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Running
on
Zero
File size: 5,766 Bytes
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from copy import deepcopy
from collections import defaultdict
from dataclasses import dataclass
from typing import Dict, Union, Tuple, List
import numpy as np
from numpy import ndarray
import os
from random import shuffle
from box import Box
from torch.onnx.symbolic_opset11 import index_copy
from .spec import ConfigSpec
@dataclass
class DatapathConfig(ConfigSpec):
'''
Config to handle input data paths.
'''
# root
input_dataset_dir: str
# use proportion data sampling
use_prob: bool
# cls: [(path_1, p_1), ...]
data_path: Dict[str, List[Tuple[str, float]]]
# how many files to return when using data sampling
num_files: Union[int, None]
@classmethod
def from_args(cls, **kwargs) -> 'DatapathConfig':
'''
Make a temporary datapath from user inputs.
'''
input = kwargs.get('input', None)
output = kwargs.get('output', None)
recursive = kwargs.get('recursive', False)
@classmethod
def parse(cls, config) -> 'DatapathConfig':
cls.check_keys(config)
return DatapathConfig(
input_dataset_dir=config.input_dataset_dir,
use_prob=config.get('use_prob', True),
data_path=config.data_path,
num_files=config.get('num_files', None),
)
def split_by_cls(self) -> Dict[str, 'DatapathConfig']:
res: Dict[str, DatapathConfig] = {}
for cls in self.data_path:
res[cls] = deepcopy(self)
res[cls].data_path = {cls: self.data_path[cls]}
return res
class Datapath():
def __init__(
self,
config: Union[DatapathConfig, None]=None,
files: Union[List[str], None]=None,
cls: Union[str, None]=None,
):
if config is not None:
self.config = config
self.file_list = []
cls_probs_first = []
cls_first = []
self.files_by_class: Dict[str, List[Dict]] = defaultdict(list)
self.class_positions: Dict[str, List[int]] = defaultdict(list)
self.cls_probs_second: Dict[str, ndarray] = defaultdict(List)
for cls in self.config.data_path:
prob = 0.
probs_second = []
for (path, p) in self.config.data_path[cls]:
prob += p
probs_second.append(p)
with open(path, 'r') as f:
file_items = []
missing = 0
for l in f.readlines():
raw_data_path = os.path.join(self.config.input_dataset_dir, l.strip(), 'raw_data.npz')
if not os.path.exists(raw_data_path):
missing += 1
continue
file_items.append({
'cls': cls,
'path': os.path.join(self.config.input_dataset_dir, l.strip()),
'prob': p
})
assert len(file_items) > 0, f"files in {path} are all missing! root: {self.config.input_dataset_dir}"
if missing > 0:
print(f"\033[31m{cls}: {missing} missing files\033[0m")
self.files_by_class[cls].append(file_items)
self.class_positions[cls].append(0)
self.file_list.extend(file_items)
probs_second = np.array(probs_second)
self.cls_probs_second[cls] = probs_second / probs_second.sum()
cls_first.append(cls)
cls_probs_first.append(prob)
cls_probs_first = np.array(cls_probs_first)
self.cls_first: List[str] = cls_first
self.cls_probs_first: Dict[str, List[float]] = cls_probs_first / cls_probs_first.sum()
elif files is not None:
if cls is None:
cls = 'inference'
self.file_list = [{'cls': cls, 'path': file} for file in files]
cls_probs_first = np.array([1.])
cls_first = []
self.files_by_class: Dict[str, List[Dict]] = {cls: self.file_list.copy()}
self.class_positions: Dict[str, List[int]] = {cls: [0]}
self.cls_probs_second: Dict[str, ndarray] = {cls: np.array([1.])}
self.config = Box({'use_prob': False})
else:
assert(0)
def __len__(self):
if self.config.use_prob:
assert self.config.num_files is not None, 'num_files is not specified'
return self.config.num_files
return len(self.file_list)
def __getitem__(self, index) -> Tuple[str, str]:
if self.config.use_prob:
# first sample a class
cls = np.random.choice(self.cls_first, p=self.cls_probs_first)
# second sample in this class
idx = np.random.choice(len(self.files_by_class[cls]), p=self.cls_probs_second[cls])
# get the current position
pos = self.class_positions[cls][idx]
files = self.files_by_class[cls][idx]
# get the item andd update position
item = files[pos]
self.class_positions[cls][idx] = (pos + 1) % len(files)
if (pos + 1) % len(files) == 0:
shuffle(self.files_by_class[cls][idx])
else:
item = self.file_list[index]
return (item['cls'], item['path'])
def get_data(self) -> List[Tuple[str, str]]:
return [self[i] for i in range(len(self))] |