-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathbackground_cnn.py
167 lines (140 loc) · 8.44 KB
/
background_cnn.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
import numpy as np
import torch
import wandb
import argparse
import utils
from model_wrapper import StandardWrapperBackground
from dataloader import BackgroundDataset, BackgroundDatasetVal
import os
import sys
import random
import pickle
def main():
commandstring = ''
for arg in sys.argv:
if ' ' in arg:
commandstring += '"{}" '.format(arg)
else:
commandstring += "{} ".format(arg)
parser = argparse.ArgumentParser(description='Foveated Convolutional layers')
parser.add_argument('-dataset', '--dataset', nargs='?', metavar='dataset',
default="background", type=str,
help='Dataset background")')
parser.add_argument('-lr', '--lr', nargs='?', metavar='dt', default=0.001, type=float,
help='model learning rate; default=0.01')
parser.add_argument('-opt', '--optimizer', default="adam", type=str,
help='Optimizer')
parser.add_argument('-wrapped_arch', '--wrapped_arch', default="vanilla", type=str,
help='Architectures (vanilla, one_layer_global)')
parser.add_argument('-aggregation_type', '--aggregation_type', default="mean", type=str,
help='Region aggreagation {mean, max}')
parser.add_argument('--grayscale', action='store_true', default=False,
help='Force grayscale frames')
parser.add_argument('-id', '--id', nargs='?', metavar='id', default='', type=str,
help='additional id; default=empty string')
parser.add_argument('-save', '--save_model_flag', action='store_true', default=False,
help='save model?; default=1')
parser.add_argument('-logdir', '--logdir', nargs='?', metavar='logdir', default='tensorboard', type=str,
help='directory where the model will be saved; default=tensorboard')
parser.add_argument('--no_cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--cuda_dev', type=int, default=0,
help='select specific CUDA device for training')
parser.add_argument('--n_gpu_use', type=int, default=1,
help='select number of CUDA device for training')
parser.add_argument('--log_interval', type=int, default=10, metavar='N',
help='logging training status cadency')
parser.add_argument('--tensorboard', action='store_true', default=False,
help='For logging the model in tensorboard')
parser.add_argument('--fixed_seed', type=str, default="False",
help='For logging the model in tensorboard')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='specify seed (default: 1)')
parser.add_argument('--batch_dim', type=int, default=32, metavar='batch',
help='Batch dims')
parser.add_argument('--num_workers', type=int, default=0, metavar='S',
help='specify number of workers')
parser.add_argument('--output_channels', type=int, default=1, metavar='out_channels',
help='Batch dims')
parser.add_argument('--kernel', type=int, default=29, metavar='kernel_size',
help='Kernel size')
parser.add_argument('--num_classes', type=int, default=3, metavar='number of classes',
help='Number of classes')
parser.add_argument('--total_epochs', type=int, default=100, metavar='number of epochs',
help='Number of epochs')
parser.add_argument('--wandb', type=str, default="False",
help='Log the model in wandb?')
parser.add_argument('--FLOPS_count', action='store_true', default=False,
help='Execution to count FLOPS')
args = parser.parse_args()
args.wandb = args.wandb in {'True', 'true'}
args.fixed_seed = args.fixed_seed in {'True', 'true'}
use_cuda = not args.no_cuda and torch.cuda.is_available()
if not use_cuda:
args.n_gpu_use = 0
device = utils.prepare_device(n_gpu_use=args.n_gpu_use, gpu_id=args.cuda_dev)
# fix seeds for reproducibility
if args.fixed_seed:
SEED = args.seed
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
cfg = StandardWrapperBackground.Config()
cfg.wandb = args.wandb
cfg.setter(
vars(args)) # transform args in dict and pass it to the setter - crate a Config instance containing params
cfg.device = device
cfg.motion_needed = False
cfg.foa_flag = False
cfg.string_command_line = "python " + commandstring.split("/")[-1]
# creating the model instance
model = StandardWrapperBackground(cfg) # instantiate the model wrapper
################# DEACTIVATE FOA for standard models ##############
deactivate_foa = True
# Create dataloader class
dset = BackgroundDatasetVal(version=os.path.join("original_preprocessed", "train"),
base_path=os.path.join("data", "task3"),
bb_path=os.path.join("original_foa_center", "train"), resize_crop=False,
resize_crop_foa=True, num_classes=args.num_classes, deactivate_foa=deactivate_foa)
folder = os.path.join("data", "task3", "original_preprocessed")
with open(os.path.join(folder, f'train_indices_{args.num_classes}_classes.pkl'), 'rb') as f:
train_idx = pickle.load(f)
with open(os.path.join(folder, f'val_indices_{args.num_classes}_classes.pkl'), 'rb') as f:
val_idx = pickle.load(f)
dset_tr = torch.utils.data.Subset(dset, train_idx)
dset_val = torch.utils.data.Subset(dset, val_idx)
# val dataset - notice the activated resize_crop on the foa
dset_test_original = BackgroundDatasetVal(version=os.path.join("bg_challenge", "original", "val"),
base_path=os.path.join("data", "task3"),
bb_path=os.path.join("val_foa_center"), resize_crop=False,
resize_crop_foa=True, num_classes=args.num_classes,
deactivate_foa=deactivate_foa)
dset_test_mixedrand = BackgroundDatasetVal(version=os.path.join("bg_challenge", "mixed_rand", "val"),
base_path=os.path.join("data", "task3"),
bb_path=os.path.join("val_foa_center"), resize_crop=False,
resize_crop_foa=True, num_classes=args.num_classes,
deactivate_foa=deactivate_foa)
dset_test_mixednext = BackgroundDatasetVal(version=os.path.join("bg_challenge", "mixed_next", "val"),
base_path=os.path.join("data", "task3"),
bb_path=os.path.join("val_foa_center"), resize_crop=False,
resize_crop_foa=True, num_classes=args.num_classes,
deactivate_foa=deactivate_foa)
dset_test_mixedsame = BackgroundDatasetVal(version=os.path.join("bg_challenge", "mixed_same", "val"),
base_path=os.path.join("data", "task3"),
bb_path=os.path.join("val_foa_center"), resize_crop=False,
resize_crop_foa=True, num_classes=args.num_classes,
deactivate_foa=deactivate_foa)
cfg.foa_options = None # offline mode
dset = {"trainset": dset_tr, "valset": dset_val, "test_original": dset_test_original,
"test_mixedrand": dset_test_mixedrand,
"test_mixednext": dset_test_mixednext, "test_mixedsame": dset_test_mixedsame
}
cfg.foa_options = None # offline mode
# # caller model class
model(dset) # decidere qui se il foa viene fatto online o meno
model.train_multiple_test_loop(args.total_epochs)
if __name__ == '__main__':
main()