-
Notifications
You must be signed in to change notification settings - Fork 140
/
logger.py
223 lines (176 loc) · 8.8 KB
/
logger.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
import numpy as np
import torch
import torch.nn.functional as F
import imageio
import os
from skimage.draw import circle
import matplotlib.pyplot as plt
import collections
def partial_state_dict_load(module, state_dict):
own_state = module.state_dict()
for name, param in state_dict.items():
if name not in own_state:
continue
if isinstance(param, torch.nn.Parameter):
# backwards compatibility for serialized parameters
param = param.data
own_state[name].copy_(param)
def load_reconstruction_module(module, checkpoint):
if 'generator' in checkpoint:
partial_state_dict_load(module, checkpoint['generator'])
else:
module.load_state_dict(checkpoint['reconstruction_module'])
def load_segmentation_module(module, checkpoint):
if 'kp_detector' in checkpoint:
partial_state_dict_load(module, checkpoint['kp_detector'])
module.state_dict()['affine.weight'].copy_(checkpoint['kp_detector']['jacobian.weight'])
module.state_dict()['affine.bias'].copy_(checkpoint['kp_detector']['jacobian.bias'])
module.state_dict()['shift.weight'].copy_(checkpoint['kp_detector']['kp.weight'])
module.state_dict()['shift.bias'].copy_(checkpoint['kp_detector']['kp.bias'])
if 'semantic_seg.weight' in checkpoint['kp_detector']:
module.state_dict()['segmentation.weight'].copy_(checkpoint['kp_detector']['semantic_seg.weight'])
module.state_dict()['segmentation.bias'].copy_(checkpoint['kp_detector']['semantic_seg.bias'])
else:
print ('Segmentation part initialized at random.')
else:
module.load_state_dict(checkpoint['segmentation_module'])
class Logger:
def __init__(self, log_dir, checkpoint_freq=100, visualizer_params=None, zfill_num=8, log_file_name='log.txt'):
self.loss_list = []
self.cpk_dir = log_dir
self.visualizations_dir = os.path.join(log_dir, 'train-vis')
if not os.path.exists(self.visualizations_dir):
os.makedirs(self.visualizations_dir)
self.log_file = open(os.path.join(log_dir, log_file_name), 'a')
self.zfill_num = zfill_num
self.visualizer = Visualizer(**visualizer_params)
self.checkpoint_freq = checkpoint_freq
self.epoch = 0
self.best_loss = float('inf')
self.names = None
def log_scores(self, loss_names):
loss_mean = np.array(self.loss_list).mean(axis=0)
loss_string = "; ".join(["%s - %.5f" % (name, value) for name, value in zip(loss_names, loss_mean)])
loss_string = str(self.epoch).zfill(self.zfill_num) + ") " + loss_string
print(loss_string, file=self.log_file)
self.loss_list = []
self.log_file.flush()
def visualize_rec(self, inp, out):
image = self.visualizer.visualize(inp['target'], inp['source'], out)
imageio.imsave(os.path.join(self.visualizations_dir, "%s-rec.png" % str(self.epoch).zfill(self.zfill_num)), image)
def save_cpk(self, emergent=False):
cpk = {k: v.state_dict() for k, v in self.models.items()}
cpk['epoch'] = self.epoch
cpk_path = os.path.join(self.cpk_dir, '%s-checkpoint.pth.tar' % str(self.epoch).zfill(self.zfill_num))
if not (os.path.exists(cpk_path) and emergent):
torch.save(cpk, cpk_path)
@staticmethod
def load_cpk(checkpoint_path, reconstruction_module=None, segmentation_module=None,
optimizer_reconstruction_module=None, optimizer_segmentation_module=None):
checkpoint = torch.load(checkpoint_path)
if reconstruction_module is not None:
load_reconstruction_module(reconstruction_module, checkpoint)
if segmentation_module is not None:
load_segmentation_module(segmentation_module, checkpoint)
if optimizer_reconstruction_module is not None and 'generator' not in checkpoint :
optimizer_reconstruction_module.load_state_dict(checkpoint['optimizer_reconstruction_module'])
if optimizer_segmentation_module is not None and 'generator' not in checkpoint :
optimizer_segmentation_module.load_state_dict(checkpoint['optimizer_segmentation_module'])
return 0 if 'generator' in checkpoint else checkpoint['epoch']
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if 'models' in self.__dict__:
self.save_cpk()
self.log_file.close()
def log_iter(self, losses):
losses = collections.OrderedDict(losses.items())
if self.names is None:
self.names = list(losses.keys())
self.loss_list.append(list(losses.values()))
def log_epoch(self, epoch, models, inp, out):
self.epoch = epoch
self.models = models
if (self.epoch + 1) % self.checkpoint_freq == 0:
self.save_cpk()
self.log_scores(self.names)
self.visualize_rec(inp, out)
class Visualizer:
def __init__(self, kp_size=5, draw_border=False, colormap='gist_rainbow'):
self.kp_size = kp_size
self.draw_border = draw_border
self.colormap = plt.get_cmap(colormap)
def draw_image_with_kp(self, image, kp_array):
image = np.copy(image)
spatial_size = np.array(image.shape[:2][::-1])[np.newaxis]
kp_array = spatial_size * (kp_array + 1) / 2
num_kp = kp_array.shape[0]
for kp_ind, kp in enumerate(kp_array):
rr, cc = circle(kp[1], kp[0], self.kp_size, shape=image.shape[:2])
image[rr, cc] = np.array(self.colormap(kp_ind / num_kp))[:3]
return image
def create_image_column_with_kp(self, images, kp):
image_array = np.array([self.draw_image_with_kp(v, k) for v, k in zip(images, kp)])
return self.create_image_column(image_array)
def create_image_column(self, images):
if self.draw_border:
images = np.copy(images)
images[:, :, [0, -1]] = (1, 1, 1)
images[:, :, [0, -1]] = (1, 1, 1)
return np.concatenate(list(images), axis=0)
def create_image_grid(self, *args):
out = []
for arg in args:
if type(arg) == tuple:
out.append(self.create_image_column_with_kp(arg[0], arg[1]))
else:
out.append(self.create_image_column(arg))
return np.concatenate(out, axis=1)
def visualize(self, target, source, out):
images = []
# Source image with keypoints
source = source.data.cpu()
source = np.transpose(source, [0, 2, 3, 1])
images.append(source)
target = target.data.cpu().numpy()
target = np.transpose(target, [0, 2, 3, 1])
images.append(target)
# Deformed image
if 'deformed' in out:
deformed = out['deformed'].data.cpu().numpy()
deformed = np.transpose(deformed, [0, 2, 3, 1])
images.append(deformed)
prediction = out['prediction'].data.cpu().numpy()
prediction = np.transpose(prediction, [0, 2, 3, 1])
images.append(prediction)
## Visibility map
if 'visibility_map' in out:
visibility_map = out['visibility_map'].data.cpu().repeat(1, 3, 1, 1)
visibility_map = F.interpolate(visibility_map, size=source.shape[1:3]).numpy()
visibility_map = np.transpose(visibility_map, [0, 2, 3, 1])
images.append(visibility_map)
if 'segmentation' in out['seg_target']:
full_mask = []
full_mask_bin = []
mask_bin = F.interpolate(out['seg_target']['segmentation'], size=source.shape[1:3], mode='bilinear')
mask_bin = (torch.max(mask_bin, dim=1, keepdim=True)[0] == mask_bin).float()
for i in range(out['seg_target']['segmentation'].shape[1]):
mask = out['seg_target']['segmentation'][:, i:(i+1)].data.cpu().repeat(1, 3, 1, 1)
mask = F.interpolate(mask, size=source.shape[1:3], mode='bilinear')
mask = np.transpose(mask.numpy(), (0, 2, 3, 1))
mask_bin_part = mask_bin[:, i:(i+1)].data.cpu().repeat(1, 3, 1, 1)
mask_bin_part = np.transpose(mask_bin_part.numpy(), (0, 2, 3, 1))
if i != 0:
color = np.array(self.colormap((i - 1) / (out['seg_target']['segmentation'].shape[1] - 1)))[:3]
else:
color = np.array((0, 0, 0))
color = color.reshape((1, 1, 1, 3))
full_mask.append(mask * color)
full_mask_bin.append(mask_bin_part * color)
images.append(sum(full_mask))
images.append(0.3 * target + 0.7 * sum(full_mask))
images.append(sum(full_mask_bin))
images.append(0.3 * target + 0.7 * sum(full_mask_bin))
image = self.create_image_grid(*images)
image = (255 * image).astype(np.uint8)
return image