-
Notifications
You must be signed in to change notification settings - Fork 300
/
common.py
387 lines (345 loc) · 11.9 KB
/
common.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
import os
import random
import torch
import numpy as np
import json
import pickle
import torch.nn as nn
from collections import OrderedDict
from pathlib import Path
import logging
logger = logging.getLogger()
def print_config(config):
info = "Running with the following configs:\n"
for k, v in config.items():
info += f"\t{k} : {str(v)}\n"
print("\n" + info + "\n")
return
def init_logger(log_file=None, log_file_level=logging.NOTSET):
'''
Example:
>>> init_logger(log_file)
>>> logger.info("abc'")
'''
if isinstance(log_file,Path):
log_file = str(log_file)
log_format = logging.Formatter(fmt='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S')
logger = logging.getLogger()
logger.setLevel(logging.INFO)
console_handler = logging.StreamHandler()
console_handler.setFormatter(log_format)
logger.handlers = [console_handler]
if log_file and log_file != '':
file_handler = logging.FileHandler(log_file)
file_handler.setLevel(log_file_level)
# file_handler.setFormatter(log_format)
logger.addHandler(file_handler)
return logger
def seed_everything(seed=1029):
'''
设置整个开发环境的seed
:param seed:
:param device:
:return:
'''
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# some cudnn methods can be random even after fixing the seed
# unless you tell it to be deterministic
torch.backends.cudnn.deterministic = True
def prepare_device(n_gpu_use):
"""
setup GPU device if available, move model into configured device
# 如果n_gpu_use为数字,则使用range生成list
# 如果输入的是一个list,则默认使用list[0]作为controller
"""
if not n_gpu_use:
device_type = 'cpu'
else:
n_gpu_use = n_gpu_use.split(",")
device_type = f"cuda:{n_gpu_use[0]}"
n_gpu = torch.cuda.device_count()
if len(n_gpu_use) > 0 and n_gpu == 0:
logger.warning("Warning: There\'s no GPU available on this machine, training will be performed on CPU.")
device_type = 'cpu'
if len(n_gpu_use) > n_gpu:
msg = f"Warning: The number of GPU\'s configured to use is {n_gpu_use}, but only {n_gpu} are available on this machine."
logger.warning(msg)
n_gpu_use = range(n_gpu)
device = torch.device(device_type)
list_ids = n_gpu_use
return device, list_ids
def model_device(n_gpu, model):
'''
判断环境 cpu还是gpu
支持单机多卡
:param n_gpu:
:param model:
:return:
'''
device, device_ids = prepare_device(n_gpu)
if len(device_ids) > 1:
logger.info(f"current {len(device_ids)} GPUs")
model = torch.nn.DataParallel(model, device_ids=device_ids)
if len(device_ids) == 1:
os.environ['CUDA_VISIBLE_DEVICES'] = str(device_ids[0])
model = model.to(device)
return model, device
def restore_checkpoint(resume_path, model=None):
'''
加载模型
:param resume_path:
:param model:
:param optimizer:
:return:
注意: 如果是加载Bert模型的话,需要调整,不能使用该模式
可以使用模块自带的Bert_model.from_pretrained(state_dict = your save state_dict)
'''
if isinstance(resume_path, Path):
resume_path = str(resume_path)
checkpoint = torch.load(resume_path)
best = checkpoint['best']
start_epoch = checkpoint['epoch'] + 1
states = checkpoint['state_dict']
if isinstance(model, nn.DataParallel):
model.module.load_state_dict(states)
else:
model.load_state_dict(states)
return [model,best,start_epoch]
def save_pickle(data, file_path):
'''
保存成pickle文件
:param data:
:param file_name:
:param pickle_path:
:return:
'''
if isinstance(file_path, Path):
file_path = str(file_path)
with open(file_path, 'wb') as f:
pickle.dump(data, f)
def load_pickle(input_file):
'''
读取pickle文件
:param pickle_path:
:param file_name:
:return:
'''
with open(str(input_file), 'rb') as f:
data = pickle.load(f)
return data
def save_json(data, file_path):
'''
保存成json文件
:param data:
:param json_path:
:param file_name:
:return:
'''
if not isinstance(file_path, Path):
file_path = Path(file_path)
# if isinstance(data,dict):
# data = json.dumps(data)
with open(str(file_path), 'w') as f:
json.dump(data, f)
def save_numpy(data, file_path):
'''
保存成.npy文件
:param data:
:param file_path:
:return:
'''
if not isinstance(file_path, Path):
file_path = Path(file_path)
np.save(str(file_path),data)
def load_numpy(file_path):
'''
加载.npy文件
:param file_path:
:return:
'''
if not isinstance(file_path, Path):
file_path = Path(file_path)
np.load(str(file_path))
def load_json(file_path):
'''
加载json文件
:param json_path:
:param file_name:
:return:
'''
if not isinstance(file_path, Path):
file_path = Path(file_path)
with open(str(file_path), 'r') as f:
data = json.load(f)
return data
def json_to_text(file_path,data):
'''
将json list写入text文件中
:param file_path:
:param data:
:return:
'''
if not isinstance(file_path, Path):
file_path = Path(file_path)
with open(str(file_path), 'w') as fw:
for line in data:
line = json.dumps(line, ensure_ascii=False)
fw.write(line + '\n')
def save_model(model, model_path):
""" 存储不含有显卡信息的state_dict或model
:param model:
:param model_name:
:param only_param:
:return:
"""
if isinstance(model_path, Path):
model_path = str(model_path)
if isinstance(model, nn.DataParallel):
model = model.module
state_dict = model.state_dict()
for key in state_dict:
state_dict[key] = state_dict[key].cpu()
torch.save(state_dict, model_path)
def load_model(model, model_path):
'''
加载模型
:param model:
:param model_name:
:param model_path:
:param only_param:
:return:
'''
if isinstance(model_path, Path):
model_path = str(model_path)
logging.info(f"loading model from {str(model_path)} .")
states = torch.load(model_path)
state = states['state_dict']
if isinstance(model, nn.DataParallel):
model.module.load_state_dict(state)
else:
model.load_state_dict(state)
return model
class AverageMeter(object):
'''
computes and stores the average and current value
Example:
>>> loss = AverageMeter()
>>> for step,batch in enumerate(train_data):
>>> pred = self.model(batch)
>>> raw_loss = self.metrics(pred,target)
>>> loss.update(raw_loss.item(),n = 1)
>>> cur_loss = loss.avg
'''
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def summary(model, *inputs, batch_size=-1, show_input=True):
'''
打印模型结构信息
:param model:
:param inputs:
:param batch_size:
:param show_input:
:return:
Example:
>>> print("model summary info: ")
>>> for step,batch in enumerate(train_data):
>>> summary(self.model,*batch,show_input=True)
>>> break
'''
def register_hook(module):
def hook(module, input, output=None):
class_name = str(module.__class__).split(".")[-1].split("'")[0]
module_idx = len(summary)
m_key = f"{class_name}-{module_idx + 1}"
summary[m_key] = OrderedDict()
summary[m_key]["input_shape"] = list(input[0].size())
summary[m_key]["input_shape"][0] = batch_size
if show_input is False and output is not None:
if isinstance(output, (list, tuple)):
for out in output:
if isinstance(out, torch.Tensor):
summary[m_key]["output_shape"] = [
[-1] + list(out.size())[1:]
][0]
else:
summary[m_key]["output_shape"] = [
[-1] + list(out[0].size())[1:]
][0]
else:
summary[m_key]["output_shape"] = list(output.size())
summary[m_key]["output_shape"][0] = batch_size
params = 0
if hasattr(module, "weight") and hasattr(module.weight, "size"):
params += torch.prod(torch.LongTensor(list(module.weight.size())))
summary[m_key]["trainable"] = module.weight.requires_grad
if hasattr(module, "bias") and hasattr(module.bias, "size"):
params += torch.prod(torch.LongTensor(list(module.bias.size())))
summary[m_key]["nb_params"] = params
if (not isinstance(module, nn.Sequential) and not isinstance(module, nn.ModuleList) and not (module == model)):
if show_input is True:
hooks.append(module.register_forward_pre_hook(hook))
else:
hooks.append(module.register_forward_hook(hook))
# create properties
summary = OrderedDict()
hooks = []
# register hook
model.apply(register_hook)
model(*inputs)
# remove these hooks
for h in hooks:
h.remove()
print("-----------------------------------------------------------------------")
if show_input is True:
line_new = f"{'Layer (type)':>25} {'Input Shape':>25} {'Param #':>15}"
else:
line_new = f"{'Layer (type)':>25} {'Output Shape':>25} {'Param #':>15}"
print(line_new)
print("=======================================================================")
total_params = 0
total_output = 0
trainable_params = 0
for layer in summary:
# input_shape, output_shape, trainable, nb_params
if show_input is True:
line_new = "{:>25} {:>25} {:>15}".format(
layer,
str(summary[layer]["input_shape"]),
"{0:,}".format(summary[layer]["nb_params"]),
)
else:
line_new = "{:>25} {:>25} {:>15}".format(
layer,
str(summary[layer]["output_shape"]),
"{0:,}".format(summary[layer]["nb_params"]),
)
total_params += summary[layer]["nb_params"]
if show_input is True:
total_output += np.prod(summary[layer]["input_shape"])
else:
total_output += np.prod(summary[layer]["output_shape"])
if "trainable" in summary[layer]:
if summary[layer]["trainable"] == True:
trainable_params += summary[layer]["nb_params"]
print(line_new)
print("=======================================================================")
print(f"Total params: {total_params:0,}")
print(f"Trainable params: {trainable_params:0,}")
print(f"Non-trainable params: {(total_params - trainable_params):0,}")
print("-----------------------------------------------------------------------")