input
stringlengths 33
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|---|---|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
init_cfg=dict(
type='Pretrained', checkpoint='./swav_800ep_pretrain.pth.tar')))
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomResize', scale=[(1333, 640), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
init_cfg=dict(
type='Pretrained', checkpoint='./swav_800ep_pretrain.pth.tar')))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
]
data = dict(train=dict(pipeline=train_pipeline))
|
_base_ = 'ssd300_voc0712.py'
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 256, 256, 256),
anchor_generator=dict(
input_size=input_size,
strides=[8, 16, 32, 64, 128, 256, 512],
basesize_ratio_range=(0.15, 0.9),
ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]))))
# dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Expand',
mean={{_base_.model.data_preprocessor.mean}},
to_rgb={{_base_.model.data_preprocessor.bgr_to_rgb}},
ratio_range=(1, 4)),
dict(
type='MinIoURandomCrop',
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
min_crop_size=0.3),
dict(type='Resize', scale=(input_size, input_size), keep_ratio=False),
dict(type='RandomFlip', prob=0.5),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(input_size, input_size), keep_ratio=False),
# avoid bboxes being resized
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=8,
num_workers=3,
dataset=dict( # RepeatDataset
# the dataset is repeated 10 times, and the training schedule is 2x,
# so the actual epoch = 12 * 10 = 120.
times=10,
dataset=dict( # ConcatDataset
datasets=[
dict(
type=dataset_type,
data_root=data_root,
ann_file='VOC2007/ImageSets/Main/trainval.txt',
data_prefix=dict(sub_data_root='VOC2007/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline),
dict(
type=dataset_type,
data_root=data_root,
ann_file='VOC2012/ImageSets/Main/trainval.txt',
data_prefix=dict(sub_data_root='VOC2012/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline)
])))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
|
_base_ = 'ssd300_voc0712.py'
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 256, 256, 256),
anchor_generator=dict(
input_size=input_size,
strides=[8, 16, 32, 64, 128, 256, 512],
basesize_ratio_range=(0.15, 0.9),
ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]))))
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Expand',
mean=img_norm_cfg['mean'],
to_rgb=img_norm_cfg['to_rgb'],
ratio_range=(1, 4)),
dict(
type='MinIoURandomCrop',
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
min_crop_size=0.3),
dict(type='Resize', img_scale=(512, 512), keep_ratio=False),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(512, 512),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(dataset=dict(pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_loaders.utils import (
map_ai_messages,
map_ai_messages_in_session,
merge_chat_runs,
merge_chat_runs_in_session,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"merge_chat_runs_in_session": "langchain_community.chat_loaders.utils",
"merge_chat_runs": "langchain_community.chat_loaders.utils",
"map_ai_messages_in_session": "langchain_community.chat_loaders.utils",
"map_ai_messages": "langchain_community.chat_loaders.utils",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"map_ai_messages",
"map_ai_messages_in_session",
"merge_chat_runs",
"merge_chat_runs_in_session",
]
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_loaders.utils import (
map_ai_messages,
map_ai_messages_in_session,
merge_chat_runs,
merge_chat_runs_in_session,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"merge_chat_runs_in_session": "langchain_community.chat_loaders.utils",
"merge_chat_runs": "langchain_community.chat_loaders.utils",
"map_ai_messages_in_session": "langchain_community.chat_loaders.utils",
"map_ai_messages": "langchain_community.chat_loaders.utils",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"merge_chat_runs_in_session",
"merge_chat_runs",
"map_ai_messages_in_session",
"map_ai_messages",
]
|
from typing import Dict, List, Tuple
import pytest
from opentelemetry.metrics import Meter
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import (
HistogramDataPoint,
InMemoryMetricReader,
Metric,
)
from jina.serve.networking.instrumentation import _NetworkingHistograms
@pytest.fixture
def metrics_setup() -> Tuple[InMemoryMetricReader, MeterProvider]:
metric_reader = InMemoryMetricReader()
meter_provider = MeterProvider(metric_readers=[metric_reader])
meter = meter_provider.get_meter('test')
yield metric_reader, meter
if hasattr(meter_provider, 'force_flush'):
metric_reader.force_flush()
if hasattr(meter_provider, 'shutdown'):
meter_provider.shutdown()
def test_get_labels():
a: _NetworkingHistograms = _NetworkingHistograms()
assert a._get_labels() == None
HIST_LABELS = {
'a': 1,
'b': 2,
}
a.histogram_metric_labels = HIST_LABELS
assert a._get_labels() == HIST_LABELS
ADD_LABELS = {
'b': 3,
'c': 4,
}
assert a._get_labels(ADD_LABELS) == {**HIST_LABELS, **ADD_LABELS}
def test_recording_methods(metrics_setup: Tuple[InMemoryMetricReader, Meter]):
metric_reader, meter = metrics_setup
a: _NetworkingHistograms = _NetworkingHistograms(
sending_requests_time_metrics=meter.create_histogram("request_time"),
send_requests_bytes_metrics=meter.create_histogram("request_bytes"),
received_response_bytes=meter.create_histogram("response_bytes"),
histogram_metric_labels=None,
)
a.record_sending_requests_time_metrics(10)
a.record_send_requests_bytes_metrics(20)
a.record_received_response_bytes(30)
histogram_metrics: List[Metric] = (
metric_reader.get_metrics_data().resource_metrics[0].scope_metrics[0].metrics
)
data_points_sums: Dict[str, HistogramDataPoint] = {
hist.name: next(iter(hist.data.data_points)).sum for hist in histogram_metrics
}
assert data_points_sums == {
'request_time': 10,
'request_bytes': 20,
'response_bytes': 30,
}
|
from typing import Dict, List, Tuple
import pytest
from opentelemetry.metrics import Meter
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import (
HistogramDataPoint,
InMemoryMetricReader,
Metric,
)
from jina.serve.networking import _NetworkingHistograms
@pytest.fixture
def metrics_setup() -> Tuple[InMemoryMetricReader, MeterProvider]:
metric_reader = InMemoryMetricReader()
meter_provider = MeterProvider(metric_readers=[metric_reader])
meter = meter_provider.get_meter('test')
yield metric_reader, meter
if hasattr(meter_provider, 'force_flush'):
metric_reader.force_flush()
if hasattr(meter_provider, 'shutdown'):
meter_provider.shutdown()
def test_get_labels():
a: _NetworkingHistograms = _NetworkingHistograms()
assert a._get_labels() == None
HIST_LABELS = {
'a': 1,
'b': 2,
}
a.histogram_metric_labels = HIST_LABELS
assert a._get_labels() == HIST_LABELS
ADD_LABELS = {
'b': 3,
'c': 4,
}
assert a._get_labels(ADD_LABELS) == {**HIST_LABELS, **ADD_LABELS}
def test_recording_methods(metrics_setup: Tuple[InMemoryMetricReader, Meter]):
metric_reader, meter = metrics_setup
a: _NetworkingHistograms = _NetworkingHistograms(
sending_requests_time_metrics=meter.create_histogram("request_time"),
send_requests_bytes_metrics=meter.create_histogram("request_bytes"),
received_response_bytes=meter.create_histogram("response_bytes"),
histogram_metric_labels=None,
)
a.record_sending_requests_time_metrics(10)
a.record_send_requests_bytes_metrics(20)
a.record_received_response_bytes(30)
histogram_metrics: List[Metric] = (
metric_reader.get_metrics_data().resource_metrics[0].scope_metrics[0].metrics
)
data_points_sums: Dict[str, HistogramDataPoint] = {
hist.name: next(iter(hist.data.data_points)).sum for hist in histogram_metrics
}
assert data_points_sums == {
'request_time': 10,
'request_bytes': 20,
'response_bytes': 30,
}
|
"""
Computes embeddings
"""
import numpy as np
from sentence_transformers import SentenceTransformer
def test_encode_token_embeddings(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
"""
Test that encode(output_value='token_embeddings') works
:return:
"""
model = paraphrase_distilroberta_base_v1_model
sent = [
"Hello Word, a test sentence",
"Here comes another sentence",
"My final sentence",
"Sentences",
"Sentence five five five five five five five",
]
emb = model.encode(sent, output_value="token_embeddings", batch_size=2)
assert len(emb) == len(sent)
for s, e in zip(sent, emb):
assert len(model.tokenize([s])["input_ids"][0]) == e.shape[0]
def test_encode_single_sentences(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
model = paraphrase_distilroberta_base_v1_model
# Single sentence
emb = model.encode("Hello Word, a test sentence")
assert emb.shape == (768,)
assert abs(np.sum(emb) - 7.9811716) < 0.001
# Single sentence as list
emb = model.encode(["Hello Word, a test sentence"])
assert emb.shape == (1, 768)
assert abs(np.sum(emb) - 7.9811716) < 0.001
# Sentence list
emb = model.encode(
[
"Hello Word, a test sentence",
"Here comes another sentence",
"My final sentence",
]
)
assert emb.shape == (3, 768)
assert abs(np.sum(emb) - 22.968266) < 0.001
def test_encode_normalize(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
model = paraphrase_distilroberta_base_v1_model
emb = model.encode(
[
"Hello Word, a test sentence",
"Here comes another sentence",
"My final sentence",
],
normalize_embeddings=True,
)
assert emb.shape == (3, 768)
for norm in np.linalg.norm(emb, axis=1):
assert abs(norm - 1) < 0.001
def test_encode_tuple_sentences(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
model = paraphrase_distilroberta_base_v1_model
# Input a sentence tuple
emb = model.encode([("Hello Word, a test sentence", "Second input for model")])
assert emb.shape == (1, 768)
assert abs(np.sum(emb) - 9.503508) < 0.001
# List of sentence tuples
emb = model.encode(
[
("Hello Word, a test sentence", "Second input for model"),
("My second tuple", "With two inputs"),
("Final tuple", "final test"),
]
)
assert emb.shape == (3, 768)
assert abs(np.sum(emb) - 32.14627) < 0.001
|
"""
Computes embeddings
"""
import numpy as np
from sentence_transformers import SentenceTransformer
def test_encode_token_embeddings(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
"""
Test that encode(output_value='token_embeddings') works
:return:
"""
model = paraphrase_distilroberta_base_v1_model
sent = [
"Hello Word, a test sentence",
"Here comes another sentence",
"My final sentence",
"Sentences",
"Sentence five five five five five five five",
]
emb = model.encode(sent, output_value="token_embeddings", batch_size=2)
assert len(emb) == len(sent)
for s, e in zip(sent, emb):
assert len(model.tokenize([s])["input_ids"][0]) == e.shape[0]
def test_encode_single_sentences(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
model = paraphrase_distilroberta_base_v1_model
# Single sentence
emb = model.encode("Hello Word, a test sentence")
assert emb.shape == (768,)
assert abs(np.sum(emb) - 7.9811716) < 0.001
# Single sentence as list
emb = model.encode(["Hello Word, a test sentence"])
assert emb.shape == (1, 768)
assert abs(np.sum(emb) - 7.9811716) < 0.001
# Sentence list
emb = model.encode(
[
"Hello Word, a test sentence",
"Here comes another sentence",
"My final sentence",
]
)
assert emb.shape == (3, 768)
assert abs(np.sum(emb) - 22.968266) < 0.001
def test_encode_normalize(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
model = paraphrase_distilroberta_base_v1_model
emb = model.encode(
[
"Hello Word, a test sentence",
"Here comes another sentence",
"My final sentence",
],
normalize_embeddings=True,
)
assert emb.shape == (3, 768)
for norm in np.linalg.norm(emb, axis=1):
assert abs(norm - 1) < 0.001
def test_encode_tuple_sentences(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
model = paraphrase_distilroberta_base_v1_model
# Input a sentence tuple
emb = model.encode([("Hello Word, a test sentence", "Second input for model")])
assert emb.shape == (1, 768)
assert abs(np.sum(emb) - 9.503508) < 0.001
# List of sentence tuples
emb = model.encode(
[
("Hello Word, a test sentence", "Second input for model"),
("My second tuple", "With two inputs"),
("Final tuple", "final test"),
]
)
assert emb.shape == (3, 768)
assert abs(np.sum(emb) - 32.14627) < 0.001
|
"""Download llama-pack as template."""
import logging
import os
import subprocess
import sys
from importlib import util
from pathlib import Path
from typing import Any, Optional, Union
import requests
from llama_index.core.download.utils import (
ChangeDirectory,
get_file_content,
initialize_directory,
get_source_files_recursive,
)
LLAMA_PACKS_CONTENTS_URL = (
"https://raw.githubusercontent.com/run-llama/llama_index/main/llama-index-packs"
)
LLAMA_PACKS_SOURCE_FILES_GITHUB_TREE_URL = (
"https://github.com/run-llama/llama_index/tree/main"
)
PY_NAMESPACE = "llama_index/packs"
PATH_TYPE = Union[str, Path]
LLAMAHUB_ANALYTICS_PROXY_SERVER = "https://llamahub.ai/api/analytics/downloads"
logger = logging.getLogger(__name__)
def download_module_and_reqs(
local_dir_path: PATH_TYPE,
remote_dir_path: PATH_TYPE,
remote_source_dir_path: PATH_TYPE,
package: str,
sub_module: str,
refresh_cache: bool = False,
) -> None:
"""Load module."""
if isinstance(local_dir_path, str):
local_dir_path = Path(local_dir_path)
module_path = f"{local_dir_path}/{PY_NAMESPACE}/{sub_module}"
if refresh_cache or not os.path.exists(module_path):
os.makedirs(module_path, exist_ok=True)
# download all source files
source_files = get_source_files_recursive(
str(remote_source_dir_path),
f"/llama-index-packs/{package}/{PY_NAMESPACE}/{sub_module}",
)
for source_file in source_files:
source_file_raw_content, _ = get_file_content(
str(remote_dir_path),
f"{source_file}",
)
local_source_file_path = (
f"{local_dir_path}/{'/'.join(source_file.split('/')[2:])}"
)
# ensure parent dir of file exists
Path(local_source_file_path).parent.absolute().mkdir(
parents=True, exist_ok=True
)
with open(local_source_file_path, "w") as f:
f.write(source_file_raw_content)
# pyproject.toml and README
pyproject_toml_path = f"{local_dir_path}/pyproject.toml"
readme_path = (
f"{local_dir_path}/README.md" # needed to install deps from pyproject.toml
)
if not os.path.exists(pyproject_toml_path):
# NOTE: need to check the status code
response_txt, status_code = get_file_content(
str(remote_dir_path), f"/{package}/pyproject.toml"
)
if status_code == 200:
with open(pyproject_toml_path, "w") as f:
f.write(response_txt)
if not os.path.exists(readme_path):
with open(readme_path, "w") as f:
f.write(
"DO NOT DELETE\nThis readme file is needed to install from pyproject.toml."
)
# Install dependencies
if os.path.exists(pyproject_toml_path):
with ChangeDirectory(str(local_dir_path)):
subprocess.check_call([sys.executable, "-m", "pip", "install", "."])
def download_llama_pack_template(
new_install_parent: str,
llama_pack_class: str,
llama_pack_url: str = LLAMA_PACKS_CONTENTS_URL,
llama_pack_source_files_dir_path: str = LLAMA_PACKS_SOURCE_FILES_GITHUB_TREE_URL,
refresh_cache: bool = False,
custom_dir: Optional[str] = None,
custom_path: Optional[str] = None,
base_file_name: str = "__init__.py",
) -> Any:
# create directory / get path
dirpath = initialize_directory(custom_path=custom_path, custom_dir=custom_dir)
sub_module = new_install_parent.replace("llama-index-packs-", "").replace("-", "_")
# download the module, install requirements
download_module_and_reqs(
local_dir_path=dirpath,
remote_dir_path=llama_pack_url,
remote_source_dir_path=llama_pack_source_files_dir_path,
package=new_install_parent,
sub_module=sub_module,
refresh_cache=refresh_cache,
)
# loads the module into memory
path = f"{dirpath}/{PY_NAMESPACE}/{sub_module}/{base_file_name}"
spec = util.spec_from_file_location("llama_index.packs._custom", location=path)
if spec is None:
raise ValueError(f"Could not find file: {path}.")
module = util.module_from_spec(spec)
spec.loader.exec_module(module) # type: ignore
return getattr(module, llama_pack_class)
def track_download(module_class: str, module_type: str) -> None:
"""
Tracks number of downloads via Llamahub proxy.
Args:
module_class: The name of the llama module being downloaded, e.g.,`GmailOpenAIAgentPack`.
module_type: Can be "loader", "tool", "llamapack", or "datasets"
"""
try:
requests.post(
LLAMAHUB_ANALYTICS_PROXY_SERVER,
json={"type": module_type, "plugin": module_class},
)
except Exception as e:
logger.info(f"Error tracking downloads for {module_class} : {e}")
|
"""Download llama-pack as template."""
import logging
import os
import subprocess
import sys
from importlib import util
from pathlib import Path
from typing import Any, Optional, Union
import requests
from llama_index.core.download.utils import (
ChangeDirectory,
get_file_content,
initialize_directory,
get_source_files_recursive,
)
LLAMA_PACKS_CONTENTS_URL = (
"https://raw.githubusercontent.com/run-llama/llama_index/main/llama-index-packs"
)
LLAMA_PACKS_SOURCE_FILES_GITHUB_TREE_URL = (
"https://github.com/run-llama/llama_index/tree/main"
)
PY_NAMESPACE = "llama_index/packs"
PATH_TYPE = Union[str, Path]
LLAMAHUB_ANALYTICS_PROXY_SERVER = "https://llamahub.ai/api/analytics/downloads"
logger = logging.getLogger(__name__)
def download_module_and_reqs(
local_dir_path: PATH_TYPE,
remote_dir_path: PATH_TYPE,
remote_source_dir_path: PATH_TYPE,
package: str,
sub_module: str,
refresh_cache: bool = False,
) -> None:
"""Load module."""
if isinstance(local_dir_path, str):
local_dir_path = Path(local_dir_path)
module_path = f"{local_dir_path}/{PY_NAMESPACE}/{sub_module}"
if refresh_cache or not os.path.exists(module_path):
os.makedirs(module_path, exist_ok=True)
# download all source files
source_files = get_source_files_recursive(
str(remote_source_dir_path),
f"/llama-index-packs/{package}/{PY_NAMESPACE}/{sub_module}",
)
for source_file in source_files:
source_file_raw_content, _ = get_file_content(
str(remote_dir_path),
f"{source_file}",
)
local_source_file_path = (
f"{local_dir_path}/{'/'.join(source_file.split('/')[2:])}"
)
# ensure parent dir of file exists
Path(local_source_file_path).parent.absolute().mkdir(
parents=True, exist_ok=True
)
with open(local_source_file_path, "w") as f:
f.write(source_file_raw_content)
# pyproject.toml and README
pyproject_toml_path = f"{local_dir_path}/pyproject.toml"
readme_path = (
f"{local_dir_path}/README.md" # needed to install deps from pyproject.toml
)
if not os.path.exists(pyproject_toml_path):
# NOTE: need to check the status code
response_txt, status_code = get_file_content(
str(remote_dir_path), f"/{package}/pyproject.toml"
)
if status_code == 200:
with open(pyproject_toml_path, "w") as f:
f.write(response_txt)
if not os.path.exists(readme_path):
with open(readme_path, "w") as f:
f.write(
"DO NOT DELETE\nThis readme file is needed to install from pyproject.toml."
)
# Install dependencies
if os.path.exists(pyproject_toml_path):
with ChangeDirectory(str(local_dir_path)):
subprocess.check_call([sys.executable, "-m", "pip", "install", "."])
def download_llama_pack_template(
new_install_parent: str,
llama_pack_class: str,
llama_pack_url: str = LLAMA_PACKS_CONTENTS_URL,
llama_pack_source_files_dir_path: str = LLAMA_PACKS_SOURCE_FILES_GITHUB_TREE_URL,
refresh_cache: bool = False,
custom_dir: Optional[str] = None,
custom_path: Optional[str] = None,
base_file_name: str = "__init__.py",
) -> Any:
# create directory / get path
dirpath = initialize_directory(custom_path=custom_path, custom_dir=custom_dir)
sub_module = new_install_parent.replace("llama-index-packs-", "").replace("-", "_")
# download the module, install requirements
download_module_and_reqs(
local_dir_path=dirpath,
remote_dir_path=llama_pack_url,
remote_source_dir_path=llama_pack_source_files_dir_path,
package=new_install_parent,
sub_module=sub_module,
refresh_cache=refresh_cache,
)
# loads the module into memory
path = f"{dirpath}/{PY_NAMESPACE}/{sub_module}/{base_file_name}"
spec = util.spec_from_file_location("llama_index.packs._custom", location=path)
if spec is None:
raise ValueError(f"Could not find file: {path}.")
module = util.module_from_spec(spec)
spec.loader.exec_module(module) # type: ignore
return getattr(module, llama_pack_class)
def track_download(module_class: str, module_type: str) -> None:
"""Tracks number of downloads via Llamahub proxy.
Args:
module_class: The name of the llama module being downloaded, e.g.,`GmailOpenAIAgentPack`.
module_type: Can be "loader", "tool", "llamapack", or "datasets"
"""
try:
requests.post(
LLAMAHUB_ANALYTICS_PROXY_SERVER,
json={"type": module_type, "plugin": module_class},
)
except Exception as e:
logger.info(f"Error tracking downloads for {module_class} : {e}")
|
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
from uuid import UUID
from pydantic import parse_obj_as
from docarray.typing.proto_register import _register_proto
from docarray.utils._internal.pydantic import is_pydantic_v2
if TYPE_CHECKING:
from docarray.proto import NodeProto
from docarray.typing.abstract_type import AbstractType
if is_pydantic_v2:
from pydantic import GetCoreSchemaHandler, GetJsonSchemaHandler
from pydantic.json_schema import JsonSchemaValue
from pydantic_core import core_schema
T = TypeVar('T', bound='ID')
@_register_proto(proto_type_name='id')
class ID(str, AbstractType):
"""
Represent an unique ID
"""
@classmethod
def _docarray_validate(
cls: Type[T],
value: Union[str, int, UUID],
) -> T:
try:
id: str = str(value)
return cls(id)
except Exception:
raise ValueError(f'Expected a str, int or UUID, got {type(value)}')
def _to_node_protobuf(self) -> 'NodeProto':
"""Convert an ID into a NodeProto message. This function should
be called when the self is nested into another Document that need to be
converted into a protobuf
:return: the nested item protobuf message
"""
from docarray.proto import NodeProto
return NodeProto(text=self, type=self._proto_type_name)
@classmethod
def from_protobuf(cls: Type[T], pb_msg: 'str') -> T:
"""
read ndarray from a proto msg
:param pb_msg:
:return: a string
"""
return parse_obj_as(cls, pb_msg)
if is_pydantic_v2:
@classmethod
def __get_pydantic_core_schema__(
cls, source: Type[Any], handler: 'GetCoreSchemaHandler'
) -> core_schema.CoreSchema:
return core_schema.with_info_plain_validator_function(
cls.validate,
)
@classmethod
def __get_pydantic_json_schema__(
cls, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
) -> JsonSchemaValue:
field_schema: dict[str, Any] = {}
field_schema.update(type='string')
return field_schema
|
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
from uuid import UUID
from pydantic import parse_obj_as
from docarray.typing.proto_register import _register_proto
from docarray.utils._internal.pydantic import is_pydantic_v2
if TYPE_CHECKING:
from docarray.proto import NodeProto
from docarray.typing.abstract_type import AbstractType
if is_pydantic_v2:
from pydantic import GetCoreSchemaHandler, GetJsonSchemaHandler
from pydantic.json_schema import JsonSchemaValue
from pydantic_core import core_schema
T = TypeVar('T', bound='ID')
@_register_proto(proto_type_name='id')
class ID(str, AbstractType):
"""
Represent an unique ID
"""
@classmethod
def _docarray_validate(
cls: Type[T],
value: Union[str, int, UUID],
) -> T:
try:
id: str = str(value)
return cls(id)
except Exception:
raise ValueError(f'Expected a str, int or UUID, got {type(value)}')
def _to_node_protobuf(self) -> 'NodeProto':
"""Convert an ID into a NodeProto message. This function should
be called when the self is nested into another Document that need to be
converted into a protobuf
:return: the nested item protobuf message
"""
from docarray.proto import NodeProto
return NodeProto(text=self, type=self._proto_type_name)
@classmethod
def from_protobuf(cls: Type[T], pb_msg: 'str') -> T:
"""
read ndarray from a proto msg
:param pb_msg:
:return: a string
"""
return parse_obj_as(cls, pb_msg)
if is_pydantic_v2:
@classmethod
def __get_pydantic_core_schema__(
cls, source: Type[Any], handler: 'GetCoreSchemaHandler'
) -> core_schema.CoreSchema:
return core_schema.general_plain_validator_function(
cls.validate,
)
@classmethod
def __get_pydantic_json_schema__(
cls, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
) -> JsonSchemaValue:
field_schema: dict[str, Any] = {}
field_schema.update(type='string')
return field_schema
|
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
|
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
|
import logging
from typing import Any
from backend.data.block import (
Block,
BlockCategory,
BlockInput,
BlockOutput,
BlockSchema,
BlockType,
get_block,
)
from backend.data.execution import ExecutionStatus
from backend.data.model import SchemaField
from backend.util import json
logger = logging.getLogger(__name__)
class AgentExecutorBlock(Block):
class Input(BlockSchema):
user_id: str = SchemaField(description="User ID")
graph_id: str = SchemaField(description="Graph ID")
graph_version: int = SchemaField(description="Graph Version")
data: BlockInput = SchemaField(description="Input data for the graph")
input_schema: dict = SchemaField(description="Input schema for the graph")
output_schema: dict = SchemaField(description="Output schema for the graph")
@classmethod
def get_input_schema(cls, data: BlockInput) -> dict[str, Any]:
return data.get("input_schema", {})
@classmethod
def get_input_defaults(cls, data: BlockInput) -> BlockInput:
return data.get("data", {})
@classmethod
def get_missing_input(cls, data: BlockInput) -> set[str]:
required_fields = cls.get_input_schema(data).get("required", [])
return set(required_fields) - set(data)
@classmethod
def get_mismatch_error(cls, data: BlockInput) -> str | None:
return json.validate_with_jsonschema(cls.get_input_schema(data), data)
class Output(BlockSchema):
pass
def __init__(self):
super().__init__(
id="e189baac-8c20-45a1-94a7-55177ea42565",
description="Executes an existing agent inside your agent",
input_schema=AgentExecutorBlock.Input,
output_schema=AgentExecutorBlock.Output,
block_type=BlockType.AGENT,
categories={BlockCategory.AGENT},
)
def run(self, input_data: Input, **kwargs) -> BlockOutput:
from backend.data.execution import ExecutionEventType
from backend.executor import utils as execution_utils
event_bus = execution_utils.get_execution_event_bus()
graph_exec = execution_utils.add_graph_execution(
graph_id=input_data.graph_id,
graph_version=input_data.graph_version,
user_id=input_data.user_id,
data=input_data.data,
)
log_id = f"Graph #{input_data.graph_id}-V{input_data.graph_version}, exec-id: {graph_exec.graph_exec_id}"
logger.info(f"Starting execution of {log_id}")
for event in event_bus.listen(
user_id=graph_exec.user_id,
graph_id=graph_exec.graph_id,
graph_exec_id=graph_exec.graph_exec_id,
):
if event.event_type == ExecutionEventType.GRAPH_EXEC_UPDATE:
if event.status in [
ExecutionStatus.COMPLETED,
ExecutionStatus.TERMINATED,
ExecutionStatus.FAILED,
]:
logger.info(f"Execution {log_id} ended with status {event.status}")
break
else:
continue
logger.debug(
f"Execution {log_id} produced input {event.input_data} output {event.output_data}"
)
if not event.block_id:
logger.warning(f"{log_id} received event without block_id {event}")
continue
block = get_block(event.block_id)
if not block or block.block_type != BlockType.OUTPUT:
continue
output_name = event.input_data.get("name")
if not output_name:
logger.warning(f"{log_id} produced an output with no name {event}")
continue
for output_data in event.output_data.get("output", []):
logger.debug(
f"Execution {log_id} produced {output_name}: {output_data}"
)
yield output_name, output_data
|
import logging
from typing import Any
from backend.data.block import (
Block,
BlockCategory,
BlockInput,
BlockOutput,
BlockSchema,
BlockType,
get_block,
)
from backend.data.execution import ExecutionStatus
from backend.data.model import SchemaField
from backend.util import json
logger = logging.getLogger(__name__)
class AgentExecutorBlock(Block):
class Input(BlockSchema):
user_id: str = SchemaField(description="User ID")
graph_id: str = SchemaField(description="Graph ID")
graph_version: int = SchemaField(description="Graph Version")
data: BlockInput = SchemaField(description="Input data for the graph")
input_schema: dict = SchemaField(description="Input schema for the graph")
output_schema: dict = SchemaField(description="Output schema for the graph")
@classmethod
def get_input_schema(cls, data: BlockInput) -> dict[str, Any]:
return data.get("input_schema", {})
@classmethod
def get_input_defaults(cls, data: BlockInput) -> BlockInput:
return data.get("data", {})
@classmethod
def get_missing_input(cls, data: BlockInput) -> set[str]:
required_fields = cls.get_input_schema(data).get("required", [])
return set(required_fields) - set(data)
@classmethod
def get_mismatch_error(cls, data: BlockInput) -> str | None:
return json.validate_with_jsonschema(cls.get_input_schema(data), data)
class Output(BlockSchema):
pass
def __init__(self):
super().__init__(
id="e189baac-8c20-45a1-94a7-55177ea42565",
description="Executes an existing agent inside your agent",
input_schema=AgentExecutorBlock.Input,
output_schema=AgentExecutorBlock.Output,
block_type=BlockType.AGENT,
categories={BlockCategory.AGENT},
)
def run(self, input_data: Input, **kwargs) -> BlockOutput:
from backend.data.execution import ExecutionEventType
from backend.executor import utils as execution_utils
event_bus = execution_utils.get_execution_event_bus()
graph_exec = execution_utils.add_graph_execution(
graph_id=input_data.graph_id,
graph_version=input_data.graph_version,
user_id=input_data.user_id,
data=input_data.data,
)
log_id = f"Graph #{input_data.graph_id}-V{input_data.graph_version}, exec-id: {graph_exec.graph_exec_id}"
logger.info(f"Starting execution of {log_id}")
for event in event_bus.listen(
user_id=graph_exec.user_id,
graph_id=graph_exec.graph_id,
graph_exec_id=graph_exec.graph_exec_id,
):
if event.event_type == ExecutionEventType.GRAPH_EXEC_UPDATE:
if event.status in [
ExecutionStatus.COMPLETED,
ExecutionStatus.TERMINATED,
ExecutionStatus.FAILED,
]:
logger.info(f"Execution {log_id} ended with status {event.status}")
break
else:
continue
logger.info(
f"Execution {log_id} produced input {event.input_data} output {event.output_data}"
)
if not event.block_id:
logger.warning(f"{log_id} received event without block_id {event}")
continue
block = get_block(event.block_id)
if not block or block.block_type != BlockType.OUTPUT:
continue
output_name = event.input_data.get("name")
if not output_name:
logger.warning(f"{log_id} produced an output with no name {event}")
continue
for output_data in event.output_data.get("output", []):
logger.info(f"Execution {log_id} produced {output_name}: {output_data}")
yield output_name, output_data
|
import pathlib
from typing import Any, Callable, Optional, Union
from .folder import default_loader
from .utils import verify_str_arg
from .vision import VisionDataset
class StanfordCars(VisionDataset):
"""Stanford Cars Dataset
The Cars dataset contains 16,185 images of 196 classes of cars. The data is
split into 8,144 training images and 8,041 testing images, where each class
has been split roughly in a 50-50 split
The original URL is https://ai.stanford.edu/~jkrause/cars/car_dataset.html,
the dataset isn't available online anymore.
.. note::
This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.
Args:
root (str or ``pathlib.Path``): Root directory of dataset
split (string, optional): The dataset split, supports ``"train"`` (default) or ``"test"``.
transform (callable, optional): A function/transform that takes in a PIL image or torch.Tensor, depends on the given loader,
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool, optional): This parameter exists for backward compatibility but it does not
download the dataset, since the original URL is not available anymore.
loader (callable, optional): A function to load an image given its path.
By default, it uses PIL as its image loader, but users could also pass in
``torchvision.io.decode_image`` for decoding image data into tensors directly.
"""
def __init__(
self,
root: Union[str, pathlib.Path],
split: str = "train",
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
loader: Callable[[str], Any] = default_loader,
) -> None:
try:
import scipy.io as sio
except ImportError:
raise RuntimeError("Scipy is not found. This dataset needs to have scipy installed: pip install scipy")
super().__init__(root, transform=transform, target_transform=target_transform)
self._split = verify_str_arg(split, "split", ("train", "test"))
self._base_folder = pathlib.Path(root) / "stanford_cars"
devkit = self._base_folder / "devkit"
if self._split == "train":
self._annotations_mat_path = devkit / "cars_train_annos.mat"
self._images_base_path = self._base_folder / "cars_train"
else:
self._annotations_mat_path = self._base_folder / "cars_test_annos_withlabels.mat"
self._images_base_path = self._base_folder / "cars_test"
if download:
self.download()
if not self._check_exists():
raise RuntimeError("Dataset not found.")
self._samples = [
(
str(self._images_base_path / annotation["fname"]),
annotation["class"] - 1, # Original target mapping starts from 1, hence -1
)
for annotation in sio.loadmat(self._annotations_mat_path, squeeze_me=True)["annotations"]
]
self.classes = sio.loadmat(str(devkit / "cars_meta.mat"), squeeze_me=True)["class_names"].tolist()
self.class_to_idx = {cls: i for i, cls in enumerate(self.classes)}
self.loader = loader
def __len__(self) -> int:
return len(self._samples)
def __getitem__(self, idx: int) -> tuple[Any, Any]:
"""Returns pil_image and class_id for given index"""
image_path, target = self._samples[idx]
image = self.loader(image_path)
if self.transform is not None:
image = self.transform(image)
if self.target_transform is not None:
target = self.target_transform(target)
return image, target
def _check_exists(self) -> bool:
if not (self._base_folder / "devkit").is_dir():
return False
return self._annotations_mat_path.exists() and self._images_base_path.is_dir()
def download(self):
raise ValueError("The original URL is broken so the StanfordCars dataset cannot be downloaded anymore.")
|
import pathlib
from typing import Any, Callable, Optional, Tuple, Union
from .folder import default_loader
from .utils import verify_str_arg
from .vision import VisionDataset
class StanfordCars(VisionDataset):
"""Stanford Cars Dataset
The Cars dataset contains 16,185 images of 196 classes of cars. The data is
split into 8,144 training images and 8,041 testing images, where each class
has been split roughly in a 50-50 split
The original URL is https://ai.stanford.edu/~jkrause/cars/car_dataset.html,
the dataset isn't available online anymore.
.. note::
This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.
Args:
root (str or ``pathlib.Path``): Root directory of dataset
split (string, optional): The dataset split, supports ``"train"`` (default) or ``"test"``.
transform (callable, optional): A function/transform that takes in a PIL image or torch.Tensor, depends on the given loader,
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool, optional): This parameter exists for backward compatibility but it does not
download the dataset, since the original URL is not available anymore.
loader (callable, optional): A function to load an image given its path.
By default, it uses PIL as its image loader, but users could also pass in
``torchvision.io.decode_image`` for decoding image data into tensors directly.
"""
def __init__(
self,
root: Union[str, pathlib.Path],
split: str = "train",
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
loader: Callable[[str], Any] = default_loader,
) -> None:
try:
import scipy.io as sio
except ImportError:
raise RuntimeError("Scipy is not found. This dataset needs to have scipy installed: pip install scipy")
super().__init__(root, transform=transform, target_transform=target_transform)
self._split = verify_str_arg(split, "split", ("train", "test"))
self._base_folder = pathlib.Path(root) / "stanford_cars"
devkit = self._base_folder / "devkit"
if self._split == "train":
self._annotations_mat_path = devkit / "cars_train_annos.mat"
self._images_base_path = self._base_folder / "cars_train"
else:
self._annotations_mat_path = self._base_folder / "cars_test_annos_withlabels.mat"
self._images_base_path = self._base_folder / "cars_test"
if download:
self.download()
if not self._check_exists():
raise RuntimeError("Dataset not found.")
self._samples = [
(
str(self._images_base_path / annotation["fname"]),
annotation["class"] - 1, # Original target mapping starts from 1, hence -1
)
for annotation in sio.loadmat(self._annotations_mat_path, squeeze_me=True)["annotations"]
]
self.classes = sio.loadmat(str(devkit / "cars_meta.mat"), squeeze_me=True)["class_names"].tolist()
self.class_to_idx = {cls: i for i, cls in enumerate(self.classes)}
self.loader = loader
def __len__(self) -> int:
return len(self._samples)
def __getitem__(self, idx: int) -> Tuple[Any, Any]:
"""Returns pil_image and class_id for given index"""
image_path, target = self._samples[idx]
image = self.loader(image_path)
if self.transform is not None:
image = self.transform(image)
if self.target_transform is not None:
target = self.target_transform(target)
return image, target
def _check_exists(self) -> bool:
if not (self._base_folder / "devkit").is_dir():
return False
return self._annotations_mat_path.exists() and self._images_base_path.is_dir()
def download(self):
raise ValueError("The original URL is broken so the StanfordCars dataset cannot be downloaded anymore.")
|
from typing import Any, Optional
from typing_extensions import override
from langchain_core.caches import RETURN_VAL_TYPE, BaseCache
from langchain_core.globals import set_llm_cache
from langchain_core.language_models import FakeListLLM
class InMemoryCache(BaseCache):
"""In-memory cache used for testing purposes."""
def __init__(self) -> None:
"""Initialize with empty cache."""
self._cache: dict[tuple[str, str], RETURN_VAL_TYPE] = {}
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
return self._cache.get((prompt, llm_string), None)
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on prompt and llm_string."""
self._cache[(prompt, llm_string)] = return_val
@override
def clear(self, **kwargs: Any) -> None:
"""Clear cache."""
self._cache = {}
async def test_local_cache_generate_async() -> None:
global_cache = InMemoryCache()
local_cache = InMemoryCache()
try:
set_llm_cache(global_cache)
llm = FakeListLLM(cache=local_cache, responses=["foo", "bar"])
output = await llm.agenerate(["foo"])
assert output.generations[0][0].text == "foo"
output = await llm.agenerate(["foo"])
assert output.generations[0][0].text == "foo"
assert global_cache._cache == {}
assert len(local_cache._cache) == 1
finally:
set_llm_cache(None)
def test_local_cache_generate_sync() -> None:
global_cache = InMemoryCache()
local_cache = InMemoryCache()
try:
set_llm_cache(global_cache)
llm = FakeListLLM(cache=local_cache, responses=["foo", "bar"])
output = llm.generate(["foo"])
assert output.generations[0][0].text == "foo"
output = llm.generate(["foo"])
assert output.generations[0][0].text == "foo"
assert global_cache._cache == {}
assert len(local_cache._cache) == 1
finally:
set_llm_cache(None)
class InMemoryCacheBad(BaseCache):
"""In-memory cache used for testing purposes."""
def __init__(self) -> None:
"""Initialize with empty cache."""
self._cache: dict[tuple[str, str], RETURN_VAL_TYPE] = {}
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
msg = "This code should not be triggered"
raise NotImplementedError(msg)
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on prompt and llm_string."""
msg = "This code should not be triggered"
raise NotImplementedError(msg)
@override
def clear(self, **kwargs: Any) -> None:
"""Clear cache."""
self._cache = {}
def test_no_cache_generate_sync() -> None:
global_cache = InMemoryCacheBad()
try:
set_llm_cache(global_cache)
llm = FakeListLLM(cache=False, responses=["foo", "bar"])
output = llm.generate(["foo"])
assert output.generations[0][0].text == "foo"
output = llm.generate(["foo"])
assert output.generations[0][0].text == "bar"
assert global_cache._cache == {}
finally:
set_llm_cache(None)
async def test_no_cache_generate_async() -> None:
global_cache = InMemoryCacheBad()
try:
set_llm_cache(global_cache)
llm = FakeListLLM(cache=False, responses=["foo", "bar"])
output = await llm.agenerate(["foo"])
assert output.generations[0][0].text == "foo"
output = await llm.agenerate(["foo"])
assert output.generations[0][0].text == "bar"
assert global_cache._cache == {}
finally:
set_llm_cache(None)
|
from typing import Any, Optional
from langchain_core.caches import RETURN_VAL_TYPE, BaseCache
from langchain_core.globals import set_llm_cache
from langchain_core.language_models import FakeListLLM
class InMemoryCache(BaseCache):
"""In-memory cache used for testing purposes."""
def __init__(self) -> None:
"""Initialize with empty cache."""
self._cache: dict[tuple[str, str], RETURN_VAL_TYPE] = {}
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
return self._cache.get((prompt, llm_string), None)
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on prompt and llm_string."""
self._cache[(prompt, llm_string)] = return_val
def clear(self, **kwargs: Any) -> None:
"""Clear cache."""
self._cache = {}
async def test_local_cache_generate_async() -> None:
global_cache = InMemoryCache()
local_cache = InMemoryCache()
try:
set_llm_cache(global_cache)
llm = FakeListLLM(cache=local_cache, responses=["foo", "bar"])
output = await llm.agenerate(["foo"])
assert output.generations[0][0].text == "foo"
output = await llm.agenerate(["foo"])
assert output.generations[0][0].text == "foo"
assert global_cache._cache == {}
assert len(local_cache._cache) == 1
finally:
set_llm_cache(None)
def test_local_cache_generate_sync() -> None:
global_cache = InMemoryCache()
local_cache = InMemoryCache()
try:
set_llm_cache(global_cache)
llm = FakeListLLM(cache=local_cache, responses=["foo", "bar"])
output = llm.generate(["foo"])
assert output.generations[0][0].text == "foo"
output = llm.generate(["foo"])
assert output.generations[0][0].text == "foo"
assert global_cache._cache == {}
assert len(local_cache._cache) == 1
finally:
set_llm_cache(None)
class InMemoryCacheBad(BaseCache):
"""In-memory cache used for testing purposes."""
def __init__(self) -> None:
"""Initialize with empty cache."""
self._cache: dict[tuple[str, str], RETURN_VAL_TYPE] = {}
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
msg = "This code should not be triggered"
raise NotImplementedError(msg)
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on prompt and llm_string."""
msg = "This code should not be triggered"
raise NotImplementedError(msg)
def clear(self, **kwargs: Any) -> None:
"""Clear cache."""
self._cache = {}
def test_no_cache_generate_sync() -> None:
global_cache = InMemoryCacheBad()
try:
set_llm_cache(global_cache)
llm = FakeListLLM(cache=False, responses=["foo", "bar"])
output = llm.generate(["foo"])
assert output.generations[0][0].text == "foo"
output = llm.generate(["foo"])
assert output.generations[0][0].text == "bar"
assert global_cache._cache == {}
finally:
set_llm_cache(None)
async def test_no_cache_generate_async() -> None:
global_cache = InMemoryCacheBad()
try:
set_llm_cache(global_cache)
llm = FakeListLLM(cache=False, responses=["foo", "bar"])
output = await llm.agenerate(["foo"])
assert output.generations[0][0].text == "foo"
output = await llm.agenerate(["foo"])
assert output.generations[0][0].text == "bar"
assert global_cache._cache == {}
finally:
set_llm_cache(None)
|
import numpy as np
import pytest
from pydantic import parse_obj_as
from docarray.base_document.document import BaseDocument
from docarray.documents import Mesh3D
from tests import TOYDATA_DIR
LOCAL_OBJ_FILE = str(TOYDATA_DIR / 'tetrahedron.obj')
REMOTE_OBJ_FILE = 'https://people.sc.fsu.edu/~jburkardt/data/obj/al.obj'
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize('file_url', [LOCAL_OBJ_FILE, REMOTE_OBJ_FILE])
def test_mesh(file_url):
mesh = Mesh3D(url=file_url)
mesh.tensors = mesh.url.load()
assert isinstance(mesh.tensors.vertices, np.ndarray)
assert isinstance(mesh.tensors.faces, np.ndarray)
def test_str_init():
t = parse_obj_as(Mesh3D, 'http://hello.ply')
assert t.url == 'http://hello.ply'
def test_doc():
class MyDoc(BaseDocument):
mesh1: Mesh3D
mesh2: Mesh3D
doc = MyDoc(mesh1='http://hello.ply', mesh2=Mesh3D(url='http://hello.ply'))
assert doc.mesh1.url == 'http://hello.ply'
assert doc.mesh2.url == 'http://hello.ply'
|
import numpy as np
import pytest
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.documents import Mesh3D
from tests import TOYDATA_DIR
LOCAL_OBJ_FILE = str(TOYDATA_DIR / 'tetrahedron.obj')
REMOTE_OBJ_FILE = 'https://people.sc.fsu.edu/~jburkardt/data/obj/al.obj'
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize('file_url', [LOCAL_OBJ_FILE, REMOTE_OBJ_FILE])
def test_mesh(file_url):
mesh = Mesh3D(url=file_url)
mesh.vertices, mesh.faces = mesh.url.load()
assert isinstance(mesh.vertices, np.ndarray)
assert isinstance(mesh.faces, np.ndarray)
def test_str_init():
t = parse_obj_as(Mesh3D, 'http://hello.ply')
assert t.url == 'http://hello.ply'
def test_doc():
class MyDoc(BaseDocument):
mesh1: Mesh3D
mesh2: Mesh3D
doc = MyDoc(mesh1='http://hello.ply', mesh2=Mesh3D(url='http://hello.ply'))
assert doc.mesh1.url == 'http://hello.ply'
assert doc.mesh2.url == 'http://hello.ply'
|
from __future__ import annotations
from typing import Union, Sequence, Literal
import torch
import torch.fft
from torch.fft import * # noqa: F403
from ._typing import Array
# Several torch fft functions do not map axes to dim
def fftn(
x: Array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> Array:
return torch.fft.fftn(x, s=s, dim=axes, norm=norm, **kwargs)
def ifftn(
x: Array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> Array:
return torch.fft.ifftn(x, s=s, dim=axes, norm=norm, **kwargs)
def rfftn(
x: Array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> Array:
return torch.fft.rfftn(x, s=s, dim=axes, norm=norm, **kwargs)
def irfftn(
x: Array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> Array:
return torch.fft.irfftn(x, s=s, dim=axes, norm=norm, **kwargs)
def fftshift(
x: Array,
/,
*,
axes: Union[int, Sequence[int]] = None,
**kwargs,
) -> Array:
return torch.fft.fftshift(x, dim=axes, **kwargs)
def ifftshift(
x: Array,
/,
*,
axes: Union[int, Sequence[int]] = None,
**kwargs,
) -> Array:
return torch.fft.ifftshift(x, dim=axes, **kwargs)
__all__ = torch.fft.__all__ + [
"fftn",
"ifftn",
"rfftn",
"irfftn",
"fftshift",
"ifftshift",
]
_all_ignore = ['torch']
|
from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
import torch
array = torch.Tensor
from typing import Union, Sequence, Literal
from torch.fft import * # noqa: F403
import torch.fft
# Several torch fft functions do not map axes to dim
def fftn(
x: array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> array:
return torch.fft.fftn(x, s=s, dim=axes, norm=norm, **kwargs)
def ifftn(
x: array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> array:
return torch.fft.ifftn(x, s=s, dim=axes, norm=norm, **kwargs)
def rfftn(
x: array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> array:
return torch.fft.rfftn(x, s=s, dim=axes, norm=norm, **kwargs)
def irfftn(
x: array,
/,
*,
s: Sequence[int] = None,
axes: Sequence[int] = None,
norm: Literal["backward", "ortho", "forward"] = "backward",
**kwargs,
) -> array:
return torch.fft.irfftn(x, s=s, dim=axes, norm=norm, **kwargs)
def fftshift(
x: array,
/,
*,
axes: Union[int, Sequence[int]] = None,
**kwargs,
) -> array:
return torch.fft.fftshift(x, dim=axes, **kwargs)
def ifftshift(
x: array,
/,
*,
axes: Union[int, Sequence[int]] = None,
**kwargs,
) -> array:
return torch.fft.ifftshift(x, dim=axes, **kwargs)
__all__ = torch.fft.__all__ + [
"fftn",
"ifftn",
"rfftn",
"irfftn",
"fftshift",
"ifftshift",
]
_all_ignore = ['torch']
|
import functools
import os
import os.path
import pathlib
from typing import Any, BinaryIO, Collection, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import FileLister, FileOpener, Filter, IterDataPipe, Mapper
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import EncodedData, EncodedImage
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling
__all__ = ["from_data_folder", "from_image_folder"]
def _is_not_top_level_file(path: str, *, root: pathlib.Path) -> bool:
rel_path = pathlib.Path(path).relative_to(root)
return rel_path.is_dir() or rel_path.parent != pathlib.Path(".")
def _prepare_sample(
data: Tuple[str, BinaryIO],
*,
root: pathlib.Path,
categories: List[str],
) -> Dict[str, Any]:
path, buffer = data
category = pathlib.Path(path).relative_to(root).parts[0]
return dict(
path=path,
data=EncodedData.from_file(buffer),
label=Label.from_category(category, categories=categories),
)
def from_data_folder(
root: Union[str, pathlib.Path],
*,
valid_extensions: Optional[Collection[str]] = None,
recursive: bool = True,
) -> Tuple[IterDataPipe, List[str]]:
root = pathlib.Path(root).expanduser().resolve()
categories = sorted(entry.name for entry in os.scandir(root) if entry.is_dir())
masks: Union[List[str], str] = [f"*.{ext}" for ext in valid_extensions] if valid_extensions is not None else ""
dp = FileLister(str(root), recursive=recursive, masks=masks)
dp: IterDataPipe = Filter(dp, functools.partial(_is_not_top_level_file, root=root))
dp = hint_sharding(dp)
dp = hint_shuffling(dp)
dp = FileOpener(dp, mode="rb")
return Mapper(dp, functools.partial(_prepare_sample, root=root, categories=categories)), categories
def _data_to_image_key(sample: Dict[str, Any]) -> Dict[str, Any]:
sample["image"] = EncodedImage(sample.pop("data").data)
return sample
def from_image_folder(
root: Union[str, pathlib.Path],
*,
valid_extensions: Collection[str] = ("jpg", "jpeg", "png", "ppm", "bmp", "pgm", "tif", "tiff", "webp"),
**kwargs: Any,
) -> Tuple[IterDataPipe, List[str]]:
valid_extensions = [valid_extension for ext in valid_extensions for valid_extension in (ext.lower(), ext.upper())]
dp, categories = from_data_folder(root, valid_extensions=valid_extensions, **kwargs)
return Mapper(dp, _data_to_image_key), categories
|
import functools
import os
import os.path
import pathlib
from typing import Any, BinaryIO, Collection, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import FileLister, FileOpener, Filter, IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import EncodedData, EncodedImage
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling
from torchvision.prototype.features import Label
__all__ = ["from_data_folder", "from_image_folder"]
def _is_not_top_level_file(path: str, *, root: pathlib.Path) -> bool:
rel_path = pathlib.Path(path).relative_to(root)
return rel_path.is_dir() or rel_path.parent != pathlib.Path(".")
def _prepare_sample(
data: Tuple[str, BinaryIO],
*,
root: pathlib.Path,
categories: List[str],
) -> Dict[str, Any]:
path, buffer = data
category = pathlib.Path(path).relative_to(root).parts[0]
return dict(
path=path,
data=EncodedData.from_file(buffer),
label=Label.from_category(category, categories=categories),
)
def from_data_folder(
root: Union[str, pathlib.Path],
*,
valid_extensions: Optional[Collection[str]] = None,
recursive: bool = True,
) -> Tuple[IterDataPipe, List[str]]:
root = pathlib.Path(root).expanduser().resolve()
categories = sorted(entry.name for entry in os.scandir(root) if entry.is_dir())
masks: Union[List[str], str] = [f"*.{ext}" for ext in valid_extensions] if valid_extensions is not None else ""
dp = FileLister(str(root), recursive=recursive, masks=masks)
dp: IterDataPipe = Filter(dp, functools.partial(_is_not_top_level_file, root=root))
dp = hint_sharding(dp)
dp = hint_shuffling(dp)
dp = FileOpener(dp, mode="rb")
return Mapper(dp, functools.partial(_prepare_sample, root=root, categories=categories)), categories
def _data_to_image_key(sample: Dict[str, Any]) -> Dict[str, Any]:
sample["image"] = EncodedImage(sample.pop("data").data)
return sample
def from_image_folder(
root: Union[str, pathlib.Path],
*,
valid_extensions: Collection[str] = ("jpg", "jpeg", "png", "ppm", "bmp", "pgm", "tif", "tiff", "webp"),
**kwargs: Any,
) -> Tuple[IterDataPipe, List[str]]:
valid_extensions = [valid_extension for ext in valid_extensions for valid_extension in (ext.lower(), ext.upper())]
dp, categories = from_data_folder(root, valid_extensions=valid_extensions, **kwargs)
return Mapper(dp, _data_to_image_key), categories
|
#!/usr/bin/env python
import functools as func
import glob
import os.path as osp
import re
import numpy as np
url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/main/'
files = sorted(glob.glob('../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirname(f.replace('../', url_prefix))
with open(f, 'r') as content_file:
content = content_file.read()
title = content.split('\n')[0].replace('# ', '').strip()
ckpts = set(x.lower().strip()
for x in re.findall(r'\[model\]\((https?.*)\)', content))
if len(ckpts) == 0:
continue
_papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)]
assert len(_papertype) > 0
papertype = _papertype[0]
paper = set([(papertype, title)])
titles.append(title)
num_ckpts += len(ckpts)
statsmsg = f"""
\t* [{papertype}] [{title}]({url}) ({len(ckpts)} ckpts)
"""
stats.append((paper, ckpts, statsmsg))
allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats])
msglist = '\n'.join(x for _, _, x in stats)
papertypes, papercounts = np.unique([t for t, _ in allpapers],
return_counts=True)
countstr = '\n'.join(
[f' - {t}: {c}' for t, c in zip(papertypes, papercounts)])
modelzoo = f"""
# Model Zoo Statistics
* Number of papers: {len(set(titles))}
{countstr}
* Number of checkpoints: {num_ckpts}
{msglist}
"""
with open('modelzoo_statistics.md', 'w') as f:
f.write(modelzoo)
|
#!/usr/bin/env python
import functools as func
import glob
import os.path as osp
import re
import numpy as np
url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/3.x/'
files = sorted(glob.glob('../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirname(f.replace('../', url_prefix))
with open(f, 'r') as content_file:
content = content_file.read()
title = content.split('\n')[0].replace('# ', '').strip()
ckpts = set(x.lower().strip()
for x in re.findall(r'\[model\]\((https?.*)\)', content))
if len(ckpts) == 0:
continue
_papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)]
assert len(_papertype) > 0
papertype = _papertype[0]
paper = set([(papertype, title)])
titles.append(title)
num_ckpts += len(ckpts)
statsmsg = f"""
\t* [{papertype}] [{title}]({url}) ({len(ckpts)} ckpts)
"""
stats.append((paper, ckpts, statsmsg))
allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats])
msglist = '\n'.join(x for _, _, x in stats)
papertypes, papercounts = np.unique([t for t, _ in allpapers],
return_counts=True)
countstr = '\n'.join(
[f' - {t}: {c}' for t, c in zip(papertypes, papercounts)])
modelzoo = f"""
# Model Zoo Statistics
* Number of papers: {len(set(titles))}
{countstr}
* Number of checkpoints: {num_ckpts}
{msglist}
"""
with open('modelzoo_statistics.md', 'w') as f:
f.write(modelzoo)
|
_base_ = './sparse-rcnn_r50_fpn_ms-480-800-3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals)))
# augmentation strategy originates from DETR.
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='RandomChoice',
transforms=[[
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
keep_ratio=True)
],
[
dict(
type='RandomChoiceResize',
scales=[(400, 1333), (500, 1333), (600, 1333)],
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
keep_ratio=True)
]]),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
|
_base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals)))
# augmentation strategy originates from DETR.
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='RandomChoice',
transforms=[[
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
keep_ratio=True)
],
[
dict(
type='RandomChoiceResize',
scales=[(400, 1333), (500, 1333), (600, 1333)],
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
keep_ratio=True)
]]),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
|
from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators
Extend this class and implement __call__ for custom evaluators.
"""
def __init__(self):
"""
Base class for all evaluators. Notably, this class introduces the ``greater_is_better`` and ``primary_metric``
attributes. The former is a boolean indicating whether a higher evaluation score is better, which is used
for choosing the best checkpoint if ``load_best_model_at_end`` is set to ``True`` in the training arguments.
The latter is a string indicating the primary metric for the evaluator. This has to be defined whenever
the evaluator returns a dictionary of metrics, and the primary metric is the key pointing to the primary
metric, i.e. the one that is used for model selection and/or logging.
"""
self.greater_is_better = True
self.primary_metric = None
def __call__(
self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1
) -> float | dict[str, float]:
"""
This is called during training to evaluate the model.
It returns a score for the evaluation with a higher score indicating a better result.
Args:
model: the model to evaluate
output_path: path where predictions and metrics are written
to
epoch: the epoch where the evaluation takes place. This is
used for the file prefixes. If this is -1, then we
assume evaluation on test data.
steps: the steps in the current epoch at time of the
evaluation. This is used for the file prefixes. If this
is -1, then we assume evaluation at the end of the
epoch.
Returns:
Either a score for the evaluation with a higher score
indicating a better result, or a dictionary with scores. If
the latter is chosen, then `evaluator.primary_metric` must
be defined
"""
pass
def prefix_name_to_metrics(self, metrics: dict[str, float], name: str) -> dict[str, float]:
if not name:
return metrics
metrics = {name + "_" + key: float(value) for key, value in metrics.items()}
if hasattr(self, "primary_metric") and not self.primary_metric.startswith(name + "_"):
self.primary_metric = name + "_" + self.primary_metric
return metrics
def store_metrics_in_model_card_data(
self, model: SentenceTransformer, metrics: dict[str, Any], epoch: int = 0, step: int = 0
) -> None:
model.model_card_data.set_evaluation_metrics(self, metrics, epoch, step)
@property
def description(self) -> str:
"""
Returns a human-readable description of the evaluator: BinaryClassificationEvaluator -> Binary Classification
1. Remove "Evaluator" from the class name
2. Add a space before every capital letter
"""
class_name = self.__class__.__name__
try:
index = class_name.index("Evaluator")
class_name = class_name[:index]
except IndexError:
pass
return re.sub(r"([a-z])([A-Z])", r"\g<1> \g<2>", class_name)
|
from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators
Extend this class and implement __call__ for custom evaluators.
"""
def __init__(self):
"""
Base class for all evaluators. Notably, this class introduces the ``greater_is_better`` and ``primary_metric``
attributes. The former is a boolean indicating whether a higher evaluation score is better, which is used
for choosing the best checkpoint if ``load_best_model_at_end`` is set to ``True`` in the training arguments.
The latter is a string indicating the primary metric for the evaluator. This has to be defined whenever
the evaluator returns a dictionary of metrics, and the primary metric is the key pointing to the primary
metric, i.e. the one that is used for model selection and/or logging.
"""
self.greater_is_better = True
self.primary_metric = None
def __call__(
self, model: SentenceTransformer, output_path: str = None, epoch: int = -1, steps: int = -1
) -> float | dict[str, float]:
"""
This is called during training to evaluate the model.
It returns a score for the evaluation with a higher score indicating a better result.
Args:
model: the model to evaluate
output_path: path where predictions and metrics are written
to
epoch: the epoch where the evaluation takes place. This is
used for the file prefixes. If this is -1, then we
assume evaluation on test data.
steps: the steps in the current epoch at time of the
evaluation. This is used for the file prefixes. If this
is -1, then we assume evaluation at the end of the
epoch.
Returns:
Either a score for the evaluation with a higher score
indicating a better result, or a dictionary with scores. If
the latter is chosen, then `evaluator.primary_metric` must
be defined
"""
pass
def prefix_name_to_metrics(self, metrics: dict[str, float], name: str) -> dict[str, float]:
if not name:
return metrics
metrics = {name + "_" + key: float(value) for key, value in metrics.items()}
if hasattr(self, "primary_metric") and not self.primary_metric.startswith(name + "_"):
self.primary_metric = name + "_" + self.primary_metric
return metrics
def store_metrics_in_model_card_data(self, model: SentenceTransformer, metrics: dict[str, Any]) -> None:
model.model_card_data.set_evaluation_metrics(self, metrics)
@property
def description(self) -> str:
"""
Returns a human-readable description of the evaluator: BinaryClassificationEvaluator -> Binary Classification
1. Remove "Evaluator" from the class name
2. Add a space before every capital letter
"""
class_name = self.__class__.__name__
try:
index = class_name.index("Evaluator")
class_name = class_name[:index]
except IndexError:
pass
return re.sub(r"([a-z])([A-Z])", r"\g<1> \g<2>", class_name)
|
import pytest
from backend.util.request import validate_url
@pytest.mark.parametrize(
"url, trusted_origins, expected_value, should_raise",
[
# Rejected IP ranges
("localhost", [], None, True),
("192.168.1.1", [], None, True),
("127.0.0.1", [], None, True),
("0.0.0.0", [], None, True),
# Normal URLs (should default to http:// if no scheme provided)
("google.com/a?b=c", [], "http://google.com/a?b=c", False),
("github.com?key=!@!@", [], "http://github.com?key=!@!@", False),
# Scheme Enforcement
("ftp://example.com", [], None, True),
("file://example.com", [], None, True),
# International domain converting to punycode (allowed if public)
("http://xn--exmple-cua.com", [], "http://xn--exmple-cua.com", False),
# Invalid domain (IDNA failure)
("http://exa◌mple.com", [], None, True),
# IPv6 addresses (loopback/blocked)
("::1", [], None, True),
("http://[::1]", [], None, True),
# Suspicious Characters in Hostname
("http://example_underscore.com", [], None, True),
("http://exa mple.com", [], None, True),
# Malformed URLs
("http://", [], None, True), # No hostname
("://missing-scheme", [], None, True), # Missing proper scheme
# Trusted Origins
(
"internal-api.company.com",
["internal-api.company.com", "10.0.0.5"],
"http://internal-api.company.com",
False,
),
("10.0.0.5", ["10.0.0.5"], "http://10.0.0.5", False),
# Special Characters in Path
(
"example.com/path%20with%20spaces",
[],
"http://example.com/path%20with%20spaces",
False,
),
# Backslashes should be replaced with forward slashes
("http://example.com\\backslash", [], "http://example.com/backslash", False),
# Check default-scheme behavior for valid domains
("example.com", [], "http://example.com", False),
("https://secure.com", [], "https://secure.com", False),
# Non-ASCII Characters in Query/Fragment
("example.com?param=äöü", [], "http://example.com?param=äöü", False),
],
)
def test_validate_url_no_dns_rebinding(
url, trusted_origins, expected_value, should_raise
):
if should_raise:
with pytest.raises(ValueError):
validate_url(url, trusted_origins, enable_dns_rebinding=False)
else:
url, host = validate_url(url, trusted_origins, enable_dns_rebinding=False)
assert url == expected_value
@pytest.mark.parametrize(
"hostname, resolved_ips, expect_error, expected_ip",
[
# Multiple public IPs, none blocked
("public-example.com", ["8.8.8.8", "9.9.9.9"], False, "8.8.8.8"),
# Includes a blocked IP (e.g. link-local 169.254.x.x) => should raise
("rebinding.com", ["1.2.3.4", "169.254.169.254"], True, None),
# Single public IP
("single-public.com", ["8.8.8.8"], False, "8.8.8.8"),
# Single blocked IP
("blocked.com", ["127.0.0.1"], True, None),
],
)
def test_dns_rebinding_fix(
monkeypatch, hostname, resolved_ips, expect_error, expected_ip
):
"""
Tests that validate_url pins the first valid public IP address, and rejects
the domain if any of the resolved IPs are blocked (i.e., DNS Rebinding scenario).
"""
def mock_getaddrinfo(host, port, *args, **kwargs):
# Simulate multiple IPs returned for the given hostname
return [(None, None, None, None, (ip, port)) for ip in resolved_ips]
# Patch socket.getaddrinfo so we control the DNS resolution in the test
monkeypatch.setattr("socket.getaddrinfo", mock_getaddrinfo)
if expect_error:
# If any IP is blocked, we expect a ValueError
with pytest.raises(ValueError):
validate_url(hostname, [])
else:
pinned_url, ascii_hostname = validate_url(hostname, [])
# The pinned_url should contain the first valid IP
assert pinned_url.startswith("http://") or pinned_url.startswith("https://")
assert expected_ip in pinned_url
# The ascii_hostname should match our original hostname after IDNA encoding
assert ascii_hostname == hostname
|
import pytest
from backend.util.request import validate_url
def test_validate_url():
# Rejected IP ranges
with pytest.raises(ValueError):
validate_url("localhost", [])
with pytest.raises(ValueError):
validate_url("192.168.1.1", [])
with pytest.raises(ValueError):
validate_url("127.0.0.1", [])
with pytest.raises(ValueError):
validate_url("0.0.0.0", [])
# Normal URLs
assert validate_url("google.com/a?b=c", []) == "http://google.com/a?b=c"
assert validate_url("github.com?key=!@!@", []) == "http://github.com?key=!@!@"
# Scheme Enforcement
with pytest.raises(ValueError):
validate_url("ftp://example.com", [])
with pytest.raises(ValueError):
validate_url("file://example.com", [])
# International domain that converts to punycode - should be allowed if public
assert validate_url("http://xn--exmple-cua.com", []) == "http://xn--exmple-cua.com"
# If the domain fails IDNA encoding or is invalid, it should raise an error
with pytest.raises(ValueError):
validate_url("http://exa◌mple.com", [])
# IPv6 Addresses
with pytest.raises(ValueError):
validate_url("::1", []) # IPv6 loopback should be blocked
with pytest.raises(ValueError):
validate_url("http://[::1]", []) # IPv6 loopback in URL form
# Suspicious Characters in Hostname
with pytest.raises(ValueError):
validate_url("http://example_underscore.com", [])
with pytest.raises(ValueError):
validate_url("http://exa mple.com", []) # Space in hostname
# Malformed URLs
with pytest.raises(ValueError):
validate_url("http://", []) # No hostname
with pytest.raises(ValueError):
validate_url("://missing-scheme", []) # Missing proper scheme
# Trusted Origins
trusted = ["internal-api.company.com", "10.0.0.5"]
assert (
validate_url("internal-api.company.com", trusted)
== "http://internal-api.company.com"
)
assert validate_url("10.0.0.5", ["10.0.0.5"]) == "http://10.0.0.5"
# Special Characters in Path or Query
assert (
validate_url("example.com/path%20with%20spaces", [])
== "http://example.com/path%20with%20spaces"
)
# Backslashes should be replaced with forward slashes
assert (
validate_url("http://example.com\\backslash", [])
== "http://example.com/backslash"
)
# Check defaulting scheme behavior for valid domains
assert validate_url("example.com", []) == "http://example.com"
assert validate_url("https://secure.com", []) == "https://secure.com"
# Non-ASCII Characters in Query/Fragment
assert validate_url("example.com?param=äöü", []) == "http://example.com?param=äöü"
|
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Tuple, Dict, List
import numpy as np
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.indexers.dump import import_vectors
class NumpySearcher(Executor):
def __init__(
self,
dump_path: str = None,
default_top_k: int = 5,
default_traversal_paths: List[str] = ['r'],
metric: str = 'cosine',
is_distance: bool = False,
**kwargs,
):
super().__init__(**kwargs)
self.default_traversal_paths = default_traversal_paths
self.is_distance = is_distance
self.metric = metric
self.dump_path = dump_path or kwargs.get('runtime_args').get('dump_path')
self.logger = get_logger(self)
self.default_top_k = default_top_k
if self.dump_path is not None:
self.logger.info(f'Importing data from {self.dump_path}')
ids, vecs = import_vectors(self.dump_path, str(self.runtime_args.pea_id))
self._ids = np.array(list(ids))
self._vecs = np.array(list(vecs))
self._ids_to_idx = {}
self.logger.info(f'Imported {len(self._ids)} documents.')
else:
self.logger.warning(
f'No dump_path provided for {self.__class__.__name__}. Use flow.rolling_update()...'
)
@requests(on='/search')
def search(self, docs: 'DocumentArray', parameters: Dict = None, **kwargs):
if not hasattr(self, '_vecs') or not self._vecs.size:
self.logger.warning('Searching an empty index')
return
top_k = int(parameters.get('top_k', self.default_top_k))
traversal_paths = parameters.get(
'traversal_paths', self.default_traversal_paths
)
doc_embeddings = docs.traverse_flat(traversal_paths).get_attributes('embedding')
if not docs:
self.logger.info('No documents to search for')
return
if not doc_embeddings:
self.logger.info('None of the docs have any embeddings')
return
doc_embeddings = np.stack(doc_embeddings)
q_emb = _ext_A(_norm(doc_embeddings))
d_emb = _ext_B(_norm(self._vecs))
if self.metric == 'cosine':
dists = _cosine(q_emb, d_emb)
elif self.metric == 'euclidean':
dists = _euclidean(q_emb, d_emb)
else:
self.logger.error(f'Metric {self.metric} not supported.')
positions, dist = self._get_sorted_top_k(dists, top_k)
for _q, _positions, _dists in zip(docs, positions, dist):
for position, dist in zip(_positions, _dists):
d = Document(id=self._ids[position], embedding=self._vecs[position])
if self.is_distance:
d.scores[self.metric] = dist
else:
if self.metric == 'cosine':
d.scores[self.metric] = 1 - dist
elif self.metric == 'euclidean':
d.scores[self.metric] = 1 / (1 + dist)
_q.matches.append(d)
@staticmethod
def _get_sorted_top_k(
dist: 'np.array', top_k: int
) -> Tuple['np.ndarray', 'np.ndarray']:
if top_k >= dist.shape[1]:
idx = dist.argsort(axis=1)[:, :top_k]
dist = np.take_along_axis(dist, idx, axis=1)
else:
idx_ps = dist.argpartition(kth=top_k, axis=1)[:, :top_k]
dist = np.take_along_axis(dist, idx_ps, axis=1)
idx_fs = dist.argsort(axis=1)
idx = np.take_along_axis(idx_ps, idx_fs, axis=1)
dist = np.take_along_axis(dist, idx_fs, axis=1)
return idx, dist
def _get_ones(x, y):
return np.ones((x, y))
def _ext_A(A):
nA, dim = A.shape
A_ext = _get_ones(nA, dim * 3)
A_ext[:, dim: 2 * dim] = A
A_ext[:, 2 * dim:] = A ** 2
return A_ext
def _ext_B(B):
nB, dim = B.shape
B_ext = _get_ones(dim * 3, nB)
B_ext[:dim] = (B ** 2).T
B_ext[dim: 2 * dim] = -2.0 * B.T
del B
return B_ext
def _euclidean(A_ext, B_ext):
sqdist = A_ext.dot(B_ext).clip(min=0)
return np.sqrt(sqdist)
def _norm(A):
return A / np.linalg.norm(A, ord=2, axis=1, keepdims=True)
def _cosine(A_norm_ext, B_norm_ext):
return A_norm_ext.dot(B_norm_ext).clip(min=0) / 2
|
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Tuple, Dict, List
import numpy as np
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.indexers.dump import import_vectors
class NumpySearcher(Executor):
def __init__(
self,
dump_path: str = None,
default_top_k: int = 5,
default_traversal_paths: List[str] = ['r'],
**kwargs,
):
super().__init__(**kwargs)
self.default_traversal_paths = default_traversal_paths
self.dump_path = dump_path or kwargs.get('runtime_args').get('dump_path')
self.logger = get_logger(self)
self.default_top_k = default_top_k
if self.dump_path is not None:
self.logger.info(f'Importing data from {self.dump_path}')
ids, vecs = import_vectors(self.dump_path, str(self.runtime_args.pea_id))
self._ids = np.array(list(ids))
self._vecs = np.array(list(vecs))
self._ids_to_idx = {}
self.logger.info(f'Imported {len(self._ids)} documents.')
else:
self.logger.warning(
f'No dump_path provided for {self.__class__.__name__}. Use flow.rolling_update()...'
)
@requests(on='/search')
def search(self, docs: 'DocumentArray', parameters: Dict = None, **kwargs):
if not hasattr(self, '_vecs') or not self._vecs.size:
self.logger.warning('Searching an empty index')
return
top_k = int(parameters.get('top_k', self.default_top_k))
traversal_paths = parameters.get(
'traversal_paths', self.default_traversal_paths
)
doc_embeddings = np.stack(
docs.traverse_flat(traversal_paths).get_attributes('embedding')
)
q_emb = _ext_A(_norm(doc_embeddings))
d_emb = _ext_B(_norm(self._vecs))
dists = _cosine(q_emb, d_emb)
positions, dist = self._get_sorted_top_k(dists, top_k)
for _q, _positions, _dists in zip(docs, positions, dist):
for position, _dist in zip(_positions, _dists):
d = Document(id=self._ids[position], embedding=self._vecs[position])
d.scores['similarity'] = 1 - _dist
_q.matches.append(d)
@staticmethod
def _get_sorted_top_k(
dist: 'np.array', top_k: int
) -> Tuple['np.ndarray', 'np.ndarray']:
if top_k >= dist.shape[1]:
idx = dist.argsort(axis=1)[:, :top_k]
dist = np.take_along_axis(dist, idx, axis=1)
else:
idx_ps = dist.argpartition(kth=top_k, axis=1)[:, :top_k]
dist = np.take_along_axis(dist, idx_ps, axis=1)
idx_fs = dist.argsort(axis=1)
idx = np.take_along_axis(idx_ps, idx_fs, axis=1)
dist = np.take_along_axis(dist, idx_fs, axis=1)
return idx, dist
def _get_ones(x, y):
return np.ones((x, y))
def _ext_A(A):
nA, dim = A.shape
A_ext = _get_ones(nA, dim * 3)
A_ext[:, dim : 2 * dim] = A
A_ext[:, 2 * dim :] = A ** 2
return A_ext
def _ext_B(B):
nB, dim = B.shape
B_ext = _get_ones(dim * 3, nB)
B_ext[:dim] = (B ** 2).T
B_ext[dim : 2 * dim] = -2.0 * B.T
del B
return B_ext
def _euclidean(A_ext, B_ext):
sqdist = A_ext.dot(B_ext).clip(min=0)
return np.sqrt(sqdist)
def _norm(A):
return A / np.linalg.norm(A, ord=2, axis=1, keepdims=True)
def _cosine(A_norm_ext, B_norm_ext):
return A_norm_ext.dot(B_norm_ext).clip(min=0) / 2
|
from __future__ import annotations
import logging
from dataclasses import dataclass
from sentence_transformers.data_collator import SentenceTransformerDataCollator
logger = logging.getLogger(__name__)
@dataclass
class SparseEncoderDataCollator(SentenceTransformerDataCollator):
"""Collator for a SparseEncoder model. Overridden from SentenceTransformerDataCollator with nothing added.
This encodes the text columns to {column}_input_ids and {column}_attention_mask columns.
This works with the two text dataset that is used as the example in the training overview:
https://www.sbert.net/docs/sentence_transformer/training_overview.html
It is important that the columns are in the expected order. For example, if your dataset has columns
"answer", "question" in that order, then the MultipleNegativesRankingLoss will consider
"answer" as the anchor and "question" as the positive, and it will (unexpectedly) optimize for
"given the answer, what is the question?".
"""
|
from __future__ import annotations
import logging
from dataclasses import dataclass
from sentence_transformers.data_collator import SentenceTransformerDataCollator
logger = logging.getLogger(__name__)
@dataclass
class SparseEncoderDataCollator(SentenceTransformerDataCollator):
"""Collator for a SparseEncoder model. Overrided from SentenceTransformerDataCollator nothing added.
This encodes the text columns to {column}_input_ids and {column}_attention_mask columns.
This works with the two text dataset that is used as the example in the training overview:
https://www.sbert.net/docs/sentence_transformer/training_overview.html
It is important that the columns are in the expected order. For example, if your dataset has columns
"answer", "question" in that order, then the MultipleNegativesRankingLoss will consider
"answer" as the anchor and "question" as the positive, and it will (unexpectedly) optimize for
"given the answer, what is the question?".
"""
|
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class MaskScoringRCNN(TwoStageDetector):
"""Mask Scoring RCNN.
https://arxiv.org/abs/1903.00241
"""
def __init__(self,
backbone,
rpn_head,
roi_head,
train_cfg,
test_cfg,
neck=None,
pretrained=None,
init_cfg=None):
super(MaskScoringRCNN, self).__init__(
backbone=backbone,
neck=neck,
rpn_head=rpn_head,
roi_head=roi_head,
train_cfg=train_cfg,
test_cfg=test_cfg,
pretrained=pretrained,
init_cfg=init_cfg)
|
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class MaskScoringRCNN(TwoStageDetector):
"""Mask Scoring RCNN.
https://arxiv.org/abs/1903.00241
"""
def __init__(self,
backbone,
rpn_head,
roi_head,
train_cfg,
test_cfg,
neck=None,
pretrained=None,
init_cfg=None):
super(MaskScoringRCNN, self).__init__(
backbone=backbone,
neck=neck,
rpn_head=rpn_head,
roi_head=roi_head,
train_cfg=train_cfg,
test_cfg=test_cfg,
pretrained=pretrained,
init_cfg=init_cfg)
|
"""
LexRank implementation
Source: https://github.com/crabcamp/lexrank/tree/dev
"""
import logging
import numpy as np
from scipy.sparse.csgraph import connected_components
from scipy.special import softmax
logger = logging.getLogger(__name__)
def degree_centrality_scores(
similarity_matrix,
threshold=None,
increase_power=True,
):
if not (threshold is None or isinstance(threshold, float) and 0 <= threshold < 1):
raise ValueError(
"'threshold' should be a floating-point number " "from the interval [0, 1) or None",
)
if threshold is None:
markov_matrix = create_markov_matrix(similarity_matrix)
else:
markov_matrix = create_markov_matrix_discrete(
similarity_matrix,
threshold,
)
scores = stationary_distribution(
markov_matrix,
increase_power=increase_power,
normalized=False,
)
return scores
def _power_method(transition_matrix, increase_power=True, max_iter=10000):
eigenvector = np.ones(len(transition_matrix))
if len(eigenvector) == 1:
return eigenvector
transition = transition_matrix.transpose()
for _ in range(max_iter):
eigenvector_next = np.dot(transition, eigenvector)
if np.allclose(eigenvector_next, eigenvector):
return eigenvector_next
eigenvector = eigenvector_next
if increase_power:
transition = np.dot(transition, transition)
logger.warning("Maximum number of iterations for power method exceeded without convergence!")
return eigenvector_next
def connected_nodes(matrix):
_, labels = connected_components(matrix)
groups = []
for tag in np.unique(labels):
group = np.where(labels == tag)[0]
groups.append(group)
return groups
def create_markov_matrix(weights_matrix):
n_1, n_2 = weights_matrix.shape
if n_1 != n_2:
raise ValueError("'weights_matrix' should be square")
row_sum = weights_matrix.sum(axis=1, keepdims=True)
# normalize probability distribution differently if we have negative transition values
if np.min(weights_matrix) <= 0:
return softmax(weights_matrix, axis=1)
return weights_matrix / row_sum
def create_markov_matrix_discrete(weights_matrix, threshold):
discrete_weights_matrix = np.zeros(weights_matrix.shape)
ixs = np.where(weights_matrix >= threshold)
discrete_weights_matrix[ixs] = 1
return create_markov_matrix(discrete_weights_matrix)
def stationary_distribution(
transition_matrix,
increase_power=True,
normalized=True,
):
n_1, n_2 = transition_matrix.shape
if n_1 != n_2:
raise ValueError("'transition_matrix' should be square")
distribution = np.zeros(n_1)
grouped_indices = connected_nodes(transition_matrix)
for group in grouped_indices:
t_matrix = transition_matrix[np.ix_(group, group)]
eigenvector = _power_method(t_matrix, increase_power=increase_power)
distribution[group] = eigenvector
if normalized:
distribution /= n_1
return distribution
|
"""
LexRank implementation
Source: https://github.com/crabcamp/lexrank/tree/dev
"""
import numpy as np
from scipy.sparse.csgraph import connected_components
from scipy.special import softmax
import logging
logger = logging.getLogger(__name__)
def degree_centrality_scores(
similarity_matrix,
threshold=None,
increase_power=True,
):
if not (threshold is None or isinstance(threshold, float) and 0 <= threshold < 1):
raise ValueError(
"'threshold' should be a floating-point number " "from the interval [0, 1) or None",
)
if threshold is None:
markov_matrix = create_markov_matrix(similarity_matrix)
else:
markov_matrix = create_markov_matrix_discrete(
similarity_matrix,
threshold,
)
scores = stationary_distribution(
markov_matrix,
increase_power=increase_power,
normalized=False,
)
return scores
def _power_method(transition_matrix, increase_power=True, max_iter=10000):
eigenvector = np.ones(len(transition_matrix))
if len(eigenvector) == 1:
return eigenvector
transition = transition_matrix.transpose()
for _ in range(max_iter):
eigenvector_next = np.dot(transition, eigenvector)
if np.allclose(eigenvector_next, eigenvector):
return eigenvector_next
eigenvector = eigenvector_next
if increase_power:
transition = np.dot(transition, transition)
logger.warning("Maximum number of iterations for power method exceeded without convergence!")
return eigenvector_next
def connected_nodes(matrix):
_, labels = connected_components(matrix)
groups = []
for tag in np.unique(labels):
group = np.where(labels == tag)[0]
groups.append(group)
return groups
def create_markov_matrix(weights_matrix):
n_1, n_2 = weights_matrix.shape
if n_1 != n_2:
raise ValueError("'weights_matrix' should be square")
row_sum = weights_matrix.sum(axis=1, keepdims=True)
# normalize probability distribution differently if we have negative transition values
if np.min(weights_matrix) <= 0:
return softmax(weights_matrix, axis=1)
return weights_matrix / row_sum
def create_markov_matrix_discrete(weights_matrix, threshold):
discrete_weights_matrix = np.zeros(weights_matrix.shape)
ixs = np.where(weights_matrix >= threshold)
discrete_weights_matrix[ixs] = 1
return create_markov_matrix(discrete_weights_matrix)
def stationary_distribution(
transition_matrix,
increase_power=True,
normalized=True,
):
n_1, n_2 = transition_matrix.shape
if n_1 != n_2:
raise ValueError("'transition_matrix' should be square")
distribution = np.zeros(n_1)
grouped_indices = connected_nodes(transition_matrix)
for group in grouped_indices:
t_matrix = transition_matrix[np.ix_(group, group)]
eigenvector = _power_method(t_matrix, increase_power=increase_power)
distribution[group] = eigenvector
if normalized:
distribution /= n_1
return distribution
|
# dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', scale=(750, 1101), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(750, 1101), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='Anno/segmentation/DeepFashion_segmentation_train.json',
data_prefix=dict(img='Img/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='Anno/segmentation/DeepFashion_segmentation_query.json',
data_prefix=dict(img='Img/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='Anno/segmentation/DeepFashion_segmentation_gallery.json',
data_prefix=dict(img='Img/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root +
'Anno/segmentation/DeepFashion_segmentation_query.json',
metric=['bbox', 'segm'],
format_only=False,
backend_args=backend_args)
test_evaluator = dict(
type='CocoMetric',
ann_file=data_root +
'Anno/segmentation/DeepFashion_segmentation_gallery.json',
metric=['bbox', 'segm'],
format_only=False,
backend_args=backend_args)
|
# dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
train_pipeline = [
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', scale=(750, 1101), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(type='Resize', scale=(750, 1101), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='Anno/segmentation/DeepFashion_segmentation_train.json',
data_prefix=dict(img='Img/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline)))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='Anno/segmentation/DeepFashion_segmentation_query.json',
data_prefix=dict(img='Img/'),
test_mode=True,
pipeline=test_pipeline))
test_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='Anno/segmentation/DeepFashion_segmentation_gallery.json',
data_prefix=dict(img='Img/'),
test_mode=True,
pipeline=test_pipeline))
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root +
'Anno/segmentation/DeepFashion_segmentation_query.json',
metric=['bbox', 'segm'],
format_only=False)
test_evaluator = dict(
type='CocoMetric',
ann_file=data_root +
'Anno/segmentation/DeepFashion_segmentation_gallery.json',
metric=['bbox', 'segm'],
format_only=False)
|
# Copyright (c) OpenMMLab. All rights reserved.
import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
@contextlib.asynccontextmanager
async def completed(trace_name='',
name='',
sleep_interval=0.05,
streams: List[torch.cuda.Stream] = None):
"""Async context manager that waits for work to complete on given CUDA
streams."""
if not torch.cuda.is_available():
yield
return
stream_before_context_switch = torch.cuda.current_stream()
if not streams:
streams = [stream_before_context_switch]
else:
streams = [s if s else stream_before_context_switch for s in streams]
end_events = [
torch.cuda.Event(enable_timing=DEBUG_COMPLETED_TIME) for _ in streams
]
if DEBUG_COMPLETED_TIME:
start = torch.cuda.Event(enable_timing=True)
stream_before_context_switch.record_event(start)
cpu_start = time.monotonic()
logger.debug('%s %s starting, streams: %s', trace_name, name, streams)
grad_enabled_before = torch.is_grad_enabled()
try:
yield
finally:
current_stream = torch.cuda.current_stream()
assert current_stream == stream_before_context_switch
if DEBUG_COMPLETED_TIME:
cpu_end = time.monotonic()
for i, stream in enumerate(streams):
event = end_events[i]
stream.record_event(event)
grad_enabled_after = torch.is_grad_enabled()
# observed change of torch.is_grad_enabled() during concurrent run of
# async_test_bboxes code
assert (grad_enabled_before == grad_enabled_after
), 'Unexpected is_grad_enabled() value change'
are_done = [e.query() for e in end_events]
logger.debug('%s %s completed: %s streams: %s', trace_name, name,
are_done, streams)
with torch.cuda.stream(stream_before_context_switch):
while not all(are_done):
await asyncio.sleep(sleep_interval)
are_done = [e.query() for e in end_events]
logger.debug(
'%s %s completed: %s streams: %s',
trace_name,
name,
are_done,
streams,
)
current_stream = torch.cuda.current_stream()
assert current_stream == stream_before_context_switch
if DEBUG_COMPLETED_TIME:
cpu_time = (cpu_end - cpu_start) * 1000
stream_times_ms = ''
for i, stream in enumerate(streams):
elapsed_time = start.elapsed_time(end_events[i])
stream_times_ms += f' {stream} {elapsed_time:.2f} ms'
logger.info('%s %s %.2f ms %s', trace_name, name, cpu_time,
stream_times_ms)
@contextlib.asynccontextmanager
async def concurrent(streamqueue: asyncio.Queue,
trace_name='concurrent',
name='stream'):
"""Run code concurrently in different streams.
:param streamqueue: asyncio.Queue instance.
Queue tasks define the pool of streams used for concurrent execution.
"""
if not torch.cuda.is_available():
yield
return
initial_stream = torch.cuda.current_stream()
with torch.cuda.stream(initial_stream):
stream = await streamqueue.get()
assert isinstance(stream, torch.cuda.Stream)
try:
with torch.cuda.stream(stream):
logger.debug('%s %s is starting, stream: %s', trace_name, name,
stream)
yield
current = torch.cuda.current_stream()
assert current == stream
logger.debug('%s %s has finished, stream: %s', trace_name,
name, stream)
finally:
streamqueue.task_done()
streamqueue.put_nowait(stream)
|
import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
@contextlib.asynccontextmanager
async def completed(trace_name='',
name='',
sleep_interval=0.05,
streams: List[torch.cuda.Stream] = None):
"""Async context manager that waits for work to complete on given CUDA
streams."""
if not torch.cuda.is_available():
yield
return
stream_before_context_switch = torch.cuda.current_stream()
if not streams:
streams = [stream_before_context_switch]
else:
streams = [s if s else stream_before_context_switch for s in streams]
end_events = [
torch.cuda.Event(enable_timing=DEBUG_COMPLETED_TIME) for _ in streams
]
if DEBUG_COMPLETED_TIME:
start = torch.cuda.Event(enable_timing=True)
stream_before_context_switch.record_event(start)
cpu_start = time.monotonic()
logger.debug('%s %s starting, streams: %s', trace_name, name, streams)
grad_enabled_before = torch.is_grad_enabled()
try:
yield
finally:
current_stream = torch.cuda.current_stream()
assert current_stream == stream_before_context_switch
if DEBUG_COMPLETED_TIME:
cpu_end = time.monotonic()
for i, stream in enumerate(streams):
event = end_events[i]
stream.record_event(event)
grad_enabled_after = torch.is_grad_enabled()
# observed change of torch.is_grad_enabled() during concurrent run of
# async_test_bboxes code
assert (grad_enabled_before == grad_enabled_after
), 'Unexpected is_grad_enabled() value change'
are_done = [e.query() for e in end_events]
logger.debug('%s %s completed: %s streams: %s', trace_name, name,
are_done, streams)
with torch.cuda.stream(stream_before_context_switch):
while not all(are_done):
await asyncio.sleep(sleep_interval)
are_done = [e.query() for e in end_events]
logger.debug(
'%s %s completed: %s streams: %s',
trace_name,
name,
are_done,
streams,
)
current_stream = torch.cuda.current_stream()
assert current_stream == stream_before_context_switch
if DEBUG_COMPLETED_TIME:
cpu_time = (cpu_end - cpu_start) * 1000
stream_times_ms = ''
for i, stream in enumerate(streams):
elapsed_time = start.elapsed_time(end_events[i])
stream_times_ms += f' {stream} {elapsed_time:.2f} ms'
logger.info('%s %s %.2f ms %s', trace_name, name, cpu_time,
stream_times_ms)
@contextlib.asynccontextmanager
async def concurrent(streamqueue: asyncio.Queue,
trace_name='concurrent',
name='stream'):
"""Run code concurrently in different streams.
:param streamqueue: asyncio.Queue instance.
Queue tasks define the pool of streams used for concurrent execution.
"""
if not torch.cuda.is_available():
yield
return
initial_stream = torch.cuda.current_stream()
with torch.cuda.stream(initial_stream):
stream = await streamqueue.get()
assert isinstance(stream, torch.cuda.Stream)
try:
with torch.cuda.stream(stream):
logger.debug('%s %s is starting, stream: %s', trace_name, name,
stream)
yield
current = torch.cuda.current_stream()
assert current == stream
logger.debug('%s %s has finished, stream: %s', trace_name,
name, stream)
finally:
streamqueue.task_done()
streamqueue.put_nowait(stream)
|
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# dataset settings
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
|
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# dataset settings
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=[(1333, 480), (1333, 960)],
resize_cfg=dict(type='Resize', keep_ratio=True)),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
|
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import Any, Dict, Optional
from docarray import BaseDoc, DocList
from docarray.typing import AnyEmbedding, AnyTensor
class LegacyDocument(BaseDoc):
"""
This Document is the LegacyDocument. It follows the same schema as in DocArray <=0.21.
It can be useful to start migrating a codebase from v1 to v2.
Nevertheless, the API is not totally compatible with DocArray <=0.21 `Document`.
Indeed, none of the method associated with `Document` are present. Only the schema
of the data is similar.
```python
from docarray import DocList
from docarray.documents.legacy import LegacyDocument
import numpy as np
doc = LegacyDocument(text='hello')
doc.url = 'http://myimg.png'
doc.tensor = np.zeros((3, 224, 224))
doc.embedding = np.zeros((100, 1))
doc.tags['price'] = 10
doc.chunks = DocList[Document]([Document() for _ in range(10)])
doc.chunks = DocList[Document]([Document() for _ in range(10)])
```
"""
tensor: Optional[AnyTensor] = None
chunks: Optional[DocList[LegacyDocument]] = None
matches: Optional[DocList[LegacyDocument]] = None
blob: Optional[bytes] = None
text: Optional[str] = None
url: Optional[str] = None
embedding: Optional[AnyEmbedding] = None
tags: Dict[str, Any] = dict()
scores: Optional[Dict[str, Any]] = None
|
from __future__ import annotations
from typing import Any, Dict, Optional
from docarray import BaseDoc, DocList
from docarray.typing import AnyEmbedding, AnyTensor
class LegacyDocument(BaseDoc):
"""
This Document is the LegacyDocument. It follows the same schema as in DocArray <=0.21.
It can be useful to start migrating a codebase from v1 to v2.
Nevertheless, the API is not totally compatible with DocArray <=0.21 `Document`.
Indeed, none of the method associated with `Document` are present. Only the schema
of the data is similar.
```python
from docarray import DocList
from docarray.documents.legacy import LegacyDocument
import numpy as np
doc = LegacyDocument(text='hello')
doc.url = 'http://myimg.png'
doc.tensor = np.zeros((3, 224, 224))
doc.embedding = np.zeros((100, 1))
doc.tags['price'] = 10
doc.chunks = DocList[Document]([Document() for _ in range(10)])
doc.chunks = DocList[Document]([Document() for _ in range(10)])
```
"""
tensor: Optional[AnyTensor] = None
chunks: Optional[DocList[LegacyDocument]] = None
matches: Optional[DocList[LegacyDocument]] = None
blob: Optional[bytes] = None
text: Optional[str] = None
url: Optional[str] = None
embedding: Optional[AnyEmbedding] = None
tags: Dict[str, Any] = dict()
scores: Optional[Dict[str, Any]] = None
|
from docarray.array.document import DocumentArray
from docarray.array.storage.annlite import StorageMixins, AnnliteConfig
__all__ = ['AnnliteConfig', 'DocumentArrayAnnlite']
class DocumentArrayAnnlite(StorageMixins, DocumentArray):
"""
DocumentArray that stores Documents in `ANNLite <https://github.com/jina-ai/annlite>`_.
.. note::
This DocumentArray requires `annlite`. You can install it via `pip install "docarray[annlite]"`.
With this implementation, :meth:`match` and :meth:`find` perform fast (approximate) vector search.
Additionally, search with filters on a :class:`~docarray.document.Document` s :attr:`~docarray.document.Document.tags` attribute is supported.
Example usage:
.. code-block:: python
from docarray import Document, DocumentArray
import numpy as np
da = DocumentArray(storage='annlite', config={'data_path': './data', 'n_dim': 10})
n_dim = 3
da = DocumentArray(
storage='annlite',
config={
'n_dim': n_dim,
'columns': [('price', 'float')],
},
)
with da:
da.extend([Document(id=f'r{i}', tags={'price': i}) for i in range(10)])
max_price = 3
n_limit = 4
filter = {'price': {'$lte': max_price}}
results = da.find(filter=filter)
.. seealso::
For further details, see our :ref:`user guide <annlite>`.
"""
def __new__(cls, *args, **kwargs):
return super().__new__(cls)
|
from .document import DocumentArray
from .storage.annlite import StorageMixins, AnnliteConfig
__all__ = ['AnnliteConfig', 'DocumentArrayAnnlite']
class DocumentArrayAnnlite(StorageMixins, DocumentArray):
"""
DocumentArray that stores Documents in `ANNLite <https://github.com/jina-ai/annlite>`_.
.. note::
This DocumentArray requires `annlite`. You can install it via `pip install "docarray[annlite]"`.
With this implementation, :meth:`match` and :meth:`find` perform fast (approximate) vector search.
Additionally, search with filters on a :class:`~docarray.document.Document` s :attr:`~docarray.document.Document.tags` attribute is supported.
Example usage:
.. code-block:: python
from docarray import Document, DocumentArray
import numpy as np
da = DocumentArray(storage='annlite', config={'data_path': './data', 'n_dim': 10})
n_dim = 3
da = DocumentArray(
storage='annlite',
config={
'n_dim': n_dim,
'columns': [('price', 'float')],
},
)
with da:
da.extend([Document(id=f'r{i}', tags={'price': i}) for i in range(10)])
max_price = 3
n_limit = 4
filter = {'price': {'$lte': max_price}}
results = da.find(filter=filter)
.. seealso::
For further details, see our :ref:`user guide <annlite>`.
"""
def __new__(cls, *args, **kwargs):
return super().__new__(cls)
|
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
# MMEngine support the following two ways, users can choose
# according to convenience
# optim_wrapper = dict(type='AmpOptimWrapper')
_base_.optim_wrapper.type = 'AmpOptimWrapper'
|
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
fp16 = dict(loss_scale=512.)
|
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AudioTorchTensor, AudioUrl
from docarray.utils.misc import is_tf_available
from tests import TOYDATA_DIR
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.typing.tensor.audio import AudioTensorFlowTensor
AUDIO_FILES = [
str(TOYDATA_DIR / 'hello.wav'),
str(TOYDATA_DIR / 'olleh.wav'),
]
REMOTE_AUDIO_FILE = 'https://github.com/docarray/docarray/blob/feat-rewrite-v2/tests/toydata/olleh.wav?raw=true' # noqa: E501
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize(
'file_url',
[*AUDIO_FILES, REMOTE_AUDIO_FILE],
)
def test_audio_url(file_url):
uri = parse_obj_as(AudioUrl, file_url)
tensor = uri.load()
assert isinstance(tensor, np.ndarray)
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize(
'file_url',
[*AUDIO_FILES, REMOTE_AUDIO_FILE],
)
def test_load_audio_url_to_audio_torch_tensor_field(file_url):
class MyAudioDoc(BaseDocument):
audio_url: AudioUrl
tensor: Optional[AudioTorchTensor]
doc = MyAudioDoc(audio_url=file_url)
doc.tensor = doc.audio_url.load()
assert isinstance(doc.tensor, torch.Tensor)
assert isinstance(doc.tensor, AudioTorchTensor)
@pytest.mark.tensorflow
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize(
'file_url',
[*AUDIO_FILES, REMOTE_AUDIO_FILE],
)
def test_load_audio_url_to_audio_tensorflow_tensor_field(file_url):
class MyAudioDoc(BaseDocument):
audio_url: AudioUrl
tensor: Optional[AudioTensorFlowTensor]
doc = MyAudioDoc(audio_url=file_url)
doc.tensor = doc.audio_url.load()
assert isinstance(doc.tensor, AudioTensorFlowTensor)
assert isinstance(doc.tensor.tensor, tf.Tensor)
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize(
'file_url',
[*AUDIO_FILES, REMOTE_AUDIO_FILE],
)
def test_load(file_url):
url = parse_obj_as(AudioUrl, file_url)
tensor = url.load()
assert isinstance(tensor, np.ndarray)
def test_json_schema():
schema_json_of(AudioUrl)
def test_dump_json():
url = parse_obj_as(AudioUrl, REMOTE_AUDIO_FILE)
orjson_dumps(url)
@pytest.mark.parametrize(
'path_to_file',
[
*[file for file in AUDIO_FILES],
REMOTE_AUDIO_FILE,
],
)
def test_validation(path_to_file):
url = parse_obj_as(AudioUrl, path_to_file)
assert isinstance(url, AudioUrl)
assert isinstance(url, str)
@pytest.mark.parametrize(
'path_to_file',
[
'illegal',
'https://www.google.com',
'my/local/text/file.txt',
'my/local/text/file.png',
],
)
def test_illegal_validation(path_to_file):
with pytest.raises(ValueError, match='AudioUrl'):
parse_obj_as(AudioUrl, path_to_file)
@pytest.mark.proto
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize(
'file_url',
[*AUDIO_FILES, REMOTE_AUDIO_FILE],
)
def test_proto_audio_url(file_url):
uri = parse_obj_as(AudioUrl, file_url)
proto = uri._to_node_protobuf()
assert 'audio_url' in str(proto)
|
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AudioTorchTensor, AudioUrl
from tests import TOYDATA_DIR
AUDIO_FILES = [
str(TOYDATA_DIR / 'hello.wav'),
str(TOYDATA_DIR / 'olleh.wav'),
]
REMOTE_AUDIO_FILE = 'https://github.com/docarray/docarray/blob/feat-rewrite-v2/tests/toydata/olleh.wav?raw=true' # noqa: E501
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize(
'file_url',
[*AUDIO_FILES, REMOTE_AUDIO_FILE],
)
def test_audio_url(file_url):
uri = parse_obj_as(AudioUrl, file_url)
tensor = uri.load()
assert isinstance(tensor, np.ndarray)
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize(
'file_url',
[*AUDIO_FILES, REMOTE_AUDIO_FILE],
)
def test_load_audio_url_to_audio_torch_tensor_field(file_url):
class MyAudioDoc(BaseDocument):
audio_url: AudioUrl
tensor: Optional[AudioTorchTensor]
doc = MyAudioDoc(audio_url=file_url)
doc.tensor = doc.audio_url.load()
assert isinstance(doc.tensor, torch.Tensor)
assert isinstance(doc.tensor, AudioTorchTensor)
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize(
'file_url',
[*AUDIO_FILES, REMOTE_AUDIO_FILE],
)
def test_load(file_url):
url = parse_obj_as(AudioUrl, file_url)
tensor = url.load()
assert isinstance(tensor, np.ndarray)
def test_json_schema():
schema_json_of(AudioUrl)
def test_dump_json():
url = parse_obj_as(AudioUrl, REMOTE_AUDIO_FILE)
orjson_dumps(url)
@pytest.mark.parametrize(
'path_to_file',
[
*[file for file in AUDIO_FILES],
REMOTE_AUDIO_FILE,
],
)
def test_validation(path_to_file):
url = parse_obj_as(AudioUrl, path_to_file)
assert isinstance(url, AudioUrl)
assert isinstance(url, str)
@pytest.mark.parametrize(
'path_to_file',
[
'illegal',
'https://www.google.com',
'my/local/text/file.txt',
'my/local/text/file.png',
],
)
def test_illegal_validation(path_to_file):
with pytest.raises(ValueError, match='AudioUrl'):
parse_obj_as(AudioUrl, path_to_file)
@pytest.mark.proto
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize(
'file_url',
[*AUDIO_FILES, REMOTE_AUDIO_FILE],
)
def test_proto_audio_url(file_url):
uri = parse_obj_as(AudioUrl, file_url)
proto = uri._to_node_protobuf()
assert 'audio_url' in str(proto)
|
# Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
import mmcv
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector)
from mmdet.registry import VISUALIZERS
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Image file')
parser.add_argument('config', help='Config file')
parser.add_argument('checkpoint', help='Checkpoint file')
parser.add_argument('--out-file', default=None, help='Path to output file')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference')
parser.add_argument(
'--palette',
default='none',
choices=['coco', 'voc', 'citys', 'random', 'none'],
help='Color palette used for visualization')
parser.add_argument(
'--score-thr', type=float, default=0.3, help='bbox score threshold')
parser.add_argument(
'--async-test',
action='store_true',
help='whether to set async options for async inference.')
args = parser.parse_args()
return args
def main(args):
# TODO: Support inference of image directory.
# build the model from a config file and a checkpoint file
model = init_detector(
args.config, args.checkpoint, palette=args.palette, device=args.device)
# init visualizer
visualizer = VISUALIZERS.build(model.cfg.visualizer)
# the dataset_meta is loaded from the checkpoint and
# then pass to the model in init_detector
visualizer.dataset_meta = model.dataset_meta
# test a single image
result = inference_detector(model, args.img)
# show the results
img = mmcv.imread(args.img)
img = mmcv.imconvert(img, 'bgr', 'rgb')
visualizer.add_datasample(
'result',
img,
data_sample=result,
draw_gt=False,
show=args.out_file is None,
wait_time=0,
out_file=args.out_file,
pred_score_thr=args.score_thr)
async def async_main(args):
# build the model from a config file and a checkpoint file
model = init_detector(args.config, args.checkpoint, device=args.device)
# init visualizer
visualizer = VISUALIZERS.build(model.cfg.visualizer)
visualizer.dataset_meta = model.dataset_meta
# test a single image
tasks = asyncio.create_task(async_inference_detector(model, args.img))
result = await asyncio.gather(tasks)
# show the results
img = mmcv.imread(args.img)
img = mmcv.imconvert(img, 'bgr', 'rgb')
visualizer.add_datasample(
'result',
img,
pred_sample=result[0],
show=args.out_file is None,
wait_time=0,
out_file=args.out_file,
pred_score_thr=args.score_thr)
if __name__ == '__main__':
args = parse_args()
assert not args.async_test, 'async inference is not supported yet.'
if args.async_test:
asyncio.run(async_main(args))
else:
main(args)
|
# Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
import mmcv
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector)
from mmdet.registry import VISUALIZERS
from mmdet.utils import register_all_modules
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Image file')
parser.add_argument('config', help='Config file')
parser.add_argument('checkpoint', help='Checkpoint file')
parser.add_argument('--out-file', default=None, help='Path to output file')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference')
parser.add_argument(
'--palette',
default='none',
choices=['coco', 'voc', 'citys', 'random', 'none'],
help='Color palette used for visualization')
parser.add_argument(
'--score-thr', type=float, default=0.3, help='bbox score threshold')
parser.add_argument(
'--async-test',
action='store_true',
help='whether to set async options for async inference.')
args = parser.parse_args()
return args
def main(args):
# register all modules in mmdet into the registries
register_all_modules()
# TODO: Support inference of image directory.
# build the model from a config file and a checkpoint file
model = init_detector(
args.config, args.checkpoint, palette=args.palette, device=args.device)
# init visualizer
visualizer = VISUALIZERS.build(model.cfg.visualizer)
# the dataset_meta is loaded from the checkpoint and
# then pass to the model in init_detector
visualizer.dataset_meta = model.dataset_meta
# test a single image
result = inference_detector(model, args.img)
# show the results
img = mmcv.imread(args.img)
img = mmcv.imconvert(img, 'bgr', 'rgb')
visualizer.add_datasample(
'result',
img,
data_sample=result,
draw_gt=False,
show=args.out_file is None,
wait_time=0,
out_file=args.out_file,
pred_score_thr=args.score_thr)
async def async_main(args):
# build the model from a config file and a checkpoint file
model = init_detector(args.config, args.checkpoint, device=args.device)
# init visualizer
visualizer = VISUALIZERS.build(model.cfg.visualizer)
visualizer.dataset_meta = model.dataset_meta
# test a single image
tasks = asyncio.create_task(async_inference_detector(model, args.img))
result = await asyncio.gather(tasks)
# show the results
img = mmcv.imread(args.img)
img = mmcv.imconvert(img, 'bgr', 'rgb')
visualizer.add_datasample(
'result',
img,
pred_sample=result[0],
show=args.out_file is None,
wait_time=0,
out_file=args.out_file,
pred_score_thr=args.score_thr)
if __name__ == '__main__':
args = parse_args()
assert not args.async_test, 'async inference is not supported yet.'
if args.async_test:
asyncio.run(async_main(args))
else:
main(args)
|
from langchain_core.prompts.chat import (
ChatPromptTemplate,
)
from langchain_core.prompts.prompt import PromptTemplate
from langchain.chains.prompt_selector import ConditionalPromptSelector, is_chat_model
DEFAULT_REFINE_PROMPT_TMPL = (
"The original question is as follows: {question}\n"
"We have provided an existing answer: {existing_answer}\n"
"We have the opportunity to refine the existing answer "
"(only if needed) with some more context below.\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"answer the question. "
"If the context isn't useful, return the original answer."
)
DEFAULT_REFINE_PROMPT = PromptTemplate.from_template(DEFAULT_REFINE_PROMPT_TMPL)
refine_template = (
"We have the opportunity to refine the existing answer "
"(only if needed) with some more context below.\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"answer the question. "
"If the context isn't useful, return the original answer."
)
CHAT_REFINE_PROMPT = ChatPromptTemplate.from_messages(
[
("human", "{question}"),
("ai", "{existing_answer}"),
("human", refine_template),
]
)
REFINE_PROMPT_SELECTOR = ConditionalPromptSelector(
default_prompt=DEFAULT_REFINE_PROMPT,
conditionals=[(is_chat_model, CHAT_REFINE_PROMPT)],
)
DEFAULT_TEXT_QA_PROMPT_TMPL = (
"Context information is below. \n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the context information and not prior knowledge, "
"answer the question: {question}\n"
)
DEFAULT_TEXT_QA_PROMPT = PromptTemplate.from_template(DEFAULT_TEXT_QA_PROMPT_TMPL)
chat_qa_prompt_template = (
"Context information is below.\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the context information and not prior knowledge, "
"answer any questions"
)
CHAT_QUESTION_PROMPT = ChatPromptTemplate.from_messages(
[
("system", chat_qa_prompt_template),
("human", "{question}"),
]
)
QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(
default_prompt=DEFAULT_TEXT_QA_PROMPT,
conditionals=[(is_chat_model, CHAT_QUESTION_PROMPT)],
)
|
# flake8: noqa
from langchain.chains.prompt_selector import ConditionalPromptSelector, is_chat_model
from langchain_core.prompts.chat import (
AIMessagePromptTemplate,
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain_core.prompts.prompt import PromptTemplate
DEFAULT_REFINE_PROMPT_TMPL = (
"The original question is as follows: {question}\n"
"We have provided an existing answer: {existing_answer}\n"
"We have the opportunity to refine the existing answer "
"(only if needed) with some more context below.\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"answer the question. "
"If the context isn't useful, return the original answer."
)
DEFAULT_REFINE_PROMPT = PromptTemplate.from_template(DEFAULT_REFINE_PROMPT_TMPL)
refine_template = (
"We have the opportunity to refine the existing answer "
"(only if needed) with some more context below.\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"answer the question. "
"If the context isn't useful, return the original answer."
)
CHAT_REFINE_PROMPT = ChatPromptTemplate.from_messages(
[
("human", "{question}"),
("ai", "{existing_answer}"),
("human", refine_template),
]
)
REFINE_PROMPT_SELECTOR = ConditionalPromptSelector(
default_prompt=DEFAULT_REFINE_PROMPT,
conditionals=[(is_chat_model, CHAT_REFINE_PROMPT)],
)
DEFAULT_TEXT_QA_PROMPT_TMPL = (
"Context information is below. \n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the context information and not prior knowledge, "
"answer the question: {question}\n"
)
DEFAULT_TEXT_QA_PROMPT = PromptTemplate.from_template(DEFAULT_TEXT_QA_PROMPT_TMPL)
chat_qa_prompt_template = (
"Context information is below.\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the context information and not prior knowledge, "
"answer any questions"
)
CHAT_QUESTION_PROMPT = ChatPromptTemplate.from_messages(
[
("system", chat_qa_prompt_template),
("human", "{question}"),
]
)
QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(
default_prompt=DEFAULT_TEXT_QA_PROMPT,
conditionals=[(is_chat_model, CHAT_QUESTION_PROMPT)],
)
|
"""Query Understanding agent pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.agent import AgentRunner
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llms.llm import LLM
from llama_index.core.tools.types import BaseTool
from llama_index.llms.openai import OpenAI
from .step import QueryUnderstandingAgentWorker
class QueryUnderstandingAgentPack(BaseLlamaPack):
"""
LLMCompilerAgent pack.
Args:
tools (List[BaseTool]): List of tools to use.
llm (Optional[LLM]): LLM to use.
"""
def __init__(
self,
tools: List[BaseTool],
llm: Optional[LLM] = None,
callback_manager: Optional[CallbackManager] = None,
agent_worker_kwargs: Optional[Dict[str, Any]] = None,
agent_runner_kwargs: Optional[Dict[str, Any]] = None,
) -> None:
"""Init params."""
self.llm = llm or OpenAI(model="gpt-4")
self.callback_manager = callback_manager or self.llm.callback_manager
self.agent_worker = QueryUnderstandingAgentWorker.from_tools(
tools,
llm=llm,
verbose=True,
callback_manager=self.callback_manager,
**(agent_worker_kwargs or {})
)
self.agent = AgentRunner(
self.agent_worker,
callback_manager=self.callback_manager,
**(agent_runner_kwargs or {})
)
def get_modules(self) -> Dict[str, Any]:
"""Get modules."""
return {
"llm": self.llm,
"callback_manager": self.callback_manager,
"agent_worker": self.agent_worker,
"agent": self.agent,
}
def run(self, *args: Any, **kwargs: Any) -> Any:
"""Run the pipeline."""
return self.agent.chat(*args, **kwargs)
|
"""Query Understanding agent pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.agent import AgentRunner
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llms.llm import LLM
from llama_index.core.tools.types import BaseTool
from llama_index.llms.openai import OpenAI
from .step import QueryUnderstandingAgentWorker
class QueryUnderstandingAgentPack(BaseLlamaPack):
"""LLMCompilerAgent pack.
Args:
tools (List[BaseTool]): List of tools to use.
llm (Optional[LLM]): LLM to use.
"""
def __init__(
self,
tools: List[BaseTool],
llm: Optional[LLM] = None,
callback_manager: Optional[CallbackManager] = None,
agent_worker_kwargs: Optional[Dict[str, Any]] = None,
agent_runner_kwargs: Optional[Dict[str, Any]] = None,
) -> None:
"""Init params."""
self.llm = llm or OpenAI(model="gpt-4")
self.callback_manager = callback_manager or self.llm.callback_manager
self.agent_worker = QueryUnderstandingAgentWorker.from_tools(
tools,
llm=llm,
verbose=True,
callback_manager=self.callback_manager,
**(agent_worker_kwargs or {})
)
self.agent = AgentRunner(
self.agent_worker,
callback_manager=self.callback_manager,
**(agent_runner_kwargs or {})
)
def get_modules(self) -> Dict[str, Any]:
"""Get modules."""
return {
"llm": self.llm,
"callback_manager": self.callback_manager,
"agent_worker": self.agent_worker,
"agent": self.agent,
}
def run(self, *args: Any, **kwargs: Any) -> Any:
"""Run the pipeline."""
return self.agent.chat(*args, **kwargs)
|
import pytest
from langchain.evaluation import ExactMatchStringEvaluator
@pytest.fixture
def exact_match_string_evaluator() -> ExactMatchStringEvaluator:
"""Create an ExactMatchStringEvaluator with default configuration."""
return ExactMatchStringEvaluator()
@pytest.fixture
def exact_match_string_evaluator_ignore_case() -> ExactMatchStringEvaluator:
"""Create an ExactMatchStringEvaluator with ignore_case set to True."""
return ExactMatchStringEvaluator(ignore_case=True)
def test_default_exact_matching(
exact_match_string_evaluator: ExactMatchStringEvaluator,
) -> None:
prediction = "Mindy is the CTO"
reference = "Mindy is the CTO"
result = exact_match_string_evaluator.evaluate_strings(
prediction=prediction,
reference=reference,
)
assert result["score"] == 1.0
reference = "Mindy is the CEO"
result = exact_match_string_evaluator.evaluate_strings(
prediction=prediction,
reference=reference,
)
assert result["score"] == 0.0
def test_exact_matching_with_ignore_case(
exact_match_string_evaluator_ignore_case: ExactMatchStringEvaluator,
) -> None:
prediction = "Mindy is the CTO"
reference = "mindy is the cto"
result = exact_match_string_evaluator_ignore_case.evaluate_strings(
prediction=prediction,
reference=reference,
)
assert result["score"] == 1.0
reference = "mindy is the CEO"
result = exact_match_string_evaluator_ignore_case.evaluate_strings(
prediction=prediction,
reference=reference,
)
assert result["score"] == 0.0
|
import pytest
from langchain.evaluation import ExactMatchStringEvaluator
@pytest.fixture
def exact_match_string_evaluator() -> ExactMatchStringEvaluator:
"""Create an ExactMatchStringEvaluator with default configuration."""
return ExactMatchStringEvaluator()
@pytest.fixture
def exact_match_string_evaluator_ignore_case() -> ExactMatchStringEvaluator:
"""Create an ExactMatchStringEvaluator with ignore_case set to True."""
return ExactMatchStringEvaluator(ignore_case=True)
def test_default_exact_matching(
exact_match_string_evaluator: ExactMatchStringEvaluator,
) -> None:
prediction = "Mindy is the CTO"
reference = "Mindy is the CTO"
result = exact_match_string_evaluator.evaluate_strings(
prediction=prediction, reference=reference
)
assert result["score"] == 1.0
reference = "Mindy is the CEO"
result = exact_match_string_evaluator.evaluate_strings(
prediction=prediction, reference=reference
)
assert result["score"] == 0.0
def test_exact_matching_with_ignore_case(
exact_match_string_evaluator_ignore_case: ExactMatchStringEvaluator,
) -> None:
prediction = "Mindy is the CTO"
reference = "mindy is the cto"
result = exact_match_string_evaluator_ignore_case.evaluate_strings(
prediction=prediction, reference=reference
)
assert result["score"] == 1.0
reference = "mindy is the CEO"
result = exact_match_string_evaluator_ignore_case.evaluate_strings(
prediction=prediction, reference=reference
)
assert result["score"] == 0.0
|
import grpc
import pytest
from jina import Flow
from jina.clients import Client
from jina.serve.helper import get_server_side_grpc_options
from jina.serve.runtimes.gateway.grpc import GRPCGateway
from tests import random_docs
@pytest.fixture(scope='function')
def flow_with_grpc():
class AuthInterceptor(grpc.aio.ServerInterceptor):
def __init__(self, key):
self._valid_metadata = ('rpc-auth-header', key)
def deny(_, context):
context.abort(grpc.StatusCode.UNAUTHENTICATED, 'Invalid key')
self._deny = grpc.unary_unary_rpc_method_handler(deny)
async def intercept_service(self, continuation, handler_call_details):
meta = handler_call_details.invocation_metadata
metas_dicts = {m.key: m.value for m in meta}
assert 'rpc-auth-header' in metas_dicts
assert (
metas_dicts['rpc-auth-header'] == 'access_key'
), f'Invalid access key detected, got {metas_dicts["rpc-auth-header"]}'
for m in meta:
if m == self._valid_metadata:
return await continuation(handler_call_details)
return self._deny
class AlternativeGRPCGateway(GRPCGateway):
def __init__(self, *args, **kwargs):
super(AlternativeGRPCGateway, self).__init__(*args, **kwargs)
self.server = grpc.aio.server(
interceptors=(AuthInterceptor('access_key'),),
options=get_server_side_grpc_options(self.grpc_server_options),
)
return Flow(protocol='grpc', uses=AlternativeGRPCGateway).add()
def test_client_grpc_kwargs(flow_with_grpc):
with flow_with_grpc:
client = Client(
port=flow_with_grpc.port,
host='localhost',
protocol='grpc',
)
meta_data = (('rpc-auth-header', 'invalid_access_key'),)
try:
client.post('', random_docs(1), request_size=1, metadata=meta_data)
except Exception as exc:
assert 'Invalid access key detected, got invalid_access_key' in repr(exc)
|
import grpc
import pytest
from jina import Flow
from jina.clients import Client
from jina.serve.helper import get_server_side_grpc_options
from jina.serve.runtimes.gateway.grpc import GRPCGateway
from tests import random_docs
@pytest.fixture(scope='function')
def flow_with_grpc(monkeypatch):
class AuthInterceptor(grpc.aio.ServerInterceptor):
def __init__(self, key):
self._valid_metadata = ('rpc-auth-header', key)
def deny(_, context):
context.abort(grpc.StatusCode.UNAUTHENTICATED, 'Invalid key')
self._deny = grpc.unary_unary_rpc_method_handler(deny)
async def intercept_service(self, continuation, handler_call_details):
meta = handler_call_details.invocation_metadata
metas_dicts = {m.key: m.value for m in meta}
assert 'rpc-auth-header' in metas_dicts
assert (
metas_dicts['rpc-auth-header'] == 'access_key'
), f'Invalid access key detected, got {metas_dicts["rpc-auth-header"]}'
for m in meta:
if m == self._valid_metadata:
return await continuation(handler_call_details)
return self._deny
class AlternativeGRPCGateway(GRPCGateway):
def __init__(self, *args, **kwargs):
super(AlternativeGRPCGateway, self).__init__(*args, **kwargs)
self.server = grpc.aio.server(
interceptors=(AuthInterceptor('access_key'),),
options=get_server_side_grpc_options(self.grpc_server_options),
)
return Flow(protocol='grpc', uses=AlternativeGRPCGateway).add()
def test_client_grpc_kwargs(flow_with_grpc):
with flow_with_grpc:
client = Client(
port=flow_with_grpc.port,
host='localhost',
protocol='grpc',
)
meta_data = (('rpc-auth-header', 'invalid_access_key'),)
try:
client.post('', random_docs(1), request_size=1, metadata=meta_data)
except Exception as exc:
assert 'Invalid access key detected, got invalid_access_key' in repr(exc)
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import (
GoogleApiClient,
GoogleApiYoutubeLoader,
YoutubeLoader,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"YoutubeLoader": "langchain_community.document_loaders",
"GoogleApiYoutubeLoader": "langchain_community.document_loaders",
"GoogleApiClient": "langchain_community.document_loaders",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"GoogleApiClient",
"GoogleApiYoutubeLoader",
"YoutubeLoader",
]
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import (
GoogleApiClient,
GoogleApiYoutubeLoader,
YoutubeLoader,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"YoutubeLoader": "langchain_community.document_loaders",
"GoogleApiYoutubeLoader": "langchain_community.document_loaders",
"GoogleApiClient": "langchain_community.document_loaders",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"YoutubeLoader",
"GoogleApiYoutubeLoader",
"GoogleApiClient",
]
|
from typing import Optional
import numpy as np
import torch
from docarray import DocumentArray
from docarray.base_document import BaseDocument
from docarray.typing import NdArray, TorchTensor
def test_proto_simple():
class CustomDoc(BaseDocument):
text: str
doc = CustomDoc(text='hello')
CustomDoc.from_protobuf(doc.to_protobuf())
def test_proto_ndarray():
class CustomDoc(BaseDocument):
tensor: NdArray
tensor = np.zeros((3, 224, 224))
doc = CustomDoc(tensor=tensor)
new_doc = CustomDoc.from_protobuf(doc.to_protobuf())
assert (new_doc.tensor == tensor).all()
def test_proto_with_nested_doc():
class CustomInnerDoc(BaseDocument):
tensor: NdArray
class CustomDoc(BaseDocument):
text: str
inner: CustomInnerDoc
doc = CustomDoc(text='hello', inner=CustomInnerDoc(tensor=np.zeros((3, 224, 224))))
CustomDoc.from_protobuf(doc.to_protobuf())
def test_proto_with_chunks_doc():
class CustomInnerDoc(BaseDocument):
tensor: NdArray
class CustomDoc(BaseDocument):
text: str
chunks: DocumentArray[CustomInnerDoc]
doc = CustomDoc(
text='hello',
chunks=DocumentArray[CustomInnerDoc](
[CustomInnerDoc(tensor=np.zeros((3, 224, 224))) for _ in range(5)],
),
)
new_doc = CustomDoc.from_protobuf(doc.to_protobuf())
for chunk1, chunk2 in zip(doc.chunks, new_doc.chunks):
assert (chunk1.tensor == chunk2.tensor).all()
def test_proto_with_nested_doc_pytorch():
class CustomInnerDoc(BaseDocument):
tensor: TorchTensor
class CustomDoc(BaseDocument):
text: str
inner: CustomInnerDoc
doc = CustomDoc(
text='hello', inner=CustomInnerDoc(tensor=torch.zeros((3, 224, 224)))
)
CustomDoc.from_protobuf(doc.to_protobuf())
def test_proto_with_chunks_doc_pytorch():
class CustomInnerDoc(BaseDocument):
tensor: TorchTensor
class CustomDoc(BaseDocument):
text: str
chunks: DocumentArray[CustomInnerDoc]
doc = CustomDoc(
text='hello',
chunks=DocumentArray[CustomInnerDoc](
[CustomInnerDoc(tensor=torch.zeros((3, 224, 224))) for _ in range(5)],
),
)
new_doc = CustomDoc.from_protobuf(doc.to_protobuf())
for chunk1, chunk2 in zip(doc.chunks, new_doc.chunks):
assert (chunk1.tensor == chunk2.tensor).all()
def test_optional_field_in_doc():
class CustomDoc(BaseDocument):
text: Optional[str]
CustomDoc.from_protobuf(CustomDoc().to_protobuf())
def test_optional_field_nested_in_doc():
class InnerDoc(BaseDocument):
title: str
class CustomDoc(BaseDocument):
text: Optional[InnerDoc]
CustomDoc.from_protobuf(CustomDoc().to_protobuf())
|
from typing import Optional
import numpy as np
import torch
from docarray import DocumentArray
from docarray.document import BaseDocument
from docarray.typing import NdArray, TorchTensor
def test_proto_simple():
class CustomDoc(BaseDocument):
text: str
doc = CustomDoc(text='hello')
CustomDoc.from_protobuf(doc.to_protobuf())
def test_proto_ndarray():
class CustomDoc(BaseDocument):
tensor: NdArray
tensor = np.zeros((3, 224, 224))
doc = CustomDoc(tensor=tensor)
new_doc = CustomDoc.from_protobuf(doc.to_protobuf())
assert (new_doc.tensor == tensor).all()
def test_proto_with_nested_doc():
class CustomInnerDoc(BaseDocument):
tensor: NdArray
class CustomDoc(BaseDocument):
text: str
inner: CustomInnerDoc
doc = CustomDoc(text='hello', inner=CustomInnerDoc(tensor=np.zeros((3, 224, 224))))
CustomDoc.from_protobuf(doc.to_protobuf())
def test_proto_with_chunks_doc():
class CustomInnerDoc(BaseDocument):
tensor: NdArray
class CustomDoc(BaseDocument):
text: str
chunks: DocumentArray[CustomInnerDoc]
doc = CustomDoc(
text='hello',
chunks=DocumentArray[CustomInnerDoc](
[CustomInnerDoc(tensor=np.zeros((3, 224, 224))) for _ in range(5)],
),
)
new_doc = CustomDoc.from_protobuf(doc.to_protobuf())
for chunk1, chunk2 in zip(doc.chunks, new_doc.chunks):
assert (chunk1.tensor == chunk2.tensor).all()
def test_proto_with_nested_doc_pytorch():
class CustomInnerDoc(BaseDocument):
tensor: TorchTensor
class CustomDoc(BaseDocument):
text: str
inner: CustomInnerDoc
doc = CustomDoc(
text='hello', inner=CustomInnerDoc(tensor=torch.zeros((3, 224, 224)))
)
CustomDoc.from_protobuf(doc.to_protobuf())
def test_proto_with_chunks_doc_pytorch():
class CustomInnerDoc(BaseDocument):
tensor: TorchTensor
class CustomDoc(BaseDocument):
text: str
chunks: DocumentArray[CustomInnerDoc]
doc = CustomDoc(
text='hello',
chunks=DocumentArray[CustomInnerDoc](
[CustomInnerDoc(tensor=torch.zeros((3, 224, 224))) for _ in range(5)],
),
)
new_doc = CustomDoc.from_protobuf(doc.to_protobuf())
for chunk1, chunk2 in zip(doc.chunks, new_doc.chunks):
assert (chunk1.tensor == chunk2.tensor).all()
def test_optional_field_in_doc():
class CustomDoc(BaseDocument):
text: Optional[str]
CustomDoc.from_protobuf(CustomDoc().to_protobuf())
def test_optional_field_nested_in_doc():
class InnerDoc(BaseDocument):
title: str
class CustomDoc(BaseDocument):
text: Optional[InnerDoc]
CustomDoc.from_protobuf(CustomDoc().to_protobuf())
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders.pyspark_dataframe import (
PySparkDataFrameLoader,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"PySparkDataFrameLoader": "langchain_community.document_loaders.pyspark_dataframe",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = ["PySparkDataFrameLoader"]
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders.pyspark_dataframe import (
PySparkDataFrameLoader,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"PySparkDataFrameLoader": "langchain_community.document_loaders.pyspark_dataframe"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = ["PySparkDataFrameLoader"]
|
"""Checks the bundled license is installed with the wheel."""
import platform
import site
from itertools import chain
from pathlib import Path
site_packages = site.getsitepackages()
site_packages_path = (Path(p) for p in site_packages)
try:
distinfo_path = next(
chain(
s
for site_package in site_packages_path
for s in site_package.glob("scikit_learn-*.dist-info")
)
)
except StopIteration as e:
raise RuntimeError("Unable to find scikit-learn's dist-info") from e
license_text = (distinfo_path / "licenses" / "COPYING").read_text()
assert "Copyright (c)" in license_text
assert (
"This binary distribution of scikit-learn also bundles the following software"
in license_text
), f"Unable to find bundled license for {platform.system()}"
|
"""Checks the bundled license is installed with the wheel."""
import platform
import site
from itertools import chain
from pathlib import Path
site_packages = site.getsitepackages()
site_packages_path = (Path(p) for p in site_packages)
try:
distinfo_path = next(
chain(
s
for site_package in site_packages_path
for s in site_package.glob("scikit_learn-*.dist-info")
)
)
except StopIteration as e:
raise RuntimeError("Unable to find scikit-learn's dist-info") from e
license_text = (distinfo_path / "COPYING").read_text()
assert "Copyright (c)" in license_text
assert (
"This binary distribution of scikit-learn also bundles the following software"
in license_text
), f"Unable to find bundled license for {platform.system()}"
|
from typing import Any, Literal, Optional, Union
from exa_py import Exa # type: ignore[untyped-import]
from exa_py.api import (
HighlightsContentsOptions, # type: ignore[untyped-import]
TextContentsOptions, # type: ignore[untyped-import]
)
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from pydantic import Field, SecretStr, model_validator
from langchain_exa._utilities import initialize_client
def _get_metadata(result: Any) -> dict[str, Any]:
"""Get the metadata from a result object."""
metadata = {
"title": result.title,
"url": result.url,
"id": result.id,
"score": result.score,
"published_date": result.published_date,
"author": result.author,
}
if getattr(result, "highlights"):
metadata["highlights"] = result.highlights
if getattr(result, "highlight_scores"):
metadata["highlight_scores"] = result.highlight_scores
return metadata
class ExaSearchRetriever(BaseRetriever):
"""Exa Search retriever."""
k: int = 10 # num_results
"""The number of search results to return."""
include_domains: Optional[list[str]] = None
"""A list of domains to include in the search."""
exclude_domains: Optional[list[str]] = None
"""A list of domains to exclude from the search."""
start_crawl_date: Optional[str] = None
"""The start date for the crawl (in YYYY-MM-DD format)."""
end_crawl_date: Optional[str] = None
"""The end date for the crawl (in YYYY-MM-DD format)."""
start_published_date: Optional[str] = None
"""The start date for when the document was published (in YYYY-MM-DD format)."""
end_published_date: Optional[str] = None
"""The end date for when the document was published (in YYYY-MM-DD format)."""
use_autoprompt: Optional[bool] = None
"""Whether to use autoprompt for the search."""
type: str = "neural"
"""The type of search, 'keyword' or 'neural'. Default: neural"""
highlights: Optional[Union[HighlightsContentsOptions, bool]] = None
"""Whether to set the page content to the highlights of the results."""
text_contents_options: Union[TextContentsOptions, Literal[True]] = True
"""How to set the page content of the results"""
client: Exa = Field(default=None)
exa_api_key: SecretStr = Field(default=None)
exa_base_url: Optional[str] = None
@model_validator(mode="before")
@classmethod
def validate_environment(cls, values: dict) -> Any:
"""Validate the environment."""
values = initialize_client(values)
return values
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> list[Document]:
response = self.client.search_and_contents( # type: ignore[misc]
query,
num_results=self.k,
text=self.text_contents_options,
highlights=self.highlights, # type: ignore
include_domains=self.include_domains,
exclude_domains=self.exclude_domains,
start_crawl_date=self.start_crawl_date,
end_crawl_date=self.end_crawl_date,
start_published_date=self.start_published_date,
end_published_date=self.end_published_date,
use_autoprompt=self.use_autoprompt,
)
results = response.results
return [
Document(
page_content=(result.text),
metadata=_get_metadata(result),
)
for result in results
]
|
from typing import Any, Dict, List, Literal, Optional, Union
from exa_py import Exa # type: ignore[untyped-import]
from exa_py.api import (
HighlightsContentsOptions, # type: ignore[untyped-import]
TextContentsOptions, # type: ignore[untyped-import]
)
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from pydantic import Field, SecretStr, model_validator
from langchain_exa._utilities import initialize_client
def _get_metadata(result: Any) -> Dict[str, Any]:
"""Get the metadata from a result object."""
metadata = {
"title": result.title,
"url": result.url,
"id": result.id,
"score": result.score,
"published_date": result.published_date,
"author": result.author,
}
if getattr(result, "highlights"):
metadata["highlights"] = result.highlights
if getattr(result, "highlight_scores"):
metadata["highlight_scores"] = result.highlight_scores
return metadata
class ExaSearchRetriever(BaseRetriever):
"""Exa Search retriever."""
k: int = 10 # num_results
"""The number of search results to return."""
include_domains: Optional[List[str]] = None
"""A list of domains to include in the search."""
exclude_domains: Optional[List[str]] = None
"""A list of domains to exclude from the search."""
start_crawl_date: Optional[str] = None
"""The start date for the crawl (in YYYY-MM-DD format)."""
end_crawl_date: Optional[str] = None
"""The end date for the crawl (in YYYY-MM-DD format)."""
start_published_date: Optional[str] = None
"""The start date for when the document was published (in YYYY-MM-DD format)."""
end_published_date: Optional[str] = None
"""The end date for when the document was published (in YYYY-MM-DD format)."""
use_autoprompt: Optional[bool] = None
"""Whether to use autoprompt for the search."""
type: str = "neural"
"""The type of search, 'keyword' or 'neural'. Default: neural"""
highlights: Optional[Union[HighlightsContentsOptions, bool]] = None
"""Whether to set the page content to the highlights of the results."""
text_contents_options: Union[TextContentsOptions, Literal[True]] = True
"""How to set the page content of the results"""
client: Exa = Field(default=None)
exa_api_key: SecretStr = Field(default=None)
exa_base_url: Optional[str] = None
@model_validator(mode="before")
@classmethod
def validate_environment(cls, values: Dict) -> Any:
"""Validate the environment."""
values = initialize_client(values)
return values
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
response = self.client.search_and_contents( # type: ignore[misc]
query,
num_results=self.k,
text=self.text_contents_options,
highlights=self.highlights, # type: ignore
include_domains=self.include_domains,
exclude_domains=self.exclude_domains,
start_crawl_date=self.start_crawl_date,
end_crawl_date=self.end_crawl_date,
start_published_date=self.start_published_date,
end_published_date=self.end_published_date,
use_autoprompt=self.use_autoprompt,
)
results = response.results
return [
Document(
page_content=(result.text),
metadata=_get_metadata(result),
)
for result in results
]
|
_base_ = './decoupled_solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOLightHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
pos_scale=0.2,
num_grids=[40, 36, 24, 16, 12],
cls_down_index=0,
loss_mask=dict(
type='DiceLoss', use_sigmoid=True, activate=False,
loss_weight=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
# TODO: Update after mmcv.RandomChoiceResize finish refactor
type='RandomChoiceResize',
scales=[(852, 512), (852, 480), (852, 448), (852, 416), (852, 384),
(852, 352)],
resize_cfg=dict(type='Resize', keep_ratio=True)),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='Resize', scale=(852, 512), keep_ratio=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
|
_base_ = './decoupled_solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOLightHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
pos_scale=0.2,
num_grids=[40, 36, 24, 16, 12],
cls_down_index=0,
loss_mask=dict(
type='DiceLoss', use_sigmoid=True, activate=False,
loss_weight=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(852, 512), (852, 480), (852, 448), (852, 416), (852, 384),
(852, 352)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(852, 512),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from keras.src.ops.nn import celu
from keras.src.ops.nn import conv
from keras.src.ops.nn import conv_transpose
from keras.src.ops.nn import ctc_decode
from keras.src.ops.nn import ctc_loss
from keras.src.ops.nn import depthwise_conv
from keras.src.ops.nn import dot_product_attention
from keras.src.ops.nn import elu
from keras.src.ops.nn import gelu
from keras.src.ops.nn import glu
from keras.src.ops.nn import hard_sigmoid
from keras.src.ops.nn import hard_silu
from keras.src.ops.nn import hard_silu as hard_swish
from keras.src.ops.nn import hard_tanh
from keras.src.ops.nn import leaky_relu
from keras.src.ops.nn import log_sigmoid
from keras.src.ops.nn import log_softmax
from keras.src.ops.nn import max_pool
from keras.src.ops.nn import moments
from keras.src.ops.nn import multi_hot
from keras.src.ops.nn import normalize
from keras.src.ops.nn import one_hot
from keras.src.ops.nn import psnr
from keras.src.ops.nn import relu
from keras.src.ops.nn import relu6
from keras.src.ops.nn import selu
from keras.src.ops.nn import separable_conv
from keras.src.ops.nn import sigmoid
from keras.src.ops.nn import silu
from keras.src.ops.nn import silu as swish
from keras.src.ops.nn import softmax
from keras.src.ops.nn import softplus
from keras.src.ops.nn import softsign
from keras.src.ops.nn import sparse_categorical_crossentropy
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from keras.src.ops.nn import celu
from keras.src.ops.nn import conv
from keras.src.ops.nn import conv_transpose
from keras.src.ops.nn import ctc_decode
from keras.src.ops.nn import ctc_loss
from keras.src.ops.nn import depthwise_conv
from keras.src.ops.nn import dot_product_attention
from keras.src.ops.nn import elu
from keras.src.ops.nn import gelu
from keras.src.ops.nn import glu
from keras.src.ops.nn import hard_sigmoid
from keras.src.ops.nn import hard_silu
from keras.src.ops.nn import hard_silu as hard_swish
from keras.src.ops.nn import leaky_relu
from keras.src.ops.nn import log_sigmoid
from keras.src.ops.nn import log_softmax
from keras.src.ops.nn import max_pool
from keras.src.ops.nn import moments
from keras.src.ops.nn import multi_hot
from keras.src.ops.nn import normalize
from keras.src.ops.nn import one_hot
from keras.src.ops.nn import psnr
from keras.src.ops.nn import relu
from keras.src.ops.nn import relu6
from keras.src.ops.nn import selu
from keras.src.ops.nn import separable_conv
from keras.src.ops.nn import sigmoid
from keras.src.ops.nn import silu
from keras.src.ops.nn import silu as swish
from keras.src.ops.nn import softmax
from keras.src.ops.nn import softplus
from keras.src.ops.nn import softsign
from keras.src.ops.nn import sparse_categorical_crossentropy
|
# flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__version__ = "2.14.1"
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
_arrow_dataset.concatenate_datasets = concatenate_datasets
_utils.DownloadConfig = DownloadConfig
_utils.DownloadManager = DownloadManager
_utils.DownloadMode = DownloadMode
_deprecated_download_manager.DownloadConfig = DownloadConfig
_deprecated_download_manager.DownloadMode = DownloadMode
_deprecated_download_manager.DownloadManager = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
|
# flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__version__ = "2.14.1.dev0"
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
_arrow_dataset.concatenate_datasets = concatenate_datasets
_utils.DownloadConfig = DownloadConfig
_utils.DownloadManager = DownloadManager
_utils.DownloadMode = DownloadMode
_deprecated_download_manager.DownloadConfig = DownloadConfig
_deprecated_download_manager.DownloadMode = DownloadMode
_deprecated_download_manager.DownloadManager = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
|
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads import StandardRoIHead # noqa
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, get_detector_cfg
def _fake_roi_head(cfg_file):
"""Set a fake roi head config."""
model = get_detector_cfg(cfg_file)
roi_head = model.roi_head
rcnn_train_cfg = model.train_cfg.rcnn if model.train_cfg is not None \
else None
roi_head.update(train_cfg=rcnn_train_cfg)
return roi_head
def _fake_proposals(img_metas, proposal_len):
"""Create a fake proposal list."""
results = []
for i in range(len(img_metas)):
result = InstanceData(metainfo=img_metas[i])
proposal = torch.randn(proposal_len, 4).to(device='cuda')
result.bboxes = proposal
results.append(result)
return results
class TestCascadeRoIHead(TestCase):
@parameterized.expand(
['cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'])
def test_init(self, cfg_file):
"""Test init standard RoI head."""
# Normal Cascade Mask R-CNN RoI head
roi_head_cfg = _fake_roi_head(cfg_file)
roi_head = MODELS.build(roi_head_cfg)
assert roi_head.with_bbox
assert roi_head.with_mask
@parameterized.expand(
['cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'])
def test_cascade_roi_head_loss(self, cfg_file):
"""Tests standard roi head loss when truth is empty and non-empty."""
if not torch.cuda.is_available():
# RoI pooling only support in GPU
return unittest.skip('test requires GPU and torch+cuda')
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
}]
roi_head_cfg = _fake_roi_head(cfg_file)
roi_head = MODELS.build(roi_head_cfg)
roi_head = roi_head.cuda()
feats = []
for i in range(len(roi_head_cfg.bbox_roi_extractor.featmap_strides)):
feats.append(
torch.rand(1, 1, s // (2**(i + 2)),
s // (2**(i + 2))).to(device='cuda'))
feats = tuple(feats)
# When truth is non-empty then both cls, box, and mask loss
# should be nonzero for random inputs
proposal_list = _fake_proposals(img_metas, 100)
packed_inputs = demo_mm_inputs(
batch_size=1,
image_shapes=[(s, s, 3)],
num_items=[1],
num_classes=4,
with_mask=True)
batch_data_samples = []
for i in range(len(packed_inputs)):
batch_data_samples.append(
packed_inputs[i]['data_sample'].to(device='cuda'))
out = roi_head.loss(feats, proposal_list, batch_data_samples)
for name, value in out.items():
if 'loss' in name:
self.assertGreaterEqual(
value.sum(), 0, msg='loss should be non-zero')
# When there is no truth, the cls loss should be nonzero but
# there should be no box and mask loss.
proposal_list = _fake_proposals(img_metas, 100)
packed_inputs = demo_mm_inputs(
batch_size=1,
image_shapes=[(s, s, 3)],
num_items=[0],
num_classes=4,
with_mask=True)
batch_data_samples = []
for i in range(len(packed_inputs)):
batch_data_samples.append(
packed_inputs[i]['data_sample'].to(device='cuda'))
out = roi_head.loss(feats, proposal_list, batch_data_samples)
for name, value in out.items():
if 'loss_cls' in name:
self.assertGreaterEqual(
value.sum(), 0, msg='loss should be non-zero')
elif 'loss_bbox' in name or 'loss_mask' in name:
self.assertEqual(value.sum(), 0)
|
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads import StandardRoIHead # noqa
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, get_detector_cfg
def _fake_roi_head(cfg_file):
"""Set a fake roi head config."""
model = get_detector_cfg(cfg_file)
roi_head = model.roi_head
rcnn_train_cfg = model.train_cfg.rcnn if model.train_cfg is not None \
else None
roi_head.update(train_cfg=rcnn_train_cfg)
return roi_head
def _fake_proposals(img_metas, proposal_len):
"""Create a fake proposal list."""
results = []
for i in range(len(img_metas)):
result = InstanceData(metainfo=img_metas[i])
proposal = torch.randn(proposal_len, 4).to(device='cuda')
result.bboxes = proposal
results.append(result)
return results
class TestStandardRoIHead(TestCase):
@parameterized.expand(
['cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'])
def test_init(self, cfg_file):
"""Test init standard RoI head."""
# Normal Cascade Mask R-CNN RoI head
roi_head_cfg = _fake_roi_head(cfg_file)
roi_head = MODELS.build(roi_head_cfg)
assert roi_head.with_bbox
assert roi_head.with_mask
@parameterized.expand(
['cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'])
def test_cascade_roi_head_loss(self, cfg_file):
"""Tests standard roi head loss when truth is empty and non-empty."""
if not torch.cuda.is_available():
# RoI pooling only support in GPU
return unittest.skip('test requires GPU and torch+cuda')
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
}]
roi_head_cfg = _fake_roi_head(cfg_file)
roi_head = MODELS.build(roi_head_cfg)
roi_head = roi_head.cuda()
feats = []
for i in range(len(roi_head_cfg.bbox_roi_extractor.featmap_strides)):
feats.append(
torch.rand(1, 1, s // (2**(i + 2)),
s // (2**(i + 2))).to(device='cuda'))
feats = tuple(feats)
# When truth is non-empty then both cls, box, and mask loss
# should be nonzero for random inputs
proposal_list = _fake_proposals(img_metas, 100)
packed_inputs = demo_mm_inputs(
batch_size=1,
image_shapes=[(s, s, 3)],
num_items=[1],
num_classes=4,
with_mask=True)
batch_data_samples = []
for i in range(len(packed_inputs)):
batch_data_samples.append(
packed_inputs[i]['data_sample'].to(device='cuda'))
out = roi_head.forward_train(feats, proposal_list, batch_data_samples)
for name, value in out.items():
if 'loss' in name:
self.assertGreaterEqual(
value.sum(), 0, msg='loss should be non-zero')
# When there is no truth, the cls loss should be nonzero but
# there should be no box and mask loss.
proposal_list = _fake_proposals(img_metas, 100)
packed_inputs = demo_mm_inputs(
batch_size=1,
image_shapes=[(s, s, 3)],
num_items=[0],
num_classes=4,
with_mask=True)
batch_data_samples = []
for i in range(len(packed_inputs)):
batch_data_samples.append(
packed_inputs[i]['data_sample'].to(device='cuda'))
out = roi_head.forward_train(feats, proposal_list, batch_data_samples)
for name, value in out.items():
if 'loss_cls' in name:
self.assertGreaterEqual(
value.sum(), 0, msg='loss should be non-zero')
elif 'loss_bbox' in name or 'loss_mask' in name:
self.assertEqual(value.sum(), 0)
|
#!/usr/bin/env python3
"""Generate feature statistics for training set.
Example:
python global_stats.py --model-type librispeech --dataset-path /home/librispeech
"""
import json
import logging
import pathlib
from argparse import ArgumentParser, RawTextHelpFormatter
import torch
import torchaudio
from common import (
MODEL_TYPE_LIBRISPEECH,
MODEL_TYPE_MUSTC,
MODEL_TYPE_TEDLIUM3,
piecewise_linear_log,
spectrogram_transform,
)
from must.dataset import MUSTC
logger = logging.getLogger()
def parse_args():
parser = ArgumentParser(description=__doc__, formatter_class=RawTextHelpFormatter)
parser.add_argument(
"--model-type", type=str, choices=[MODEL_TYPE_LIBRISPEECH, MODEL_TYPE_TEDLIUM3, MODEL_TYPE_MUSTC], required=True
)
parser.add_argument(
"--dataset-path",
required=True,
type=pathlib.Path,
help="Path to dataset. "
"For LibriSpeech, all of 'train-clean-360', 'train-clean-100', and 'train-other-500' must exist.",
)
parser.add_argument(
"--output-path",
default=pathlib.Path("global_stats.json"),
type=pathlib.Path,
help="File to save feature statistics to. (Default: './global_stats.json')",
)
return parser.parse_args()
def generate_statistics(samples):
E_x = 0
E_x_2 = 0
N = 0
for idx, sample in enumerate(samples):
mel_spec = spectrogram_transform(sample[0].squeeze()).transpose(1, 0)
scaled_mel_spec = piecewise_linear_log(mel_spec)
sum = scaled_mel_spec.sum(0)
sq_sum = scaled_mel_spec.pow(2).sum(0)
M = scaled_mel_spec.size(0)
E_x = E_x * (N / (N + M)) + sum / (N + M)
E_x_2 = E_x_2 * (N / (N + M)) + sq_sum / (N + M)
N += M
if idx % 100 == 0:
logger.info(f"Processed {idx}")
return E_x, (E_x_2 - E_x**2) ** 0.5
def get_dataset(args):
if args.model_type == MODEL_TYPE_LIBRISPEECH:
return torch.utils.data.ConcatDataset(
[
torchaudio.datasets.LIBRISPEECH(args.dataset_path, url="train-clean-360"),
torchaudio.datasets.LIBRISPEECH(args.dataset_path, url="train-clean-100"),
torchaudio.datasets.LIBRISPEECH(args.dataset_path, url="train-other-500"),
]
)
elif args.model_type == MODEL_TYPE_TEDLIUM3:
return torchaudio.datasets.TEDLIUM(args.dataset_path, release="release3", subset="train")
elif args.model_type == MODEL_TYPE_MUSTC:
return MUSTC(args.dataset_path, subset="train")
else:
raise ValueError(f"Encountered unsupported model type {args.model_type}.")
def cli_main():
args = parse_args()
dataset = get_dataset(args)
dataloader = torch.utils.data.DataLoader(dataset, num_workers=4)
mean, stddev = generate_statistics(iter(dataloader))
json_str = json.dumps({"mean": mean.tolist(), "invstddev": (1 / stddev).tolist()}, indent=2)
with open(args.output_path, "w") as f:
f.write(json_str)
if __name__ == "__main__":
cli_main()
|
#!/usr/bin/env python3
"""Generate feature statistics for training set.
Example:
python global_stats.py --model-type librispeech --dataset-path /home/librispeech
"""
import json
import logging
import pathlib
from argparse import ArgumentParser, RawTextHelpFormatter
import torch
import torchaudio
from common import (
MODEL_TYPE_LIBRISPEECH,
MODEL_TYPE_TEDLIUM3,
MODEL_TYPE_MUSTC,
piecewise_linear_log,
spectrogram_transform,
)
from must.dataset import MUSTC
logger = logging.getLogger()
def parse_args():
parser = ArgumentParser(description=__doc__, formatter_class=RawTextHelpFormatter)
parser.add_argument(
"--model-type", type=str, choices=[MODEL_TYPE_LIBRISPEECH, MODEL_TYPE_TEDLIUM3, MODEL_TYPE_MUSTC], required=True
)
parser.add_argument(
"--dataset-path",
required=True,
type=pathlib.Path,
help="Path to dataset. "
"For LibriSpeech, all of 'train-clean-360', 'train-clean-100', and 'train-other-500' must exist.",
)
parser.add_argument(
"--output-path",
default=pathlib.Path("global_stats.json"),
type=pathlib.Path,
help="File to save feature statistics to. (Default: './global_stats.json')",
)
return parser.parse_args()
def generate_statistics(samples):
E_x = 0
E_x_2 = 0
N = 0
for idx, sample in enumerate(samples):
mel_spec = spectrogram_transform(sample[0].squeeze()).transpose(1, 0)
scaled_mel_spec = piecewise_linear_log(mel_spec)
sum = scaled_mel_spec.sum(0)
sq_sum = scaled_mel_spec.pow(2).sum(0)
M = scaled_mel_spec.size(0)
E_x = E_x * (N / (N + M)) + sum / (N + M)
E_x_2 = E_x_2 * (N / (N + M)) + sq_sum / (N + M)
N += M
if idx % 100 == 0:
logger.info(f"Processed {idx}")
return E_x, (E_x_2 - E_x**2) ** 0.5
def get_dataset(args):
if args.model_type == MODEL_TYPE_LIBRISPEECH:
return torch.utils.data.ConcatDataset(
[
torchaudio.datasets.LIBRISPEECH(args.dataset_path, url="train-clean-360"),
torchaudio.datasets.LIBRISPEECH(args.dataset_path, url="train-clean-100"),
torchaudio.datasets.LIBRISPEECH(args.dataset_path, url="train-other-500"),
]
)
elif args.model_type == MODEL_TYPE_TEDLIUM3:
return torchaudio.datasets.TEDLIUM(args.dataset_path, release="release3", subset="train")
elif args.model_type == MODEL_TYPE_MUSTC:
return MUSTC(args.dataset_path, subset="train")
else:
raise ValueError(f"Encountered unsupported model type {args.model_type}.")
def cli_main():
args = parse_args()
dataset = get_dataset(args)
dataloader = torch.utils.data.DataLoader(dataset, num_workers=4)
mean, stddev = generate_statistics(iter(dataloader))
json_str = json.dumps({"mean": mean.tolist(), "invstddev": (1 / stddev).tolist()}, indent=2)
with open(args.output_path, "w") as f:
f.write(json_str)
if __name__ == "__main__":
cli_main()
|
# mypy: allow-untyped-defs
r"""Autograd anomaly mode."""
import warnings
import torch
__all__ = ["detect_anomaly", "set_detect_anomaly"]
class detect_anomaly:
r"""Context-manager that enable anomaly detection for the autograd engine.
This does two things:
- Running the forward pass with detection enabled will allow the backward
pass to print the traceback of the forward operation that created the failing
backward function.
- If ``check_nan`` is ``True``, any backward computation that generate "nan"
value will raise an error. Default ``True``.
.. warning::
This mode should be enabled only for debugging as the different tests
will slow down your program execution.
Example:
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ANOMALY)
>>> import torch
>>> from torch import autograd
>>> class MyFunc(autograd.Function):
... @staticmethod
... def forward(ctx, inp):
... return inp.clone()
...
... @staticmethod
... def backward(ctx, gO):
... # Error during the backward pass
... raise RuntimeError("Some error in backward")
... return gO.clone()
>>> def run_fn(a):
... out = MyFunc.apply(a)
... return out.sum()
>>> inp = torch.rand(10, 10, requires_grad=True)
>>> out = run_fn(inp)
>>> out.backward()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
allow_unreachable=True) # allow_unreachable flag
File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
return self._forward_cls.backward(self, *args)
File "<stdin>", line 8, in backward
RuntimeError: Some error in backward
>>> with autograd.detect_anomaly():
... inp = torch.rand(10, 10, requires_grad=True)
... out = run_fn(inp)
... out.backward()
Traceback of forward call that caused the error:
File "tmp.py", line 53, in <module>
out = run_fn(inp)
File "tmp.py", line 44, in run_fn
out = MyFunc.apply(a)
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
allow_unreachable=True) # allow_unreachable flag
File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
return self._forward_cls.backward(self, *args)
File "<stdin>", line 8, in backward
RuntimeError: Some error in backward
"""
def __init__(self, check_nan=True) -> None: # noqa: D107
self.prev = torch.is_anomaly_enabled()
self.check_nan = check_nan
self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
warnings.warn(
"Anomaly Detection has been enabled. "
"This mode will increase the runtime "
"and should only be enabled for debugging.",
stacklevel=2,
)
def __enter__(self) -> None: # noqa: D105
torch.set_anomaly_enabled(True, self.check_nan)
def __exit__(self, *args: object) -> None: # noqa: D105
torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
class set_detect_anomaly:
r"""Context-manager that sets the anomaly detection for the autograd engine on or off.
``set_detect_anomaly`` will enable or disable the autograd anomaly detection
based on its argument :attr:`mode`.
It can be used as a context-manager or as a function.
See ``detect_anomaly`` above for details of the anomaly detection behaviour.
Args:
mode (bool): Flag whether to enable anomaly detection (``True``),
or disable (``False``).
check_nan (bool): Flag whether to raise an error when the backward
generate "nan"
"""
def __init__(self, mode: bool, check_nan: bool = True) -> None: # noqa: D107
self.prev = torch.is_anomaly_enabled()
self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
torch.set_anomaly_enabled(mode, check_nan)
def __enter__(self) -> None: # noqa: D105
pass
def __exit__(self, *args: object) -> None: # noqa: D105
torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
|
# mypy: allow-untyped-defs
r"""Autograd anomaly mode."""
import warnings
import torch
__all__ = ["detect_anomaly", "set_detect_anomaly"]
class detect_anomaly:
r"""Context-manager that enable anomaly detection for the autograd engine.
This does two things:
- Running the forward pass with detection enabled will allow the backward
pass to print the traceback of the forward operation that created the failing
backward function.
- If ``check_nan`` is ``True``, any backward computation that generate "nan"
value will raise an error. Default ``True``.
.. warning::
This mode should be enabled only for debugging as the different tests
will slow down your program execution.
Example:
>>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ANOMALY)
>>> import torch
>>> from torch import autograd
>>> class MyFunc(autograd.Function):
... @staticmethod
... def forward(ctx, inp):
... return inp.clone()
... @staticmethod
... def backward(ctx, gO):
... # Error during the backward pass
... raise RuntimeError("Some error in backward")
... return gO.clone()
>>> def run_fn(a):
... out = MyFunc.apply(a)
... return out.sum()
>>> inp = torch.rand(10, 10, requires_grad=True)
>>> out = run_fn(inp)
>>> out.backward()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
allow_unreachable=True) # allow_unreachable flag
File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
return self._forward_cls.backward(self, *args)
File "<stdin>", line 8, in backward
RuntimeError: Some error in backward
>>> with autograd.detect_anomaly():
... inp = torch.rand(10, 10, requires_grad=True)
... out = run_fn(inp)
... out.backward()
Traceback of forward call that caused the error:
File "tmp.py", line 53, in <module>
out = run_fn(inp)
File "tmp.py", line 44, in run_fn
out = MyFunc.apply(a)
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
allow_unreachable=True) # allow_unreachable flag
File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
return self._forward_cls.backward(self, *args)
File "<stdin>", line 8, in backward
RuntimeError: Some error in backward
"""
def __init__(self, check_nan=True) -> None: # noqa: D107
self.prev = torch.is_anomaly_enabled()
self.check_nan = check_nan
self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
warnings.warn(
"Anomaly Detection has been enabled. "
"This mode will increase the runtime "
"and should only be enabled for debugging.",
stacklevel=2,
)
def __enter__(self) -> None: # noqa: D105
torch.set_anomaly_enabled(True, self.check_nan)
def __exit__(self, *args: object) -> None: # noqa: D105
torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
class set_detect_anomaly:
r"""Context-manager that sets the anomaly detection for the autograd engine on or off.
``set_detect_anomaly`` will enable or disable the autograd anomaly detection
based on its argument :attr:`mode`.
It can be used as a context-manager or as a function.
See ``detect_anomaly`` above for details of the anomaly detection behaviour.
Args:
mode (bool): Flag whether to enable anomaly detection (``True``),
or disable (``False``).
check_nan (bool): Flag whether to raise an error when the backward
generate "nan"
"""
def __init__(self, mode: bool, check_nan: bool = True) -> None: # noqa: D107
self.prev = torch.is_anomaly_enabled()
self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
torch.set_anomaly_enabled(mode, check_nan)
def __enter__(self) -> None: # noqa: D105
pass
def __exit__(self, *args: object) -> None: # noqa: D105
torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
|
import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RescalingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_rescaling_basics(self):
self.run_layer_test(
layers.Rescaling,
init_kwargs={"scale": 1.0 / 255, "offset": 0.5},
input_shape=(2, 3),
expected_output_shape=(2, 3),
expected_num_trainable_weights=0,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
expected_num_losses=0,
supports_masking=True,
)
@pytest.mark.requires_trainable_backend
def test_rescaling_dtypes(self):
# int scale
self.run_layer_test(
layers.Rescaling,
init_kwargs={"scale": 2, "offset": 0.5},
input_shape=(2, 3),
expected_output_shape=(2, 3),
expected_num_trainable_weights=0,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
expected_num_losses=0,
supports_masking=True,
)
# int offset
self.run_layer_test(
layers.Rescaling,
init_kwargs={"scale": 1.0, "offset": 2},
input_shape=(2, 3),
expected_output_shape=(2, 3),
expected_num_trainable_weights=0,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
expected_num_losses=0,
supports_masking=True,
)
# int inputs
self.run_layer_test(
layers.Rescaling,
init_kwargs={"scale": 1.0 / 255, "offset": 0.5},
input_shape=(2, 3),
input_dtype="int16",
expected_output_shape=(2, 3),
expected_num_trainable_weights=0,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
expected_num_losses=0,
supports_masking=True,
)
def test_rescaling_correctness(self):
layer = layers.Rescaling(scale=1.0 / 255, offset=0.5)
x = np.random.random((3, 10, 10, 3)) * 255
out = layer(x)
self.assertAllClose(out, x / 255 + 0.5)
def test_tf_data_compatibility(self):
layer = layers.Rescaling(scale=1.0 / 255, offset=0.5)
x = np.random.random((3, 10, 10, 3)) * 255
ds = tf_data.Dataset.from_tensor_slices(x).batch(3).map(layer)
next(iter(ds)).numpy()
def test_rescaling_with_channels_first_and_vector_scale(self):
config = backend.image_data_format()
backend.set_image_data_format("channels_first")
layer = layers.Rescaling(
scale=[1.0 / 255, 1.5 / 255, 2.0 / 255], offset=0.5
)
x = np.random.random((2, 3, 10, 10)) * 255
layer(x)
backend.set_image_data_format(config)
@pytest.mark.requires_trainable_backend
def test_numpy_args(self):
# https://github.com/keras-team/keras/issues/20072
self.run_layer_test(
layers.Rescaling,
init_kwargs={
"scale": np.array(1.0 / 255.0),
"offset": np.array(0.5),
},
input_shape=(2, 3),
expected_output_shape=(2, 3),
expected_num_trainable_weights=0,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
expected_num_losses=0,
supports_masking=True,
)
|
import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RescalingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_rescaling_basics(self):
self.run_layer_test(
layers.Rescaling,
init_kwargs={"scale": 1.0 / 255, "offset": 0.5},
input_shape=(2, 3),
expected_output_shape=(2, 3),
expected_num_trainable_weights=0,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
expected_num_losses=0,
supports_masking=True,
)
@pytest.mark.requires_trainable_backend
def test_rescaling_dtypes(self):
# int scale
self.run_layer_test(
layers.Rescaling,
init_kwargs={"scale": 2, "offset": 0.5},
input_shape=(2, 3),
expected_output_shape=(2, 3),
expected_num_trainable_weights=0,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
expected_num_losses=0,
supports_masking=True,
)
# int offset
self.run_layer_test(
layers.Rescaling,
init_kwargs={"scale": 1.0, "offset": 2},
input_shape=(2, 3),
expected_output_shape=(2, 3),
expected_num_trainable_weights=0,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
expected_num_losses=0,
supports_masking=True,
)
# int inputs
self.run_layer_test(
layers.Rescaling,
init_kwargs={"scale": 1.0 / 255, "offset": 0.5},
input_shape=(2, 3),
input_dtype="int16",
expected_output_shape=(2, 3),
expected_num_trainable_weights=0,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
expected_num_losses=0,
supports_masking=True,
)
def test_rescaling_correctness(self):
layer = layers.Rescaling(scale=1.0 / 255, offset=0.5)
x = np.random.random((3, 10, 10, 3)) * 255
out = layer(x)
self.assertAllClose(out, x / 255 + 0.5)
def test_tf_data_compatibility(self):
layer = layers.Rescaling(scale=1.0 / 255, offset=0.5)
x = np.random.random((3, 10, 10, 3)) * 255
ds = tf_data.Dataset.from_tensor_slices(x).batch(3).map(layer)
for output in ds.take(1):
output.numpy()
def test_rescaling_with_channels_first_and_vector_scale(self):
config = backend.image_data_format()
backend.set_image_data_format("channels_first")
layer = layers.Rescaling(
scale=[1.0 / 255, 1.5 / 255, 2.0 / 255], offset=0.5
)
x = np.random.random((2, 3, 10, 10)) * 255
layer(x)
backend.set_image_data_format(config)
@pytest.mark.requires_trainable_backend
def test_numpy_args(self):
# https://github.com/keras-team/keras/issues/20072
self.run_layer_test(
layers.Rescaling,
init_kwargs={
"scale": np.array(1.0 / 255.0),
"offset": np.array(0.5),
},
input_shape=(2, 3),
expected_output_shape=(2, 3),
expected_num_trainable_weights=0,
expected_num_non_trainable_weights=0,
expected_num_seed_generators=0,
expected_num_losses=0,
supports_masking=True,
)
|
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import torch
def _calculate_bin_centers(boundaries: torch.Tensor) -> torch.Tensor:
step = boundaries[1] - boundaries[0]
bin_centers = boundaries + step / 2
bin_centers = torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1)], dim=0)
return bin_centers
def _calculate_expected_aligned_error(
alignment_confidence_breaks: torch.Tensor,
aligned_distance_error_probs: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
bin_centers = _calculate_bin_centers(alignment_confidence_breaks)
return (
torch.sum(aligned_distance_error_probs * bin_centers, dim=-1),
bin_centers[-1],
)
def compute_predicted_aligned_error(
logits: torch.Tensor,
max_bin: int = 31,
no_bins: int = 64,
**kwargs,
) -> dict[str, torch.Tensor]:
"""Computes aligned confidence metrics from logits.
Args:
logits: [*, num_res, num_res, num_bins] the logits output from
PredictedAlignedErrorHead.
max_bin: Maximum bin value
no_bins: Number of bins
Returns:
aligned_confidence_probs: [*, num_res, num_res, num_bins] the predicted
aligned error probabilities over bins for each residue pair.
predicted_aligned_error: [*, num_res, num_res] the expected aligned distance
error for each pair of residues.
max_predicted_aligned_error: [*] the maximum predicted error possible.
"""
boundaries = torch.linspace(0, max_bin, steps=(no_bins - 1), device=logits.device)
aligned_confidence_probs = torch.nn.functional.softmax(logits, dim=-1)
predicted_aligned_error, max_predicted_aligned_error = _calculate_expected_aligned_error(
alignment_confidence_breaks=boundaries,
aligned_distance_error_probs=aligned_confidence_probs,
)
return {
"aligned_confidence_probs": aligned_confidence_probs,
"predicted_aligned_error": predicted_aligned_error,
"max_predicted_aligned_error": max_predicted_aligned_error,
}
def compute_tm(
logits: torch.Tensor,
residue_weights: Optional[torch.Tensor] = None,
max_bin: int = 31,
no_bins: int = 64,
eps: float = 1e-8,
**kwargs,
) -> torch.Tensor:
if residue_weights is None:
residue_weights = logits.new_ones(logits.shape[-2])
boundaries = torch.linspace(0, max_bin, steps=(no_bins - 1), device=logits.device)
bin_centers = _calculate_bin_centers(boundaries)
torch.sum(residue_weights)
n = logits.shape[-2]
clipped_n = max(n, 19)
d0 = 1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8
probs = torch.nn.functional.softmax(logits, dim=-1)
tm_per_bin = 1.0 / (1 + (bin_centers**2) / (d0**2))
predicted_tm_term = torch.sum(probs * tm_per_bin, dim=-1)
normed_residue_mask = residue_weights / (eps + residue_weights.sum())
per_alignment = torch.sum(predicted_tm_term * normed_residue_mask, dim=-1)
weighted = per_alignment * residue_weights
argmax = (weighted == torch.max(weighted)).nonzero()[0]
return per_alignment[tuple(argmax)]
|
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict, Optional, Tuple
import torch
def _calculate_bin_centers(boundaries: torch.Tensor) -> torch.Tensor:
step = boundaries[1] - boundaries[0]
bin_centers = boundaries + step / 2
bin_centers = torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1)], dim=0)
return bin_centers
def _calculate_expected_aligned_error(
alignment_confidence_breaks: torch.Tensor,
aligned_distance_error_probs: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
bin_centers = _calculate_bin_centers(alignment_confidence_breaks)
return (
torch.sum(aligned_distance_error_probs * bin_centers, dim=-1),
bin_centers[-1],
)
def compute_predicted_aligned_error(
logits: torch.Tensor,
max_bin: int = 31,
no_bins: int = 64,
**kwargs,
) -> Dict[str, torch.Tensor]:
"""Computes aligned confidence metrics from logits.
Args:
logits: [*, num_res, num_res, num_bins] the logits output from
PredictedAlignedErrorHead.
max_bin: Maximum bin value
no_bins: Number of bins
Returns:
aligned_confidence_probs: [*, num_res, num_res, num_bins] the predicted
aligned error probabilities over bins for each residue pair.
predicted_aligned_error: [*, num_res, num_res] the expected aligned distance
error for each pair of residues.
max_predicted_aligned_error: [*] the maximum predicted error possible.
"""
boundaries = torch.linspace(0, max_bin, steps=(no_bins - 1), device=logits.device)
aligned_confidence_probs = torch.nn.functional.softmax(logits, dim=-1)
predicted_aligned_error, max_predicted_aligned_error = _calculate_expected_aligned_error(
alignment_confidence_breaks=boundaries,
aligned_distance_error_probs=aligned_confidence_probs,
)
return {
"aligned_confidence_probs": aligned_confidence_probs,
"predicted_aligned_error": predicted_aligned_error,
"max_predicted_aligned_error": max_predicted_aligned_error,
}
def compute_tm(
logits: torch.Tensor,
residue_weights: Optional[torch.Tensor] = None,
max_bin: int = 31,
no_bins: int = 64,
eps: float = 1e-8,
**kwargs,
) -> torch.Tensor:
if residue_weights is None:
residue_weights = logits.new_ones(logits.shape[-2])
boundaries = torch.linspace(0, max_bin, steps=(no_bins - 1), device=logits.device)
bin_centers = _calculate_bin_centers(boundaries)
torch.sum(residue_weights)
n = logits.shape[-2]
clipped_n = max(n, 19)
d0 = 1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8
probs = torch.nn.functional.softmax(logits, dim=-1)
tm_per_bin = 1.0 / (1 + (bin_centers**2) / (d0**2))
predicted_tm_term = torch.sum(probs * tm_per_bin, dim=-1)
normed_residue_mask = residue_weights / (eps + residue_weights.sum())
per_alignment = torch.sum(predicted_tm_term * normed_residue_mask, dim=-1)
weighted = per_alignment * residue_weights
argmax = (weighted == torch.max(weighted)).nonzero()[0]
return per_alignment[tuple(argmax)]
|
from __future__ import annotations
import json
from json import JSONDecodeError
from typing import Annotated, Any, Optional, TypeVar, Union
import jsonpatch # type: ignore[import]
import pydantic
from pydantic import SkipValidation
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers.format_instructions import JSON_FORMAT_INSTRUCTIONS
from langchain_core.output_parsers.transform import BaseCumulativeTransformOutputParser
from langchain_core.outputs import Generation
from langchain_core.utils.json import (
parse_and_check_json_markdown,
parse_json_markdown,
parse_partial_json,
)
from langchain_core.utils.pydantic import PYDANTIC_MAJOR_VERSION
if PYDANTIC_MAJOR_VERSION < 2:
PydanticBaseModel = pydantic.BaseModel
else:
from pydantic.v1 import BaseModel
# Union type needs to be last assignment to PydanticBaseModel to make mypy happy.
PydanticBaseModel = Union[BaseModel, pydantic.BaseModel] # type: ignore
TBaseModel = TypeVar("TBaseModel", bound=PydanticBaseModel)
class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]):
"""Parse the output of an LLM call to a JSON object.
When used in streaming mode, it will yield partial JSON objects containing
all the keys that have been returned so far.
In streaming, if `diff` is set to `True`, yields JSONPatch operations
describing the difference between the previous and the current object.
"""
pydantic_object: Annotated[Optional[type[TBaseModel]], SkipValidation()] = None # type: ignore
"""The Pydantic object to use for validation.
If None, no validation is performed."""
def _diff(self, prev: Optional[Any], next: Any) -> Any:
return jsonpatch.make_patch(prev, next).patch
def _get_schema(self, pydantic_object: type[TBaseModel]) -> dict[str, Any]:
if issubclass(pydantic_object, pydantic.BaseModel):
return pydantic_object.model_json_schema()
elif issubclass(pydantic_object, pydantic.v1.BaseModel):
return pydantic_object.schema()
def parse_result(self, result: list[Generation], *, partial: bool = False) -> Any:
"""Parse the result of an LLM call to a JSON object.
Args:
result: The result of the LLM call.
partial: Whether to parse partial JSON objects.
If True, the output will be a JSON object containing
all the keys that have been returned so far.
If False, the output will be the full JSON object.
Default is False.
Returns:
The parsed JSON object.
Raises:
OutputParserException: If the output is not valid JSON.
"""
text = result[0].text
text = text.strip()
if partial:
try:
return parse_json_markdown(text)
except JSONDecodeError:
return None
else:
try:
return parse_json_markdown(text)
except JSONDecodeError as e:
msg = f"Invalid json output: {text}"
raise OutputParserException(msg, llm_output=text) from e
def parse(self, text: str) -> Any:
"""Parse the output of an LLM call to a JSON object.
Args:
text: The output of the LLM call.
Returns:
The parsed JSON object.
"""
return self.parse_result([Generation(text=text)])
def get_format_instructions(self) -> str:
"""Return the format instructions for the JSON output.
Returns:
The format instructions for the JSON output.
"""
if self.pydantic_object is None:
return "Return a JSON object."
else:
# Copy schema to avoid altering original Pydantic schema.
schema = dict(self._get_schema(self.pydantic_object).items())
# Remove extraneous fields.
reduced_schema = schema
if "title" in reduced_schema:
del reduced_schema["title"]
if "type" in reduced_schema:
del reduced_schema["type"]
# Ensure json in context is well-formed with double quotes.
schema_str = json.dumps(reduced_schema, ensure_ascii=False)
return JSON_FORMAT_INSTRUCTIONS.format(schema=schema_str)
@property
def _type(self) -> str:
return "simple_json_output_parser"
# For backwards compatibility
SimpleJsonOutputParser = JsonOutputParser
__all__ = [
"JsonOutputParser",
"SimpleJsonOutputParser", # For backwards compatibility
"parse_partial_json", # For backwards compatibility
"parse_and_check_json_markdown", # For backwards compatibility
]
|
from __future__ import annotations
import json
from json import JSONDecodeError
from typing import Annotated, Any, Optional, TypeVar, Union
import jsonpatch # type: ignore[import]
import pydantic
from pydantic import SkipValidation
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers.format_instructions import JSON_FORMAT_INSTRUCTIONS
from langchain_core.output_parsers.transform import BaseCumulativeTransformOutputParser
from langchain_core.outputs import Generation
from langchain_core.utils.json import (
parse_and_check_json_markdown,
parse_json_markdown,
parse_partial_json,
)
from langchain_core.utils.pydantic import PYDANTIC_MAJOR_VERSION
if PYDANTIC_MAJOR_VERSION < 2:
PydanticBaseModel = pydantic.BaseModel
else:
from pydantic.v1 import BaseModel
# Union type needs to be last assignment to PydanticBaseModel to make mypy happy.
PydanticBaseModel = Union[BaseModel, pydantic.BaseModel] # type: ignore
TBaseModel = TypeVar("TBaseModel", bound=PydanticBaseModel)
class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]):
"""Parse the output of an LLM call to a JSON object.
When used in streaming mode, it will yield partial JSON objects containing
all the keys that have been returned so far.
In streaming, if `diff` is set to `True`, yields JSONPatch operations
describing the difference between the previous and the current object.
"""
pydantic_object: Annotated[Optional[type[TBaseModel]], SkipValidation()] = None # type: ignore
"""The Pydantic object to use for validation.
If None, no validation is performed."""
def _diff(self, prev: Optional[Any], next: Any) -> Any:
return jsonpatch.make_patch(prev, next).patch
def _get_schema(self, pydantic_object: type[TBaseModel]) -> dict[str, Any]:
if issubclass(pydantic_object, pydantic.BaseModel):
return pydantic_object.model_json_schema()
elif issubclass(pydantic_object, pydantic.v1.BaseModel):
return pydantic_object.schema()
def parse_result(self, result: list[Generation], *, partial: bool = False) -> Any:
"""Parse the result of an LLM call to a JSON object.
Args:
result: The result of the LLM call.
partial: Whether to parse partial JSON objects.
If True, the output will be a JSON object containing
all the keys that have been returned so far.
If False, the output will be the full JSON object.
Default is False.
Returns:
The parsed JSON object.
Raises:
OutputParserException: If the output is not valid JSON.
"""
text = result[0].text
text = text.strip()
if partial:
try:
return parse_json_markdown(text)
except JSONDecodeError:
return None
else:
try:
return parse_json_markdown(text)
except JSONDecodeError as e:
msg = f"Invalid json output: {text}"
raise OutputParserException(msg, llm_output=text) from e
def parse(self, text: str) -> Any:
"""Parse the output of an LLM call to a JSON object.
Args:
text: The output of the LLM call.
Returns:
The parsed JSON object.
"""
return self.parse_result([Generation(text=text)])
def get_format_instructions(self) -> str:
"""Return the format instructions for the JSON output.
Returns:
The format instructions for the JSON output.
"""
if self.pydantic_object is None:
return "Return a JSON object."
else:
# Copy schema to avoid altering original Pydantic schema.
schema = dict(self._get_schema(self.pydantic_object).items())
# Remove extraneous fields.
reduced_schema = schema
if "title" in reduced_schema:
del reduced_schema["title"]
if "type" in reduced_schema:
del reduced_schema["type"]
# Ensure json in context is well-formed with double quotes.
schema_str = json.dumps(reduced_schema, ensure_ascii=False)
return JSON_FORMAT_INSTRUCTIONS.format(schema=schema_str)
@property
def _type(self) -> str:
return "simple_json_output_parser"
# For backwards compatibility
SimpleJsonOutputParser = JsonOutputParser
parse_partial_json = parse_partial_json
parse_and_check_json_markdown = parse_and_check_json_markdown
|
from jina import Client
from docarray import DocList
from docarray.documents import TextDoc
if __name__ == '__main__':
c = Client(host='grpc://0.0.0.0:54321')
da = c.post(
'/', DocList[TextDoc]([TextDoc(), TextDoc()]), return_type=DocList[TextDoc]
)
print(da.text)
|
from jina import Client
from docarray import DocList
from docarray.documents import TextDoc
if __name__ == '__main__':
c = Client(host='grpc://0.0.0.0:54321')
da = c.post('/', DocList[TextDoc]([TextDoc(), TextDoc()]), return_type=DocList[TextDoc])
print(da.text)
|
import json
import logging
from enum import Enum
from typing import Any
from requests.exceptions import HTTPError, RequestException
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
logger = logging.getLogger(name=__name__)
class HttpMethod(Enum):
GET = "GET"
POST = "POST"
PUT = "PUT"
DELETE = "DELETE"
PATCH = "PATCH"
OPTIONS = "OPTIONS"
HEAD = "HEAD"
class SendWebRequestBlock(Block):
class Input(BlockSchema):
url: str = SchemaField(
description="The URL to send the request to",
placeholder="https://api.example.com",
)
method: HttpMethod = SchemaField(
description="The HTTP method to use for the request",
default=HttpMethod.POST,
)
headers: dict[str, str] = SchemaField(
description="The headers to include in the request",
default_factory=dict,
)
json_format: bool = SchemaField(
title="JSON format",
description="Whether to send and receive body as JSON",
default=True,
)
body: Any = SchemaField(
description="The body of the request",
default=None,
)
class Output(BlockSchema):
response: object = SchemaField(description="The response from the server")
client_error: object = SchemaField(description="Errors on 4xx status codes")
server_error: object = SchemaField(description="Errors on 5xx status codes")
error: str = SchemaField(description="Errors for all other exceptions")
def __init__(self):
super().__init__(
id="6595ae1f-b924-42cb-9a41-551a0611c4b4",
description="This block makes an HTTP request to the given URL.",
categories={BlockCategory.OUTPUT},
input_schema=SendWebRequestBlock.Input,
output_schema=SendWebRequestBlock.Output,
)
def run(self, input_data: Input, **kwargs) -> BlockOutput:
body = input_data.body
if input_data.json_format:
if isinstance(body, str):
try:
# Try to parse as JSON first
body = json.loads(body)
except json.JSONDecodeError:
# If it's not valid JSON and just plain text,
# we should send it as plain text instead
input_data.json_format = False
try:
response = requests.request(
input_data.method.value,
input_data.url,
headers=input_data.headers,
json=body if input_data.json_format else None,
data=body if not input_data.json_format else None,
)
if input_data.json_format:
if response.status_code == 204 or not response.content.strip():
result = None
else:
result = response.json()
else:
result = response.text
yield "response", result
except HTTPError as e:
# Handle error responses
try:
result = e.response.json() if input_data.json_format else str(e)
except json.JSONDecodeError:
result = str(e)
if 400 <= e.response.status_code < 500:
yield "client_error", result
elif 500 <= e.response.status_code < 600:
yield "server_error", result
else:
error_msg = (
"Unexpected status code "
f"{e.response.status_code} '{e.response.reason}'"
)
logger.warning(error_msg)
yield "error", error_msg
except RequestException as e:
# Handle other request-related exceptions
yield "error", str(e)
except Exception as e:
# Catch any other unexpected exceptions
yield "error", str(e)
|
import json
import logging
from enum import Enum
from typing import Any
from requests.exceptions import HTTPError, RequestException
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
logger = logging.getLogger(name=__name__)
class HttpMethod(Enum):
GET = "GET"
POST = "POST"
PUT = "PUT"
DELETE = "DELETE"
PATCH = "PATCH"
OPTIONS = "OPTIONS"
HEAD = "HEAD"
class SendWebRequestBlock(Block):
class Input(BlockSchema):
url: str = SchemaField(
description="The URL to send the request to",
placeholder="https://api.example.com",
)
method: HttpMethod = SchemaField(
description="The HTTP method to use for the request",
default=HttpMethod.POST,
)
headers: dict[str, str] = SchemaField(
description="The headers to include in the request",
default_factory=dict,
)
json_format: bool = SchemaField(
title="JSON format",
description="Whether to send and receive body as JSON",
default=True,
)
body: Any = SchemaField(
description="The body of the request",
default=None,
)
class Output(BlockSchema):
response: object = SchemaField(description="The response from the server")
client_error: object = SchemaField(description="Errors on 4xx status codes")
server_error: object = SchemaField(description="Errors on 5xx status codes")
error: str = SchemaField(description="Errors for all other exceptions")
def __init__(self):
super().__init__(
id="6595ae1f-b924-42cb-9a41-551a0611c4b4",
description="This block makes an HTTP request to the given URL.",
categories={BlockCategory.OUTPUT},
input_schema=SendWebRequestBlock.Input,
output_schema=SendWebRequestBlock.Output,
)
def run(self, input_data: Input, **kwargs) -> BlockOutput:
body = input_data.body
if input_data.json_format:
if isinstance(body, str):
try:
# Try to parse as JSON first
body = json.loads(body)
except json.JSONDecodeError:
# If it's not valid JSON and just plain text,
# we should send it as plain text instead
input_data.json_format = False
try:
response = requests.request(
input_data.method.value,
input_data.url,
headers=input_data.headers,
json=body if input_data.json_format else None,
data=body if not input_data.json_format else None,
)
result = response.json() if input_data.json_format else response.text
yield "response", result
except HTTPError as e:
# Handle error responses
try:
result = e.response.json() if input_data.json_format else str(e)
except json.JSONDecodeError:
result = str(e)
if 400 <= e.response.status_code < 500:
yield "client_error", result
elif 500 <= e.response.status_code < 600:
yield "server_error", result
else:
error_msg = (
"Unexpected status code "
f"{e.response.status_code} '{e.response.reason}'"
)
logger.warning(error_msg)
yield "error", error_msg
except RequestException as e:
# Handle other request-related exceptions
yield "error", str(e)
except Exception as e:
# Catch any other unexpected exceptions
yield "error", str(e)
|
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.builder import NECKS
@NECKS.register_module()
class CTResNetNeck(BaseModule):
"""The neck used in `CenterNet <https://arxiv.org/abs/1904.07850>`_ for
object classification and box regression.
Args:
in_channel (int): Number of input channels.
num_deconv_filters (tuple[int]): Number of filters per stage.
num_deconv_kernels (tuple[int]): Number of kernels per stage.
use_dcn (bool): If True, use DCNv2. Default: True.
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(self,
in_channel,
num_deconv_filters,
num_deconv_kernels,
use_dcn=True,
init_cfg=None):
super(CTResNetNeck, self).__init__(init_cfg)
assert len(num_deconv_filters) == len(num_deconv_kernels)
self.fp16_enabled = False
self.use_dcn = use_dcn
self.in_channel = in_channel
self.deconv_layers = self._make_deconv_layer(num_deconv_filters,
num_deconv_kernels)
def _make_deconv_layer(self, num_deconv_filters, num_deconv_kernels):
"""use deconv layers to upsample backbone's output."""
layers = []
for i in range(len(num_deconv_filters)):
feat_channel = num_deconv_filters[i]
conv_module = ConvModule(
self.in_channel,
feat_channel,
3,
padding=1,
conv_cfg=dict(type='DCNv2') if self.use_dcn else None,
norm_cfg=dict(type='BN'))
layers.append(conv_module)
upsample_module = ConvModule(
feat_channel,
feat_channel,
num_deconv_kernels[i],
stride=2,
padding=1,
conv_cfg=dict(type='deconv'),
norm_cfg=dict(type='BN'))
layers.append(upsample_module)
self.in_channel = feat_channel
return nn.Sequential(*layers)
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.ConvTranspose2d):
# In order to be consistent with the source code,
# reset the ConvTranspose2d initialization parameters
m.reset_parameters()
# Simulated bilinear upsampling kernel
w = m.weight.data
f = math.ceil(w.size(2) / 2)
c = (2 * f - 1 - f % 2) / (2. * f)
for i in range(w.size(2)):
for j in range(w.size(3)):
w[0, 0, i, j] = \
(1 - math.fabs(i / f - c)) * (
1 - math.fabs(j / f - c))
for c in range(1, w.size(0)):
w[c, 0, :, :] = w[0, 0, :, :]
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# self.use_dcn is False
elif not self.use_dcn and isinstance(m, nn.Conv2d):
# In order to be consistent with the source code,
# reset the Conv2d initialization parameters
m.reset_parameters()
@auto_fp16()
def forward(self, inputs):
assert isinstance(inputs, (list, tuple))
outs = self.deconv_layers(inputs[-1])
return outs,
|
import math
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.builder import NECKS
@NECKS.register_module()
class CTResNetNeck(BaseModule):
"""The neck used in `CenterNet <https://arxiv.org/abs/1904.07850>`_ for
object classification and box regression.
Args:
in_channel (int): Number of input channels.
num_deconv_filters (tuple[int]): Number of filters per stage.
num_deconv_kernels (tuple[int]): Number of kernels per stage.
use_dcn (bool): If True, use DCNv2. Default: True.
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(self,
in_channel,
num_deconv_filters,
num_deconv_kernels,
use_dcn=True,
init_cfg=None):
super(CTResNetNeck, self).__init__(init_cfg)
assert len(num_deconv_filters) == len(num_deconv_kernels)
self.fp16_enabled = False
self.use_dcn = use_dcn
self.in_channel = in_channel
self.deconv_layers = self._make_deconv_layer(num_deconv_filters,
num_deconv_kernels)
def _make_deconv_layer(self, num_deconv_filters, num_deconv_kernels):
"""use deconv layers to upsample backbone's output."""
layers = []
for i in range(len(num_deconv_filters)):
feat_channel = num_deconv_filters[i]
conv_module = ConvModule(
self.in_channel,
feat_channel,
3,
padding=1,
conv_cfg=dict(type='DCNv2') if self.use_dcn else None,
norm_cfg=dict(type='BN'))
layers.append(conv_module)
upsample_module = ConvModule(
feat_channel,
feat_channel,
num_deconv_kernels[i],
stride=2,
padding=1,
conv_cfg=dict(type='deconv'),
norm_cfg=dict(type='BN'))
layers.append(upsample_module)
self.in_channel = feat_channel
return nn.Sequential(*layers)
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.ConvTranspose2d):
# In order to be consistent with the source code,
# reset the ConvTranspose2d initialization parameters
m.reset_parameters()
# Simulated bilinear upsampling kernel
w = m.weight.data
f = math.ceil(w.size(2) / 2)
c = (2 * f - 1 - f % 2) / (2. * f)
for i in range(w.size(2)):
for j in range(w.size(3)):
w[0, 0, i, j] = \
(1 - math.fabs(i / f - c)) * (
1 - math.fabs(j / f - c))
for c in range(1, w.size(0)):
w[c, 0, :, :] = w[0, 0, :, :]
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# self.use_dcn is False
elif not self.use_dcn and isinstance(m, nn.Conv2d):
# In order to be consistent with the source code,
# reset the Conv2d initialization parameters
m.reset_parameters()
@auto_fp16()
def forward(self, inputs):
assert isinstance(inputs, (list, tuple))
outs = self.deconv_layers(inputs[-1])
return outs,
|
"""This module contains the core type definitions and protocols used throughout Dynamo.
The types defined here fall into several categories:
- Guard related types (GuardFn, GuardFail, GuardedCode): Used for tracking and managing guards that protect compiled code
- Frame and cache types (FrameState, CacheEntry): Used for managing interpreter frame state and caching
- Callback protocols (DynamoCallbackFn): Define the interface for frame evaluation callbacks
- Hook protocols (DynamoGuardHook, ProfilerStartHook, ProfilerEndHook, BytecodeHook): Define various hook points for
instrumentation and customization
These types provide the foundational interfaces that enable Dynamo's dynamic compilation and optimization system,
ensuring type safety and clear contracts between different components of the system.
"""
import dataclasses
import types
from typing import Any, Callable, NamedTuple, Optional, Protocol, Union
# CacheEntry has a `guard_manager` field for the guard, and a `code` field for the code object.
from torch._C._dynamo.eval_frame import (
_CacheEntry as CacheEntry,
_ExtraState as ExtraState,
_FrameAction as FrameAction,
_FrameExecStrategy as FrameExecStrategy,
_PyInterpreterFrame as DynamoFrameType,
)
from torch._guards import CompileId, Guard
# We use a dict to store additional data per frame.
FrameState = dict[Any, Any]
class GuardFail(NamedTuple):
# A string repr of the piece of failed guard code we eval-ed
reason: str
# A code object where we failed a guard
orig_code: types.CodeType
@dataclasses.dataclass(frozen=True)
class GuardFilterEntry:
name: str
has_value: bool
value: object
guard_type: str
derived_guard_types: tuple[str, ...]
is_global: bool
orig_guard: Guard
class GuardFn(Protocol):
closure_vars: dict[str, object]
args: list[str]
code_parts: list[str]
verbose_code_parts: list[str]
global_scope: dict[str, object]
guard_fail_fn: Optional[Callable[[GuardFail], None]]
cache_entry: Optional[CacheEntry]
extra_state: Optional[ExtraState]
# maps locals of user function to bool
def __call__(self, f_locals: dict[str, object]) -> bool: ...
@dataclasses.dataclass
class GuardedCode:
code: types.CodeType
guard_manager: GuardFn
compile_id: CompileId
trace_annotation: str = "Unknown"
@dataclasses.dataclass
class ConvertFrameReturn:
# default return is no compiled code (i.e. `return None`):
# strategy is to skip non-recursively, for all future intercepted frames too
# eval frame execution strategy for this frame
frame_exec_strategy: FrameExecStrategy = dataclasses.field(
default_factory=lambda: FrameExecStrategy(FrameAction.SKIP, FrameAction.DEFAULT)
)
# also apply frame_exec strategy to future frames with same code
apply_to_code: bool = True
guarded_code: Optional[GuardedCode] = None
def wrap_guarded_code(guarded_code: GuardedCode) -> ConvertFrameReturn:
return ConvertFrameReturn(
frame_exec_strategy=FrameExecStrategy(FrameAction.DEFAULT, FrameAction.DEFAULT),
guarded_code=guarded_code,
)
class DynamoCallbackFn(Protocol):
def __call__(
self,
frame: DynamoFrameType,
cache_entry: Optional[CacheEntry],
frame_state: FrameState,
) -> ConvertFrameReturn: ...
DynamoCallback = Union[DynamoCallbackFn, None, bool]
class DynamoGuardHook(Protocol):
def __call__(
self,
guard_manager: GuardFn,
code: types.CodeType,
f_locals: dict[str, object],
index: int,
last: bool,
) -> None: ...
class ProfilerStartHook(Protocol):
def __call__(
self,
name: str,
# TODO(whc) how do I annotate a _RecordFunction here?
) -> Any: ...
class ProfilerEndHook(Protocol):
def __call__(self, record: Any) -> None: ...
class BytecodeHook(Protocol):
def __call__(
self, code: types.CodeType, new_code: types.CodeType
) -> Optional[types.CodeType]: ...
|
"""This module contains the core type definitions and protocols used throughout Dynamo.
The types defined here fall into several categories:
- Guard related types (GuardFn, GuardFail, GuardedCode): Used for tracking and managing guards that protect compiled code
- Frame and cache types (FrameState, CacheEntry): Used for managing interpreter frame state and caching
- Callback protocols (DynamoCallbackFn): Define the interface for frame evaluation callbacks
- Hook protocols (DynamoGuardHook, ProfilerStartHook, ProfilerEndHook, BytecodeHook): Define various hook points for
instrumentation and customization
These types provide the foundational interfaces that enable Dynamo's dynamic compilation and optimization system,
ensuring type safety and clear contracts between different components of the system.
"""
import dataclasses
import types
from typing import Any, Callable, NamedTuple, Optional, Protocol, Union
# CacheEntry has a `guard_manager` field for the guard, and a `code` field for the code object.
from torch._C._dynamo.eval_frame import (
_CacheEntry as CacheEntry,
_ExtraState as ExtraState,
_FrameAction as FrameAction,
_FrameExecStrategy as FrameExecStrategy,
_PyInterpreterFrame as DynamoFrameType,
)
from torch._guards import CompileId, Guard
# We use a dict to store additional data per frame.
FrameState = dict[Any, Any]
class GuardFail(NamedTuple):
# A string repr of the piece of failed guard code we eval-ed
reason: str
# A code object where we failed a guard
orig_code: types.CodeType
@dataclasses.dataclass(frozen=True)
class GuardFilterEntry:
name: str
has_value: bool
value: object
guard_type: str
derived_guard_types: tuple[str, ...]
is_global: bool
orig_guard: Guard
class GuardFn(Protocol):
closure_vars: dict[str, object]
args: list[str]
code_parts: list[str]
verbose_code_parts: list[str]
global_scope: dict[str, object]
guard_fail_fn: Optional[Callable[[GuardFail], None]]
cache_entry: Optional[CacheEntry]
extra_state: Optional[ExtraState]
# maps locals of user function to bool
def __call__(self, f_locals: dict[str, object]) -> bool: ...
@dataclasses.dataclass
class GuardedCode:
code: types.CodeType
guard_manager: GuardFn
compile_id: CompileId
trace_annotation: str = "Unknown"
@dataclasses.dataclass
class ConvertFrameReturn:
# default return is no compiled code (i.e. `return None`):
# strategy is to skip non-recursively, for all future intercepted frames too
# eval fram execution strategy for this frame
frame_exec_strategy: FrameExecStrategy = dataclasses.field(
default_factory=lambda: FrameExecStrategy(FrameAction.SKIP, FrameAction.DEFAULT)
)
# also apply frame_exec strategy to future frames with same code
apply_to_code: bool = True
guarded_code: Optional[GuardedCode] = None
def wrap_guarded_code(guarded_code: GuardedCode) -> ConvertFrameReturn:
return ConvertFrameReturn(
frame_exec_strategy=FrameExecStrategy(FrameAction.DEFAULT, FrameAction.DEFAULT),
guarded_code=guarded_code,
)
class DynamoCallbackFn(Protocol):
def __call__(
self,
frame: DynamoFrameType,
cache_entry: Optional[CacheEntry],
frame_state: FrameState,
) -> ConvertFrameReturn: ...
DynamoCallback = Union[DynamoCallbackFn, None, bool]
class DynamoGuardHook(Protocol):
def __call__(
self,
guard_manager: GuardFn,
code: types.CodeType,
f_locals: dict[str, object],
index: int,
last: bool,
) -> None: ...
class ProfilerStartHook(Protocol):
def __call__(
self,
name: str,
# TODO(whc) how do I annotate a _RecordFunction here?
) -> Any: ...
class ProfilerEndHook(Protocol):
def __call__(self, record: Any) -> None: ...
class BytecodeHook(Protocol):
def __call__(
self, code: types.CodeType, new_code: types.CodeType
) -> Optional[types.CodeType]: ...
|
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_video_torch_encoder():
model_state_dict_path = os.path.join(cur_dir, '../model/model_state_dict.pth')
input_dim = 224
test_img = np.random.rand(3, input_dim, input_dim)
docs = DocumentArray([Document(blob=test_img), Document(blob=test_img)])
f = Flow().add(
uses={
'jtype': 'CustomImageTorchEncoder',
'with': {
'model_state_dict_path': model_state_dict_path,
'layer_name': 'conv1',
'model_definition_file': os.path.join(
cur_dir, '../model/external_model.py'
),
'model_class_name': 'ExternalModel',
},
}
)
with f:
resp = f.post(on='/test', inputs=docs, return_results=True)
assert resp[0].docs[0].embedding.shape == (10,)
assert resp[0].docs[1].embedding.shape == (10,)
@pytest.mark.docker
def test_docker_runtime(build_docker_image: str):
with pytest.raises(subprocess.TimeoutExpired):
subprocess.run(
['jina', 'executor', f'--uses=docker://{build_docker_image}'],
timeout=30,
check=True,
)
@pytest.mark.gpu
@pytest.mark.docker
def test_docker_runtime_gpu(build_docker_image_gpu: str):
with pytest.raises(subprocess.TimeoutExpired):
subprocess.run(
[
'jina',
'pea',
f'--uses=docker://{build_docker_image_gpu}',
'--gpus',
'all',
'--uses-with',
'device:cuda',
],
timeout=30,
check=True,
)
|
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import numpy as np
from jina import Document, Flow, DocumentArray
from ...custom_image_torch_encoder import CustomImageTorchEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_video_torch_encoder():
model_state_dict_path = os.path.join(cur_dir, '../model/model_state_dict.pth')
input_dim = 224
test_img = np.random.rand(3, input_dim, input_dim)
docs = DocumentArray([Document(blob=test_img), Document(blob=test_img)])
f = Flow().add(uses={'jtype': 'CustomImageTorchEncoder',
'with': {'model_state_dict_path': model_state_dict_path,
'layer_name': 'conv1',
'model_definition_file': os.path.join(cur_dir, '../model/external_model.py'),
'model_class_name': 'ExternalModel'}})
with f:
resp = f.post(on='/test', inputs=docs,
return_results=True)
assert resp[0].docs[0].embedding.shape == (10,)
assert resp[0].docs[1].embedding.shape == (10,)
|
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseTranslationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model, not mutilingual but hope to see some on the hub soon
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
# Load a parallel sentences dataset
dataset = load_dataset("sentence-transformers/parallel-sentences-news-commentary", "en-nl", split="train[:1000]")
# Initialize the TranslationEvaluator using the same texts from two languages
translation_evaluator = SparseTranslationEvaluator(
source_sentences=dataset["english"],
target_sentences=dataset["non_english"],
name="news-commentary-en-nl",
)
results = translation_evaluator(model)
"""
Evaluating translation matching Accuracy of the model on the news-commentary-en-nl dataset:
Accuracy src2trg: 41.40
Accuracy trg2src: 47.70
Model Sparsity Stats: Row Non-Zero Mean: 113.6150016784668, Row Sparsity Mean: 0.9962776005268097
"""
# Print the results
print(f"Primary metric: {translation_evaluator.primary_metric}")
# => Primary metric: news-commentary-en-nl_mean_accuracy
print(f"Primary metric value: {results[translation_evaluator.primary_metric]:.4f}")
# => Primary metric value: 0.4455
|
import logging
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseTranslationEvaluator,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
# Initialize the SPLADE model
model_name = "naver/splade-cocondenser-ensembledistil"
model = SparseEncoder(
modules=[
MLMTransformer(model_name),
SpladePooling(pooling_strategy="max"), # You can also use 'sum'
],
device="cuda:0",
)
# Load a parallel sentences dataset
dataset = load_dataset("sentence-transformers/parallel-sentences-news-commentary", "en-nl", split="train[:1000]")
# Initialize the TranslationEvaluator using the same texts from two languages
translation_evaluator = SparseTranslationEvaluator(
source_sentences=dataset["english"],
target_sentences=dataset["non_english"],
name="news-commentary-en-nl",
)
results = translation_evaluator(model)
# Print the results
print(f"Primary metric: {translation_evaluator.primary_metric}")
print(f"Primary metric value: {results[translation_evaluator.primary_metric]:.4f}")
|
# Copyright (c) OpenMMLab. All rights reserved.
from .builder import build_match_cost
from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost
__all__ = [
'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost',
'FocalLossCost'
]
|
from .builder import build_match_cost
from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost
__all__ = [
'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost',
'FocalLossCost'
]
|
_base_ = './mask-rcnn_hrnetv2p-w18-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
gamma=0.1)
]
|
_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
gamma=0.1)
]
|
import functools
import importlib
import os
import re
from pathlib import Path
from typing import TYPE_CHECKING, TypeVar
if TYPE_CHECKING:
from backend.data.block import Block
T = TypeVar("T")
@functools.cache
def load_all_blocks() -> dict[str, type["Block"]]:
from backend.data.block import Block
# Dynamically load all modules under backend.blocks
current_dir = Path(__file__).parent
modules = [
str(f.relative_to(current_dir))[:-3].replace(os.path.sep, ".")
for f in current_dir.rglob("*.py")
if f.is_file() and f.name != "__init__.py" and not f.name.startswith("test_")
]
for module in modules:
if not re.match("^[a-z0-9_.]+$", module):
raise ValueError(
f"Block module {module} error: module name must be lowercase, "
"and contain only alphanumeric characters and underscores."
)
importlib.import_module(f".{module}", package=__name__)
# Load all Block instances from the available modules
available_blocks: dict[str, type["Block"]] = {}
for block_cls in all_subclasses(Block):
class_name = block_cls.__name__
if class_name.endswith("Base"):
continue
if not class_name.endswith("Block"):
raise ValueError(
f"Block class {class_name} does not end with 'Block'. "
"If you are creating an abstract class, "
"please name the class with 'Base' at the end"
)
block = block_cls.create()
if not isinstance(block.id, str) or len(block.id) != 36:
raise ValueError(
f"Block ID {block.name} error: {block.id} is not a valid UUID"
)
if block.id in available_blocks:
raise ValueError(
f"Block ID {block.name} error: {block.id} is already in use"
)
input_schema = block.input_schema.model_fields
output_schema = block.output_schema.model_fields
# Make sure `error` field is a string in the output schema
if "error" in output_schema and output_schema["error"].annotation is not str:
raise ValueError(
f"{block.name} `error` field in output_schema must be a string"
)
# Ensure all fields in input_schema and output_schema are annotated SchemaFields
for field_name, field in [*input_schema.items(), *output_schema.items()]:
if field.annotation is None:
raise ValueError(
f"{block.name} has a field {field_name} that is not annotated"
)
if field.json_schema_extra is None:
raise ValueError(
f"{block.name} has a field {field_name} not defined as SchemaField"
)
for field in block.input_schema.model_fields.values():
if field.annotation is bool and field.default not in (True, False):
raise ValueError(
f"{block.name} has a boolean field with no default value"
)
available_blocks[block.id] = block_cls
return available_blocks
__all__ = ["load_all_blocks"]
def all_subclasses(cls: type[T]) -> list[type[T]]:
subclasses = cls.__subclasses__()
for subclass in subclasses:
subclasses += all_subclasses(subclass)
return subclasses
|
import functools
import importlib
import os
import re
from pathlib import Path
from typing import TYPE_CHECKING, TypeVar
if TYPE_CHECKING:
from backend.data.block import Block
T = TypeVar("T")
@functools.cache
def load_all_blocks() -> dict[str, type["Block"]]:
from backend.data.block import Block
# Dynamically load all modules under backend.blocks
current_dir = Path(__file__).parent
modules = [
str(f.relative_to(current_dir))[:-3].replace(os.path.sep, ".")
for f in current_dir.rglob("*.py")
if f.is_file() and f.name != "__init__.py"
]
for module in modules:
if not re.match("^[a-z0-9_.]+$", module):
raise ValueError(
f"Block module {module} error: module name must be lowercase, "
"and contain only alphanumeric characters and underscores."
)
importlib.import_module(f".{module}", package=__name__)
# Load all Block instances from the available modules
available_blocks: dict[str, type["Block"]] = {}
for block_cls in all_subclasses(Block):
class_name = block_cls.__name__
if class_name.endswith("Base"):
continue
if not class_name.endswith("Block"):
raise ValueError(
f"Block class {class_name} does not end with 'Block'. "
"If you are creating an abstract class, "
"please name the class with 'Base' at the end"
)
block = block_cls.create()
if not isinstance(block.id, str) or len(block.id) != 36:
raise ValueError(
f"Block ID {block.name} error: {block.id} is not a valid UUID"
)
if block.id in available_blocks:
raise ValueError(
f"Block ID {block.name} error: {block.id} is already in use"
)
input_schema = block.input_schema.model_fields
output_schema = block.output_schema.model_fields
# Make sure `error` field is a string in the output schema
if "error" in output_schema and output_schema["error"].annotation is not str:
raise ValueError(
f"{block.name} `error` field in output_schema must be a string"
)
# Ensure all fields in input_schema and output_schema are annotated SchemaFields
for field_name, field in [*input_schema.items(), *output_schema.items()]:
if field.annotation is None:
raise ValueError(
f"{block.name} has a field {field_name} that is not annotated"
)
if field.json_schema_extra is None:
raise ValueError(
f"{block.name} has a field {field_name} not defined as SchemaField"
)
for field in block.input_schema.model_fields.values():
if field.annotation is bool and field.default not in (True, False):
raise ValueError(
f"{block.name} has a boolean field with no default value"
)
available_blocks[block.id] = block_cls
return available_blocks
__all__ = ["load_all_blocks"]
def all_subclasses(cls: type[T]) -> list[type[T]]:
subclasses = cls.__subclasses__()
for subclass in subclasses:
subclasses += all_subclasses(subclass)
return subclasses
|
import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.digits, k=6))
return f'JINA_TEST_WORKSPACE_{rand}'
@pytest.fixture(scope='function')
def test_metas(tmpdir, random_workspace_name):
from jina.serve.executors.metas import get_default_metas
os.environ[random_workspace_name] = str(tmpdir)
metas = get_default_metas()
metas['workspace'] = os.environ[random_workspace_name]
yield metas
del os.environ[random_workspace_name]
@pytest.fixture()
def docker_compose(request):
os.system(
f"docker compose -f {request.param} --project-directory . up --build -d --remove-orphans"
)
time.sleep(10)
yield
os.system(
f"docker compose -f {request.param} --project-directory . down --remove-orphans"
)
@pytest.fixture(scope='function')
def port_generator():
generated_ports = set()
def random_port():
port = helper.random_port()
while port in generated_ports:
port = helper.random_port()
generated_ports.add(port)
return port
return random_port
@pytest.fixture(autouse=True)
def test_log_level(monkeypatch):
monkeypatch.setenv('JINA_LOG_LEVEL', 'DEBUG')
@pytest.fixture(autouse=True)
def test_grpc_fork_support_true(monkeypatch):
monkeypatch.setenv('GRPC_ENABLE_FORK_SUPPORT', 'true')
@pytest.fixture(autouse=True)
def test_timeout_ctrl_time(monkeypatch):
monkeypatch.setenv('JINA_DEFAULT_TIMEOUT_CTRL', '500')
@pytest.fixture(autouse=True)
def test_disable_telemetry(monkeypatch):
monkeypatch.setenv('JINA_OPTOUT_TELEMETRY', 'True')
@pytest.fixture(autouse=True)
def tmpfile(tmpdir):
tmpfile = f'jina_test_{next(tempfile._get_candidate_names())}.db'
return tmpdir / tmpfile
@pytest.fixture(scope='session')
def event_loop(request):
"""
Valid only for `pytest.mark.asyncio` tests
"""
loop = asyncio.get_event_loop_policy().new_event_loop()
yield loop
loop.close()
@pytest.fixture(autouse=True)
def set_test_pip_version() -> None:
os.environ['JINA_GATEWAY_IMAGE'] = 'jinaai/jina:test-pip'
yield
if 'JINA_GATEWAY_IMAGE' in os.environ: # maybe another fixture has already removed
del os.environ['JINA_GATEWAY_IMAGE']
|
import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.digits, k=6))
return f'JINA_TEST_WORKSPACE_{rand}'
@pytest.fixture(scope='function')
def test_metas(tmpdir, random_workspace_name):
from jina.serve.executors.metas import get_default_metas
os.environ[random_workspace_name] = str(tmpdir)
metas = get_default_metas()
metas['workspace'] = os.environ[random_workspace_name]
yield metas
del os.environ[random_workspace_name]
@pytest.fixture()
def docker_compose(request):
os.system(
f"docker-compose -f {request.param} --project-directory . up --build -d --remove-orphans"
)
time.sleep(10)
yield
os.system(
f"docker-compose -f {request.param} --project-directory . down --remove-orphans"
)
@pytest.fixture(scope='function')
def port_generator():
generated_ports = set()
def random_port():
port = helper.random_port()
while port in generated_ports:
port = helper.random_port()
generated_ports.add(port)
return port
return random_port
@pytest.fixture(autouse=True)
def test_log_level(monkeypatch):
monkeypatch.setenv('JINA_LOG_LEVEL', 'DEBUG')
@pytest.fixture(autouse=True)
def test_grpc_fork_support_true(monkeypatch):
monkeypatch.setenv('GRPC_ENABLE_FORK_SUPPORT', 'true')
@pytest.fixture(autouse=True)
def test_timeout_ctrl_time(monkeypatch):
monkeypatch.setenv('JINA_DEFAULT_TIMEOUT_CTRL', '500')
@pytest.fixture(autouse=True)
def test_disable_telemetry(monkeypatch):
monkeypatch.setenv('JINA_OPTOUT_TELEMETRY', 'True')
@pytest.fixture(autouse=True)
def tmpfile(tmpdir):
tmpfile = f'jina_test_{next(tempfile._get_candidate_names())}.db'
return tmpdir / tmpfile
@pytest.fixture(scope='session')
def event_loop(request):
"""
Valid only for `pytest.mark.asyncio` tests
"""
loop = asyncio.get_event_loop_policy().new_event_loop()
yield loop
loop.close()
@pytest.fixture(autouse=True)
def set_test_pip_version() -> None:
os.environ['JINA_GATEWAY_IMAGE'] = 'jinaai/jina:test-pip'
yield
if 'JINA_GATEWAY_IMAGE' in os.environ: # maybe another fixture has already removed
del os.environ['JINA_GATEWAY_IMAGE']
|
from typing import Iterable, Type
from docarray.array.abstract_array import AbstractDocumentArray
from docarray.array.mixins import GetAttributeArrayMixin, ProtoArrayMixin
from docarray.document import AnyDocument, BaseDocument, BaseNode
class DocumentArray(
list,
ProtoArrayMixin,
GetAttributeArrayMixin,
AbstractDocumentArray,
BaseNode,
):
"""
a DocumentArray is a list-like container of Document of the same schema
:param docs: iterable of Document
"""
document_type: Type[BaseDocument] = AnyDocument
def __init__(self, docs: Iterable[BaseDocument]):
super().__init__(doc_ for doc_ in docs)
def __class_getitem__(cls, item: Type[BaseDocument]):
if not issubclass(item, BaseDocument):
raise ValueError(
f'DocumentArray[item] item should be a Document not a {item} '
)
class _DocumenArrayTyped(DocumentArray):
document_type: Type[BaseDocument] = item
for field in _DocumenArrayTyped.document_type.__fields__.keys():
def _proprety_generator(val: str):
return property(lambda self: self._get_documents_attribute(val))
setattr(_DocumenArrayTyped, field, _proprety_generator(field))
# this generates property on the fly based on the schema of the item
_DocumenArrayTyped.__name__ = f'DocumentArray{item.__name__}'
return _DocumenArrayTyped
|
from typing import Iterable, Type
from docarray.array.abstract_array import AbstractDocumentArray
from docarray.array.mixins import GetAttributeArrayMixin, ProtoArrayMixin
from docarray.document import AnyDocument, BaseDocument, BaseNode
from docarray.document.abstract_document import AbstractDocument
class DocumentArray(
list,
ProtoArrayMixin,
GetAttributeArrayMixin,
AbstractDocumentArray,
BaseNode,
):
"""
a DocumentArray is a list-like container of Document of the same schema
:param docs: iterable of Document
"""
document_type: Type[BaseDocument] = AnyDocument
def __init__(self, docs: Iterable[AbstractDocument]):
super().__init__(doc_ for doc_ in docs)
def __class_getitem__(cls, item: Type[BaseDocument]):
if not issubclass(item, BaseDocument):
raise ValueError(
f'DocumentArray[item] item should be a Document not a {item} '
)
class _DocumenArrayTyped(DocumentArray):
document_type = item
for field in _DocumenArrayTyped.document_type.__fields__.keys():
def _proprety_generator(val: str):
return property(lambda self: self._get_documents_attribute(val))
setattr(_DocumenArrayTyped, field, _proprety_generator(field))
# this generates property on the fly based on the schema of the item
_DocumenArrayTyped.__name__ = f'DocumentArray{item.__name__}'
return _DocumenArrayTyped
|
from __future__ import annotations
import os
import pytest
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.util import is_datasets_available
from tests.utils import SafeTemporaryDirectory
if is_datasets_available():
from datasets import DatasetDict, load_dataset
@pytest.fixture()
def stsb_bert_tiny_model() -> SentenceTransformer:
return SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-safetensors")
@pytest.fixture(scope="session")
def stsb_bert_tiny_model_reused() -> SentenceTransformer:
return SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-safetensors")
@pytest.fixture()
def stsb_bert_tiny_model_onnx() -> SentenceTransformer:
return SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-onnx")
@pytest.fixture()
def stsb_bert_tiny_model_openvino() -> SentenceTransformer:
return SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-openvino")
@pytest.fixture()
def paraphrase_distilroberta_base_v1_model() -> SentenceTransformer:
return SentenceTransformer("paraphrase-distilroberta-base-v1")
@pytest.fixture()
def clip_vit_b_32_model() -> SentenceTransformer:
return SentenceTransformer("clip-ViT-B-32")
@pytest.fixture()
def distilbert_base_uncased_model() -> SentenceTransformer:
word_embedding_model = Transformer("distilbert-base-uncased")
pooling_model = Pooling(word_embedding_model.get_word_embedding_dimension())
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
return model
@pytest.fixture(scope="session")
def stsb_dataset_dict() -> DatasetDict:
return load_dataset("sentence-transformers/stsb")
@pytest.fixture()
def cache_dir():
"""
In the CI environment, we use a temporary directory as `cache_dir`
to avoid keeping the downloaded models on disk after the test.
"""
if os.environ.get("CI", None):
# Note: `ignore_cleanup_errors=True` is used to avoid NotADirectoryError in Windows on GitHub Actions.
# See https://github.com/python/cpython/issues/107408, https://www.scivision.dev/python-tempfile-permission-error-windows/
with SafeTemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield None
|
from __future__ import annotations
import os
import pytest
from sentence_transformers import CrossEncoder, SentenceTransformer
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.util import is_datasets_available
from tests.utils import SafeTemporaryDirectory
if is_datasets_available():
from datasets import DatasetDict, load_dataset
@pytest.fixture()
def stsb_bert_tiny_model() -> SentenceTransformer:
return SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-safetensors")
@pytest.fixture(scope="session")
def stsb_bert_tiny_model_reused() -> SentenceTransformer:
return SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-safetensors")
@pytest.fixture()
def stsb_bert_tiny_model_onnx() -> SentenceTransformer:
return SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-onnx")
@pytest.fixture()
def stsb_bert_tiny_model_openvino() -> SentenceTransformer:
return SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-openvino")
@pytest.fixture()
def paraphrase_distilroberta_base_v1_model() -> SentenceTransformer:
return SentenceTransformer("paraphrase-distilroberta-base-v1")
@pytest.fixture()
def distilroberta_base_ce_model() -> CrossEncoder:
return CrossEncoder("distilroberta-base", num_labels=1)
@pytest.fixture()
def clip_vit_b_32_model() -> SentenceTransformer:
return SentenceTransformer("clip-ViT-B-32")
@pytest.fixture()
def distilbert_base_uncased_model() -> SentenceTransformer:
word_embedding_model = Transformer("distilbert-base-uncased")
pooling_model = Pooling(word_embedding_model.get_word_embedding_dimension())
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
return model
@pytest.fixture(scope="session")
def stsb_dataset_dict() -> DatasetDict:
return load_dataset("mteb/stsbenchmark-sts")
@pytest.fixture()
def cache_dir():
"""
In the CI environment, we use a temporary directory as `cache_dir`
to avoid keeping the downloaded models on disk after the test.
"""
if os.environ.get("CI", None):
# Note: `ignore_cleanup_errors=True` is used to avoid NotADirectoryError in Windows on GitHub Actions.
# See https://github.com/python/cpython/issues/107408, https://www.scivision.dev/python-tempfile-permission-error-windows/
with SafeTemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield None
|
from typing import TYPE_CHECKING
from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, deprecate
from ..utils.import_utils import is_peft_available, is_torch_available, is_transformers_available
def text_encoder_lora_state_dict(text_encoder):
deprecate(
"text_encoder_load_state_dict in `models`",
"0.27.0",
"`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co/docs/peft/v0.6.2/en/quicktour#peftmodel for more information.",
)
state_dict = {}
for name, module in text_encoder_attn_modules(text_encoder):
for k, v in module.q_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.q_proj.lora_linear_layer.{k}"] = v
for k, v in module.k_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.k_proj.lora_linear_layer.{k}"] = v
for k, v in module.v_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.v_proj.lora_linear_layer.{k}"] = v
for k, v in module.out_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.out_proj.lora_linear_layer.{k}"] = v
return state_dict
if is_transformers_available():
def text_encoder_attn_modules(text_encoder):
deprecate(
"text_encoder_attn_modules in `models`",
"0.27.0",
"`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co/docs/peft/v0.6.2/en/quicktour#peftmodel for more information.",
)
from transformers import CLIPTextModel, CLIPTextModelWithProjection
attn_modules = []
if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
for i, layer in enumerate(text_encoder.text_model.encoder.layers):
name = f"text_model.encoder.layers.{i}.self_attn"
mod = layer.self_attn
attn_modules.append((name, mod))
else:
raise ValueError(f"do not know how to get attention modules for: {text_encoder.__class__.__name__}")
return attn_modules
_import_structure = {}
if is_torch_available():
_import_structure["single_file_model"] = ["FromOriginalModelMixin"]
_import_structure["unet"] = ["UNet2DConditionLoadersMixin"]
_import_structure["utils"] = ["AttnProcsLayers"]
if is_transformers_available():
_import_structure["single_file"] = ["FromSingleFileMixin"]
_import_structure["lora_pipeline"] = [
"AmusedLoraLoaderMixin",
"StableDiffusionLoraLoaderMixin",
"SD3LoraLoaderMixin",
"StableDiffusionXLLoraLoaderMixin",
"LoraLoaderMixin",
"FluxLoraLoaderMixin",
"CogVideoXLoraLoaderMixin",
"Mochi1LoraLoaderMixin",
]
_import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
_import_structure["ip_adapter"] = ["IPAdapterMixin"]
_import_structure["peft"] = ["PeftAdapterMixin"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
if is_torch_available():
from .single_file_model import FromOriginalModelMixin
from .unet import UNet2DConditionLoadersMixin
from .utils import AttnProcsLayers
if is_transformers_available():
from .ip_adapter import IPAdapterMixin
from .lora_pipeline import (
AmusedLoraLoaderMixin,
CogVideoXLoraLoaderMixin,
FluxLoraLoaderMixin,
LoraLoaderMixin,
Mochi1LoraLoaderMixin,
SD3LoraLoaderMixin,
StableDiffusionLoraLoaderMixin,
StableDiffusionXLLoraLoaderMixin,
)
from .single_file import FromSingleFileMixin
from .textual_inversion import TextualInversionLoaderMixin
from .peft import PeftAdapterMixin
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
|
from typing import TYPE_CHECKING
from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, deprecate
from ..utils.import_utils import is_peft_available, is_torch_available, is_transformers_available
def text_encoder_lora_state_dict(text_encoder):
deprecate(
"text_encoder_load_state_dict in `models`",
"0.27.0",
"`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co/docs/peft/v0.6.2/en/quicktour#peftmodel for more information.",
)
state_dict = {}
for name, module in text_encoder_attn_modules(text_encoder):
for k, v in module.q_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.q_proj.lora_linear_layer.{k}"] = v
for k, v in module.k_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.k_proj.lora_linear_layer.{k}"] = v
for k, v in module.v_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.v_proj.lora_linear_layer.{k}"] = v
for k, v in module.out_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.out_proj.lora_linear_layer.{k}"] = v
return state_dict
if is_transformers_available():
def text_encoder_attn_modules(text_encoder):
deprecate(
"text_encoder_attn_modules in `models`",
"0.27.0",
"`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co/docs/peft/v0.6.2/en/quicktour#peftmodel for more information.",
)
from transformers import CLIPTextModel, CLIPTextModelWithProjection
attn_modules = []
if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
for i, layer in enumerate(text_encoder.text_model.encoder.layers):
name = f"text_model.encoder.layers.{i}.self_attn"
mod = layer.self_attn
attn_modules.append((name, mod))
else:
raise ValueError(f"do not know how to get attention modules for: {text_encoder.__class__.__name__}")
return attn_modules
_import_structure = {}
if is_torch_available():
_import_structure["single_file_model"] = ["FromOriginalModelMixin"]
_import_structure["unet"] = ["UNet2DConditionLoadersMixin"]
_import_structure["utils"] = ["AttnProcsLayers"]
if is_transformers_available():
_import_structure["single_file"] = ["FromSingleFileMixin"]
_import_structure["lora_pipeline"] = [
"AmusedLoraLoaderMixin",
"StableDiffusionLoraLoaderMixin",
"SD3LoraLoaderMixin",
"StableDiffusionXLLoraLoaderMixin",
"LoraLoaderMixin",
"FluxLoraLoaderMixin",
"CogVideoXLoraLoaderMixin",
]
_import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
_import_structure["ip_adapter"] = ["IPAdapterMixin"]
_import_structure["peft"] = ["PeftAdapterMixin"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
if is_torch_available():
from .single_file_model import FromOriginalModelMixin
from .unet import UNet2DConditionLoadersMixin
from .utils import AttnProcsLayers
if is_transformers_available():
from .ip_adapter import IPAdapterMixin
from .lora_pipeline import (
AmusedLoraLoaderMixin,
CogVideoXLoraLoaderMixin,
FluxLoraLoaderMixin,
LoraLoaderMixin,
SD3LoraLoaderMixin,
StableDiffusionLoraLoaderMixin,
StableDiffusionXLLoraLoaderMixin,
)
from .single_file import FromSingleFileMixin
from .textual_inversion import TextualInversionLoaderMixin
from .peft import PeftAdapterMixin
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
|
_base_ = 'mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py' # noqa
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
|
_base_ = 'mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
|
# pants requires this import to recognize the dep
import pytest_asyncio # noqa: F401
import pytest
import os
from llama_index.embeddings.nvidia import NVIDIAEmbedding as Interface
from llama_index.embeddings.nvidia.base import DEFAULT_MODEL
from typing import Generator
# this fixture is used to mask the NVIDIA_API_KEY environment variable and restore it
# after the test. it also returns the value of the NVIDIA_API_KEY environment variable
# before it was masked so that it can be used in the test.
@pytest.fixture()
def masked_env_var() -> Generator[str, None, None]:
var = "NVIDIA_API_KEY"
try:
if val := os.environ.get(var, None):
del os.environ[var]
yield val
finally:
if val:
os.environ[var] = val
@pytest.fixture(params=[Interface])
def public_class(request: pytest.FixtureRequest) -> type:
return request.param
def pytest_collection_modifyitems(config, items):
if "NVIDIA_API_KEY" not in os.environ:
skip_marker = pytest.mark.skip(
reason="requires NVIDIA_API_KEY environment variable or --nim-endpoint option"
)
for item in items:
if "integration" in item.keywords and not config.getoption(
"--nim-endpoint"
):
item.add_marker(skip_marker)
def pytest_addoption(parser: pytest.Parser) -> None:
parser.addoption(
"--all-models",
action="store_true",
help="Run tests across all models",
)
parser.addoption(
"--model-id",
action="store",
help="Run tests for a specific chat model",
)
parser.addoption(
"--nim-endpoint",
type=str,
help="Run tests using NIM mode",
)
def get_mode(config: pytest.Config) -> dict:
nim_endpoint = config.getoption("--nim-endpoint")
if nim_endpoint:
return {"base_url": nim_endpoint}
return {}
def pytest_generate_tests(metafunc: pytest.Metafunc) -> None:
mode = get_mode(metafunc.config)
if "model" in metafunc.fixturenames:
models = [DEFAULT_MODEL]
if model := metafunc.config.getoption("--model-id"):
models = [model]
elif metafunc.config.getoption("--all-models"):
models = [model.id for model in Interface(**mode).available_models]
metafunc.parametrize("model", models, ids=models)
@pytest.fixture()
def mode(request: pytest.FixtureRequest) -> dict:
return get_mode(request.config)
|
import pytest
import os
from llama_index.embeddings.nvidia import NVIDIAEmbedding as Interface
from llama_index.embeddings.nvidia.base import DEFAULT_MODEL
from typing import Generator
# this fixture is used to mask the NVIDIA_API_KEY environment variable and restore it
# after the test. it also returns the value of the NVIDIA_API_KEY environment variable
# before it was masked so that it can be used in the test.
@pytest.fixture()
def masked_env_var() -> Generator[str, None, None]:
var = "NVIDIA_API_KEY"
try:
if val := os.environ.get(var, None):
del os.environ[var]
yield val
finally:
if val:
os.environ[var] = val
@pytest.fixture(params=[Interface])
def public_class(request: pytest.FixtureRequest) -> type:
return request.param
def pytest_collection_modifyitems(config, items):
if "NVIDIA_API_KEY" not in os.environ:
skip_marker = pytest.mark.skip(
reason="requires NVIDIA_API_KEY environment variable or --nim-endpoint option"
)
for item in items:
if "integration" in item.keywords and not config.getoption(
"--nim-endpoint"
):
item.add_marker(skip_marker)
def pytest_addoption(parser: pytest.Parser) -> None:
parser.addoption(
"--all-models",
action="store_true",
help="Run tests across all models",
)
parser.addoption(
"--model-id",
action="store",
help="Run tests for a specific chat model",
)
parser.addoption(
"--nim-endpoint",
type=str,
help="Run tests using NIM mode",
)
def get_mode(config: pytest.Config) -> dict:
nim_endpoint = config.getoption("--nim-endpoint")
if nim_endpoint:
return {"base_url": nim_endpoint}
return {}
def pytest_generate_tests(metafunc: pytest.Metafunc) -> None:
mode = get_mode(metafunc.config)
if "model" in metafunc.fixturenames:
models = [DEFAULT_MODEL]
if model := metafunc.config.getoption("--model-id"):
models = [model]
elif metafunc.config.getoption("--all-models"):
models = [model.id for model in Interface(**mode).available_models]
metafunc.parametrize("model", models, ids=models)
@pytest.fixture()
def mode(request: pytest.FixtureRequest) -> dict:
return get_mode(request.config)
|
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class FlaxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def test_stable_diffusion_inpaint_pipeline(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"
)
model_id = "xvjiarui/stable-diffusion-2-inpainting"
pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
init_image = num_samples * [init_image]
mask_image = num_samples * [mask_image]
prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(prompt, init_image, mask_image)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)
processed_masked_images = shard(processed_masked_images)
processed_masks = shard(processed_masks)
output = pipeline(
prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True
)
images = output.images.reshape(num_samples, 512, 512, 3)
image_slice = images[0, 253:256, 253:256, -1]
output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
expected_slice = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]
)
assert jnp.abs(output_slice - expected_slice).max() < 1e-2
|
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class FlaxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def test_stable_diffusion_inpaint_pipeline(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"
)
model_id = "xvjiarui/stable-diffusion-2-inpainting"
pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
init_image = num_samples * [init_image]
mask_image = num_samples * [mask_image]
prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(prompt, init_image, mask_image)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)
processed_masked_images = shard(processed_masked_images)
processed_masks = shard(processed_masks)
output = pipeline(
prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True
)
images = output.images.reshape(num_samples, 512, 512, 3)
image_slice = images[0, 253:256, 253:256, -1]
output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
expected_slice = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]
)
print(f"output_slice: {output_slice}")
assert jnp.abs(output_slice - expected_slice).max() < 1e-2
|
import collections.abc
import dataclasses
from typing import Optional, Sequence
import pytest
import torch
from torch.nn.functional import one_hot
from torchvision.prototype import datapoints
from transforms_v2_legacy_utils import combinations_grid, DEFAULT_EXTRA_DIMS, from_loader, from_loaders, TensorLoader
@dataclasses.dataclass
class LabelLoader(TensorLoader):
categories: Optional[Sequence[str]]
def _parse_categories(categories):
if categories is None:
num_categories = int(torch.randint(1, 11, ()))
elif isinstance(categories, int):
num_categories = categories
categories = [f"category{idx}" for idx in range(num_categories)]
elif isinstance(categories, collections.abc.Sequence) and all(isinstance(category, str) for category in categories):
categories = list(categories)
num_categories = len(categories)
else:
raise pytest.UsageError(
f"`categories` can either be `None` (default), an integer, or a sequence of strings, "
f"but got '{categories}' instead."
)
return categories, num_categories
def make_label_loader(*, extra_dims=(), categories=None, dtype=torch.int64):
categories, num_categories = _parse_categories(categories)
def fn(shape, dtype, device):
# The idiom `make_tensor(..., dtype=torch.int64).to(dtype)` is intentional to only get integer values,
# regardless of the requested dtype, e.g. 0 or 0.0 rather than 0 or 0.123
data = torch.testing.make_tensor(shape, low=0, high=num_categories, dtype=torch.int64, device=device).to(dtype)
return datapoints.Label(data, categories=categories)
return LabelLoader(fn, shape=extra_dims, dtype=dtype, categories=categories)
make_label = from_loader(make_label_loader)
@dataclasses.dataclass
class OneHotLabelLoader(TensorLoader):
categories: Optional[Sequence[str]]
def make_one_hot_label_loader(*, categories=None, extra_dims=(), dtype=torch.int64):
categories, num_categories = _parse_categories(categories)
def fn(shape, dtype, device):
if num_categories == 0:
data = torch.empty(shape, dtype=dtype, device=device)
else:
# The idiom `make_label_loader(..., dtype=torch.int64); ...; one_hot(...).to(dtype)` is intentional
# since `one_hot` only supports int64
label = make_label_loader(extra_dims=extra_dims, categories=num_categories, dtype=torch.int64).load(device)
data = one_hot(label, num_classes=num_categories).to(dtype)
return datapoints.OneHotLabel(data, categories=categories)
return OneHotLabelLoader(fn, shape=(*extra_dims, num_categories), dtype=dtype, categories=categories)
def make_one_hot_label_loaders(
*,
categories=(1, 0, None),
extra_dims=DEFAULT_EXTRA_DIMS,
dtypes=(torch.int64, torch.float32),
):
for params in combinations_grid(categories=categories, extra_dims=extra_dims, dtype=dtypes):
yield make_one_hot_label_loader(**params)
make_one_hot_labels = from_loaders(make_one_hot_label_loaders)
|
import collections.abc
import dataclasses
from typing import Optional, Sequence
import pytest
import torch
from common_utils import combinations_grid, DEFAULT_EXTRA_DIMS, from_loader, from_loaders, TensorLoader
from torch.nn.functional import one_hot
from torchvision.prototype import datapoints
@dataclasses.dataclass
class LabelLoader(TensorLoader):
categories: Optional[Sequence[str]]
def _parse_categories(categories):
if categories is None:
num_categories = int(torch.randint(1, 11, ()))
elif isinstance(categories, int):
num_categories = categories
categories = [f"category{idx}" for idx in range(num_categories)]
elif isinstance(categories, collections.abc.Sequence) and all(isinstance(category, str) for category in categories):
categories = list(categories)
num_categories = len(categories)
else:
raise pytest.UsageError(
f"`categories` can either be `None` (default), an integer, or a sequence of strings, "
f"but got '{categories}' instead."
)
return categories, num_categories
def make_label_loader(*, extra_dims=(), categories=None, dtype=torch.int64):
categories, num_categories = _parse_categories(categories)
def fn(shape, dtype, device):
# The idiom `make_tensor(..., dtype=torch.int64).to(dtype)` is intentional to only get integer values,
# regardless of the requested dtype, e.g. 0 or 0.0 rather than 0 or 0.123
data = torch.testing.make_tensor(shape, low=0, high=num_categories, dtype=torch.int64, device=device).to(dtype)
return datapoints.Label(data, categories=categories)
return LabelLoader(fn, shape=extra_dims, dtype=dtype, categories=categories)
make_label = from_loader(make_label_loader)
@dataclasses.dataclass
class OneHotLabelLoader(TensorLoader):
categories: Optional[Sequence[str]]
def make_one_hot_label_loader(*, categories=None, extra_dims=(), dtype=torch.int64):
categories, num_categories = _parse_categories(categories)
def fn(shape, dtype, device):
if num_categories == 0:
data = torch.empty(shape, dtype=dtype, device=device)
else:
# The idiom `make_label_loader(..., dtype=torch.int64); ...; one_hot(...).to(dtype)` is intentional
# since `one_hot` only supports int64
label = make_label_loader(extra_dims=extra_dims, categories=num_categories, dtype=torch.int64).load(device)
data = one_hot(label, num_classes=num_categories).to(dtype)
return datapoints.OneHotLabel(data, categories=categories)
return OneHotLabelLoader(fn, shape=(*extra_dims, num_categories), dtype=dtype, categories=categories)
def make_one_hot_label_loaders(
*,
categories=(1, 0, None),
extra_dims=DEFAULT_EXTRA_DIMS,
dtypes=(torch.int64, torch.float32),
):
for params in combinations_grid(categories=categories, extra_dims=extra_dims, dtype=dtypes):
yield make_one_hot_label_loader(**params)
make_one_hot_labels = from_loaders(make_one_hot_label_loaders)
|
import types
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.typing.tensor.image.image_tensor import ImageTensor
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docarray.typing.tensor.image.image_jax_array import ImageJaxArray # noqa
from docarray.typing.tensor.image.image_tensorflow_tensor import ( # noqa
ImageTensorFlowTensor,
)
from docarray.typing.tensor.image.image_torch_tensor import ImageTorchTensor # noqa
__all__ = ['ImageNdArray', 'ImageTensor']
def __getattr__(name: str):
lib: types.ModuleType
if name == 'ImageTorchTensor':
import_library('torch', raise_error=True)
import docarray.typing.tensor.image.image_torch_tensor as lib
elif name == 'ImageTensorFlowTensor':
import_library('tensorflow', raise_error=True)
import docarray.typing.tensor.image.image_tensorflow_tensor as lib
elif name == 'ImageJaxArray':
import_library('jax', raise_error=True)
import docarray.typing.tensor.image.image_jax_array as lib
else:
raise ImportError(
f'cannot import name \'{name}\' from \'{_get_path_from_docarray_root_level(__file__)}\''
)
tensor_cls = getattr(lib, name)
if name not in __all__:
__all__.append(name)
return tensor_cls
|
import types
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.typing.tensor.image.image_tensor import ImageTensor
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docarray.typing.tensor.image.image_tensorflow_tensor import ( # noqa
ImageTensorFlowTensor,
)
from docarray.typing.tensor.image.image_torch_tensor import ImageTorchTensor # noqa
__all__ = ['ImageNdArray', 'ImageTensor']
def __getattr__(name: str):
lib: types.ModuleType
if name == 'ImageTorchTensor':
import_library('torch', raise_error=True)
import docarray.typing.tensor.image.image_torch_tensor as lib
elif name == 'ImageTensorFlowTensor':
import_library('tensorflow', raise_error=True)
import docarray.typing.tensor.image.image_tensorflow_tensor as lib
else:
raise ImportError(
f'cannot import name \'{name}\' from \'{_get_path_from_docarray_root_level(__file__)}\''
)
tensor_cls = getattr(lib, name)
if name not in __all__:
__all__.append(name)
return tensor_cls
|
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
import transformers
from PIL import Image
from sentence_transformers.models.Asym import InputModule
class CLIPModel(InputModule):
save_in_root: bool = True
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None) -> None:
super().__init__()
if processor_name is None:
processor_name = model_name
self.model = transformers.CLIPModel.from_pretrained(model_name)
self.processor = transformers.CLIPProcessor.from_pretrained(processor_name)
def __repr__(self) -> str:
return "CLIPModel()"
@property
def max_seq_length(self) -> int:
return self.processor.tokenizer.model_max_length
@max_seq_length.setter
def max_seq_length(self, value: int) -> None:
self.processor.tokenizer.model_max_length = value
def forward(self, features: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
image_embeds = []
text_embeds = []
if "pixel_values" in features:
vision_outputs = self.model.vision_model(pixel_values=features["pixel_values"])
image_embeds = self.model.visual_projection(vision_outputs[1])
if "input_ids" in features:
text_outputs = self.model.text_model(
input_ids=features.get("input_ids"),
attention_mask=features.get("attention_mask", None),
position_ids=features.get("position_ids", None),
output_attentions=features.get("output_attentions", None),
output_hidden_states=features.get("output_hidden_states", None),
)
text_embeds = self.model.text_projection(text_outputs[1])
sentence_embedding = []
image_features = iter(image_embeds)
text_features = iter(text_embeds)
for idx, input_type in enumerate(features["image_text_info"]):
if input_type == 0:
sentence_embedding.append(next(image_features))
else:
sentence_embedding.append(next(text_features))
features["sentence_embedding"] = torch.stack(sentence_embedding).float()
return features
def tokenize(self, texts, padding: str | bool = True) -> dict[str, torch.Tensor]:
images = []
texts_values = []
image_text_info = []
for idx, data in enumerate(texts):
if isinstance(data, Image.Image): # An Image
images.append(data)
image_text_info.append(0)
else: # A text
texts_values.append(data)
image_text_info.append(1)
encoding = {}
if len(texts_values):
encoding = self.processor.tokenizer(texts_values, padding=padding, truncation=True, return_tensors="pt")
if len(images):
image_features = self.processor.image_processor(images, return_tensors="pt")
encoding["pixel_values"] = image_features.pixel_values
encoding["image_text_info"] = image_text_info
return dict(encoding)
@property
def tokenizer(self) -> transformers.CLIPProcessor:
return self.processor
def save(self, output_path: str, *args, safe_serialization: bool = True, **kwargs) -> None:
self.model.save_pretrained(output_path, safe_serialization=safe_serialization)
self.processor.save_pretrained(output_path)
@classmethod
def load(
cls,
model_name_or_path: str,
subfolder: str = "",
token: bool | str | None = None,
cache_folder: str | None = None,
revision: str | None = None,
local_files_only: bool = False,
**kwargs,
) -> Self:
local_path = cls.load_dir_path(
model_name_or_path=model_name_or_path,
subfolder=subfolder,
token=token,
cache_folder=cache_folder,
revision=revision,
local_files_only=local_files_only,
)
return cls(local_path)
|
from __future__ import annotations
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
save_in_root: bool = True
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None) -> None:
super().__init__()
if processor_name is None:
processor_name = model_name
self.model = transformers.CLIPModel.from_pretrained(model_name)
self.processor = transformers.CLIPProcessor.from_pretrained(processor_name)
def __repr__(self) -> str:
return "CLIPModel()"
@property
def max_seq_length(self) -> int:
return self.processor.tokenizer.model_max_length
@max_seq_length.setter
def max_seq_length(self, value: int) -> None:
self.processor.tokenizer.model_max_length = value
def forward(self, features: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
image_embeds = []
text_embeds = []
if "pixel_values" in features:
vision_outputs = self.model.vision_model(pixel_values=features["pixel_values"])
image_embeds = self.model.visual_projection(vision_outputs[1])
if "input_ids" in features:
text_outputs = self.model.text_model(
input_ids=features.get("input_ids"),
attention_mask=features.get("attention_mask", None),
position_ids=features.get("position_ids", None),
output_attentions=features.get("output_attentions", None),
output_hidden_states=features.get("output_hidden_states", None),
)
text_embeds = self.model.text_projection(text_outputs[1])
sentence_embedding = []
image_features = iter(image_embeds)
text_features = iter(text_embeds)
for idx, input_type in enumerate(features["image_text_info"]):
if input_type == 0:
sentence_embedding.append(next(image_features))
else:
sentence_embedding.append(next(text_features))
features["sentence_embedding"] = torch.stack(sentence_embedding).float()
return features
def tokenize(self, texts, padding: str | bool = True) -> dict[str, torch.Tensor]:
images = []
texts_values = []
image_text_info = []
for idx, data in enumerate(texts):
if isinstance(data, Image.Image): # An Image
images.append(data)
image_text_info.append(0)
else: # A text
texts_values.append(data)
image_text_info.append(1)
encoding = {}
if len(texts_values):
encoding = self.processor.tokenizer(texts_values, padding=padding, truncation=True, return_tensors="pt")
if len(images):
image_features = self.processor.image_processor(images, return_tensors="pt")
encoding["pixel_values"] = image_features.pixel_values
encoding["image_text_info"] = image_text_info
return dict(encoding)
@property
def tokenizer(self) -> transformers.CLIPProcessor:
return self.processor
def save(self, output_path: str) -> None:
self.model.save_pretrained(output_path)
self.processor.save_pretrained(output_path)
@staticmethod
def load(input_path: str) -> CLIPModel:
return CLIPModel(model_name=input_path)
|
from __future__ import annotations
from collections.abc import Iterable
from enum import Enum
from typing import Any
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import pairwise_cos_sim, pairwise_euclidean_sim, pairwise_manhattan_sim
class TripletDistanceMetric(Enum):
"""The metric for the triplet loss"""
COSINE = lambda x, y: 1 - pairwise_cos_sim(x, y)
EUCLIDEAN = lambda x, y: pairwise_euclidean_sim(x, y)
MANHATTAN = lambda x, y: pairwise_manhattan_sim(x, y)
class TripletLoss(nn.Module):
def __init__(
self, model: SentenceTransformer, distance_metric=TripletDistanceMetric.EUCLIDEAN, triplet_margin: float = 5
) -> None:
"""
This class implements triplet loss. Given a triplet of (anchor, positive, negative),
the loss minimizes the distance between anchor and positive while it maximizes the distance
between anchor and negative. It compute the following loss function:
``loss = max(||anchor - positive|| - ||anchor - negative|| + margin, 0)``.
Margin is an important hyperparameter and needs to be tuned respectively.
Args:
model: SentenceTransformerModel
distance_metric: Function to compute distance between two
embeddings. The class TripletDistanceMetric contains
common distance metrices that can be used.
triplet_margin: The negative should be at least this much
further away from the anchor than the positive.
References:
- For further details, see: https://en.wikipedia.org/wiki/Triplet_loss
Requirements:
1. (anchor, positive, negative) triplets
Inputs:
+---------------------------------------+--------+
| Texts | Labels |
+=======================================+========+
| (anchor, positive, negative) triplets | none |
+---------------------------------------+--------+
Example:
::
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, losses
from datasets import Dataset
model = SentenceTransformer("microsoft/mpnet-base")
train_dataset = Dataset.from_dict({
"anchor": ["It's nice weather outside today.", "He drove to work."],
"positive": ["It's so sunny.", "He took the car to the office."],
"negative": ["It's quite rainy, sadly.", "She walked to the store."],
})
loss = losses.TripletLoss(model=model)
trainer = SentenceTransformerTrainer(
model=model,
train_dataset=train_dataset,
loss=loss,
)
trainer.train()
"""
super().__init__()
self.model = model
self.distance_metric = distance_metric
self.triplet_margin = triplet_margin
def forward(self, sentence_features: Iterable[dict[str, Tensor]], labels: Tensor) -> Tensor:
embeddings = [self.model(sentence_feature)["sentence_embedding"] for sentence_feature in sentence_features]
return self.compute_loss_from_embeddings(embeddings, labels)
def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor:
"""
Compute the CoSENT loss from embeddings.
Args:
embeddings: List of embeddings
Returns:
Loss value
"""
rep_anchor, rep_pos, rep_neg = embeddings
distance_pos = self.distance_metric(rep_anchor, rep_pos)
distance_neg = self.distance_metric(rep_anchor, rep_neg)
losses = F.relu(distance_pos - distance_neg + self.triplet_margin)
return losses.mean()
def get_config_dict(self) -> dict[str, Any]:
distance_metric_name = self.distance_metric.__name__
for name, value in vars(TripletDistanceMetric).items():
if value == self.distance_metric:
distance_metric_name = f"TripletDistanceMetric.{name}"
break
return {"distance_metric": distance_metric_name, "triplet_margin": self.triplet_margin}
@property
def citation(self) -> str:
return """
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
"""
|
from __future__ import annotations
from collections.abc import Iterable
from enum import Enum
from typing import Any
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import pairwise_cos_sim, pairwise_euclidean_sim, pairwise_manhattan_sim
class TripletDistanceMetric(Enum):
"""The metric for the triplet loss"""
COSINE = lambda x, y: 1 - pairwise_cos_sim(x, y)
EUCLIDEAN = lambda x, y: pairwise_euclidean_sim(x, y)
MANHATTAN = lambda x, y: pairwise_manhattan_sim(x, y)
class TripletLoss(nn.Module):
def __init__(
self, model: SentenceTransformer, distance_metric=TripletDistanceMetric.EUCLIDEAN, triplet_margin: float = 5
) -> None:
"""
This class implements triplet loss. Given a triplet of (anchor, positive, negative),
the loss minimizes the distance between anchor and positive while it maximizes the distance
between anchor and negative. It compute the following loss function:
``loss = max(||anchor - positive|| - ||anchor - negative|| + margin, 0)``.
Margin is an important hyperparameter and needs to be tuned respectively.
Args:
model: SentenceTransformerModel
distance_metric: Function to compute distance between two
embeddings. The class TripletDistanceMetric contains
common distance metrices that can be used.
triplet_margin: The negative should be at least this much
further away from the anchor than the positive.
References:
- For further details, see: https://en.wikipedia.org/wiki/Triplet_loss
Requirements:
1. (anchor, positive, negative) triplets
Inputs:
+---------------------------------------+--------+
| Texts | Labels |
+=======================================+========+
| (anchor, positive, negative) triplets | none |
+---------------------------------------+--------+
Example:
::
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, losses
from datasets import Dataset
model = SentenceTransformer("microsoft/mpnet-base")
train_dataset = Dataset.from_dict({
"anchor": ["It's nice weather outside today.", "He drove to work."],
"positive": ["It's so sunny.", "He took the car to the office."],
"negative": ["It's quite rainy, sadly.", "She walked to the store."],
})
loss = losses.TripletLoss(model=model)
trainer = SentenceTransformerTrainer(
model=model,
train_dataset=train_dataset,
loss=loss,
)
trainer.train()
"""
super().__init__()
self.model = model
self.distance_metric = distance_metric
self.triplet_margin = triplet_margin
def forward(self, sentence_features: Iterable[dict[str, Tensor]], labels: Tensor) -> Tensor:
reps = [self.model(sentence_feature)["sentence_embedding"] for sentence_feature in sentence_features]
rep_anchor, rep_pos, rep_neg = reps
distance_pos = self.distance_metric(rep_anchor, rep_pos)
distance_neg = self.distance_metric(rep_anchor, rep_neg)
losses = F.relu(distance_pos - distance_neg + self.triplet_margin)
return losses.mean()
def get_config_dict(self) -> dict[str, Any]:
distance_metric_name = self.distance_metric.__name__
for name, value in vars(TripletDistanceMetric).items():
if value == self.distance_metric:
distance_metric_name = f"TripletDistanceMetric.{name}"
break
return {"distance_metric": distance_metric_name, "triplet_margin": self.triplet_margin}
@property
def citation(self) -> str:
return """
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
"""
|
"""LLMResult class."""
from __future__ import annotations
from copy import deepcopy
from typing import Literal, Optional, Union
from pydantic import BaseModel
from langchain_core.outputs.chat_generation import ChatGeneration, ChatGenerationChunk
from langchain_core.outputs.generation import Generation, GenerationChunk
from langchain_core.outputs.run_info import RunInfo
class LLMResult(BaseModel):
"""A container for results of an LLM call.
Both chat models and LLMs generate an LLMResult object. This object contains
the generated outputs and any additional information that the model provider
wants to return.
"""
generations: list[
list[Union[Generation, ChatGeneration, GenerationChunk, ChatGenerationChunk]]
]
"""Generated outputs.
The first dimension of the list represents completions for different input
prompts.
The second dimension of the list represents different candidate generations
for a given prompt.
When returned from an LLM the type is list[list[Generation]].
When returned from a chat model the type is list[list[ChatGeneration]].
ChatGeneration is a subclass of Generation that has a field for a structured
chat message.
"""
llm_output: Optional[dict] = None
"""For arbitrary LLM provider specific output.
This dictionary is a free-form dictionary that can contain any information that the
provider wants to return. It is not standardized and is provider-specific.
Users should generally avoid relying on this field and instead rely on
accessing relevant information from standardized fields present in
AIMessage.
"""
run: Optional[list[RunInfo]] = None
"""List of metadata info for model call for each input."""
type: Literal["LLMResult"] = "LLMResult"
"""Type is used exclusively for serialization purposes."""
def flatten(self) -> list[LLMResult]:
"""Flatten generations into a single list.
Unpack list[list[Generation]] -> list[LLMResult] where each returned LLMResult
contains only a single Generation. If token usage information is available,
it is kept only for the LLMResult corresponding to the top-choice
Generation, to avoid over-counting of token usage downstream.
Returns:
List of LLMResults where each returned LLMResult contains a single
Generation.
"""
llm_results = []
for i, gen_list in enumerate(self.generations):
# Avoid double counting tokens in OpenAICallback
if i == 0:
llm_results.append(
LLMResult(
generations=[gen_list],
llm_output=self.llm_output,
)
)
else:
if self.llm_output is not None:
llm_output = deepcopy(self.llm_output)
llm_output["token_usage"] = {}
else:
llm_output = None
llm_results.append(
LLMResult(
generations=[gen_list],
llm_output=llm_output,
)
)
return llm_results
def __eq__(self, other: object) -> bool:
"""Check for LLMResult equality by ignoring any metadata related to runs."""
if not isinstance(other, LLMResult):
return NotImplemented
return (
self.generations == other.generations
and self.llm_output == other.llm_output
)
__hash__ = None # type: ignore[assignment]
|
"""LLMResult class."""
from __future__ import annotations
from copy import deepcopy
from typing import Literal, Optional, Union
from pydantic import BaseModel
from langchain_core.outputs.chat_generation import ChatGeneration, ChatGenerationChunk
from langchain_core.outputs.generation import Generation, GenerationChunk
from langchain_core.outputs.run_info import RunInfo
class LLMResult(BaseModel):
"""A container for results of an LLM call.
Both chat models and LLMs generate an LLMResult object. This object contains
the generated outputs and any additional information that the model provider
wants to return.
"""
generations: list[
list[Union[Generation, ChatGeneration, GenerationChunk, ChatGenerationChunk]]
]
"""Generated outputs.
The first dimension of the list represents completions for different input
prompts.
The second dimension of the list represents different candidate generations
for a given prompt.
When returned from an LLM the type is list[list[Generation]].
When returned from a chat model the type is list[list[ChatGeneration]].
ChatGeneration is a subclass of Generation that has a field for a structured
chat message.
"""
llm_output: Optional[dict] = None
"""For arbitrary LLM provider specific output.
This dictionary is a free-form dictionary that can contain any information that the
provider wants to return. It is not standardized and is provider-specific.
Users should generally avoid relying on this field and instead rely on
accessing relevant information from standardized fields present in
AIMessage.
"""
run: Optional[list[RunInfo]] = None
"""List of metadata info for model call for each input."""
type: Literal["LLMResult"] = "LLMResult"
"""Type is used exclusively for serialization purposes."""
def flatten(self) -> list[LLMResult]:
"""Flatten generations into a single list.
Unpack list[list[Generation]] -> list[LLMResult] where each returned LLMResult
contains only a single Generation. If token usage information is available,
it is kept only for the LLMResult corresponding to the top-choice
Generation, to avoid over-counting of token usage downstream.
Returns:
List of LLMResults where each returned LLMResult contains a single
Generation.
"""
llm_results = []
for i, gen_list in enumerate(self.generations):
# Avoid double counting tokens in OpenAICallback
if i == 0:
llm_results.append(
LLMResult(
generations=[gen_list],
llm_output=self.llm_output,
)
)
else:
if self.llm_output is not None:
llm_output = deepcopy(self.llm_output)
llm_output["token_usage"] = {}
else:
llm_output = None
llm_results.append(
LLMResult(
generations=[gen_list],
llm_output=llm_output,
)
)
return llm_results
def __eq__(self, other: object) -> bool:
"""Check for LLMResult equality by ignoring any metadata related to runs."""
if not isinstance(other, LLMResult):
return NotImplemented
return (
self.generations == other.generations
and self.llm_output == other.llm_output
)
|
# Copyright (c) OpenMMLab. All rights reserved.
from .visualizer import Visualizer
from .writer import (BaseWriter, ComposedWriter, LocalWriter,
TensorboardWriter, WandbWriter)
__all__ = [
'Visualizer', 'BaseWriter', 'LocalWriter', 'WandbWriter',
'TensorboardWriter', 'ComposedWriter'
]
|
# Copyright (c) OpenMMLab. All rights reserved.
from .visualizer import Visualizer
__all__ = ['Visualizer']
|
"""
This examples measures the inference speed of a certain model
Usage:
python evaluation_inference_speed.py
OR
python evaluation_inference_speed.py model_name
"""
import sys
import time
import torch
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
# Limit torch to 4 threads
torch.set_num_threads(4)
model_name = sys.argv[1] if len(sys.argv) > 1 else "bert-base-nli-mean-tokens"
# Load a sentence transformer model
model = SentenceTransformer(model_name)
max_sentences = 100_000
all_nli_dataset = load_dataset("sentence-transformers/all-nli", "pair", split="train")
sentences = list(set(all_nli_dataset["anchor"]))[:max_sentences]
print("Model Name:", model_name)
print("Number of sentences:", len(sentences))
for i in range(3):
print("Run", i)
start_time = time.time()
emb = model.encode(sentences, batch_size=32)
end_time = time.time()
diff_time = end_time - start_time
print("Done after {:.2f} seconds".format(diff_time))
print("Speed: {:.2f} sentences / second".format(len(sentences) / diff_time))
print("=====")
|
"""
This examples measures the inference speed of a certain model
Usage:
python evaluation_inference_speed.py
OR
python evaluation_inference_speed.py model_name
"""
from sentence_transformers import SentenceTransformer
import sys
import time
import torch
from datasets import load_dataset
# Limit torch to 4 threads
torch.set_num_threads(4)
model_name = sys.argv[1] if len(sys.argv) > 1 else "bert-base-nli-mean-tokens"
# Load a sentence transformer model
model = SentenceTransformer(model_name)
max_sentences = 100_000
all_nli_dataset = load_dataset("sentence-transformers/all-nli", "pair", split="train")
sentences = list(set(all_nli_dataset["anchor"]))[:max_sentences]
print("Model Name:", model_name)
print("Number of sentences:", len(sentences))
for i in range(3):
print("Run", i)
start_time = time.time()
emb = model.encode(sentences, batch_size=32)
end_time = time.time()
diff_time = end_time - start_time
print("Done after {:.2f} seconds".format(diff_time))
print("Speed: {:.2f} sentences / second".format(len(sentences) / diff_time))
print("=====")
|
from typing import Iterable, Dict
from ..base.getsetdel import BaseGetSetDelMixin
from ..base.helper import Offset2ID
from .... import Document
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``,
and ``__delitem__`` for ``DocumentArrayElastic``"""
def _document_to_elastic(self, doc: 'Document') -> Dict:
request = {
'_op_type': 'index',
'_id': doc.id,
'_index': self._config.index_name,
'embedding': self._map_embedding(doc.embedding),
'blob': doc.to_base64(),
}
if self._config.tag_indices:
for index in self._config.tag_indices:
request[index] = doc.tags.get(index)
if doc.text:
request['text'] = doc.text
return request
def _getitem(self, doc_id: str) -> 'Document':
"""Helper method for getting item with elastic as storage
:param doc_id: id of the document
:raises KeyError: raise error when elastic id does not exist in storage
:return: Document
"""
try:
result = self._client.get(index=self._config.index_name, id=doc_id)
doc = Document.from_base64(result['_source']['blob'])
return doc
except Exception as ex:
raise KeyError(doc_id) from ex
def _get_doc_by_id(self, _id: str) -> 'Document':
"""Concrete implementation of base class' ``_get_doc_by_id``
:param _id: the id of the document
:return: the retrieved document from elastic
"""
return self._getitem(_id)
def _set_doc_by_id(self, _id: str, value: 'Document'):
"""Concrete implementation of base class' ``_set_doc_by_id``
:param _id: the id of doc to update
:param value: the document to update to
"""
if _id != value.id:
self._del_doc_by_id(_id)
request = [self._document_to_elastic(value)]
self._send_requests(request)
self._refresh(self._config.index_name)
def _set_docs_by_ids(self, ids, docs: Iterable['Document'], mismatch_ids: Dict):
"""Overridden implementation of _set_docs_by_ids in order to add docs in batches and flush at the end
:param ids: the ids used for indexing
"""
for _id, doc in zip(ids, docs):
self._set_doc_by_id(_id, doc)
self._refresh(self._config.index_name)
def _del_doc_by_id(self, _id: str):
"""Concrete implementation of base class' ``_del_doc_by_id``
:param _id: the id of the document to delete
"""
if self._doc_id_exists(_id):
self._client.delete(index=self._config.index_name, id=_id)
self._refresh(self._config.index_name)
def _clear_storage(self):
"""Concrete implementation of base class' ``_clear_storage``"""
self._client.indices.delete(index=self._config.index_name)
def _load_offset2ids(self):
ids = self._get_offset2ids_meta()
self._offset2ids = Offset2ID(ids)
def _save_offset2ids(self):
self._update_offset2ids_meta()
|
from typing import Iterable, Dict
from ..base.getsetdel import BaseGetSetDelMixin
from ..base.helper import Offset2ID
from .... import Document
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``,
and ``__delitem__`` for ``DocumentArrayElastic``"""
def _getitem(self, doc_id: str) -> 'Document':
"""Helper method for getting item with elastic as storage
:param doc_id: id of the document
:raises KeyError: raise error when elastic id does not exist in storage
:return: Document
"""
try:
result = self._client.get(index=self._config.index_name, id=doc_id)
doc = Document.from_base64(result['_source']['blob'])
return doc
except Exception as ex:
raise KeyError(doc_id) from ex
def _get_doc_by_id(self, _id: str) -> 'Document':
"""Concrete implementation of base class' ``_get_doc_by_id``
:param _id: the id of the document
:return: the retrieved document from elastic
"""
return self._getitem(_id)
def _set_doc_by_id(self, _id: str, value: 'Document'):
"""Concrete implementation of base class' ``_set_doc_by_id``
:param _id: the id of doc to update
:param value: the document to update to
"""
if _id != value.id:
self._del_doc_by_id(_id)
request = [
{
"_op_type": "index",
'_id': value.id,
'_index': self._config.index_name,
'embedding': self._map_embedding(value.embedding),
'blob': value.to_base64(),
}
]
self._send_requests(request)
self._refresh(self._config.index_name)
def _set_docs_by_ids(self, ids, docs: Iterable['Document'], mismatch_ids: Dict):
"""Overridden implementation of _set_docs_by_ids in order to add docs in batches and flush at the end
:param ids: the ids used for indexing
"""
for _id, doc in zip(ids, docs):
self._set_doc_by_id(_id, doc)
self._refresh(self._config.index_name)
def _del_doc_by_id(self, _id: str):
"""Concrete implementation of base class' ``_del_doc_by_id``
:param _id: the id of the document to delete
"""
if self._doc_id_exists(_id):
self._client.delete(index=self._config.index_name, id=_id)
self._refresh(self._config.index_name)
def _clear_storage(self):
""" Concrete implementation of base class' ``_clear_storage``"""
self._client.indices.delete(index=self._config.index_name)
def _load_offset2ids(self):
ids = self._get_offset2ids_meta()
self._offset2ids = Offset2ID(ids)
def _save_offset2ids(self):
self._update_offset2ids_meta()
|
from docarray.array.any_array import AnyDocArray
from docarray.array.doc_list.doc_list import DocList
from docarray.array.doc_vec.doc_vec import DocVec
__all__ = ['DocList', 'DocVec', 'AnyDocArray']
|
from docarray.array.array.array import DocArray
from docarray.array.stacked.array_stacked import DocArrayStacked
__all__ = ['DocArray', 'DocArrayStacked']
|
import logging
import time
from datetime import datetime
from apscheduler.schedulers.background import BackgroundScheduler
from apscheduler.triggers.cron import CronTrigger
from autogpt_libs.utils.cache import thread_cached
from backend.data.block import BlockInput
from backend.data.schedule import (
ExecutionSchedule,
add_schedule,
get_active_schedules,
get_schedules,
update_schedule,
)
from backend.executor.manager import ExecutionManager
from backend.util.service import AppService, expose, get_service_client
from backend.util.settings import Config
logger = logging.getLogger(__name__)
def log(msg, **kwargs):
logger.warning("[ExecutionScheduler] " + msg, **kwargs)
class ExecutionScheduler(AppService):
def __init__(self, refresh_interval=10):
super().__init__()
self.use_db = True
self.last_check = datetime.min
self.refresh_interval = refresh_interval
@classmethod
def get_port(cls) -> int:
return Config().execution_scheduler_port
@property
@thread_cached
def execution_client(self) -> ExecutionManager:
return get_service_client(ExecutionManager)
def run_service(self):
scheduler = BackgroundScheduler()
scheduler.start()
while True:
self.__refresh_jobs_from_db(scheduler)
time.sleep(self.refresh_interval)
def __refresh_jobs_from_db(self, scheduler: BackgroundScheduler):
schedules = self.run_and_wait(get_active_schedules(self.last_check))
for schedule in schedules:
if schedule.last_updated:
self.last_check = max(self.last_check, schedule.last_updated)
if not schedule.is_enabled:
log(f"Removing recurring job {schedule.id}: {schedule.schedule}")
scheduler.remove_job(schedule.id)
continue
log(f"Adding recurring job {schedule.id}: {schedule.schedule}")
scheduler.add_job(
self.__execute_graph,
CronTrigger.from_crontab(schedule.schedule),
id=schedule.id,
args=[schedule.graph_id, schedule.input_data, schedule.user_id],
replace_existing=True,
)
def __execute_graph(self, graph_id: str, input_data: dict, user_id: str):
try:
log(f"Executing recurring job for graph #{graph_id}")
self.execution_client.add_execution(graph_id, input_data, user_id)
except Exception as e:
logger.exception(f"Error executing graph {graph_id}: {e}")
@expose
def update_schedule(self, schedule_id: str, is_enabled: bool, user_id: str) -> str:
self.run_and_wait(update_schedule(schedule_id, is_enabled, user_id))
return schedule_id
@expose
def add_execution_schedule(
self,
graph_id: str,
graph_version: int,
cron: str,
input_data: BlockInput,
user_id: str,
) -> str:
schedule = ExecutionSchedule(
graph_id=graph_id,
user_id=user_id,
graph_version=graph_version,
schedule=cron,
input_data=input_data,
)
return self.run_and_wait(add_schedule(schedule)).id
@expose
def get_execution_schedules(self, graph_id: str, user_id: str) -> dict[str, str]:
schedules = self.run_and_wait(get_schedules(graph_id, user_id=user_id))
return {v.id: v.schedule for v in schedules}
|
import logging
import time
from datetime import datetime
from apscheduler.schedulers.background import BackgroundScheduler
from apscheduler.triggers.cron import CronTrigger
from autogpt_libs.utils.cache import thread_cached_property
from backend.data.block import BlockInput
from backend.data.schedule import (
ExecutionSchedule,
add_schedule,
get_active_schedules,
get_schedules,
update_schedule,
)
from backend.executor.manager import ExecutionManager
from backend.util.service import AppService, expose, get_service_client
from backend.util.settings import Config
logger = logging.getLogger(__name__)
def log(msg, **kwargs):
logger.warning("[ExecutionScheduler] " + msg, **kwargs)
class ExecutionScheduler(AppService):
def __init__(self, refresh_interval=10):
super().__init__()
self.use_db = True
self.last_check = datetime.min
self.refresh_interval = refresh_interval
@classmethod
def get_port(cls) -> int:
return Config().execution_scheduler_port
@thread_cached_property
def execution_client(self) -> ExecutionManager:
return get_service_client(ExecutionManager)
def run_service(self):
scheduler = BackgroundScheduler()
scheduler.start()
while True:
self.__refresh_jobs_from_db(scheduler)
time.sleep(self.refresh_interval)
def __refresh_jobs_from_db(self, scheduler: BackgroundScheduler):
schedules = self.run_and_wait(get_active_schedules(self.last_check))
for schedule in schedules:
if schedule.last_updated:
self.last_check = max(self.last_check, schedule.last_updated)
if not schedule.is_enabled:
log(f"Removing recurring job {schedule.id}: {schedule.schedule}")
scheduler.remove_job(schedule.id)
continue
log(f"Adding recurring job {schedule.id}: {schedule.schedule}")
scheduler.add_job(
self.__execute_graph,
CronTrigger.from_crontab(schedule.schedule),
id=schedule.id,
args=[schedule.graph_id, schedule.input_data, schedule.user_id],
replace_existing=True,
)
def __execute_graph(self, graph_id: str, input_data: dict, user_id: str):
try:
log(f"Executing recurring job for graph #{graph_id}")
self.execution_client.add_execution(graph_id, input_data, user_id)
except Exception as e:
logger.exception(f"Error executing graph {graph_id}: {e}")
@expose
def update_schedule(self, schedule_id: str, is_enabled: bool, user_id: str) -> str:
self.run_and_wait(update_schedule(schedule_id, is_enabled, user_id))
return schedule_id
@expose
def add_execution_schedule(
self,
graph_id: str,
graph_version: int,
cron: str,
input_data: BlockInput,
user_id: str,
) -> str:
schedule = ExecutionSchedule(
graph_id=graph_id,
user_id=user_id,
graph_version=graph_version,
schedule=cron,
input_data=input_data,
)
return self.run_and_wait(add_schedule(schedule)).id
@expose
def get_execution_schedules(self, graph_id: str, user_id: str) -> dict[str, str]:
schedules = self.run_and_wait(get_schedules(graph_id, user_id=user_id))
return {v.id: v.schedule for v in schedules}
|
# Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
from mmengine import DefaultScope
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fork` to speed up the training
if platform.system() != 'Windows':
mp_start_method = cfg.get('mp_start_method', 'fork')
current_method = mp.get_start_method(allow_none=True)
if current_method is not None and current_method != mp_start_method:
warnings.warn(
f'Multi-processing start method `{mp_start_method}` is '
f'different from the previous setting `{current_method}`.'
f'It will be force set to `{mp_start_method}`. You can change '
f'this behavior by changing `mp_start_method` in your config.')
mp.set_start_method(mp_start_method, force=True)
# disable opencv multithreading to avoid system being overloaded
opencv_num_threads = cfg.get('opencv_num_threads', 0)
cv2.setNumThreads(opencv_num_threads)
# setup OMP threads
# This code is referred from https://github.com/pytorch/pytorch/blob/master/torch/distributed/run.py # noqa
workers_per_gpu = cfg.data.get('workers_per_gpu', 1)
if 'train_dataloader' in cfg.data:
workers_per_gpu = \
max(cfg.data.train_dataloader.get('workers_per_gpu', 1),
workers_per_gpu)
if 'OMP_NUM_THREADS' not in os.environ and workers_per_gpu > 1:
omp_num_threads = 1
warnings.warn(
f'Setting OMP_NUM_THREADS environment variable for each process '
f'to be {omp_num_threads} in default, to avoid your system being '
f'overloaded, please further tune the variable for optimal '
f'performance in your application as needed.')
os.environ['OMP_NUM_THREADS'] = str(omp_num_threads)
# setup MKL threads
if 'MKL_NUM_THREADS' not in os.environ and workers_per_gpu > 1:
mkl_num_threads = 1
warnings.warn(
f'Setting MKL_NUM_THREADS environment variable for each process '
f'to be {mkl_num_threads} in default, to avoid your system being '
f'overloaded, please further tune the variable for optimal '
f'performance in your application as needed.')
os.environ['MKL_NUM_THREADS'] = str(mkl_num_threads)
def register_all_modules(init_default_scope: bool = True) -> None:
"""Register all modules in mmdet into the registries.
Args:
init_default_scope (bool): Whether initialize the mmdet default scope.
When `init_default_scope=True`, the global default scope will be
set to `mmdet`, and all registries will build modules from mmdet's
registry node. To understand more about the registry, please refer
to https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/registry.md
Defaults to True.
""" # noqa
import mmdet.core # noqa: F401,F403
import mmdet.datasets # noqa: F401,F403
import mmdet.metrics # noqa: F401,F403
import mmdet.models # noqa: F401,F403
if init_default_scope:
DefaultScope.get_instance('mmdet', scope_name='mmdet')
|
# Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fork` to speed up the training
if platform.system() != 'Windows':
mp_start_method = cfg.get('mp_start_method', 'fork')
current_method = mp.get_start_method(allow_none=True)
if current_method is not None and current_method != mp_start_method:
warnings.warn(
f'Multi-processing start method `{mp_start_method}` is '
f'different from the previous setting `{current_method}`.'
f'It will be force set to `{mp_start_method}`. You can change '
f'this behavior by changing `mp_start_method` in your config.')
mp.set_start_method(mp_start_method, force=True)
# disable opencv multithreading to avoid system being overloaded
opencv_num_threads = cfg.get('opencv_num_threads', 0)
cv2.setNumThreads(opencv_num_threads)
# setup OMP threads
# This code is referred from https://github.com/pytorch/pytorch/blob/master/torch/distributed/run.py # noqa
workers_per_gpu = cfg.data.get('workers_per_gpu', 1)
if 'train_dataloader' in cfg.data:
workers_per_gpu = \
max(cfg.data.train_dataloader.get('workers_per_gpu', 1),
workers_per_gpu)
if 'OMP_NUM_THREADS' not in os.environ and workers_per_gpu > 1:
omp_num_threads = 1
warnings.warn(
f'Setting OMP_NUM_THREADS environment variable for each process '
f'to be {omp_num_threads} in default, to avoid your system being '
f'overloaded, please further tune the variable for optimal '
f'performance in your application as needed.')
os.environ['OMP_NUM_THREADS'] = str(omp_num_threads)
# setup MKL threads
if 'MKL_NUM_THREADS' not in os.environ and workers_per_gpu > 1:
mkl_num_threads = 1
warnings.warn(
f'Setting MKL_NUM_THREADS environment variable for each process '
f'to be {mkl_num_threads} in default, to avoid your system being '
f'overloaded, please further tune the variable for optimal '
f'performance in your application as needed.')
os.environ['MKL_NUM_THREADS'] = str(mkl_num_threads)
|
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import Cutmix, Mixup, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
from ._color import (
ColorJitter,
Grayscale,
RandomAdjustSharpness,
RandomAutocontrast,
RandomEqualize,
RandomGrayscale,
RandomInvert,
RandomPhotometricDistort,
RandomPosterize,
RandomSolarize,
)
from ._container import Compose, RandomApply, RandomChoice, RandomOrder
from ._geometry import (
CenterCrop,
ElasticTransform,
FiveCrop,
Pad,
RandomAffine,
RandomCrop,
RandomHorizontalFlip,
RandomIoUCrop,
RandomPerspective,
RandomResize,
RandomResizedCrop,
RandomRotation,
RandomShortestSize,
RandomVerticalFlip,
RandomZoomOut,
Resize,
ScaleJitter,
TenCrop,
)
from ._meta import ClampBoundingBox, ConvertBoundingBoxFormat
from ._misc import (
ConvertImageDtype,
GaussianBlur,
Identity,
Lambda,
LinearTransformation,
Normalize,
SanitizeBoundingBox,
ToDtype,
)
from ._temporal import UniformTemporalSubsample
from ._type_conversion import PILToTensor, ToImagePIL, ToImageTensor, ToPILImage
from ._deprecated import ToTensor # usort: skip
from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
if _WARN_ABOUT_BETA_TRANSFORMS:
import warnings
warnings.warn(_BETA_TRANSFORMS_WARNING)
|
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import Cutmix, Mixup, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
from ._color import (
ColorJitter,
Grayscale,
RandomAdjustSharpness,
RandomAutocontrast,
RandomEqualize,
RandomGrayscale,
RandomInvert,
RandomPhotometricDistort,
RandomPosterize,
RandomSolarize,
)
from ._container import Compose, RandomApply, RandomChoice, RandomOrder
from ._geometry import (
CenterCrop,
ElasticTransform,
FiveCrop,
Pad,
RandomAffine,
RandomCrop,
RandomHorizontalFlip,
RandomIoUCrop,
RandomPerspective,
RandomResize,
RandomResizedCrop,
RandomRotation,
RandomShortestSize,
RandomVerticalFlip,
RandomZoomOut,
Resize,
ScaleJitter,
TenCrop,
)
from ._meta import ClampBoundingBox, ConvertBoundingBoxFormat, ConvertImageDtype
from ._misc import GaussianBlur, Identity, Lambda, LinearTransformation, Normalize, SanitizeBoundingBox, ToDtype
from ._temporal import UniformTemporalSubsample
from ._type_conversion import PILToTensor, ToImagePIL, ToImageTensor, ToPILImage
from ._deprecated import ToTensor # usort: skip
from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
if _WARN_ABOUT_BETA_TRANSFORMS:
import warnings
warnings.warn(_BETA_TRANSFORMS_WARNING)
|
import numpy as np
from docarray import BaseDocument
from docarray.typing import NdArray
def test_set_tensor():
class MyDocument(BaseDocument):
tensor: NdArray
d = MyDocument(tensor=np.zeros((3, 224, 224)))
assert isinstance(d.tensor, NdArray)
assert isinstance(d.tensor, np.ndarray)
assert (d.tensor == np.zeros((3, 224, 224))).all()
|
import numpy as np
from docarray import Document
from docarray.typing import NdArray
def test_set_tensor():
class MyDocument(Document):
tensor: NdArray
d = MyDocument(tensor=np.zeros((3, 224, 224)))
assert isinstance(d.tensor, NdArray)
assert isinstance(d.tensor, np.ndarray)
assert (d.tensor == np.zeros((3, 224, 224))).all()
|
import os
from pathlib import Path
from typing import List, Optional, Tuple, Union
import torch
import torchaudio
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive
_URL = "https://zenodo.org/record/3338373/files/musdb18hq.zip"
_CHECKSUM = "baac80d0483c61d74b2e5f3be75fa557eec52898339e6aa45c1fa48833c5d21d"
_EXT = ".wav"
_SAMPLE_RATE = 44100
_VALIDATION_SET = [
"Actions - One Minute Smile",
"Clara Berry And Wooldog - Waltz For My Victims",
"Johnny Lokke - Promises & Lies",
"Patrick Talbot - A Reason To Leave",
"Triviul - Angelsaint",
"Alexander Ross - Goodbye Bolero",
"Fergessen - Nos Palpitants",
"Leaf - Summerghost",
"Skelpolu - Human Mistakes",
"Young Griffo - Pennies",
"ANiMAL - Rockshow",
"James May - On The Line",
"Meaxic - Take A Step",
"Traffic Experiment - Sirens",
]
class MUSDB_HQ(Dataset):
"""Create *MUSDB_HQ* [:footcite:`MUSDB18HQ`] Dataset
Args:
root (str or Path): Root directory where the dataset's top level directory is found
subset (str): Subset of the dataset to use. Options: [``"train"``, ``"test"``].
sources (List[str] or None, optional): Sources extract data from.
List can contain the following options: [``"bass"``, ``"drums"``, ``"other"``, ``"mixture"``, ``"vocals"``].
If ``None``, dataset consists of tracks except mixture.
(default: ``None``)
split (str or None, optional): Whether to split training set into train and validation set.
If ``None``, no splitting occurs. If ``train`` or ``validation``, returns respective set.
(default: ``None``)
download (bool, optional): Whether to download the dataset if it is not found at root path.
(default: ``False``)
"""
def __init__(
self,
root: Union[str, Path],
subset: str,
sources: Optional[List[str]] = None,
split: Optional[str] = None,
download: bool = False,
) -> None:
self.sources = ["bass", "drums", "other", "vocals"] if not sources else sources
self.split = split
basename = os.path.basename(_URL)
archive = os.path.join(root, basename)
basename = basename.rsplit(".", 2)[0]
if subset not in ["test", "train"]:
raise ValueError("`subset` must be one of ['test', 'train']")
if self.split is not None and self.split not in ["train", "validation"]:
raise ValueError("`split` must be one of ['train', 'validation']")
base_path = os.path.join(root, basename)
self._path = os.path.join(base_path, subset)
if not os.path.isdir(self._path):
if not os.path.isfile(archive):
if not download:
raise RuntimeError("Dataset not found. Please use `download=True` to download")
download_url_to_file(_URL, archive, hash_prefix=_CHECKSUM)
os.makedirs(base_path, exist_ok=True)
extract_archive(archive, base_path)
self.names = self._collect_songs()
def _get_track(self, name, source):
return Path(self._path) / name / f"{source}{_EXT}"
def _load_sample(self, n: int) -> Tuple[torch.Tensor, int, int, str]:
name = self.names[n]
wavs = []
num_frames = None
for source in self.sources:
track = self._get_track(name, source)
wav, sr = torchaudio.load(str(track))
if sr != _SAMPLE_RATE:
raise ValueError(f"expected sample rate {_SAMPLE_RATE}, but got {sr}")
if num_frames is None:
num_frames = wav.shape[-1]
else:
if wav.shape[-1] != num_frames:
raise ValueError("num_frames do not match across sources")
wavs.append(wav)
stacked = torch.stack(wavs)
return stacked, _SAMPLE_RATE, num_frames, name
def _collect_songs(self):
if self.split == "validation":
return _VALIDATION_SET
path = Path(self._path)
names = []
for root, folders, _ in os.walk(path, followlinks=True):
root = Path(root)
if root.name.startswith(".") or folders or root == path:
continue
name = str(root.relative_to(path))
if self.split and name in _VALIDATION_SET:
continue
names.append(name)
return sorted(names)
def __getitem__(self, n: int) -> Tuple[torch.Tensor, int, int, str]:
"""Load the n-th sample from the dataset.
Args:
n (int): The index of the sample to be loaded
Returns:
(Tensor, int, int, str): ``(waveforms, sample_rate, num_frames, track_name)``
"""
return self._load_sample(n)
def __len__(self) -> int:
return len(self.names)
|
import os
from pathlib import Path
from typing import List, Optional, Tuple, Union
import torch
import torchaudio
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive
_URL = "https://zenodo.org/record/3338373/files/musdb18hq.zip"
_CHECKSUM = "baac80d0483c61d74b2e5f3be75fa557eec52898339e6aa45c1fa48833c5d21d"
_EXT = ".wav"
_SAMPLE_RATE = 44100
_VALIDATION_SET = [
"Actions - One Minute Smile",
"Clara Berry And Wooldog - Waltz For My Victims",
"Johnny Lokke - Promises & Lies",
"Patrick Talbot - A Reason To Leave",
"Triviul - Angelsaint",
"Alexander Ross - Goodbye Bolero",
"Fergessen - Nos Palpitants",
"Leaf - Summerghost",
"Skelpolu - Human Mistakes",
"Young Griffo - Pennies",
"ANiMAL - Rockshow",
"James May - On The Line",
"Meaxic - Take A Step",
"Traffic Experiment - Sirens",
]
class MUSDB_HQ(Dataset):
"""Create *MUSDB_HQ* [:footcite:`MUSDB18HQ`] Dataset
Args:
root (str or Path): Root directory where the dataset's top level directory is found
subset (str): Subset of the dataset to use. Options: [``"train"``, ``"test"``].
sources (List[str] or None, optional): Sources extract data from.
List can contain the following options: [``"bass"``, ``"drums"``, ``"other"``, ``"mixture"``, ``"vocals"``].
If ``None``, dataset consists of tracks except mixture.
(default: ``None``)
split (str or None, optional): Whether to split training set into train and validation set.
If ``None``, no splitting occurs. If ``train`` or ``validation``, returns respective set.
(default: ``None``)
download (bool, optional): Whether to download the dataset if it is not found at root path.
(default: ``False``)
"""
def __init__(
self,
root: Union[str, Path],
subset: str,
sources: Optional[List[str]] = None,
split: Optional[str] = None,
download: bool = False,
) -> None:
self.sources = ["bass", "drums", "other", "vocals"] if not sources else sources
self.split = split
basename = os.path.basename(_URL)
archive = os.path.join(root, basename)
basename = basename.rsplit(".", 2)[0]
assert subset in ["test", "train"], "`subset` must be one of ['test', 'train']"
assert self.split is None or self.split in [
"train",
"validation",
], "`split` must be one of ['train', 'validation']"
base_path = os.path.join(root, basename)
self._path = os.path.join(base_path, subset)
if not os.path.isdir(self._path):
if not os.path.isfile(archive):
if not download:
raise RuntimeError("Dataset not found. Please use `download=True` to download")
download_url_to_file(_URL, archive, hash_prefix=_CHECKSUM)
os.makedirs(base_path, exist_ok=True)
extract_archive(archive, base_path)
self.names = self._collect_songs()
def _get_track(self, name, source):
return Path(self._path) / name / f"{source}{_EXT}"
def _load_sample(self, n: int) -> Tuple[torch.Tensor, int, int, str]:
name = self.names[n]
wavs = []
num_frames = None
for source in self.sources:
track = self._get_track(name, source)
wav, sr = torchaudio.load(str(track))
assert sr == _SAMPLE_RATE, f"expected sample rate {_SAMPLE_RATE}, but got {sr}"
if num_frames is None:
num_frames = wav.shape[-1]
else:
assert wav.shape[-1] == num_frames, "num_frames do not match across sources"
wavs.append(wav)
stacked = torch.stack(wavs)
return stacked, _SAMPLE_RATE, num_frames, name
def _collect_songs(self):
if self.split == "validation":
return _VALIDATION_SET
path = Path(self._path)
names = []
for root, folders, _ in os.walk(path, followlinks=True):
root = Path(root)
if root.name.startswith(".") or folders or root == path:
continue
name = str(root.relative_to(path))
if self.split and name in _VALIDATION_SET:
continue
names.append(name)
return sorted(names)
def __getitem__(self, n: int) -> Tuple[torch.Tensor, int, int, str]:
"""Load the n-th sample from the dataset.
Args:
n (int): The index of the sample to be loaded
Returns:
(Tensor, int, int, str): ``(waveforms, sample_rate, num_frames, track_name)``
"""
return self._load_sample(n)
def __len__(self) -> int:
return len(self.names)
|
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import bbox_overlaps, get_box_tensor
def cast_tensor_type(x, scale=1., dtype=None):
if dtype == 'fp16':
# scale is for preventing overflows
x = (x / scale).half()
return x
@TASK_UTILS.register_module()
class BboxOverlaps2D:
"""2D Overlaps (e.g. IoUs, GIoUs) Calculator."""
def __init__(self, scale=1., dtype=None):
self.scale = scale
self.dtype = dtype
def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
"""Calculate IoU between 2D bboxes.
Args:
bboxes1 (Tensor or :obj:`BaseBoxes`): bboxes have shape (m, 4)
in <x1, y1, x2, y2> format, or shape (m, 5) in <x1, y1, x2,
y2, score> format.
bboxes2 (Tensor or :obj:`BaseBoxes`): bboxes have shape (m, 4)
in <x1, y1, x2, y2> format, shape (m, 5) in <x1, y1, x2, y2,
score> format, or be empty. If ``is_aligned `` is ``True``,
then m and n must be equal.
mode (str): "iou" (intersection over union), "iof" (intersection
over foreground), or "giou" (generalized intersection over
union).
is_aligned (bool, optional): If True, then m and n must be equal.
Default False.
Returns:
Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
"""
bboxes1 = get_box_tensor(bboxes1)
bboxes2 = get_box_tensor(bboxes2)
assert bboxes1.size(-1) in [0, 4, 5]
assert bboxes2.size(-1) in [0, 4, 5]
if bboxes2.size(-1) == 5:
bboxes2 = bboxes2[..., :4]
if bboxes1.size(-1) == 5:
bboxes1 = bboxes1[..., :4]
if self.dtype == 'fp16':
# change tensor type to save cpu and cuda memory and keep speed
bboxes1 = cast_tensor_type(bboxes1, self.scale, self.dtype)
bboxes2 = cast_tensor_type(bboxes2, self.scale, self.dtype)
overlaps = bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
if not overlaps.is_cuda and overlaps.dtype == torch.float16:
# resume cpu float32
overlaps = overlaps.float()
return overlaps
return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
def __repr__(self):
"""str: a string describing the module"""
repr_str = self.__class__.__name__ + f'(' \
f'scale={self.scale}, dtype={self.dtype})'
return repr_str
@TASK_UTILS.register_module()
class BboxOverlaps2D_GLIP(BboxOverlaps2D):
def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
TO_REMOVE = 1
area1 = (bboxes1[:, 2] - bboxes1[:, 0] + TO_REMOVE) * (
bboxes1[:, 3] - bboxes1[:, 1] + TO_REMOVE)
area2 = (bboxes2[:, 2] - bboxes2[:, 0] + TO_REMOVE) * (
bboxes2[:, 3] - bboxes2[:, 1] + TO_REMOVE)
lt = torch.max(bboxes1[:, None, :2], bboxes2[:, :2]) # [N,M,2]
rb = torch.min(bboxes1[:, None, 2:], bboxes2[:, 2:]) # [N,M,2]
wh = (rb - lt + TO_REMOVE).clamp(min=0) # [N,M,2]
inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
iou = inter / (area1[:, None] + area2 - inter)
return iou
|
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import bbox_overlaps, get_box_tensor
def cast_tensor_type(x, scale=1., dtype=None):
if dtype == 'fp16':
# scale is for preventing overflows
x = (x / scale).half()
return x
@TASK_UTILS.register_module()
class BboxOverlaps2D:
"""2D Overlaps (e.g. IoUs, GIoUs) Calculator."""
def __init__(self, scale=1., dtype=None):
self.scale = scale
self.dtype = dtype
def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
"""Calculate IoU between 2D bboxes.
Args:
bboxes1 (Tensor or :obj:`BaseBoxes`): bboxes have shape (m, 4)
in <x1, y1, x2, y2> format, or shape (m, 5) in <x1, y1, x2,
y2, score> format.
bboxes2 (Tensor or :obj:`BaseBoxes`): bboxes have shape (m, 4)
in <x1, y1, x2, y2> format, shape (m, 5) in <x1, y1, x2, y2,
score> format, or be empty. If ``is_aligned `` is ``True``,
then m and n must be equal.
mode (str): "iou" (intersection over union), "iof" (intersection
over foreground), or "giou" (generalized intersection over
union).
is_aligned (bool, optional): If True, then m and n must be equal.
Default False.
Returns:
Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
"""
bboxes1 = get_box_tensor(bboxes1)
bboxes2 = get_box_tensor(bboxes2)
assert bboxes1.size(-1) in [0, 4, 5]
assert bboxes2.size(-1) in [0, 4, 5]
if bboxes2.size(-1) == 5:
bboxes2 = bboxes2[..., :4]
if bboxes1.size(-1) == 5:
bboxes1 = bboxes1[..., :4]
if self.dtype == 'fp16':
# change tensor type to save cpu and cuda memory and keep speed
bboxes1 = cast_tensor_type(bboxes1, self.scale, self.dtype)
bboxes2 = cast_tensor_type(bboxes2, self.scale, self.dtype)
overlaps = bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
if not overlaps.is_cuda and overlaps.dtype == torch.float16:
# resume cpu float32
overlaps = overlaps.float()
return overlaps
return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
def __repr__(self):
"""str: a string describing the module"""
repr_str = self.__class__.__name__ + f'(' \
f'scale={self.scale}, dtype={self.dtype})'
return repr_str
|
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.optim import SGD
from mmengine.evaluator import BaseMetric
from mmengine.model import BaseModel
from mmengine.runner import Runner
class MMResNet50(BaseModel):
def __init__(self):
super().__init__()
self.resnet = torchvision.models.resnet50()
def forward(self, imgs, labels, mode):
x = self.resnet(imgs)
if mode == 'loss':
return {'loss': F.cross_entropy(x, labels)}
elif mode == 'predict':
return x, labels
class Accuracy(BaseMetric):
def process(self, data_batch, data_samples):
score, gt = data_samples
self.results.append({
'batch_size': len(gt),
'correct': (score.argmax(dim=1) == gt).sum().cpu(),
})
def compute_metrics(self, results):
total_correct = sum(item['correct'] for item in results)
total_size = sum(item['batch_size'] for item in results)
return dict(accuracy=100 * total_correct / total_size)
def parse_args():
parser = argparse.ArgumentParser(description='Distributed Training')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
return args
def main():
args = parse_args()
norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201])
train_set = torchvision.datasets.CIFAR10(
'data/cifar10',
train=True,
download=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(**norm_cfg)
]))
valid_set = torchvision.datasets.CIFAR10(
'data/cifar10',
train=False,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(**norm_cfg)]))
train_dataloader = dict(
batch_size=32,
dataset=train_set,
sampler=dict(type='DefaultSampler', shuffle=True),
collate_fn=dict(type='default_collate'))
val_dataloader = dict(
batch_size=32,
dataset=valid_set,
sampler=dict(type='DefaultSampler', shuffle=False),
collate_fn=dict(type='default_collate'))
runner = Runner(
model=MMResNet50(),
work_dir='./work_dir',
train_dataloader=train_dataloader,
optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
train_cfg=dict(by_epoch=True, max_epochs=2, val_interval=1),
val_dataloader=val_dataloader,
val_cfg=dict(),
val_evaluator=dict(type=Accuracy),
launcher=args.launcher,
)
runner.train()
if __name__ == '__main__':
main()
|
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.optim import SGD
from torch.utils.data import DataLoader
from mmengine.evaluator import BaseMetric
from mmengine.model import BaseModel
from mmengine.runner import Runner
class MMResNet50(BaseModel):
def __init__(self):
super().__init__()
self.resnet = torchvision.models.resnet50()
def forward(self, imgs, labels, mode):
x = self.resnet(imgs)
if mode == 'loss':
return {'loss': F.cross_entropy(x, labels)}
elif mode == 'predict':
return x, labels
class Accuracy(BaseMetric):
def process(self, data_batch, data_samples):
score, gt = data_samples
self.results.append({
'batch_size': len(gt),
'correct': (score.argmax(dim=1) == gt).sum().cpu(),
})
def compute_metrics(self, results):
total_correct = sum(item['correct'] for item in results)
total_size = sum(item['batch_size'] for item in results)
return dict(accuracy=100 * total_correct / total_size)
def parse_args():
parser = argparse.ArgumentParser(description='Distributed Training')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
return args
def main():
args = parse_args()
norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201])
train_dataloader = DataLoader(
batch_size=32,
shuffle=True,
dataset=torchvision.datasets.CIFAR10(
'data/cifar10',
train=True,
download=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(**norm_cfg)
])))
val_dataloader = DataLoader(
batch_size=32,
shuffle=False,
dataset=torchvision.datasets.CIFAR10(
'data/cifar10',
train=False,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(**norm_cfg)])))
runner = Runner(
model=MMResNet50(),
work_dir='./work_dir',
train_dataloader=train_dataloader,
optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
train_cfg=dict(by_epoch=True, max_epochs=2, val_interval=1),
val_dataloader=val_dataloader,
val_cfg=dict(),
val_evaluator=dict(type=Accuracy),
launcher=args.launcher,
)
runner.train()
if __name__ == '__main__':
main()
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PAA',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=5),
bbox_head=dict(
type='PAAHead',
reg_decoded_bbox=True,
score_voting=True,
topk=9,
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
octave_base_scale=8,
scales_per_octave=1,
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.1,
neg_iou_thr=0.1,
min_pos_iou=0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PAA',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=5),
bbox_head=dict(
type='PAAHead',
reg_decoded_bbox=True,
score_voting=True,
topk=9,
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
octave_base_scale=8,
scales_per_octave=1,
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.1,
neg_iou_thr=0.1,
min_pos_iou=0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
|
__version__ = '0.16.2'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
__version__ = '0.16.1'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Image(TVTensor):
""":class:`torch.Tensor` subclass for images with shape ``[..., C, H, W]``.
.. note::
In the :ref:`transforms <transforms>`, ``Image`` instances are largely
interchangeable with pure :class:`torch.Tensor`. See
:ref:`this note <passthrough_heuristic>` for more details.
Args:
data (tensor-like, PIL.Image.Image): Any data that can be turned into a tensor with :func:`torch.as_tensor` as
well as PIL images.
dtype (torch.dtype, optional): Desired data type. If omitted, will be inferred from
``data``.
device (torch.device, optional): Desired device. If omitted and ``data`` is a
:class:`torch.Tensor`, the device is taken from it. Otherwise, the image is constructed on the CPU.
requires_grad (bool, optional): Whether autograd should record operations. If omitted and
``data`` is a :class:`torch.Tensor`, the value is taken from it. Otherwise, defaults to ``False``.
"""
def __new__(
cls,
data: Any,
*,
dtype: Optional[torch.dtype] = None,
device: Optional[Union[torch.device, str, int]] = None,
requires_grad: Optional[bool] = None,
) -> Image:
if isinstance(data, PIL.Image.Image):
from torchvision.transforms.v2 import functional as F
data = F.pil_to_tensor(data)
tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
if tensor.ndim < 2:
raise ValueError
elif tensor.ndim == 2:
tensor = tensor.unsqueeze(0)
return tensor.as_subclass(cls)
def __repr__(self, *, tensor_contents: Any = None) -> str: # type: ignore[override]
return self._make_repr()
|
from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Image(TVTensor):
""":class:`torch.Tensor` subclass for images.
.. note::
In the :ref:`transforms <transforms>`, ``Image`` instances are largely
interchangeable with pure :class:`torch.Tensor`. See
:ref:`this note <passthrough_heuristic>` for more details.
Args:
data (tensor-like, PIL.Image.Image): Any data that can be turned into a tensor with :func:`torch.as_tensor` as
well as PIL images.
dtype (torch.dtype, optional): Desired data type. If omitted, will be inferred from
``data``.
device (torch.device, optional): Desired device. If omitted and ``data`` is a
:class:`torch.Tensor`, the device is taken from it. Otherwise, the image is constructed on the CPU.
requires_grad (bool, optional): Whether autograd should record operations. If omitted and
``data`` is a :class:`torch.Tensor`, the value is taken from it. Otherwise, defaults to ``False``.
"""
def __new__(
cls,
data: Any,
*,
dtype: Optional[torch.dtype] = None,
device: Optional[Union[torch.device, str, int]] = None,
requires_grad: Optional[bool] = None,
) -> Image:
if isinstance(data, PIL.Image.Image):
from torchvision.transforms.v2 import functional as F
data = F.pil_to_tensor(data)
tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
if tensor.ndim < 2:
raise ValueError
elif tensor.ndim == 2:
tensor = tensor.unsqueeze(0)
return tensor.as_subclass(cls)
def __repr__(self, *, tensor_contents: Any = None) -> str: # type: ignore[override]
return self._make_repr()
|
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
file_client_args = dict(backend='disk')
# comment out the code below to use different file client
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
train_pipeline = [
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomResize',
scale=image_size,
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=image_size,
recompute_bbox=True,
allow_negative_crop=True),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
# Use RepeatDataset to speed up training
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=4, # simply change this from 2 to 16 for 50e - 400e training.
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline)))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/instances_val2017.json',
metric=['bbox', 'segm'],
format_only=False)
test_evaluator = val_evaluator
max_epochs = 25
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=5)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# optimizer assumes bs=64
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004))
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[22, 24],
gamma=0.1)
]
# only keep latest 2 checkpoints
default_hooks = dict(checkpoint=dict(max_keep_ckpts=2))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (32 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)
|
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
file_client_args = dict(backend='disk')
# comment out the code below to use different file client
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
train_pipeline = [
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomResize',
scale=image_size,
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=image_size,
recompute_bbox=True,
allow_negative_crop=True),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
# Use RepeatDataset to speed up training
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=4, # simply change this from 2 to 16 for 50e - 400e training.
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline)))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/instances_val2017.json',
metric=['bbox', 'segm'],
format_only=False)
test_evaluator = val_evaluator
max_epochs = 25
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=5)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# optimizer assumes bs=64
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004))
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[22, 24],
gamma=0.1)
]
# only keep latest 2 checkpoints
default_hooks = dict(checkpoint=dict(max_keep_ckpts=2))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (32 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)
|
_base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='SingleStageDetector',
backbone=dict(
type='MobileNetV2',
out_indices=(4, 7),
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
neck=dict(
type='SSDNeck',
in_channels=(96, 1280),
out_channels=(96, 1280, 512, 256, 256, 128),
level_strides=(2, 2, 2, 2),
level_paddings=(1, 1, 1, 1),
l2_norm_scale=None,
use_depthwise=True,
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
act_cfg=dict(type='ReLU6'),
init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
bbox_head=dict(
type='SSDHead',
in_channels=(96, 1280, 512, 256, 256, 128),
num_classes=80,
use_depthwise=True,
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
act_cfg=dict(type='ReLU6'),
init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),
# set anchor size manually instead of using the predefined
# SSD300 setting.
anchor_generator=dict(
type='SSDAnchorGenerator',
scale_major=False,
strides=[16, 32, 64, 107, 160, 320],
ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
min_sizes=[48, 100, 150, 202, 253, 304],
max_sizes=[100, 150, 202, 253, 304, 320]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2])),
# model training and testing settings
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.,
ignore_iof_thr=-1,
gt_max_assign_all=False),
smoothl1_beta=1.,
allowed_border=-1,
pos_weight=-1,
neg_pos_ratio=3,
debug=False),
test_cfg=dict(
nms_pre=1000,
nms=dict(type='nms', iou_threshold=0.45),
min_bbox_size=0,
score_thr=0.02,
max_per_img=200))
cudnn_benchmark = True
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Expand',
mean=img_norm_cfg['mean'],
to_rgb=img_norm_cfg['to_rgb'],
ratio_range=(1, 4)),
dict(
type='MinIoURandomCrop',
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
min_crop_size=0.3),
dict(type='Resize', img_scale=(320, 320), keep_ratio=False),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=320),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(320, 320),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=320),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=24,
workers_per_gpu=4,
train=dict(
_delete_=True,
type='RepeatDataset', # use RepeatDataset to speed up training
times=5,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=4.0e-5)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
min_lr=0)
runner = dict(type='EpochBasedRunner', max_epochs=120)
# Avoid evaluation and saving weights too frequently
evaluation = dict(interval=5, metric='bbox')
checkpoint_config = dict(interval=5)
custom_hooks = [
dict(type='NumClassCheckHook'),
dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
]
|
_base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='SingleStageDetector',
backbone=dict(
type='MobileNetV2',
out_indices=(4, 7),
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
neck=dict(
type='SSDNeck',
in_channels=(96, 1280),
out_channels=(96, 1280, 512, 256, 256, 128),
level_strides=(2, 2, 2, 2),
level_paddings=(1, 1, 1, 1),
l2_norm_scale=None,
use_depthwise=True,
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
act_cfg=dict(type='ReLU6'),
init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
bbox_head=dict(
type='SSDHead',
in_channels=(96, 1280, 512, 256, 256, 128),
num_classes=80,
use_depthwise=True,
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
act_cfg=dict(type='ReLU6'),
init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),
# set anchor size manually instead of using the predefined
# SSD300 setting.
anchor_generator=dict(
type='SSDAnchorGenerator',
scale_major=False,
strides=[16, 32, 64, 107, 160, 320],
ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
min_sizes=[48, 100, 150, 202, 253, 304],
max_sizes=[100, 150, 202, 253, 304, 320]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2])),
# model training and testing settings
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.,
ignore_iof_thr=-1,
gt_max_assign_all=False),
smoothl1_beta=1.,
allowed_border=-1,
pos_weight=-1,
neg_pos_ratio=3,
debug=False),
test_cfg=dict(
nms_pre=1000,
nms=dict(type='nms', iou_threshold=0.45),
min_bbox_size=0,
score_thr=0.02,
max_per_img=200))
cudnn_benchmark = True
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(
type='Expand',
mean=img_norm_cfg['mean'],
to_rgb=img_norm_cfg['to_rgb'],
ratio_range=(1, 4)),
dict(
type='MinIoURandomCrop',
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
min_crop_size=0.3),
dict(type='Resize', img_scale=(320, 320), keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Pad', size_divisor=320),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(320, 320),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=False),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=320),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=24,
workers_per_gpu=4,
train=dict(
_delete_=True,
type='RepeatDataset', # use RepeatDataset to speed up training
times=5,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=4.0e-5)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
min_lr=0)
runner = dict(type='EpochBasedRunner', max_epochs=120)
# Avoid evaluation and saving weights too frequently
evaluation = dict(interval=5, metric='bbox')
checkpoint_config = dict(interval=5)
custom_hooks = [
dict(type='NumClassCheckHook'),
dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
]
|
"""**Text Splitters** are classes for splitting text.
**Class hierarchy:**
.. code-block::
BaseDocumentTransformer --> TextSplitter --> <name>TextSplitter # Example: CharacterTextSplitter
RecursiveCharacterTextSplitter --> <name>TextSplitter
Note: **MarkdownHeaderTextSplitter** and **HTMLHeaderTextSplitter do not derive from TextSplitter.
**Main helpers:**
.. code-block::
Document, Tokenizer, Language, LineType, HeaderType
""" # noqa: E501
from langchain_text_splitters.base import (
Language,
TextSplitter,
Tokenizer,
TokenTextSplitter,
split_text_on_tokens,
)
from langchain_text_splitters.character import (
CharacterTextSplitter,
RecursiveCharacterTextSplitter,
)
from langchain_text_splitters.html import (
ElementType,
HTMLHeaderTextSplitter,
HTMLSectionSplitter,
HTMLSemanticPreservingSplitter,
)
from langchain_text_splitters.json import RecursiveJsonSplitter
from langchain_text_splitters.jsx import JSFrameworkTextSplitter
from langchain_text_splitters.konlpy import KonlpyTextSplitter
from langchain_text_splitters.latex import LatexTextSplitter
from langchain_text_splitters.markdown import (
ExperimentalMarkdownSyntaxTextSplitter,
HeaderType,
LineType,
MarkdownHeaderTextSplitter,
MarkdownTextSplitter,
)
from langchain_text_splitters.nltk import NLTKTextSplitter
from langchain_text_splitters.python import PythonCodeTextSplitter
from langchain_text_splitters.sentence_transformers import (
SentenceTransformersTokenTextSplitter,
)
from langchain_text_splitters.spacy import SpacyTextSplitter
__all__ = [
"CharacterTextSplitter",
"ElementType",
"ExperimentalMarkdownSyntaxTextSplitter",
"HTMLHeaderTextSplitter",
"HTMLSectionSplitter",
"HTMLSemanticPreservingSplitter",
"HeaderType",
"JSFrameworkTextSplitter",
"KonlpyTextSplitter",
"Language",
"LatexTextSplitter",
"LineType",
"MarkdownHeaderTextSplitter",
"MarkdownTextSplitter",
"NLTKTextSplitter",
"PythonCodeTextSplitter",
"RecursiveCharacterTextSplitter",
"RecursiveJsonSplitter",
"SentenceTransformersTokenTextSplitter",
"SpacyTextSplitter",
"TextSplitter",
"TokenTextSplitter",
"Tokenizer",
"split_text_on_tokens",
]
|
"""**Text Splitters** are classes for splitting text.
**Class hierarchy:**
.. code-block::
BaseDocumentTransformer --> TextSplitter --> <name>TextSplitter # Example: CharacterTextSplitter
RecursiveCharacterTextSplitter --> <name>TextSplitter
Note: **MarkdownHeaderTextSplitter** and **HTMLHeaderTextSplitter do not derive from TextSplitter.
**Main helpers:**
.. code-block::
Document, Tokenizer, Language, LineType, HeaderType
""" # noqa: E501
from langchain_text_splitters.base import (
Language,
TextSplitter,
Tokenizer,
TokenTextSplitter,
split_text_on_tokens,
)
from langchain_text_splitters.character import (
CharacterTextSplitter,
RecursiveCharacterTextSplitter,
)
from langchain_text_splitters.html import (
ElementType,
HTMLHeaderTextSplitter,
HTMLSectionSplitter,
HTMLSemanticPreservingSplitter,
)
from langchain_text_splitters.json import RecursiveJsonSplitter
from langchain_text_splitters.jsx import JSFrameworkTextSplitter
from langchain_text_splitters.konlpy import KonlpyTextSplitter
from langchain_text_splitters.latex import LatexTextSplitter
from langchain_text_splitters.markdown import (
ExperimentalMarkdownSyntaxTextSplitter,
HeaderType,
LineType,
MarkdownHeaderTextSplitter,
MarkdownTextSplitter,
)
from langchain_text_splitters.nltk import NLTKTextSplitter
from langchain_text_splitters.python import PythonCodeTextSplitter
from langchain_text_splitters.sentence_transformers import (
SentenceTransformersTokenTextSplitter,
)
from langchain_text_splitters.spacy import SpacyTextSplitter
__all__ = [
"TokenTextSplitter",
"TextSplitter",
"Tokenizer",
"Language",
"RecursiveCharacterTextSplitter",
"RecursiveJsonSplitter",
"LatexTextSplitter",
"JSFrameworkTextSplitter",
"PythonCodeTextSplitter",
"KonlpyTextSplitter",
"SpacyTextSplitter",
"NLTKTextSplitter",
"split_text_on_tokens",
"SentenceTransformersTokenTextSplitter",
"ElementType",
"HeaderType",
"LineType",
"HTMLHeaderTextSplitter",
"HTMLSectionSplitter",
"HTMLSemanticPreservingSplitter",
"MarkdownHeaderTextSplitter",
"MarkdownTextSplitter",
"CharacterTextSplitter",
"ExperimentalMarkdownSyntaxTextSplitter",
]
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.regularizers import deserialize as deserialize
from keras.src.regularizers import get as get
from keras.src.regularizers import serialize as serialize
from keras.src.regularizers.regularizers import L1 as L1
from keras.src.regularizers.regularizers import L1 as l1
from keras.src.regularizers.regularizers import L1L2 as L1L2
from keras.src.regularizers.regularizers import L1L2 as l1_l2
from keras.src.regularizers.regularizers import L2 as L2
from keras.src.regularizers.regularizers import L2 as l2
from keras.src.regularizers.regularizers import (
OrthogonalRegularizer as OrthogonalRegularizer,
)
from keras.src.regularizers.regularizers import (
OrthogonalRegularizer as orthogonal_regularizer,
)
from keras.src.regularizers.regularizers import Regularizer as Regularizer
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.regularizers import deserialize
from keras.src.regularizers import get
from keras.src.regularizers import serialize
from keras.src.regularizers.regularizers import L1
from keras.src.regularizers.regularizers import L1 as l1
from keras.src.regularizers.regularizers import L1L2
from keras.src.regularizers.regularizers import L1L2 as l1_l2
from keras.src.regularizers.regularizers import L2
from keras.src.regularizers.regularizers import L2 as l2
from keras.src.regularizers.regularizers import OrthogonalRegularizer
from keras.src.regularizers.regularizers import (
OrthogonalRegularizer as orthogonal_regularizer,
)
from keras.src.regularizers.regularizers import Regularizer
|
from PIL import Image
from sentence_transformers import SentenceTransformer, models, util
###########
image = Image.open("two_dogs_in_snow.jpg")
from transformers import CLIPModel, CLIPProcessor
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
inputs = processor(text=["a cat", "a dog"], images=[image], return_tensors="pt", padding=True)
output = model(**inputs)
# vision_outputs = model.vision_model(pixel_values=inputs['pixel_values'])
# image_embeds = model.visual_projection(vision_outputs[1])
# print(image_embeds.shape)
# exit()
# Load CLIP model
clip = models.CLIPModel()
model = SentenceTransformer(modules=[clip])
model.save("tmp-clip-model")
model = SentenceTransformer("tmp-clip-model")
# Encode an image:
img_emb = model.encode(Image.open("two_dogs_in_snow.jpg"))
# Encode text descriptions
text_emb = model.encode(["Two dogs in the snow", "A cat on a table", "A picture of London at night"])
# Compute cosine similarities
cos_scores = util.cos_sim(img_emb, text_emb)
print(cos_scores)
|
from PIL import Image
from sentence_transformers import SentenceTransformer, models, util
###########
image = Image.open("two_dogs_in_snow.jpg")
from transformers import CLIPModel, CLIPProcessor
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
inputs = processor(texts=["a cat", "a dog"], images=[image], return_tensors="pt", padding=True)
output = model(**inputs)
# vision_outputs = model.vision_model(pixel_values=inputs['pixel_values'])
# image_embeds = model.visual_projection(vision_outputs[1])
# print(image_embeds.shape)
# exit()
# Load CLIP model
clip = models.CLIPModel()
model = SentenceTransformer(modules=[clip])
model.save("tmp-clip-model")
model = SentenceTransformer("tmp-clip-model")
# Encode an image:
img_emb = model.encode(Image.open("two_dogs_in_snow.jpg"))
# Encode text descriptions
text_emb = model.encode(["Two dogs in the snow", "A cat on a table", "A picture of London at night"])
# Compute cosine similarities
cos_scores = util.cos_sim(img_emb, text_emb)
print(cos_scores)
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.datasets.california_housing import load_data as load_data
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.datasets.california_housing import load_data
|
from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init__(
self, model: SparseEncoder, distance_metric=TripletDistanceMetric.EUCLIDEAN, triplet_margin: float = 5
) -> None:
"""
This class implements triplet loss. Given a triplet of (anchor, positive, negative),
the loss minimizes the distance between anchor and positive while it maximizes the distance
between anchor and negative. It compute the following loss function:
``loss = max(||anchor - positive|| - ||anchor - negative|| + margin, 0)``.
Margin is an important hyperparameter and needs to be tuned respectively.
Args:
model: SparseEncoder
distance_metric: Function to compute distance between two
embeddings. The class TripletDistanceMetric contains
common distance metrices that can be used.
triplet_margin: The negative should be at least this much
further away from the anchor than the positive.
References:
- For further details, see: https://en.wikipedia.org/wiki/Triplet_loss
Requirements:
1. Need to be used in SpladeLoss or CSRLoss as a loss function.
2. (anchor, positive, negative) triplets
Inputs:
+---------------------------------------+--------+
| Texts | Labels |
+=======================================+========+
| (anchor, positive, negative) triplets | none |
+---------------------------------------+--------+
Example:
::
from datasets import Dataset
from sentence_transformers.sparse_encoder import SparseEncoder, SparseEncoderTrainer, losses
model = SparseEncoder("distilbert/distilbert-base-uncased")
train_dataset = Dataset.from_dict(
{
"anchor": ["It's nice weather outside today.", "He drove to work."],
"positive": ["It's so sunny.", "He took the car to the office."],
"negative": ["It's quite rainy, sadly.", "She walked to the store."],
}
)
loss = losses.SpladeLoss(model=model, loss=losses.SparseTripletLoss(model), lambda_corpus=3e-5, lambda_query=5e-5)
trainer = SparseEncoderTrainer(model=model, train_dataset=train_dataset, loss=loss)
trainer.train()
"""
super().__init__(model, distance_metric=distance_metric, triplet_margin=triplet_margin)
def forward(self, sentence_features: Iterable[dict[str, Tensor]], labels: Tensor) -> Tensor:
raise AttributeError("SparseTripletLoss shold not be used alone. Use it with SpladeLoss or CSRLoss.")
|
from __future__ import annotations
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init__(
self, model: SparseEncoder, distance_metric=TripletDistanceMetric.EUCLIDEAN, triplet_margin: float = 5
) -> None:
"""
This class implements triplet loss. Given a triplet of (anchor, positive, negative),
the loss minimizes the distance between anchor and positive while it maximizes the distance
between anchor and negative. It compute the following loss function:
``loss = max(||anchor - positive|| - ||anchor - negative|| + margin, 0)``.
Margin is an important hyperparameter and needs to be tuned respectively.
Args:
model: SparseEncoder
distance_metric: Function to compute distance between two
embeddings. The class TripletDistanceMetric contains
common distance metrices that can be used.
triplet_margin: The negative should be at least this much
further away from the anchor than the positive.
References:
- For further details, see: https://en.wikipedia.org/wiki/Triplet_loss
Requirements:
1. (anchor, positive, negative) triplets
Inputs:
+---------------------------------------+--------+
| Texts | Labels |
+=======================================+========+
| (anchor, positive, negative) triplets | none |
+---------------------------------------+--------+
Example:
::
from datasets import Dataset
from sentence_transformers.sparse_encoder import SparseEncoder, SparseEncoderTrainer, losses
model = SparseEncoder("distilbert/distilbert-base-uncased")
train_dataset = Dataset.from_dict(
{
"anchor": ["It's nice weather outside today.", "He drove to work."],
"positive": ["It's so sunny.", "He took the car to the office."],
"negative": ["It's quite rainy, sadly.", "She walked to the store."],
}
)
loss = losses.SpladeLoss(model=model, loss=losses.SparseTripletLoss(model), lambda_corpus=3e-5, lambda_query=5e-5)
trainer = SparseEncoderTrainer(model=model, train_dataset=train_dataset, loss=loss)
trainer.train()
"""
super().__init__(model, distance_metric=distance_metric, triplet_margin=triplet_margin)
|
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
from mmdet.utils import register_all_modules
class TestGridRoIHead(TestCase):
def setUp(self):
register_all_modules()
self.roi_head_cfg = get_roi_head_cfg(
'grid_rcnn/grid-rcnn_r50_fpn_gn-head_2x_coco.py')
def test_init(self):
roi_head = MODELS.build(self.roi_head_cfg)
self.assertTrue(roi_head.with_bbox)
@parameterized.expand(['cpu', 'cuda'])
def test_grid_roi_head_loss(self, device):
"""Tests trident roi head predict."""
if device == 'cuda':
if not torch.cuda.is_available():
return unittest.skip('test requires GPU and torch+cuda')
roi_head = MODELS.build(self.roi_head_cfg)
roi_head = roi_head.to(device=device)
s = 256
feats = []
for i in range(len(roi_head.bbox_roi_extractor.featmap_strides)):
feats.append(
torch.rand(1, 256, s // (2**(i + 2)),
s // (2**(i + 2))).to(device=device))
image_shapes = [(3, s, s)]
batch_data_samples = demo_mm_inputs(
batch_size=1,
image_shapes=image_shapes,
num_items=[1],
num_classes=4,
with_mask=True,
device=device)['data_samples']
proposals_list = demo_mm_proposals(
image_shapes=image_shapes, num_proposals=100, device=device)
out = roi_head.loss(feats, proposals_list, batch_data_samples)
loss_cls = out['loss_cls']
loss_grid = out['loss_grid']
self.assertGreater(loss_cls.sum(), 0, 'cls loss should be non-zero')
self.assertGreater(loss_grid.sum(), 0, 'grid loss should be non-zero')
batch_data_samples = demo_mm_inputs(
batch_size=1,
image_shapes=image_shapes,
num_items=[0],
num_classes=4,
with_mask=True,
device=device)['data_samples']
proposals_list = demo_mm_proposals(
image_shapes=image_shapes, num_proposals=100, device=device)
out = roi_head.loss(feats, proposals_list, batch_data_samples)
empty_cls_loss = out['loss_cls']
self.assertGreater(empty_cls_loss.sum(), 0,
'cls loss should be non-zero')
self.assertNotIn(
'loss_grid', out,
'grid loss should be passed when there are no true boxes')
@parameterized.expand(['cpu', 'cuda'])
def test_grid_roi_head_predict(self, device):
"""Tests trident roi head predict."""
if device == 'cuda':
if not torch.cuda.is_available():
return unittest.skip('test requires GPU and torch+cuda')
roi_head = MODELS.build(self.roi_head_cfg)
roi_head = roi_head.to(device=device)
s = 256
feats = []
for i in range(len(roi_head.bbox_roi_extractor.featmap_strides)):
feats.append(
torch.rand(1, 256, s // (2**(i + 2)),
s // (2**(i + 2))).to(device=device))
image_shapes = [(3, s, s)]
batch_data_samples = demo_mm_inputs(
batch_size=1,
image_shapes=image_shapes,
num_items=[0],
num_classes=4,
with_mask=True,
device=device)['data_samples']
proposals_list = demo_mm_proposals(
image_shapes=image_shapes, num_proposals=100, device=device)
roi_head.predict(feats, proposals_list, batch_data_samples)
@parameterized.expand(['cpu', 'cuda'])
def test_grid_roi_head_forward(self, device):
"""Tests trident roi head forward."""
if device == 'cuda':
if not torch.cuda.is_available():
return unittest.skip('test requires GPU and torch+cuda')
roi_head = MODELS.build(self.roi_head_cfg)
roi_head = roi_head.to(device=device)
s = 256
feats = []
for i in range(len(roi_head.bbox_roi_extractor.featmap_strides)):
feats.append(
torch.rand(1, 256, s // (2**(i + 2)),
s // (2**(i + 2))).to(device=device))
image_shapes = [(3, s, s)]
proposals_list = demo_mm_proposals(
image_shapes=image_shapes, num_proposals=100, device=device)
roi_head.forward(feats, proposals_list)
|
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
from mmdet.utils import register_all_modules
class TestGridRoIHead(TestCase):
def setUp(self):
register_all_modules()
self.roi_head_cfg = get_roi_head_cfg(
'grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py')
def test_init(self):
roi_head = MODELS.build(self.roi_head_cfg)
self.assertTrue(roi_head.with_bbox)
@parameterized.expand(['cpu', 'cuda'])
def test_grid_roi_head_loss(self, device):
"""Tests trident roi head predict."""
if device == 'cuda':
if not torch.cuda.is_available():
return unittest.skip('test requires GPU and torch+cuda')
roi_head = MODELS.build(self.roi_head_cfg)
roi_head = roi_head.to(device=device)
s = 256
feats = []
for i in range(len(roi_head.bbox_roi_extractor.featmap_strides)):
feats.append(
torch.rand(1, 256, s // (2**(i + 2)),
s // (2**(i + 2))).to(device=device))
image_shapes = [(3, s, s)]
batch_data_samples = demo_mm_inputs(
batch_size=1,
image_shapes=image_shapes,
num_items=[1],
num_classes=4,
with_mask=True,
device=device)['data_samples']
proposals_list = demo_mm_proposals(
image_shapes=image_shapes, num_proposals=100, device=device)
out = roi_head.loss(feats, proposals_list, batch_data_samples)
loss_cls = out['loss_cls']
loss_grid = out['loss_grid']
self.assertGreater(loss_cls.sum(), 0, 'cls loss should be non-zero')
self.assertGreater(loss_grid.sum(), 0, 'grid loss should be non-zero')
batch_data_samples = demo_mm_inputs(
batch_size=1,
image_shapes=image_shapes,
num_items=[0],
num_classes=4,
with_mask=True,
device=device)['data_samples']
proposals_list = demo_mm_proposals(
image_shapes=image_shapes, num_proposals=100, device=device)
out = roi_head.loss(feats, proposals_list, batch_data_samples)
empty_cls_loss = out['loss_cls']
self.assertGreater(empty_cls_loss.sum(), 0,
'cls loss should be non-zero')
self.assertNotIn(
'loss_grid', out,
'grid loss should be passed when there are no true boxes')
@parameterized.expand(['cpu', 'cuda'])
def test_grid_roi_head_predict(self, device):
"""Tests trident roi head predict."""
if device == 'cuda':
if not torch.cuda.is_available():
return unittest.skip('test requires GPU and torch+cuda')
roi_head = MODELS.build(self.roi_head_cfg)
roi_head = roi_head.to(device=device)
s = 256
feats = []
for i in range(len(roi_head.bbox_roi_extractor.featmap_strides)):
feats.append(
torch.rand(1, 256, s // (2**(i + 2)),
s // (2**(i + 2))).to(device=device))
image_shapes = [(3, s, s)]
batch_data_samples = demo_mm_inputs(
batch_size=1,
image_shapes=image_shapes,
num_items=[0],
num_classes=4,
with_mask=True,
device=device)['data_samples']
proposals_list = demo_mm_proposals(
image_shapes=image_shapes, num_proposals=100, device=device)
roi_head.predict(feats, proposals_list, batch_data_samples)
@parameterized.expand(['cpu', 'cuda'])
def test_grid_roi_head_forward(self, device):
"""Tests trident roi head forward."""
if device == 'cuda':
if not torch.cuda.is_available():
return unittest.skip('test requires GPU and torch+cuda')
roi_head = MODELS.build(self.roi_head_cfg)
roi_head = roi_head.to(device=device)
s = 256
feats = []
for i in range(len(roi_head.bbox_roi_extractor.featmap_strides)):
feats.append(
torch.rand(1, 256, s // (2**(i + 2)),
s // (2**(i + 2))).to(device=device))
image_shapes = [(3, s, s)]
proposals_list = demo_mm_proposals(
image_shapes=image_shapes, num_proposals=100, device=device)
roi_head.forward(feats, proposals_list)
|
from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.typing.tensor.video import VideoNdArray
from docarray.typing.url import (
AnyUrl,
AudioUrl,
ImageUrl,
Mesh3DUrl,
PointCloud3DUrl,
TextUrl,
VideoUrl,
)
__all__ = [
'NdArray',
'NdArrayEmbedding',
'AudioNdArray',
'VideoNdArray',
'AnyEmbedding',
'ImageUrl',
'AudioUrl',
'TextUrl',
'Mesh3DUrl',
'PointCloud3DUrl',
'VideoUrl',
'AnyUrl',
'ID',
'AnyTensor',
'NdArrayEmbedding',
'ImageBytes',
'ImageTensor',
'ImageNdArray',
]
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor import TorchEmbedding, TorchTensor # noqa: F401
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor # noqa
from docarray.typing.tensor.image import ImageTorchTensor # noqa: F401
from docarray.typing.tensor.video.video_torch_tensor import VideoTorchTensor # noqa
__all__.extend(
[
'AudioTorchTensor',
'TorchEmbedding',
'TorchTensor',
'VideoTorchTensor',
'ImageTorchTensor',
]
)
tf_available = is_tf_available()
if tf_available:
from docarray.typing.tensor import TensorFlowTensor # noqa: F401
from docarray.typing.tensor.audio import AudioTensorFlowTensor # noqa: F401
from docarray.typing.tensor.embedding import TensorFlowEmbedding # noqa: F401
from docarray.typing.tensor.image import ImageTensorFlowTensor # noqa: F401
from docarray.typing.tensor.video import VideoTensorFlowTensor # noqa
__all__.extend(
[
'TensorFlowTensor',
'TensorFlowEmbedding',
'AudioTensorFlowTensor',
'ImageTensorFlowTensor',
'VideoTensorFlowTensor',
]
)
|
from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.typing.tensor.video import VideoNdArray
from docarray.typing.url import (
AnyUrl,
AudioUrl,
ImageUrl,
Mesh3DUrl,
PointCloud3DUrl,
TextUrl,
VideoUrl,
)
__all__ = [
'NdArray',
'NdArrayEmbedding',
'AudioNdArray',
'VideoNdArray',
'AnyEmbedding',
'ImageUrl',
'AudioUrl',
'TextUrl',
'Mesh3DUrl',
'PointCloud3DUrl',
'VideoUrl',
'AnyUrl',
'ID',
'AnyTensor',
'TensorFlowTensor',
'NdArrayEmbedding',
'ImageBytes',
'ImageTensor',
'ImageNdArray',
]
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor import TorchEmbedding, TorchTensor # noqa: F401
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor # noqa
from docarray.typing.tensor.image import ImageTorchTensor # noqa: F401
from docarray.typing.tensor.video.video_torch_tensor import VideoTorchTensor # noqa
__all__.extend(
[
'AudioTorchTensor',
'TorchEmbedding',
'TorchTensor',
'VideoTorchTensor',
'ImageTorchTensor',
]
)
tf_available = is_tf_available()
if tf_available:
from docarray.typing.tensor import TensorFlowTensor
from docarray.typing.tensor.audio import AudioTensorFlowTensor # noqa: F401
from docarray.typing.tensor.embedding import TensorFlowEmbedding # noqa: F401
from docarray.typing.tensor.image import ImageTensorFlowTensor # noqa: F401
from docarray.typing.tensor.video import VideoTensorFlowTensor # noqa
__all__.extend(
[
'TensorFlowTensor',
'TensorFlowEmbedding',
'AudioTensorFlowTensor',
'ImageTensorFlowTensor',
'VideoTensorFlowTensor',
]
)
|
from langchain_core.output_parsers.json import (
SimpleJsonOutputParser,
)
from langchain_core.utils.json import (
parse_and_check_json_markdown,
parse_json_markdown,
parse_partial_json,
)
__all__ = [
"SimpleJsonOutputParser",
"parse_and_check_json_markdown",
"parse_json_markdown",
"parse_partial_json",
]
|
from langchain_core.output_parsers.json import (
SimpleJsonOutputParser,
)
from langchain_core.utils.json import (
parse_and_check_json_markdown,
parse_json_markdown,
parse_partial_json,
)
__all__ = [
"SimpleJsonOutputParser",
"parse_partial_json",
"parse_json_markdown",
"parse_and_check_json_markdown",
]
|
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_additional_imports = {}
_import_structure = {"pipeline_output": ["ChromaPipelineOutput"]}
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils import dummy_torch_and_transformers_objects # noqa F403
_dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
else:
_import_structure["pipeline_chroma"] = ["ChromaPipeline"]
_import_structure["pipeline_chroma_img2img"] = ["ChromaImg2ImgPipeline"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_chroma import ChromaPipeline
from .pipeline_chroma_img2img import ChromaImg2ImgPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
module_spec=__spec__,
)
for name, value in _dummy_objects.items():
setattr(sys.modules[__name__], name, value)
for name, value in _additional_imports.items():
setattr(sys.modules[__name__], name, value)
|
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_additional_imports = {}
_import_structure = {"pipeline_output": ["ChromaPipelineOutput"]}
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils import dummy_torch_and_transformers_objects # noqa F403
_dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
else:
_import_structure["pipeline_chroma"] = ["ChromaPipeline"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_chroma import ChromaPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
module_spec=__spec__,
)
for name, value in _dummy_objects.items():
setattr(sys.modules[__name__], name, value)
for name, value in _additional_imports.items():
setattr(sys.modules[__name__], name, value)
|
"""
Mbox parser.
Contains simple parser for mbox files.
"""
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional
from fsspec import AbstractFileSystem
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
class MboxReader(BaseReader):
"""
Mbox parser.
Extract messages from mailbox files.
Returns string including date, subject, sender, receiver and
content for each message.
"""
DEFAULT_MESSAGE_FORMAT: str = (
"Date: {_date}\n"
"From: {_from}\n"
"To: {_to}\n"
"Subject: {_subject}\n"
"Content: {_content}"
)
def __init__(
self,
*args: Any,
max_count: int = 0,
message_format: str = DEFAULT_MESSAGE_FORMAT,
**kwargs: Any,
) -> None:
"""Init params."""
try:
from bs4 import BeautifulSoup # noqa
except ImportError:
raise ImportError(
"`beautifulsoup4` package not found: `pip install beautifulsoup4`"
)
super().__init__(*args, **kwargs)
self.max_count = max_count
self.message_format = message_format
def load_data(
self,
file: Path,
extra_info: Optional[Dict] = None,
fs: Optional[AbstractFileSystem] = None,
) -> List[Document]:
"""Parse file into string."""
# Import required libraries
import mailbox
from email.parser import BytesParser
from email.policy import default
from bs4 import BeautifulSoup
if fs:
logger.warning(
"fs was specified but MboxReader doesn't support loading "
"from fsspec filesystems. Will load from local filesystem instead."
)
i = 0
results: List[str] = []
# Load file using mailbox
bytes_parser = BytesParser(policy=default).parse
mbox = mailbox.mbox(file, factory=bytes_parser) # type: ignore
# Iterate through all messages
for _, _msg in enumerate(mbox):
try:
msg: mailbox.mboxMessage = _msg
# Parse multipart messages
if msg.is_multipart():
for part in msg.walk():
ctype = part.get_content_type()
cdispo = str(part.get("Content-Disposition"))
if ctype == "text/plain" and "attachment" not in cdispo:
content = part.get_payload(decode=True) # decode
break
# Get plain message payload for non-multipart messages
else:
content = msg.get_payload(decode=True)
# Parse message HTML content and remove unneeded whitespace
soup = BeautifulSoup(content)
stripped_content = " ".join(soup.get_text().split())
# Format message to include date, sender, receiver and subject
msg_string = self.message_format.format(
_date=msg["date"],
_from=msg["from"],
_to=msg["to"],
_subject=msg["subject"],
_content=stripped_content,
)
# Add message string to results
results.append(msg_string)
except Exception as e:
logger.warning(f"Failed to parse message:\n{_msg}\n with exception {e}")
# Increment counter and return if max count is met
i += 1
if self.max_count > 0 and i >= self.max_count:
break
return [Document(text=result, metadata=extra_info or {}) for result in results]
|
"""Mbox parser.
Contains simple parser for mbox files.
"""
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional
from fsspec import AbstractFileSystem
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
class MboxReader(BaseReader):
"""Mbox parser.
Extract messages from mailbox files.
Returns string including date, subject, sender, receiver and
content for each message.
"""
DEFAULT_MESSAGE_FORMAT: str = (
"Date: {_date}\n"
"From: {_from}\n"
"To: {_to}\n"
"Subject: {_subject}\n"
"Content: {_content}"
)
def __init__(
self,
*args: Any,
max_count: int = 0,
message_format: str = DEFAULT_MESSAGE_FORMAT,
**kwargs: Any,
) -> None:
"""Init params."""
try:
from bs4 import BeautifulSoup # noqa
except ImportError:
raise ImportError(
"`beautifulsoup4` package not found: `pip install beautifulsoup4`"
)
super().__init__(*args, **kwargs)
self.max_count = max_count
self.message_format = message_format
def load_data(
self,
file: Path,
extra_info: Optional[Dict] = None,
fs: Optional[AbstractFileSystem] = None,
) -> List[Document]:
"""Parse file into string."""
# Import required libraries
import mailbox
from email.parser import BytesParser
from email.policy import default
from bs4 import BeautifulSoup
if fs:
logger.warning(
"fs was specified but MboxReader doesn't support loading "
"from fsspec filesystems. Will load from local filesystem instead."
)
i = 0
results: List[str] = []
# Load file using mailbox
bytes_parser = BytesParser(policy=default).parse
mbox = mailbox.mbox(file, factory=bytes_parser) # type: ignore
# Iterate through all messages
for _, _msg in enumerate(mbox):
try:
msg: mailbox.mboxMessage = _msg
# Parse multipart messages
if msg.is_multipart():
for part in msg.walk():
ctype = part.get_content_type()
cdispo = str(part.get("Content-Disposition"))
if ctype == "text/plain" and "attachment" not in cdispo:
content = part.get_payload(decode=True) # decode
break
# Get plain message payload for non-multipart messages
else:
content = msg.get_payload(decode=True)
# Parse message HTML content and remove unneeded whitespace
soup = BeautifulSoup(content)
stripped_content = " ".join(soup.get_text().split())
# Format message to include date, sender, receiver and subject
msg_string = self.message_format.format(
_date=msg["date"],
_from=msg["from"],
_to=msg["to"],
_subject=msg["subject"],
_content=stripped_content,
)
# Add message string to results
results.append(msg_string)
except Exception as e:
logger.warning(f"Failed to parse message:\n{_msg}\n with exception {e}")
# Increment counter and return if max count is met
i += 1
if self.max_count > 0 and i >= self.max_count:
break
return [Document(text=result, metadata=extra_info or {}) for result in results]
|
import torch
from torch import nn
from typing import List
import os
import json
class CNN(nn.Module):
"""CNN-layer with multiple kernel-sizes over the word embeddings"""
def __init__(
self,
in_word_embedding_dimension: int,
out_channels: int = 256,
kernel_sizes: List[int] = [1, 3, 5],
stride_sizes: List[int] = None,
):
nn.Module.__init__(self)
self.config_keys = ["in_word_embedding_dimension", "out_channels", "kernel_sizes"]
self.in_word_embedding_dimension = in_word_embedding_dimension
self.out_channels = out_channels
self.kernel_sizes = kernel_sizes
self.embeddings_dimension = out_channels * len(kernel_sizes)
self.convs = nn.ModuleList()
in_channels = in_word_embedding_dimension
if stride_sizes is None:
stride_sizes = [1] * len(kernel_sizes)
for kernel_size, stride in zip(kernel_sizes, stride_sizes):
padding_size = int((kernel_size - 1) / 2)
conv = nn.Conv1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding_size,
)
self.convs.append(conv)
def forward(self, features):
token_embeddings = features["token_embeddings"]
token_embeddings = token_embeddings.transpose(1, -1)
vectors = [conv(token_embeddings) for conv in self.convs]
out = torch.cat(vectors, 1).transpose(1, -1)
features.update({"token_embeddings": out})
return features
def get_word_embedding_dimension(self) -> int:
return self.embeddings_dimension
def tokenize(self, text: str, **kwargs) -> List[int]:
raise NotImplementedError()
def save(self, output_path: str):
with open(os.path.join(output_path, "cnn_config.json"), "w") as fOut:
json.dump(self.get_config_dict(), fOut, indent=2)
torch.save(self.state_dict(), os.path.join(output_path, "pytorch_model.bin"))
def get_config_dict(self):
return {key: self.__dict__[key] for key in self.config_keys}
@staticmethod
def load(input_path: str):
with open(os.path.join(input_path, "cnn_config.json"), "r") as fIn:
config = json.load(fIn)
weights = torch.load(os.path.join(input_path, "pytorch_model.bin"), map_location=torch.device("cpu"))
model = CNN(**config)
model.load_state_dict(weights)
return model
|
import torch
from torch import nn
from typing import List
import os
import json
class CNN(nn.Module):
"""CNN-layer with multiple kernel-sizes over the word embeddings"""
def __init__(
self,
in_word_embedding_dimension: int,
out_channels: int = 256,
kernel_sizes: List[int] = [1, 3, 5],
stride_sizes: List[int] = None,
):
nn.Module.__init__(self)
self.config_keys = ["in_word_embedding_dimension", "out_channels", "kernel_sizes"]
self.in_word_embedding_dimension = in_word_embedding_dimension
self.out_channels = out_channels
self.kernel_sizes = kernel_sizes
self.embeddings_dimension = out_channels * len(kernel_sizes)
self.convs = nn.ModuleList()
in_channels = in_word_embedding_dimension
if stride_sizes is None:
stride_sizes = [1] * len(kernel_sizes)
for kernel_size, stride in zip(kernel_sizes, stride_sizes):
padding_size = int((kernel_size - 1) / 2)
conv = nn.Conv1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding_size,
)
self.convs.append(conv)
def forward(self, features):
token_embeddings = features["token_embeddings"]
token_embeddings = token_embeddings.transpose(1, -1)
vectors = [conv(token_embeddings) for conv in self.convs]
out = torch.cat(vectors, 1).transpose(1, -1)
features.update({"token_embeddings": out})
return features
def get_word_embedding_dimension(self) -> int:
return self.embeddings_dimension
def tokenize(self, text: str) -> List[int]:
raise NotImplementedError()
def save(self, output_path: str):
with open(os.path.join(output_path, "cnn_config.json"), "w") as fOut:
json.dump(self.get_config_dict(), fOut, indent=2)
torch.save(self.state_dict(), os.path.join(output_path, "pytorch_model.bin"))
def get_config_dict(self):
return {key: self.__dict__[key] for key in self.config_keys}
@staticmethod
def load(input_path: str):
with open(os.path.join(input_path, "cnn_config.json"), "r") as fIn:
config = json.load(fIn)
weights = torch.load(os.path.join(input_path, "pytorch_model.bin"), map_location=torch.device("cpu"))
model = CNN(**config)
model.load_state_dict(weights)
return model
|
from typing import Any, Optional
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
from docarray.typing.id import ID
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Tensor or an Optional Tensor type."""
return isinstance(type_, type) and issubclass(type_, AbstractTensor)
def is_tensor_union(type_: Any) -> bool:
"""Return True if type is a Union of type Tensors."""
is_union = is_union_type(type_)
if is_union is None:
return False
else:
return is_union and all(
(is_type_tensor(t) or issubclass(t, type(None))) for t in get_args(type_)
)
def change_cls_name(cls: type, new_name: str, scope: Optional[dict] = None) -> None:
"""Change the name of a class.
:param cls: the class to change the name of
:param new_name: the new name
:param scope: the scope in which the class is defined
"""
if scope:
scope[new_name] = cls
cls.__qualname__ = cls.__qualname__[: -len(cls.__name__)] + new_name
cls.__name__ = new_name
def safe_issubclass(x: type, a_tuple: type) -> bool:
"""
This is a modified version of the built-in 'issubclass' function to support non-class input.
Traditional 'issubclass' calls can result in a crash if the input is non-class type (e.g. list/tuple).
:param x: A class 'x'
:param a_tuple: A class, or a tuple of classes.
:return: A boolean value - 'True' if 'x' is a subclass of 'A_tuple', 'False' otherwise.
Note that if the origin of 'x' is a list or tuple, the function immediately returns 'False'.
"""
if (get_origin(x) in (list, tuple, dict, set)) or is_typevar(x) or x == ID:
return False
return issubclass(x, a_tuple)
|
from typing import Any, Optional
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Tensor or an Optional Tensor type."""
return isinstance(type_, type) and issubclass(type_, AbstractTensor)
def is_tensor_union(type_: Any) -> bool:
"""Return True if type is a Union of type Tensors."""
is_union = is_union_type(type_)
if is_union is None:
return False
else:
return is_union and all(
(is_type_tensor(t) or issubclass(t, type(None))) for t in get_args(type_)
)
def change_cls_name(cls: type, new_name: str, scope: Optional[dict] = None) -> None:
"""Change the name of a class.
:param cls: the class to change the name of
:param new_name: the new name
:param scope: the scope in which the class is defined
"""
if scope:
scope[new_name] = cls
cls.__qualname__ = cls.__qualname__[: -len(cls.__name__)] + new_name
cls.__name__ = new_name
def safe_issubclass(x: type, a_tuple: type) -> bool:
"""
This is a modified version of the built-in 'issubclass' function to support non-class input.
Traditional 'issubclass' calls can result in a crash if the input is non-class type (e.g. list/tuple).
:param x: A class 'x'
:param a_tuple: A class, or a tuple of classes.
:return: A boolean value - 'True' if 'x' is a subclass of 'A_tuple', 'False' otherwise.
Note that if the origin of 'x' is a list or tuple, the function immediately returns 'False'.
"""
if (get_origin(x) in (list, tuple, dict, set)) or is_typevar(x):
return False
return issubclass(x, a_tuple)
|
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TorchEmbedding, TorchTensor
def test_proto_tensor():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
tensor._to_node_protobuf()
def test_json_schema():
schema_json_of(TorchTensor)
def test_dump_json():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
orjson_dumps(tensor)
def test_unwrap():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
ndarray = tensor.unwrap()
assert not isinstance(ndarray, TorchTensor)
assert isinstance(tensor, TorchTensor)
assert isinstance(ndarray, torch.Tensor)
assert tensor.data_ptr() == ndarray.data_ptr()
assert (ndarray == torch.zeros(3, 224, 224)).all()
def test_parametrized():
# correct shape, single axis
tensor = parse_obj_as(TorchTensor[128], torch.zeros(128))
assert isinstance(tensor, TorchTensor)
assert isinstance(tensor, torch.Tensor)
assert tensor.shape == (128,)
# correct shape, multiple axis
tensor = parse_obj_as(TorchTensor[3, 224, 224], torch.zeros(3, 224, 224))
assert isinstance(tensor, TorchTensor)
assert isinstance(tensor, torch.Tensor)
assert tensor.shape == (3, 224, 224)
# wrong but reshapable shape
tensor = parse_obj_as(TorchTensor[3, 224, 224], torch.zeros(224, 3, 224))
assert isinstance(tensor, TorchTensor)
assert isinstance(tensor, torch.Tensor)
assert tensor.shape == (3, 224, 224)
# wrong and not reshapable shape
with pytest.raises(ValueError):
parse_obj_as(TorchTensor[3, 224, 224], torch.zeros(224, 224))
# test independent variable dimensions
tensor = parse_obj_as(TorchTensor[3, 'x', 'y'], torch.zeros(3, 224, 224))
assert isinstance(tensor, TorchTensor)
assert isinstance(tensor, torch.Tensor)
assert tensor.shape == (3, 224, 224)
tensor = parse_obj_as(TorchTensor[3, 'x', 'y'], torch.zeros(3, 60, 128))
assert isinstance(tensor, TorchTensor)
assert isinstance(tensor, torch.Tensor)
assert tensor.shape == (3, 60, 128)
with pytest.raises(ValueError):
parse_obj_as(TorchTensor[3, 'x', 'y'], torch.zeros(4, 224, 224))
with pytest.raises(ValueError):
parse_obj_as(TorchTensor[3, 'x', 'y'], torch.zeros(100, 1))
# test dependent variable dimensions
tensor = parse_obj_as(TorchTensor[3, 'x', 'x'], torch.zeros(3, 224, 224))
assert isinstance(tensor, TorchTensor)
assert isinstance(tensor, torch.Tensor)
assert tensor.shape == (3, 224, 224)
with pytest.raises(ValueError):
tensor = parse_obj_as(TorchTensor[3, 'x', 'x'], torch.zeros(3, 60, 128))
with pytest.raises(ValueError):
tensor = parse_obj_as(TorchTensor[3, 'x', 'x'], torch.zeros(3, 60))
@pytest.mark.parametrize('shape', [(3, 224, 224), (224, 224, 3)])
def test_parameterized_tensor_class_name(shape):
tensor = parse_obj_as(TorchTensor[3, 224, 224], torch.zeros(shape))
assert tensor.__class__.__name__ == 'TorchTensor[3, 224, 224]'
assert tensor.__class__.__qualname__ == 'TorchTensor[3, 224, 224]'
assert f'{tensor[0][0][0]}' == 'TorchTensor[3, 224, 224](0.)'
def test_torch_embedding():
# correct shape
tensor = parse_obj_as(TorchEmbedding[128], torch.zeros(128))
assert isinstance(tensor, TorchEmbedding)
assert isinstance(tensor, torch.Tensor)
assert tensor.shape == (128,)
# wrong shape at data setting time
with pytest.raises(ValueError):
parse_obj_as(TorchEmbedding[128], torch.zeros(256))
# illegal shape at class creation time
with pytest.raises(ValueError):
parse_obj_as(TorchEmbedding[128, 128], torch.zeros(128, 128))
def test_parametrized_subclass():
c1 = TorchTensor[128]
c2 = TorchTensor[128]
assert issubclass(c1, c2)
assert issubclass(c1, TorchTensor)
assert issubclass(c1, torch.Tensor)
assert not issubclass(c1, TorchTensor[256])
def test_parametrized_instance():
t = parse_obj_as(TorchTensor[128], torch.zeros(128))
assert isinstance(t, TorchTensor[128])
assert isinstance(t, TorchTensor)
assert isinstance(t, torch.Tensor)
assert not isinstance(t, TorchTensor[256])
def test_parametrized_equality():
t1 = parse_obj_as(TorchTensor[128], torch.zeros(128))
t2 = parse_obj_as(TorchTensor[128], torch.zeros(128))
t3 = parse_obj_as(TorchTensor[256], torch.zeros(256))
assert (t1 == t2).all()
assert not t1 == t3
def test_parametrized_operations():
t1 = parse_obj_as(TorchTensor[128], torch.zeros(128))
t2 = parse_obj_as(TorchTensor[128], torch.zeros(128))
t_result = t1 + t2
assert isinstance(t_result, torch.Tensor)
assert isinstance(t_result, TorchTensor)
assert isinstance(t_result, TorchTensor[128])
|
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TorchEmbedding, TorchTensor
def test_proto_tensor():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
tensor._to_node_protobuf()
def test_json_schema():
schema_json_of(TorchTensor)
def test_dump_json():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
orjson_dumps(tensor)
def test_unwrap():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
ndarray = tensor.unwrap()
assert not isinstance(ndarray, TorchTensor)
assert isinstance(tensor, TorchTensor)
assert isinstance(ndarray, torch.Tensor)
assert tensor.data_ptr() == ndarray.data_ptr()
assert (ndarray == torch.zeros(3, 224, 224)).all()
def test_parametrized():
# correct shape, single axis
tensor = parse_obj_as(TorchTensor[128], torch.zeros(128))
assert isinstance(tensor, TorchTensor)
assert isinstance(tensor, torch.Tensor)
assert tensor.shape == (128,)
# correct shape, multiple axis
tensor = parse_obj_as(TorchTensor[3, 224, 224], torch.zeros(3, 224, 224))
assert isinstance(tensor, TorchTensor)
assert isinstance(tensor, torch.Tensor)
assert tensor.shape == (3, 224, 224)
# wrong but reshapable shape
tensor = parse_obj_as(TorchTensor[3, 224, 224], torch.zeros(224, 3, 224))
assert isinstance(tensor, TorchTensor)
assert isinstance(tensor, torch.Tensor)
assert tensor.shape == (3, 224, 224)
# wrong and not reshapable shape
with pytest.raises(ValueError):
parse_obj_as(TorchTensor[3, 224, 224], torch.zeros(224, 224))
@pytest.mark.parametrize('shape', [(3, 224, 224), (224, 224, 3)])
def test_parameterized_tensor_class_name(shape):
tensor = parse_obj_as(TorchTensor[3, 224, 224], torch.zeros(shape))
assert tensor.__class__.__name__ == 'TorchTensor[3, 224, 224]'
assert tensor.__class__.__qualname__ == 'TorchTensor[3, 224, 224]'
assert f'{tensor[0][0][0]}' == 'TorchTensor[3, 224, 224](0.)'
def test_torch_embedding():
# correct shape
tensor = parse_obj_as(TorchEmbedding[128], torch.zeros(128))
assert isinstance(tensor, TorchEmbedding)
assert isinstance(tensor, torch.Tensor)
assert tensor.shape == (128,)
# wrong shape at data setting time
with pytest.raises(ValueError):
parse_obj_as(TorchEmbedding[128], torch.zeros(256))
# illegal shape at class creation time
with pytest.raises(ValueError):
parse_obj_as(TorchEmbedding[128, 128], torch.zeros(128, 128))
def test_parametrized_subclass():
c1 = TorchTensor[128]
c2 = TorchTensor[128]
assert issubclass(c1, c2)
assert issubclass(c1, TorchTensor)
assert issubclass(c1, torch.Tensor)
assert not issubclass(c1, TorchTensor[256])
def test_parametrized_instance():
t = parse_obj_as(TorchTensor[128], torch.zeros(128))
assert isinstance(t, TorchTensor[128])
assert isinstance(t, TorchTensor)
assert isinstance(t, torch.Tensor)
assert not isinstance(t, TorchTensor[256])
def test_parametrized_equality():
t1 = parse_obj_as(TorchTensor[128], torch.zeros(128))
t2 = parse_obj_as(TorchTensor[128], torch.zeros(128))
t3 = parse_obj_as(TorchTensor[256], torch.zeros(256))
assert (t1 == t2).all()
assert not t1 == t3
def test_parametrized_operations():
t1 = parse_obj_as(TorchTensor[128], torch.zeros(128))
t2 = parse_obj_as(TorchTensor[128], torch.zeros(128))
t_result = t1 + t2
assert isinstance(t_result, torch.Tensor)
assert isinstance(t_result, TorchTensor)
assert isinstance(t_result, TorchTensor[128])
|
import asyncio
import numpy as np
from typing import Any, List, Literal, Optional
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.bridge.pydantic import Field, PrivateAttr, ConfigDict
from fastembed import TextEmbedding
class FastEmbedEmbedding(BaseEmbedding):
"""
Qdrant FastEmbedding models.
FastEmbed is a lightweight, fast, Python library built for embedding generation.
See more documentation at:
* https://github.com/qdrant/fastembed/
* https://qdrant.github.io/fastembed/.
To use this class, you must install the `fastembed` Python package.
`pip install fastembed`
Example:
from llama_index.embeddings.fastembed import FastEmbedEmbedding
fastembed = FastEmbedEmbedding()
"""
model_config = ConfigDict(
protected_namespaces=("pydantic_model_",),
arbitrary_types_allowed=True,
use_attribute_docstrings=True,
)
model_name: str = Field(
default="BAAI/bge-small-en-v1.5",
description=(
"Name of the FastEmbedding model to use. "
"Find the list of supported models at "
"https://qdrant.github.io/fastembed/examples/Supported_Models/"
),
)
cache_dir: Optional[str] = Field(
default=None,
description="The path to the cache directory. Defaults to fastembed_cache in the system's temp directory.",
)
threads: Optional[int] = Field(
default=None,
description="The number of threads single onnxruntime session can use. Defaults to None.",
)
doc_embed_type: Literal["default", "passage"] = Field(
default="default",
description="Type of embedding method to use for documents. Available options are 'default' and 'passage'.",
)
providers: Optional[List[str]] = Field(
default=None, description="The ONNX providers to use for the embedding model."
)
_model: TextEmbedding = PrivateAttr()
def __init__(
self,
model_name: str = "BAAI/bge-small-en-v1.5",
cache_dir: Optional[str] = None,
threads: Optional[int] = None,
doc_embed_type: Literal["default", "passage"] = "default",
providers: Optional[List[str]] = None,
**kwargs: Any,
):
super().__init__(
model_name=model_name,
threads=threads,
doc_embed_type=doc_embed_type,
providers=providers,
cache_dir=cache_dir,
**kwargs,
)
self._model = TextEmbedding(
model_name=model_name,
cache_dir=cache_dir,
threads=threads,
providers=providers,
**kwargs,
)
@classmethod
def class_name(cls) -> str:
return "FastEmbedEmbedding"
def _get_text_embedding(self, text: str) -> List[float]:
return self._get_text_embeddings([text])[0]
async def _aget_text_embedding(self, text: str) -> List[float]:
return await asyncio.to_thread(self._get_text_embedding, text)
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
embeddings: List[np.ndarray]
if self.doc_embed_type == "passage":
embeddings = list(self._model.passage_embed(texts))
else:
embeddings = list(self._model.embed(texts))
return [embedding.tolist() for embedding in embeddings]
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
return await asyncio.to_thread(self._get_text_embeddings, texts)
def _get_query_embedding(self, query: str) -> List[float]:
query_embeddings: list[np.ndarray] = list(self._model.query_embed(query))
return query_embeddings[0].tolist()
async def _aget_query_embedding(self, query: str) -> List[float]:
return await asyncio.to_thread(self._get_query_embedding, query)
|
from typing import Any, List, Literal, Optional
import numpy as np
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from fastembed import TextEmbedding
class FastEmbedEmbedding(BaseEmbedding):
"""
Qdrant FastEmbedding models.
FastEmbed is a lightweight, fast, Python library built for embedding generation.
See more documentation at:
* https://github.com/qdrant/fastembed/
* https://qdrant.github.io/fastembed/.
To use this class, you must install the `fastembed` Python package.
`pip install fastembed`
Example:
from llama_index.embeddings.fastembed import FastEmbedEmbedding
fastembed = FastEmbedEmbedding()
"""
model_name: str = Field(
"BAAI/bge-small-en-v1.5",
description="Name of the FastEmbedding model to use.\n"
"Defaults to 'BAAI/bge-small-en-v1.5'.\n"
"Find the list of supported models at "
"https://qdrant.github.io/fastembed/examples/Supported_Models/",
)
max_length: int = Field(
512,
description="The maximum number of tokens. Defaults to 512.\n"
"Unknown behavior for values > 512.",
)
cache_dir: Optional[str] = Field(
None,
description="The path to the cache directory.\n"
"Defaults to `local_cache` in the parent directory",
)
threads: Optional[int] = Field(
None,
description="The number of threads single onnxruntime session can use.\n"
"Defaults to None",
)
doc_embed_type: Literal["default", "passage"] = Field(
"default",
description="Type of embedding method to use for documents.\n"
"Available options are 'default' and 'passage'.",
)
providers: Optional[List[str]] = Field(
default=None,
description="The ONNX providers to use for the embedding model.",
)
_model: Any = PrivateAttr()
@classmethod
def class_name(self) -> str:
return "FastEmbedEmbedding"
def __init__(
self,
model_name: Optional[str] = "BAAI/bge-small-en-v1.5",
max_length: Optional[int] = 512,
cache_dir: Optional[str] = None,
threads: Optional[int] = None,
doc_embed_type: Literal["default", "passage"] = "default",
providers: Optional[List[str]] = None,
**kwargs: Any,
):
super().__init__(
model_name=model_name,
max_length=max_length,
threads=threads,
doc_embed_type=doc_embed_type,
providers=providers,
**kwargs,
)
self._model = TextEmbedding(
model_name=model_name,
max_length=max_length,
cache_dir=cache_dir,
threads=threads,
providers=providers,
**kwargs,
)
def _get_text_embedding(self, text: str) -> List[float]:
embeddings: List[np.ndarray]
if self.doc_embed_type == "passage":
embeddings = list(self._model.passage_embed(text))
else:
embeddings = list(self._model.embed(text))
return embeddings[0].tolist()
def _get_query_embedding(self, query: str) -> List[float]:
query_embeddings: np.ndarray = next(self._model.query_embed(query))
return query_embeddings.tolist()
async def _aget_query_embedding(self, query: str) -> List[float]:
return self._get_query_embedding(query)
|
import io
import logging
from enum import Enum
import replicate
import replicate.exceptions
import requests
from replicate.helpers import FileOutput
from backend.data.graph import Graph
from backend.util.settings import Settings
logger = logging.getLogger(__name__)
class ImageSize(str, Enum):
LANDSCAPE = "1024x768"
class ImageStyle(str, Enum):
DIGITAL_ART = "digital art"
async def generate_agent_image(agent: Graph) -> io.BytesIO:
"""
Generate an image for an agent using Flux model via Replicate API.
Args:
agent (Graph): The agent to generate an image for
Returns:
io.BytesIO: The generated image as bytes
"""
try:
settings = Settings()
if not settings.secrets.replicate_api_key:
raise ValueError("Missing Replicate API key in settings")
# Construct prompt from agent details
prompt = f"Create a visually engaging app store thumbnail for the AI agent that highlights what it does in a clear and captivating way:\n- **Name**: {agent.name}\n- **Description**: {agent.description}\nFocus on showcasing its core functionality with an appealing design."
# Set up Replicate client
client = replicate.Client(api_token=settings.secrets.replicate_api_key)
# Model parameters
input_data = {
"prompt": prompt,
"width": 1024,
"height": 768,
"aspect_ratio": "4:3",
"output_format": "jpg",
"output_quality": 90,
"num_inference_steps": 30,
"guidance": 3.5,
"negative_prompt": "blurry, low quality, distorted, deformed",
"disable_safety_checker": True,
}
try:
# Run model
output = client.run("black-forest-labs/flux-1.1-pro", input=input_data)
# Depending on the model output, extract the image URL or bytes
# If the output is a list of FileOutput or URLs
if isinstance(output, list) and output:
if isinstance(output[0], FileOutput):
image_bytes = output[0].read()
else:
# If it's a URL string, fetch the image bytes
result_url = output[0]
response = requests.get(result_url)
response.raise_for_status()
image_bytes = response.content
elif isinstance(output, FileOutput):
image_bytes = output.read()
elif isinstance(output, str):
# Output is a URL
response = requests.get(output)
response.raise_for_status()
image_bytes = response.content
else:
raise RuntimeError("Unexpected output format from the model.")
return io.BytesIO(image_bytes)
except replicate.exceptions.ReplicateError as e:
if e.status == 401:
raise RuntimeError("Invalid Replicate API token") from e
raise RuntimeError(f"Replicate API error: {str(e)}") from e
except Exception as e:
logger.exception("Failed to generate agent image")
raise RuntimeError(f"Image generation failed: {str(e)}")
|
import io
import logging
from enum import Enum
import replicate
import replicate.exceptions
import requests
from replicate.helpers import FileOutput
from backend.data.graph import Graph
from backend.util.settings import Settings
logger = logging.getLogger(__name__)
class ImageSize(str, Enum):
LANDSCAPE = "1024x768"
class ImageStyle(str, Enum):
DIGITAL_ART = "digital art"
async def generate_agent_image(agent: Graph) -> io.BytesIO:
"""
Generate an image for an agent using Flux model via Replicate API.
Args:
agent (Graph): The agent to generate an image for
Returns:
io.BytesIO: The generated image as bytes
"""
try:
settings = Settings()
if not settings.secrets.replicate_api_key:
raise ValueError("Missing Replicate API key in settings")
# Construct prompt from agent details
prompt = f"App store image for AI agent that gives a cool visual representation of what the agent does: - {agent.name} - {agent.description}"
# Set up Replicate client
client = replicate.Client(api_token=settings.secrets.replicate_api_key)
# Model parameters
input_data = {
"prompt": prompt,
"width": 1024,
"height": 768,
"aspect_ratio": "4:3",
"output_format": "jpg",
"output_quality": 90,
"num_inference_steps": 30,
"guidance": 3.5,
"negative_prompt": "blurry, low quality, distorted, deformed",
"disable_safety_checker": True,
}
try:
# Run model
output = client.run("black-forest-labs/flux-pro", input=input_data)
# Depending on the model output, extract the image URL or bytes
# If the output is a list of FileOutput or URLs
if isinstance(output, list) and output:
if isinstance(output[0], FileOutput):
image_bytes = output[0].read()
else:
# If it's a URL string, fetch the image bytes
result_url = output[0]
response = requests.get(result_url)
response.raise_for_status()
image_bytes = response.content
elif isinstance(output, FileOutput):
image_bytes = output.read()
elif isinstance(output, str):
# Output is a URL
response = requests.get(output)
response.raise_for_status()
image_bytes = response.content
else:
raise RuntimeError("Unexpected output format from the model.")
return io.BytesIO(image_bytes)
except replicate.exceptions.ReplicateError as e:
if e.status == 401:
raise RuntimeError("Invalid Replicate API token") from e
raise RuntimeError(f"Replicate API error: {str(e)}") from e
except Exception as e:
logger.exception("Failed to generate agent image")
raise RuntimeError(f"Image generation failed: {str(e)}")
|
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