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# -*- coding: utf-8 -*-
import datetime
import os
import subprocess
import fsspec
import torch
from trainer.logger import logger
def to_cuda(x: torch.Tensor) -> torch.Tensor:
if x is None:
return None
if torch.is_tensor(x):
x = x.contiguous()
if torch.cuda.is_available():
x = x.cuda(non_blocking=True)
return x
def get_cuda():
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
return use_cuda, device
def get_git_branch():
try:
out = subprocess.check_output(["git", "branch"]).decode("utf8")
current = next(line for line in out.split("\n") if line.startswith("*"))
current.replace("* ", "")
except subprocess.CalledProcessError:
current = "inside_docker"
except FileNotFoundError:
current = "unknown"
return current
def get_commit_hash():
"""https://stackoverflow.com/questions/14989858/get-the-current-git-hash-in-a-python-script"""
try:
commit = subprocess.check_output(["git", "rev-parse", "--short", "HEAD"]).decode().strip()
# Not copying .git folder into docker container
except (subprocess.CalledProcessError, FileNotFoundError):
commit = "0000000"
return commit
def get_experiment_folder_path(root_path, model_name):
"""Get an experiment folder path with the current date and time"""
date_str = datetime.datetime.now().strftime("%B-%d-%Y_%I+%M%p")
commit_hash = get_commit_hash()
output_folder = os.path.join(root_path, model_name + "-" + date_str + "-" + commit_hash)
return output_folder
def remove_experiment_folder(experiment_path):
"""Check folder if there is a checkpoint, otherwise remove the folder"""
fs = fsspec.get_mapper(experiment_path).fs
checkpoint_files = fs.glob(experiment_path + "/*.pth")
if not checkpoint_files:
if fs.exists(experiment_path):
fs.rm(experiment_path, recursive=True)
logger.info(" ! Run is removed from %s", experiment_path)
else:
logger.info(" ! Run is kept in %s", experiment_path)
def count_parameters(model):
r"""Count number of trainable parameters in a network"""
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def set_partial_state_dict(model_dict, checkpoint_state, c):
# Partial initialization: if there is a mismatch with new and old layer, it is skipped.
for k, v in checkpoint_state.items():
if k not in model_dict:
logger.info(" | > Layer missing in the model definition: %s", k)
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in checkpoint_state.items() if k in model_dict}
# 2. filter out different size layers
pretrained_dict = {k: v for k, v in pretrained_dict.items() if v.numel() == model_dict[k].numel()}
# 3. skip reinit layers
if c.has("reinit_layers") and c.reinit_layers is not None:
for reinit_layer_name in c.reinit_layers:
pretrained_dict = {k: v for k, v in pretrained_dict.items() if reinit_layer_name not in k}
# 4. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
logger.info(" | > %i / %i layers are restored.", len(pretrained_dict), len(model_dict))
return model_dict
class KeepAverage:
def __init__(self):
self.avg_values = {}
self.iters = {}
def __getitem__(self, key):
return self.avg_values[key]
def items(self):
return self.avg_values.items()
def add_value(self, name, init_val=0, init_iter=0):
self.avg_values[name] = init_val
self.iters[name] = init_iter
def update_value(self, name, value, weighted_avg=False):
if name not in self.avg_values:
# add value if not exist before
self.add_value(name, init_val=value)
else:
# else update existing value
if weighted_avg:
self.avg_values[name] = 0.99 * self.avg_values[name] + 0.01 * value
self.iters[name] += 1
else:
self.avg_values[name] = self.avg_values[name] * self.iters[name] + value
self.iters[name] += 1
self.avg_values[name] /= self.iters[name]
def add_values(self, name_dict):
for key, value in name_dict.items():
self.add_value(key, init_val=value)
def update_values(self, value_dict):
for key, value in value_dict.items():
self.update_value(key, value)
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