/
main_icarl.py
201 lines (166 loc) · 8.68 KB
/
main_icarl.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
from comet_ml import Experiment
from model.iCaRL import iCaRL
import torch
from model.temporalShiftModule.ops.transforms import *
# from utils.icarl_dataset import CILSetTask
from utils.icarl_dataset_frames import CILSetTask
import argparse
import yaml, pickle
import torch.nn as nn
import os
import random
random.seed(10)
def parse_conf(conf, new_dict = {}):
for k, v in conf.items():
if type(v) == dict:
new_dict = parse_conf(v, new_dict)
else:
new_dict[k] = v
return new_dict
def main():
global dict_conf, device, experiment, data, list_val_acc_ii, memory_size, type_sampling
list_val_acc_ii = []
parser = argparse.ArgumentParser(description="iCaRL TSN Baseline")
parser.add_argument("-conf","--conf_path", default = './conf/conf_ucf101_icarl_tsn.yaml')
args = parser.parse_args()
conf_file = open(args.conf_path, 'r')
print("Conf file dir: ",conf_file)
dict_conf = yaml.load(conf_file)
conf_model = dict_conf['model']
api_key = dict_conf['comet']['api_key']
workspace = dict_conf['comet']['workspace']
project_name = dict_conf['comet']['project_name']
experiment = Experiment(api_key=api_key,
project_name=project_name, workspace=workspace)
experiment.log_parameters(parse_conf(dict_conf))
experiment.set_name(dict_conf['comet']['name'])
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
path_data = dict_conf['dataset']['path_data']
with open(path_data, 'rb') as handle:
data = pickle.load(handle)
num_class = len(data['train'][0].keys())
type_sampling = dict_conf['memory']['type_mem'] if 'type_mem' in dict_conf['memory'] else 'icarl'
print('sampling strategy:', type_sampling)
model = iCaRL(conf_model, num_class, dict_conf['checkpoints'])
crop_size = model.crop_size
scale_size = model.scale_size
input_mean = model.input_mean
input_std = model.input_std
policies = model.get_optim_policies()
dataset_name = dict_conf['dataset']['name']
train_augmentation = model.get_augmentation(flip=False if 'something' in dataset_name or 'jester' in dataset_name else True)
optimizer = torch.optim.SGD(policies,
conf_model['lr'],
momentum=conf_model['momentum'],
weight_decay=conf_model['weight_decay'])
path_frames = dict_conf['dataset']['path_frames']
memory_size = dict_conf['memory']['memory_size']
batch_size = conf_model['batch_size']
num_workers = conf_model['num_workers']
arch = conf_model['arch']
modality = conf_model['modality']
num_segments = conf_model['num_segments']
# Data loading code
if modality != 'RGBDiff':
normalize = GroupNormalize(input_mean, input_std)
else:
normalize = IdentityTransform()
if modality == 'RGB':
data_length = 1
elif args.modality in ['Flow', 'RGBDiff']:
data_length = 5
train_transforms = torchvision.transforms.Compose([
train_augmentation,
Stack(roll=(arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(arch not in ['BNInception', 'InceptionV3'])),
normalize
])
val_transforms = torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=(arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(arch not in ['BNInception', 'InceptionV3'])),
normalize,
])
train_cilDatasetList = CILSetTask(data['train'], path_frames, memory_size, batch_size, shuffle=True,
num_workers=num_workers, num_frame_to_save = conf_model['num_frame_to_save'], drop_last=True,
pin_memory=True, num_segments=num_segments, new_length=data_length, modality=modality,
transform=train_transforms, dense_sample=False, train_enable = True)
val_cilDatasetList = CILSetTask(data['val'], path_frames, memory_size, batch_size, shuffle=False,
num_workers=num_workers, pin_memory=True, num_frame_to_save = conf_model['num_frame_to_save'],
num_segments=num_segments, new_length=data_length, modality=modality,
transform=val_transforms, random_shift=False, dense_sample=False,
train_enable = False)
test_cilDatasetList = CILSetTask(data['test'], path_frames, memory_size, batch_size, shuffle=False,
num_workers=num_workers, pin_memory=True, num_frame_to_save = conf_model['num_frame_to_save'],
num_segments=num_segments, new_length=data_length, modality=modality,
transform=val_transforms, random_shift=False, dense_sample=False,
train_enable = False)
# define loss function (criterion) and optimizer
cls_loss = nn.CrossEntropyLoss().to(device)
dist_loss = nn.BCEWithLogitsLoss().to(device)
model.set_losses(cls_loss, dist_loss)
for group in policies:
print(('group: {} has {} params, lr_mult: {}, decay_mult: {}'.format(
group['name'], len(group['params']), group['lr_mult'], group['decay_mult'])))
path_model = dict_conf['checkpoints']['path_model']
if dict_conf['checkpoints']['train_mode']:
best_prec1 = 0
current_task = 0
current_epoch = 0
path_best_model = path_model.format('Best_Model')
if os.path.exists(path_best_model):
checkpoint_dict = torch.load(path_best_model)
model.load_state_dict(checkpoint_dict['state_dict'])
print("load parameters model - to train")
best_prec1 = checkpoint_dict['accuracy']
current_task = checkpoint_dict['current_task']
current_epoch = checkpoint_dict['current_epoch'] + 1
train_loop(model, optimizer, train_cilDatasetList, val_cilDatasetList, test_cilDatasetList)
def train_loop(model, optimizer, train_cilDatasetList, val_cilDatasetList, test_cilDatasetList):
iter_trainDataloader = iter(train_cilDatasetList)
num_tasks = train_cilDatasetList.num_tasks
eval_freq = dict_conf['checkpoints']['eval_freq']
path_model = dict_conf['checkpoints']['path_model']
num_epochs = dict_conf['model']['epochs']
for j in range(num_tasks):
classes, data, train_loader_i, len_data, num_next_classes = next(iter_trainDataloader)
model.train(train_loader_i, len_data, optimizer, num_epochs, experiment, j, val_cilDatasetList)
if torch.cuda.device_count() > 1:
m = memory_size // model.feature_encoder.module.new_fc.out_features
else:
m = memory_size // model.feature_encoder.new_fc.out_features
model.add_samples_to_mem(val_cilDatasetList, data, m, type_sampling)
train_cilDatasetList.memory = model.memory
model.n_known = len(model.memory)
print('n_known_classes: ',model.n_known)
batch_size = dict_conf['model']['batch_size']
train_eval_loader = val_cilDatasetList.get_dataloader(data, batch_size, model.memory)
total_train = 0.0
correct_train = 0.0
print('Init classification for training set')
for _, _, videos, labels in train_eval_loader:
videos = videos.to(device)
preds = model.classify(videos, val_cilDatasetList)
total_train += labels.size(0)
correct_train += (preds.data.cpu() == labels).sum()
acc = (100 * correct_train / total_train)
experiment.log_metric("Train_Acc_task_{}".format(j+1), acc)
print('Train Accuracy: %d %%' % acc)
with experiment.validate():
total_acc_val = model.final_validate(val_cilDatasetList, j, experiment)
print('Val Accuracy: %d %%' % total_acc_val)
with experiment.test():
total_acc_test = model.final_validate(test_cilDatasetList, j, experiment)
print('Test Accuracy: %d %%' % total_acc_test)
if num_next_classes != None:
model.augment_classification(num_next_classes, device)
print('Classifier augmented')
policies = model.get_optim_policies()
conf_model = dict_conf['model']
optimizer = torch.optim.SGD(policies,
conf_model['lr'],
momentum=conf_model['momentum'],
weight_decay=conf_model['weight_decay'])
if __name__ == '__main__':
main()