-
Notifications
You must be signed in to change notification settings - Fork 1
/
utils.py
179 lines (137 loc) · 7.53 KB
/
utils.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
import os
import torch
import shutil
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import cv2
import dataloaders.read_flow_lib as read_flow_lib_
cmap = plt.cm.jet
#cmap = plt.cm.viridis
def parse_command():
model_names = ['squeezenet','shufflenetv2','vgg11','densenet121','densenet121_skipadd' ,
'resnet18', 'resnet50', 'resnet18skipadd', 'resnet18skipadd_dw', 'resnet18skipconcat',
'mobilenet', 'mobilenetskipadd', 'mobilenetskipconcat']
loss_names = ['l1', 'l2', 'custom', 'smoothl1', 'inversedepthsmoothness']
data_names = ['nyudepthv2', 'kitti', 'kitti_eigen']
from models import Decoder
decoder_names = Decoder.names
print(decoder_names)
modality_names = ['rgb_flow', 'rgb_flownet', 'yuv_flow', 'rgb_flow_edges', 'yuv_flow_edges', 'rgb', 'flow', 'flownet', 'flow_edges']
import argparse
parser = argparse.ArgumentParser(description='Parallax-Depth')
parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18skipadd', choices=model_names,
help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet18)')
parser.add_argument('--modality', '-m', metavar='MODALITY', default='rgb_flownet', choices=modality_names,
help='modality: ' + ' | '.join(modality_names) + ' (default: rgb_flow)')
parser.add_argument('--data', metavar='DATA', default='kitti_eigen',
choices=data_names,
help='dataset: ' + ' | '.join(data_names) + ' (default: kitti_eigen)')
parser.add_argument('--decoder', '-d', metavar='DECODER', default='nnconv5dw', choices=decoder_names,
help='decoder: ' + ' | '.join(decoder_names) + ' (default: nnconv5dw)')
parser.add_argument('-j', '--workers', default=6, type=int, metavar='N',
help='number of data loading workers (default: 8)')
parser.add_argument('--epochs', default=12, type=int, metavar='N',
help='number of total epochs to run (default: 5)')
parser.add_argument('-c', '--criterion', metavar='LOSS', default='smoothl1', choices=loss_names,
help='loss function: ' + ' | '.join(loss_names) + ' (default: l2)')
parser.add_argument('-b', '--batch-size', default=8, type=int, help='mini-batch size (default: 8)')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
metavar='LR', help='initial learning rate (default 0.1)')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-3, type=float,
metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', type=str, default='',
help='evaluate model on validation set')
parser.add_argument('--no-pretrain', dest='pretrained', action='store_false',
help='not to use ImageNet pre-trained weights')
parser.add_argument('--min_depth', default=1e-5, type=float)
parser.add_argument('--max_depth', default=80.0, type=float)
parser.set_defaults(pretrained=False)
args = parser.parse_args()
return args
def save_checkpoint(state, is_best, epoch, output_directory):
checkpoint_filename = os.path.join(output_directory, 'checkpoint-' + str(epoch) + '.pth.tar')
torch.save(state, checkpoint_filename)
if is_best:
best_filename = os.path.join(output_directory, 'model_best.pth.tar')
shutil.copyfile(checkpoint_filename, best_filename)
if epoch > 0:
prev_checkpoint_filename = os.path.join(output_directory, 'checkpoint-' + str(epoch-1) + '.pth.tar')
if os.path.exists(prev_checkpoint_filename):
os.remove(prev_checkpoint_filename)
def adjust_learning_rate(optimizer, epoch, lr_init):
"""Sets the learning rate to the initial LR decayed by 1/10 every 3 epochs"""
lr = lr_init * (0.1 ** (epoch // 3))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def get_output_directory(args):
output_directory = os.path.join('results',
'{}.modality={}.arch={}.decoder={}.criterion={}.lr={}.bs={}.pretrained={}'.
format(args.data, args.modality, \
args.arch, args.decoder, args.criterion, args.lr, args.batch_size, \
args.pretrained))
return output_directory
def colored_depthmap(depth, d_min=None, d_max=None):
if d_min is None:
d_min = np.min(depth)
if d_max is None:
d_max = np.max(depth)
depth_relative = (depth - d_min) / (d_max - d_min)
return 255 * cmap(depth_relative)[:,:,:3] # H, W, C
def merge_into_row(input, depth_target, depth_pred):
rgb = 255 * np.transpose(np.squeeze(input.cpu().numpy()), (1,2,0)) # H, W, C
depth_target_cpu = np.squeeze(depth_target.cpu().numpy())
depth_pred_cpu = np.squeeze(depth_pred.data.cpu().numpy())
d_min = min(np.min(depth_target_cpu), np.min(depth_pred_cpu))
d_max = max(np.max(depth_target_cpu), np.max(depth_pred_cpu))
depth_target_col = colored_depthmap(depth_target_cpu, d_min, d_max)
depth_pred_col = colored_depthmap(depth_pred_cpu, d_min, d_max)
img_merge = np.vstack([rgb, depth_target_col, depth_pred_col])
return img_merge
def merge_into_row_with_gt(input, flow, depth_target, depth_pred, pretrained, modality):
if not modality == 'flownet':
rgb = np.transpose(np.squeeze(input.cpu().numpy()), (1,2,0)) # H, W, C
else:
rgb = input
#revert normalization
if pretrained:
rgb[:,:,0] = rgb[:,:,0] * 0.229
rgb[:,:,0] = rgb[:,:,0] + 0.485
rgb[:,:,1] = rgb[:,:,1] * 0.224
rgb[:,:,1] = rgb[:,:,1] + 0.456
rgb[:,:,2] = rgb[:,:,2] * 0.225
rgb[:,:,2] = rgb[:,:,2] + 0.406
rgb = 255 *rgb
if modality == 'rgb':
flow_cpu = flow
flow_input_col = colored_depthmap(flow_cpu, 0, 80)
elif (modality == 'rgb_flownet' or modality == 'flownet'):
flow_cpu = np.squeeze(flow.cpu().numpy())
flow_cpu = cv2.normalize(flow_cpu,None,0,255,cv2.NORM_MINMAX)
flow_input_col = cv2.cvtColor(flow_cpu,cv2.COLOR_GRAY2RGB)
else:
flow_cpu = 255 * np.squeeze(flow.cpu().numpy())
flow_input_col = cv2.cvtColor(flow_cpu,cv2.COLOR_GRAY2RGB)
depth_target_cpu = np.squeeze(depth_target.cpu().numpy())
depth_pred_cpu = np.squeeze(depth_pred.data.cpu().numpy())
#d_min = min(np.min(depth_input_cpu), np.min(depth_target_cpu), np.min(depth_pred_cpu))
#d_max = max(np.max(depth_input_cpu), np.max(depth_target_cpu), np.max(depth_pred_cpu))
d_min = 0
d_max = 80
#flow_input_col = colored_depthmap(depth_input_cpu, d_min, d_max)
#flow_input_col = cv2.applyColorMap(flow_cpu.astype('uint8'), cv2.COLORMAP_JET)
depth_target_col = colored_depthmap(depth_target_cpu, d_min, d_max)
depth_pred_col = colored_depthmap(depth_pred_cpu, d_min, d_max)
img_merge = np.vstack([rgb, flow_input_col, depth_target_col, depth_pred_col])
return img_merge
def add_row(img_merge, row):
return np.vstack([img_merge, row])
def save_image(img_merge, filename):
img_merge = Image.fromarray(img_merge.astype('uint8'))
img_merge.save(filename)