forked from HongduanTian/CoPA
-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_net_mdl.py
167 lines (144 loc) · 7.53 KB
/
train_net_mdl.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
"""
This code allows you to train a vanilla multi-domain learning network.
"""
import os
import sys
import torch
import numpy as np
import tensorflow as tf
from time import sleep
from tqdm import tqdm, trange
from torch.utils.tensorboard import SummaryWriter
from data.meta_dataset_reader import (MetaDatasetBatchReader,
MetaDatasetEpisodeReader)
from models.losses import cross_entropy_loss, prototype_loss
from models.model_utils import (CheckPointer, UniformStepLR,
CosineAnnealRestartLR, ExpDecayLR)
from models.model_helpers import get_model, get_optimizer
from utils import Accumulator
from config import args, BATCHSIZES, LOSSWEIGHTS
def train():
# initialize datasets and loaders
trainsets, valsets, testsets = args['data.train'], args['data.val'], args['data.test']
train_loaders = []
num_train_classes = dict()
for t_indx, trainset in enumerate(trainsets):
train_loaders.append(MetaDatasetBatchReader('train', [trainset], valsets, testsets,
batch_size=BATCHSIZES[trainset]))
num_train_classes[trainset] = train_loaders[t_indx].num_classes('train')
val_loader = MetaDatasetEpisodeReader('val', trainsets, valsets, testsets)
model = get_model(list(num_train_classes.values()), args)
model.train()
optimizer = get_optimizer(model, args, params=model.get_parameters())
# restoring the last checkpoint
checkpointer = CheckPointer(args, model, optimizer=optimizer)
if os.path.isfile(checkpointer.last_ckpt) and args['train.resume']:
start_iter, best_val_loss, best_val_acc =\
checkpointer.restore_model(ckpt='last')
else:
print('No checkpoint restoration')
best_val_loss = 999999999
best_val_acc = start_iter = 0
# define learning rate policy
if args['train.lr_policy'] == "step":
lr_manager = UniformStepLR(optimizer, args, start_iter)
elif "exp_decay" in args['train.lr_policy']:
lr_manager = ExpDecayLR(optimizer, args, start_iter)
elif "cosine" in args['train.lr_policy']:
lr_manager = CosineAnnealRestartLR(optimizer, args, start_iter)
# defining the summary writer
writer = SummaryWriter(checkpointer.out_path)
# Training loop
max_iter = args['train.max_iter']
epoch_loss = {name: [] for name in trainsets}
epoch_acc = {name: [] for name in trainsets}
epoch_val_loss = {name: [] for name in valsets}
epoch_val_acc = {name: [] for name in valsets}
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = False
with tf.compat.v1.Session(config=config) as session:
for i in tqdm(range(max_iter)):
if i < start_iter:
continue
optimizer.zero_grad()
samples = []
images = []
num_samples = []
for t_indx, train_loader in enumerate(train_loaders):
sample = train_loader.get_train_batch(session)
samples.append(sample)
images.append(sample['images'])
num_samples.append(sample['images'].size(0))
images = torch.cat(images, dim=0)
logits = model.forward(images, num_samples)
batch_losses, stats_dicts = [], []
for t_indx, trainset in enumerate(trainsets):
batch_loss, stats_dict, _ = cross_entropy_loss(logits[t_indx], samples[t_indx]['labels'])
batch_losses.append(batch_loss*LOSSWEIGHTS[trainset])
stats_dicts.append(stats_dict)
batch_dataset = samples[t_indx]['dataset_name']
epoch_loss[batch_dataset].append(stats_dict['loss'])
epoch_acc[batch_dataset].append(stats_dict['acc'])
batch_loss = torch.stack(batch_losses).sum()
batch_loss.backward()
optimizer.step()
lr_manager.step(i)
if (i + 1) % 200 == 0:
for dataset_name in trainsets:
writer.add_scalar(f"loss/{dataset_name}-train_acc",
np.mean(epoch_loss[dataset_name]), i)
writer.add_scalar(f"accuracy/{dataset_name}-train_acc",
np.mean(epoch_acc[dataset_name]), i)
epoch_loss[dataset_name], epoch_acc[dataset_name] = [], []
writer.add_scalar('learning_rate',
optimizer.param_groups[0]['lr'], i)
# Evaluation inside the training loop
if (i + 1) % args['train.eval_freq'] == 0:
model.eval()
dataset_accs, dataset_losses = [], []
for valset in valsets:
val_losses, val_accs = [], []
for j in tqdm(range(args['train.eval_size'])):
with torch.no_grad():
sample = val_loader.get_validation_task(session, valset)
context_features = model.embed(sample['context_images'])
target_features = model.embed(sample['target_images'])
context_labels = sample['context_labels']
target_labels = sample['target_labels']
_, stats_dict, _ = prototype_loss(context_features, context_labels,
target_features, target_labels)
val_losses.append(stats_dict['loss'])
val_accs.append(stats_dict['acc'])
# write summaries per validation set
dataset_acc, dataset_loss = np.mean(val_accs) * 100, np.mean(val_losses)
dataset_accs.append(dataset_acc)
dataset_losses.append(dataset_loss)
epoch_val_loss[valset].append(dataset_loss)
epoch_val_acc[valset].append(dataset_acc)
writer.add_scalar(f"loss/{valset}/val_loss", dataset_loss, i)
writer.add_scalar(f"accuracy/{valset}/val_acc", dataset_acc, i)
print(f"{valset}: val_acc {dataset_acc:.2f}%, val_loss {dataset_loss:.3f}")
# write summaries averaged over datasets
avg_val_loss, avg_val_acc = np.mean(dataset_losses), np.mean(dataset_accs)
writer.add_scalar(f"loss/avg_val_loss", avg_val_loss, i)
writer.add_scalar(f"accuracy/avg_val_acc", avg_val_acc, i)
# saving checkpoints
if avg_val_acc > best_val_acc:
best_val_loss, best_val_acc = avg_val_loss, avg_val_acc
is_best = True
print('Best model so far!')
else:
is_best = False
extra_dict = {'epoch_loss': epoch_loss, 'epoch_acc': epoch_acc, 'epoch_val_loss': epoch_val_loss, 'epoch_val_acc': epoch_val_acc}
checkpointer.save_checkpoint(i, best_val_acc, best_val_loss,
is_best, optimizer=optimizer,
state_dict=model.get_state_dict(), extra=extra_dict)
model.train()
print(f"Trained and evaluated at {i}")
writer.close()
if start_iter < max_iter:
print(f"""Done training with best_mean_val_loss: {best_val_loss:.3f}, best_avg_val_acc: {best_val_acc:.2f}%""")
else:
print(f"""No training happened. Loaded checkpoint at {start_iter}, while max_iter was {max_iter}""")
if __name__ == '__main__':
train()