forked from cfchen-duke/ProtoPNet
-
Notifications
You must be signed in to change notification settings - Fork 1
/
main.py
189 lines (164 loc) · 8.16 KB
/
main.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
import os
import shutil
import torch
import torch.utils.data
# import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import argparse
import re
from helpers import makedir
import model
import push
import prune
import train_and_test as tnt
import save
from log import create_logger
from preprocess import mean, std, preprocess_input_function
parser = argparse.ArgumentParser()
parser.add_argument('-gpuid', nargs=1, type=str, default='0') # python3 main.py -gpuid=0,1,2,3
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpuid[0]
print(os.environ['CUDA_VISIBLE_DEVICES'])
# book keeping namings and code
from settings import base_architecture, img_size, prototype_shape, num_classes, \
prototype_activation_function, add_on_layers_type, experiment_run
base_architecture_type = re.match('^[a-z]*', base_architecture).group(0)
model_dir = './saved_models/' + base_architecture + '/' + experiment_run + '/'
makedir(model_dir)
shutil.copy(src=os.path.join(os.getcwd(), __file__), dst=model_dir)
shutil.copy(src=os.path.join(os.getcwd(), 'settings.py'), dst=model_dir)
shutil.copy(src=os.path.join(os.getcwd(), base_architecture_type + '_features.py'), dst=model_dir)
shutil.copy(src=os.path.join(os.getcwd(), 'model.py'), dst=model_dir)
shutil.copy(src=os.path.join(os.getcwd(), 'train_and_test.py'), dst=model_dir)
log, logclose = create_logger(log_filename=os.path.join(model_dir, 'train.log'))
img_dir = os.path.join(model_dir, 'img')
makedir(img_dir)
weight_matrix_filename = 'outputL_weights'
prototype_img_filename_prefix = 'prototype-img'
prototype_self_act_filename_prefix = 'prototype-self-act'
proto_bound_boxes_filename_prefix = 'bb'
# load the data
from settings import train_dir, test_dir, train_push_dir, \
train_batch_size, test_batch_size, train_push_batch_size
normalize = transforms.Normalize(mean=mean,
std=std)
# all datasets
# train set
train_dataset = datasets.ImageFolder(
train_dir,
transforms.Compose([
transforms.Resize(size=(img_size, img_size)),
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=train_batch_size, shuffle=True,
num_workers=4, pin_memory=False)
# push set
train_push_dataset = datasets.ImageFolder(
train_push_dir,
transforms.Compose([
transforms.Resize(size=(img_size, img_size)),
transforms.ToTensor(),
]))
train_push_loader = torch.utils.data.DataLoader(
train_push_dataset, batch_size=train_push_batch_size, shuffle=False,
num_workers=4, pin_memory=False)
# test set
test_dataset = datasets.ImageFolder(
test_dir,
transforms.Compose([
transforms.Resize(size=(img_size, img_size)),
transforms.ToTensor(),
normalize,
]))
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=test_batch_size, shuffle=False,
num_workers=4, pin_memory=False)
# we should look into distributed sampler more carefully at torch.utils.data.distributed.DistributedSampler(train_dataset)
log('training set size: {0}'.format(len(train_loader.dataset)))
log('push set size: {0}'.format(len(train_push_loader.dataset)))
log('test set size: {0}'.format(len(test_loader.dataset)))
log('batch size: {0}'.format(train_batch_size))
# construct the model
ppnet = model.construct_PPNet(base_architecture=base_architecture,
pretrained=True, img_size=img_size,
prototype_shape=prototype_shape,
num_classes=num_classes,
prototype_activation_function=prototype_activation_function,
add_on_layers_type=add_on_layers_type)
#if prototype_activation_function == 'linear':
# ppnet.set_last_layer_incorrect_connection(incorrect_strength=0)
ppnet = ppnet.cuda()
ppnet_multi = torch.nn.DataParallel(ppnet)
class_specific = True
# define optimizer
from settings import joint_optimizer_lrs, joint_lr_step_size
joint_optimizer_specs = \
[{'params': ppnet.features.parameters(), 'lr': joint_optimizer_lrs['features'], 'weight_decay': 1e-3}, # bias are now also being regularized
{'params': ppnet.add_on_layers.parameters(), 'lr': joint_optimizer_lrs['add_on_layers'], 'weight_decay': 1e-3},
{'params': ppnet.prototype_vectors, 'lr': joint_optimizer_lrs['prototype_vectors']},
]
joint_optimizer = torch.optim.Adam(joint_optimizer_specs)
joint_lr_scheduler = torch.optim.lr_scheduler.StepLR(joint_optimizer, step_size=joint_lr_step_size, gamma=0.1)
from settings import warm_optimizer_lrs
warm_optimizer_specs = \
[{'params': ppnet.add_on_layers.parameters(), 'lr': warm_optimizer_lrs['add_on_layers'], 'weight_decay': 1e-3},
{'params': ppnet.prototype_vectors, 'lr': warm_optimizer_lrs['prototype_vectors']},
]
warm_optimizer = torch.optim.Adam(warm_optimizer_specs)
from settings import last_layer_optimizer_lr
last_layer_optimizer_specs = [{'params': ppnet.last_layer.parameters(), 'lr': last_layer_optimizer_lr}]
last_layer_optimizer = torch.optim.Adam(last_layer_optimizer_specs)
# weighting of different training losses
from settings import coefs
# number of training epochs, number of warm epochs, push start epoch, push epochs
from settings import num_train_epochs, num_warm_epochs, push_start, push_epochs
# train the model
log('start training')
import copy
for epoch in range(num_train_epochs):
log('epoch: \t{0}'.format(epoch))
if epoch < num_warm_epochs:
tnt.warm_only(model=ppnet_multi, log=log)
_ = tnt.train(model=ppnet_multi, dataloader=train_loader, optimizer=warm_optimizer,
class_specific=class_specific, coefs=coefs, log=log)
else:
tnt.joint(model=ppnet_multi, log=log)
joint_lr_scheduler.step()
_ = tnt.train(model=ppnet_multi, dataloader=train_loader, optimizer=joint_optimizer,
class_specific=class_specific, coefs=coefs, log=log)
accu = tnt.test(model=ppnet_multi, dataloader=test_loader,
class_specific=class_specific, log=log)
save.save_model_w_condition(model=ppnet, model_dir=model_dir, model_name=str(epoch) + 'nopush', accu=accu,
target_accu=0.70, log=log)
if epoch >= push_start and epoch in push_epochs:
push.push_prototypes(
train_push_loader, # pytorch dataloader (must be unnormalized in [0,1])
prototype_network_parallel=ppnet_multi, # pytorch network with prototype_vectors
class_specific=class_specific,
preprocess_input_function=preprocess_input_function, # normalize if needed
prototype_layer_stride=1,
root_dir_for_saving_prototypes=img_dir, # if not None, prototypes will be saved here
epoch_number=epoch, # if not provided, prototypes saved previously will be overwritten
prototype_img_filename_prefix=prototype_img_filename_prefix,
prototype_self_act_filename_prefix=prototype_self_act_filename_prefix,
proto_bound_boxes_filename_prefix=proto_bound_boxes_filename_prefix,
save_prototype_class_identity=True,
log=log)
accu = tnt.test(model=ppnet_multi, dataloader=test_loader,
class_specific=class_specific, log=log)
save.save_model_w_condition(model=ppnet, model_dir=model_dir, model_name=str(epoch) + 'push', accu=accu,
target_accu=0.70, log=log)
if prototype_activation_function != 'linear':
tnt.last_only(model=ppnet_multi, log=log)
for i in range(20):
log('iteration: \t{0}'.format(i))
_ = tnt.train(model=ppnet_multi, dataloader=train_loader, optimizer=last_layer_optimizer,
class_specific=class_specific, coefs=coefs, log=log)
accu = tnt.test(model=ppnet_multi, dataloader=test_loader,
class_specific=class_specific, log=log)
save.save_model_w_condition(model=ppnet, model_dir=model_dir, model_name=str(epoch) + '_' + str(i) + 'push', accu=accu,
target_accu=0.70, log=log)
logclose()