-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
344 lines (293 loc) · 15.3 KB
/
train.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
import torch
import numpy as np
import argparse
from load_model import load_model
from tensorboardX import SummaryWriter
import torch.nn.functional as F
from clevr_data import ClevrDataLoader, load_vocab
from pathlib import Path
import torchvision.utils as vutils
from torchvision import transforms
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
import matplotlib.pyplot as plt
import textwrap
def clip_grads(net):
parameters = list(filter(lambda p: p.grad is not None, net.parameters()))
for p in parameters:
p.grad.data.clamp_(args.min_grad, args.max_grad)
def parse_arguments():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--model', type=str, default="PG_endtoend",
help='Model: SAN, SAN_wbw, PG, PG_memory, PG_endtoend', metavar='')
parser.add_argument('--question_size', type=int, default=92,
help='Number of words in question dictionary', metavar='')
parser.add_argument('--stem_dim', type=int, default=256,
help='Number of feature-maps ', metavar='')
parser.add_argument('--n_channel', type=int, default=1024,
help='Number of features channels ', metavar='')
parser.add_argument('--answer_size', type=int, default=31,
help='Number of words in answers dictionary', metavar='')
parser.add_argument('--batch_size', type=int, default=15,
help='Batch size', metavar='')
parser.add_argument('--min_grad', type=float, default=-10,
help='Minimum value of gradient clipping', metavar='')
parser.add_argument('--max_grad', type=float, default=10,
help='Maximum value of gradient clipping', metavar='')
parser.add_argument('--load_model_path', type=str, default='./checkpoint/PG_endtoend.combined.model',
help='Checkpoint path', metavar='')
parser.add_argument('--load_model_mode', type=str, default='PG+EE',
help='Load model checkpoint (PG, EE, PG+EE)', metavar='')
parser.add_argument('--save_model', type=bool, default=True,
help='Save model checkpoint', metavar='')
parser.add_argument('--clevr_dataset', type=str, default='/nas/softechict/CLEVR_v1.0/data_h5/',
help='Clevr dataset features,questions', metavar='')
parser.add_argument('--clevr_val_images', type=str, default='/nas/softechict/CLEVR_v1.0/images/val/',
help='Clevr dataset validation images path', metavar='')
parser.add_argument('--num_iterations', type=int, default=1000,
help='Num iteration per epoch', metavar='')
parser.add_argument('--num_val_samples', type=int, default=200,
help='Num samples from test dataset', metavar='')
parser.add_argument('--batch_multiplier', type=int, default=1,
help='Virtual batch size (min: 1)', metavar='')
parser.add_argument('--train_mode', type=str, default='PG',
help='Train mode (PG, EE, PG+EE)', metavar='')
parser.add_argument('--decoder_mode', type=str, default='hard+penalty',
help='Seq2seq decoder mode: (soft, gumbel, hard)', metavar='')
parser.add_argument('--use_curriculum', type=bool, default=False,
help='Use curriculum to learn program generator', metavar='')
return parser.parse_args()
def plot_grad_flow(named_parameters):
ave_grads = []
layers = []
for n, p in named_parameters:
if p.requires_grad and ("bias" not in n) and (p.grad is not None):
layers.append(n)
ave_grads.append(p.grad.abs().mean())
plt.plot(ave_grads, alpha=0.3, color="b")
plt.hlines(0, 0, len(ave_grads)+1, linewidth=1, color="k")
plt.xticks(range(0,len(ave_grads), 1), layers, rotation="vertical")
plt.xlim(xmin=0, xmax=len(ave_grads))
plt.xlabel("Layers")
plt.ylabel("average gradient")
plt.title("Gradient flow")
plt.grid(True)
plt.show()
def image_to_tensor(image_path, text):
'''
Load image file, add text, convert to tensor
'''
img1 = Image.open(image_path)
img1 = img1.crop((0, 0, 450, 500))
draw = ImageDraw.Draw(img1)
draw.rectangle((0, 320, 450, 500), fill="white")
font = ImageFont.truetype('font/Roboto-Black.ttf', size=22)
lines = textwrap.wrap(text, width=40)
y_text = 320
for line in lines:
width, height = font.getsize(line)
draw.text(((450 - width) / 2, y_text), line, font=font, fill='black')
y_text += height
content_transform = transforms.Compose([
transforms.ToTensor()
])
pic_tensor = content_transform(img1)
return pic_tensor
def check_accuracy(val_loader, model, vocab):
num_correct, num_samples = 0, 0
model.eval()
torch.set_grad_enabled(False)
for question, image, feats, answers, programs in val_loader:
if not (question.size(0) == feats.size(0) and feats.size(0) == args.batch_size):
continue
feats = feats.to(device)
question = question.to(device)
programs = programs.to(device)
if 'SAN' in args.model or 'endtoend' in args.model:
outs = model(feats, question)
else:
outs = model(feats, programs)
_, preds = outs.data.cpu().max(1)
num_correct += (preds.to('cpu') == answers).sum()
num_samples += preds.size(0)
if num_samples % args.batch_size*3 == 0:
image_path = args.clevr_val_images + "CLEVR_val_" + str(int(image[0])).zfill(6) + ".png"
question_text = "Q: "
answer_text = vocab['answer_idx_to_token'][int(preds[0])]
for q in question[0]:
if int(q) == 0:
break
if int(q) not in (1, 2, 3):
question_text += vocab['question_idx_to_token'][int(q)] + " "
pic = image_to_tensor(image_path, question_text + "? A: " + answer_text)
writer.add_image('Test Images', pic, num_samples)
if 'SAN' in args.model:
att_map = model.getData()
pic1 = vutils.make_grid(att_map, normalize=True, scale_each=True)
writer.add_image('Test Attention Map', pic1, num_samples)
if num_samples >= args.num_val_samples or num_samples == len(val_loader):
break
accuracy = float(int(num_correct) / num_samples)
model.train()
return accuracy
def train_loop(model, train_loader, val_loader, vocab):
criterion = torch.nn.CrossEntropyLoss().to(device)
optimizer_model, optimizer_pg = None, None
if 'endtoend'in args.model:
optimizer_pg = torch.optim.Adam([param for name, param in model.named_parameters()
if 'program_generator' in name], lr=1e-04)
optimizer_model = torch.optim.Adam([param for name, param in model.named_parameters()
if 'program_generator' not in name], lr=1e-04)
else:
optimizer_model = torch.optim.Adam(model.parameters(), lr=1e-04)
best_accuracy = float(0)
t, epoch = 0, 0
raw_reward, raw_penalty, reward_moving_average, penalty_moving_average = 0, 0, 0, 0
loss, rew_mean, entropy_a, entropy_b = 0, 0, 0, 0
centered_reward, centered_penalty = 0, 0
q_fun = 0
accuracy = check_accuracy(val_loader, model, vocab)
writer.add_scalar('Test Accuracy', accuracy, 0)
while epoch < 1000:
print('------- Starting epoch %d ---------' % epoch)
torch.set_grad_enabled(True)
count = 1
for question, _, feats, answers, programs in train_loader:
# Check batch_size
if not (question.size(0) == feats.size(0) and feats.size(0) == args.batch_size):
continue
feats = feats.to(device)
question = question.to(device)
programs = programs.to(device)
if 'SAN' in args.model or 'endtoend'in args.model:
outs = model(feats, question)
else:
outs = model(feats, programs)
_, preds = outs.data.cpu().max(1)
if 'hard' not in args.decoder_mode:
loss = criterion(outs, answers.to(device)) / args.batch_multiplier
loss.backward()
# Optimizer virtual-batch
count -= 1
if count == 0:
if 'PG' in args.train_mode:
if 'hard' in args.decoder_mode:
optimizer_pg.zero_grad()
if 'penalty' in args.decoder_mode:
# Penalty = Cross entropy loss(Y, Y_pred)
raw_penalty = F.nll_loss(F.log_softmax(outs.to(device).data, -1), answers.to(device).data,
reduction='none').neg()
# Penalty Baseline
penalty_moving_average *= 0.9
penalty_moving_average += (1.0 - 0.9) * raw_penalty.mean()
centered_penalty = (raw_penalty - penalty_moving_average).to(device)
# Reward Baseline
raw_reward = (preds == answers).float()
reward_moving_average *= 0.9
reward_moving_average += (1.0 - 0.9) * raw_reward.mean()
centered_reward = (raw_reward - reward_moving_average).to(device)
# REINFORCE
if 'penalty' in args.decoder_mode:
loss, rew_mean, entropy_a, entropy_b = model.program_generator.reinforce_penalty(centered_reward, centered_penalty)
else:
loss, rew_mean, entropy_a, entropy_b = model.program_generator.reinforce_reward(centered_reward)
# GRADIENT DEBUG
# plot_grad_flow(model.named_parameters())
optimizer_pg.step()
else:
optimizer_pg.step()
optimizer_pg.zero_grad()
if 'EE' in args.train_mode:
optimizer_model.step()
optimizer_model.zero_grad()
count = args.batch_multiplier
# Log on Tensorboard
if t % 5 == 0:
if 'SAN' in args.model:
att_map = model.getData()
pic1 = vutils.make_grid(att_map, normalize=True, scale_each=True)
writer.add_image('Attention Map', pic1, t+epoch*args.num_iterations)
elif "memory" in args.model or "endtoend" in args.model:
addr_u, addr_b = model.getData()
if addr_u:
pic1 = vutils.make_grid(addr_u, normalize=True, scale_each=True)
writer.add_image('Addressing unary', pic1, t + epoch * args.num_iterations)
if addr_b:
pic2 = vutils.make_grid(addr_b, normalize=True, scale_each=True)
writer.add_image('Addressing binary', pic2, t + epoch * args.num_iterations)
if 'hard' not in args.decoder_mode:
print("Loss: ", loss.item() * args.batch_multiplier)
writer.add_scalar('Train Loss', loss.item() * args.batch_multiplier,
t + epoch * args.num_iterations)
else:
# print("Norm Reward AVG: ", raw_reward.mean() * args.batch_multiplier)
writer.add_scalar('Norm Reward AVG', raw_reward.mean() * args.batch_multiplier,
t + epoch * args.num_iterations)
# print("Policy Loss: ", loss.item() * args.batch_multiplier)
writer.add_scalar('Policy Loss', loss.item() * args.batch_multiplier,
t + epoch * args.num_iterations)
if 'penalty' in args.decoder_mode:
writer.add_scalar('Cross entropy Loss', raw_penalty.mean() * args.batch_multiplier,
t + epoch * args.num_iterations)
writer.add_scalar('Entropy_a', entropy_a.item() * args.batch_multiplier,
t + epoch * args.num_iterations)
writer.add_scalar('Entropy_b', entropy_b.item() * args.batch_multiplier,
t + epoch * args.num_iterations)
# Check accuracy on test dataset
if t >= args.num_iterations:
t = 0
epoch += 1
accuracy = check_accuracy(val_loader, model, vocab)
writer.add_scalar('Test Accuracy', accuracy, epoch)
if accuracy >= 0 and args.save_model:
best_accuracy = accuracy
torch.save(model.state_dict(), './checkpoint/' + args.model + '.model')
break
t += 1
def main():
print("--------- Load vocab -----------")
vocab = load_vocab('/homes/rdicarlo/scripts/vocab.json')
train_loader_kwargs = {
# /nas/softechict/CLEVR_v1.0/data_h5/ D://VQA//data
'question_h5': Path(args.clevr_dataset + 'train_questions.h5'),
'feature_h5': Path(args.clevr_dataset + 'train_features.h5'),
'batch_size': args.batch_size,
'num_workers': 0,
'shuffle': True
}
val_loader_kwargs = {
'question_h5': Path(args.clevr_dataset + 'val_questions.h5'),
'feature_h5': Path(args.clevr_dataset + 'val_features.h5'),
'batch_size': args.batch_size,
'num_workers': 0,
'shuffle': True
}
model = load_model(args, vocab)
print(model)
print("--------- Number of parameters -----------")
print(model.calculate_num_params())
print("--------- Loading checkpoint -----------")
model = load_checkpoint(model)
print("--------- Start training -----------")
with ClevrDataLoader(**train_loader_kwargs) as train_loader, ClevrDataLoader(**val_loader_kwargs) as val_loader:
train_loop(model, train_loader, val_loader, vocab)
def load_checkpoint(model):
# Load checkpoint, partial or full
if args.load_model_mode != '':
model_dict = model.state_dict()
print(args.load_model_path)
checkpoint = torch.load(args.load_model_path)
for key, val in checkpoint.copy().items():
if 'program_generator' in key and args.load_model_mode == 'EE':
checkpoint.pop(key, None)
elif 'program_generator' not in key and args.load_model_mode == 'PG':
checkpoint.pop(key, None)
model_dict.update(checkpoint)
model.load_state_dict(model_dict)
return model
if __name__ == "__main__":
args = parse_arguments()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
writer = SummaryWriter()
main()