-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathtrainer.py
244 lines (211 loc) · 11.1 KB
/
trainer.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
import torch
import numpy as np
import os
import math
import shutil
import sys
from torchvision.utils import make_grid
from data.datasets import inv_normalize
device_ids = [0, 1, 2]
def fit(train_loader, val_loader, model, loss_fn, optimizer, scheduler, writer,
metrics=[], exp_decay=math.exp(-0.01), args=None):
"""
Loaders, model, loss function and metrics should work together for a given task,
i.e. The model should be able to process data output of loaders,
loss function should process target output of loaders and outputs from the model
Examples: Classification: batch loader, classification model, NLL loss, accuracy metric
Siamese network: Siamese loader, siamese model, contrastive loss
Online triplet learning: batch loader, embedding model, online triplet loss
"""
min_loss = float('inf')
start_epoch = 0
parallel_model = model
if args.cuda:
parallel_model = torch.nn.DataParallel(model, device_ids=device_ids, dim=0).cuda() # Encapsulate the model
if args.resume:
args.resume = os.path.join(args.directory, args.resume)
if os.path.isfile(args.resume):
# load checkpoint weights and update model and optimizer
print(">> Loading checkpoint:\n>> '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch']
min_loss = checkpoint['min_loss']
print('min_loss:', min_loss)
parallel_model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print(">>>> loaded checkpoint:\n>>>> '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
# important not to forget scheduler updating
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=exp_decay, last_epoch=checkpoint['epoch']-1)
else:
print(">> No checkpoint found at '{}'".format(args.resume))
epochs = args.epochs
for epoch in range(start_epoch, epochs):
val_loss, metrics = test_epoch(val_loader, parallel_model, loss_fn, metrics, writer, args)
val_loss /= len(val_loader)
message += '\nEpoch: {}/{}. Validation set: Average loss: {:.4f}'.format(epoch + 1, epochs,
val_loss)
for metric in metrics:
message += '\t{}: {}'.format(metric.name(), metric.value())
print('minloss:', min_loss, 'current test mean loss:', val_loss)
print('metric message:', message)
is_best = val_loss < min_loss
min_loss = min(val_loss, min_loss)
save_checkpoint({
'epoch': epoch + 1,
'state_dict': parallel_model.state_dict(),
'min_loss': min_loss,
'optimizer': optimizer.state_dict(),
}, is_best, args.directory)
scheduler.step()
print("curr learning rate:", scheduler.get_lr())
# Train stage
train_loss, metrics = train_epoch(train_loader, parallel_model, loss_fn, optimizer,
metrics, writer, args, epoch)
message = 'Epoch: {}/{}. Train set: Average loss: {:.4f}'.format(epoch + 1, epochs, train_loss)
for metric in metrics:
message += '\t{}: {}'.format(metric.name(), metric.value())
def save_checkpoint(state, is_best, directory):
filename = os.path.join(directory, 'model_epoch%d.pth.tar' % state['epoch'])
torch.save(state, filename)
print("epoch model saved")
if is_best:
filename_best = os.path.join(directory, 'model_best.pth.tar')
shutil.copyfile(filename, filename_best)
print("global best model updated")
def train_epoch(train_loader, model, loss_fn, optimizer, metrics, writer, args, epoch):
for metric in metrics:
metric.reset()
model.train()
losses = []
total_loss = 0
for batch_idx, (data, target) in enumerate(train_loader):
target = target if len(target) > 0 else None
if not type(data) in (tuple, list):
data = (data,)
if args.cuda:
data = tuple(d.cuda() for d in data)
if target is not None:
target = target.cuda()
optimizer.zero_grad()
outputs = model(*data)
if type(outputs) not in (tuple, list):
outputs = (outputs,)
loss_inputs = outputs
if target is not None:
target = (target,)
loss_inputs += target
[loss_outputs, distance_pos, distance_neg, pairs, len_pairs] = loss_fn(*loss_inputs)
loss = loss_outputs
# print("loss: ", loss)
losses.append(loss.item())
total_loss += loss.item()
loss.backward()
optimizer.step()
for metric in metrics:
metric(outputs, target, len_pairs)
if batch_idx % args.log_interval == 0:
message = 'Train: [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
batch_idx * len(data[0]), len(train_loader.dataset),
100. * batch_idx / len(train_loader), np.mean(losses))
for metric in metrics:
message += '\t{}: {}'.format(metric.name(), metric.value())
print('metric message:', message)
losses = []
if args.loss.startswith('OnlineContrastive'):
indice_show = np.random.choice(range(pair[0].shape[0]), args.train_show_pairs)
writer.add_image('position image',
make_grid(inv_normalize(data[0][pair[0][indice_show]], args.train_show_pairs*2)))
writer.add_image('negative image',
make_grid(inv_normalize(data[0][pair[1][indice_show]], args.train_show_pairs*2)))
if 'cos' in args.loss:
writer.add_scalars('data/TRAIN_SIA_COS_DISTANCE', {
'TRAIN_COS_POS': distance_pos,
'TRAIN_COS_NEG': distance_neg
}, global_step=epoch*len(train_loader) + batch_idx)
elif 'Euc' in args.loss:
writer.add_scalars('data/TRAIN_SIA_EUC_DISTANCE', {
'TRAIN_EUC_POS': distance_pos,
'TRAIN_EUC_POS': distance_neg
}, global_step=epoch*len(train_loader) + batch_idx)
elif args.loss == 'OnlineTriplet':
indice_show = np.random.choice(range(pairs.shape[0]), args.train_show_pairs)
# print('inv_normalize(data[0][pairs[indice_show, 0]]:', data[0][pairs[indice_show, 0]].shape, inv_normalize(data[0][pairs[indice_show, 0]]))
# x =
# print("fdsafs:", x.size(), args.train_show_pairs)
# y = make_grid(x, nrow=args.train_show_pairs, padding=0)
# print(y.size())
# a = input()
writer.add_image('anchors image', make_grid(inv_normalize(data[0][pairs[indice_show, 0]]), nrow=args.train_show_pairs))
writer.add_image('position image', make_grid(inv_normalize(data[0][pairs[indice_show, 1]]), nrow=args.train_show_pairs))
writer.add_image('negative image', make_grid(inv_normalize(data[0][pairs[indice_show, 2]]), nrow=args.train_show_pairs))
writer.add_scalars('data/TRAIN_TRI_EUC_DISTANCE', {
'TRAIN_EUC_POS': distance_pos,
'TRAIN_EUC_NEG': distance_neg
}, global_step=epoch*len(train_loader) + batch_idx)
return total_loss, metrics
def test_epoch(val_loader, model, loss_fn, metrics, writer, args):
with torch.no_grad():
for metric in metrics:
metric.reset()
model.eval()
val_loss = 0
for batch_idx, (data, target) in enumerate(val_loader):
target = target if len(target) > 0 else None
if not type(data) in (tuple, list):
data = (data,)
if args.cuda:
data = tuple(d.cuda() for d in data)
if target is not None:
target = target.cuda()
outputs = model(*data)
if type(outputs) not in (tuple, list):
outputs = (outputs,)
loss_inputs = outputs
if target is not None:
target = (target,)
loss_inputs += target
[loss_outputs, distance_pos, distance_neg, pairs, len_pairs] = loss_fn(*loss_inputs)
loss = loss_outputs
val_loss += loss.item()
for metric in metrics:
metric(outputs, target, len_pairs)
if batch_idx % args.log_interval == 0:
# message = 'Valid: [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
# batch_idx * len(data[0]), len(val_loader.dataset),
# 100. * batch_idx / len(val_loader), np.mean(losses))
# for metric in metrics:
# message += '\t{}: {}'.format(metric.name(), metric.value())
#
# print('metric message:', message)
if args.loss.startswith('OnlineContrastive'):
# indice_show = np.random.choice(range(pair[0].shape[0]), args.train_show_pairs)
# writer.add_images('position image',
# make_grid(
# inv_normalize(data[0][pair[0][indice_show]], args.train_show_pairs * 2)))
# writer.add_images('negative image',
# make_grid(
# inv_normalize(data[0][pair[1][indice_show]], args.train_show_pairs * 2)))
if 'cos' in args.loss:
writer.add_scalars('data/TEST_SIA_COS_DISTANCE', {
'TEST_COS_POS': distance_pos,
'TEST_COS_NEG': distance_neg
}, batch_idx)
elif 'Euc' in args.loss:
writer.add_scalars('data/TEST_SIA_EUC_DISTANCE', {
'TEST_EUC_POS': distance_pos,
'TEST_EUC_NEG': distance_neg
}, batch_idx)
elif args.loss == 'OnlineTriplet':
# indice_show = np.random.choice(range(pair.shape[0]), args.train_show_pairs)
# writer.add_images('anchors image',
# make_grid(inv_normalize(data[0][pairs[indice_show, 0]]), args.train_show_pairs))
# writer.add_images('position image',
# make_grid(inv_normalize(data[0][pairs[indice_show, 1]]), args.train_show_pairs))
# writer.add_images('negative image',
# make_grid(inv_normalize(data[0][pairs[indice_show, 2]]), args.train_show_pairs))
writer.add_scalars('data/TEST_TRI_EUC_DISTANCE', {
'TEST_EUC_POS': distance_pos,
'TEST_EUC_NEG': distance_neg
}, batch_idx)
return val_loss, metrics