-
Notifications
You must be signed in to change notification settings - Fork 94
/
eval_helper.py
199 lines (153 loc) · 6.18 KB
/
eval_helper.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
import torch
import torch.nn.functional as F
from util.util import resize_max_side
def safe_forward(model, im, seg, inter_s8=None, inter_s4=None):
"""
Slightly pads the input image such that its length is a multiple of 8
"""
b, _, ph, pw = seg.shape
if (ph % 8 != 0) or (pw % 8 != 0):
newH = ((ph//8+1)*8)
newW = ((pw//8+1)*8)
p_im = torch.zeros(b, 3, newH, newW).cuda()
p_seg = torch.zeros(b, 1, newH, newW).cuda() - 1
p_im[:,:,0:ph,0:pw] = im
p_seg[:,:,0:ph,0:pw] = seg
im = p_im
seg = p_seg
if inter_s8 is not None:
p_inter_s8 = torch.zeros(b, 1, newH, newW).cuda() - 1
p_inter_s8[:,:,0:ph,0:pw] = inter_s8
inter_s8 = p_inter_s8
if inter_s4 is not None:
p_inter_s4 = torch.zeros(b, 1, newH, newW).cuda() - 1
p_inter_s4[:,:,0:ph,0:pw] = inter_s4
inter_s4 = p_inter_s4
images = model(im, seg, inter_s8, inter_s4)
return_im = {}
for key in ['pred_224', 'pred_28_3', 'pred_56_2']:
return_im[key] = images[key][:,:,0:ph,0:pw]
del images
return return_im
def process_high_res_im(model, im, seg, para, name=None, aggre_device='cpu:0'):
im = im.to(aggre_device)
seg = seg.to(aggre_device)
max_L = para['L']
stride = para['stride']
_, _, h, w = seg.shape
"""
Global Step
"""
if max(h, w) > max_L:
im_small = resize_max_side(im, max_L, 'area')
seg_small = resize_max_side(seg, max_L, 'area')
else:
im_small = im
seg_small = seg
images = safe_forward(model, im_small, seg_small)
pred_224 = images['pred_224'].to(aggre_device)
pred_56 = images['pred_56_2'].to(aggre_device)
# del images
if para['clear']:
torch.cuda.empty_cache()
"""
Local step
"""
for new_size in [max(h, w)]:
im_small = resize_max_side(im, new_size, 'area')
seg_small = resize_max_side(seg, new_size, 'area')
_, _, h, w = seg_small.shape
combined_224 = torch.zeros_like(seg_small)
combined_weight = torch.zeros_like(seg_small)
r_pred_224 = (F.interpolate(pred_224, size=(h, w), mode='bilinear', align_corners=False)>0.5).float()*2-1
r_pred_56 = F.interpolate(pred_56, size=(h, w), mode='bilinear', align_corners=False)*2-1
padding = 16
step_size = stride - padding*2
step_len = max_L
used_start_idx = {}
for x_idx in range((w)//step_size+1):
for y_idx in range((h)//step_size+1):
start_x = x_idx * step_size
start_y = y_idx * step_size
end_x = start_x + step_len
end_y = start_y + step_len
# Shift when required
if end_y > h:
end_y = h
start_y = h - step_len
if end_x > w:
end_x = w
start_x = w - step_len
# Bound x/y range
start_x = max(0, start_x)
start_y = max(0, start_y)
end_x = min(w, end_x)
end_y = min(h, end_y)
# The same crop might appear twice due to bounding/shifting
start_idx = start_y*w + start_x
if start_idx in used_start_idx:
continue
else:
used_start_idx[start_idx] = True
# Take crop
im_part = im_small[:,:,start_y:end_y, start_x:end_x]
seg_224_part = r_pred_224[:,:,start_y:end_y, start_x:end_x]
seg_56_part = r_pred_56[:,:,start_y:end_y, start_x:end_x]
# Skip when it is not an interesting crop anyway
seg_part_norm = (seg_224_part>0).float()
high_thres = 0.9
low_thres = 0.1
if (seg_part_norm.mean() > high_thres) or (seg_part_norm.mean() < low_thres):
continue
grid_images = safe_forward(model, im_part, seg_224_part, seg_56_part)
grid_pred_224 = grid_images['pred_224'].to(aggre_device)
# Padding
pred_sx = pred_sy = 0
pred_ex = step_len
pred_ey = step_len
if start_x != 0:
start_x += padding
pred_sx += padding
if start_y != 0:
start_y += padding
pred_sy += padding
if end_x != w:
end_x -= padding
pred_ex -= padding
if end_y != h:
end_y -= padding
pred_ey -= padding
combined_224[:,:,start_y:end_y, start_x:end_x] += grid_pred_224[:,:,pred_sy:pred_ey,pred_sx:pred_ex]
del grid_pred_224
if para['clear']:
torch.cuda.empty_cache()
# Used for averaging
combined_weight[:,:,start_y:end_y, start_x:end_x] += 1
# Final full resolution output
seg_norm = (r_pred_224/2+0.5)
pred_224 = combined_224 / combined_weight
pred_224 = torch.where(combined_weight==0, seg_norm, pred_224)
_, _, h, w = seg.shape
images = {}
images['pred_224'] = F.interpolate(pred_224, size=(h, w), mode='bilinear', align_corners=False)
if para['clear']:
torch.cuda.empty_cache()
return images
def process_im_single_pass(model, im, seg, min_size, para):
"""
A single pass version, aka global step only.
"""
max_size = para['L']
_, _, h, w = im.shape
if max(h, w) < min_size:
im = resize_max_side(im, min_size, 'bicubic')
seg = resize_max_side(seg, min_size, 'bilinear')
if max(h, w) > max_size:
im = resize_max_side(im, max_size, 'area')
seg = resize_max_side(seg, max_size, 'area')
images = safe_forward(model, im, seg)
if max(h, w) < min_size:
images['pred_224'] = F.interpolate(images['pred_224'], size=(h, w), mode='area')
elif max(h, w) > max_size:
images['pred_224'] = F.interpolate(images['pred_224'], size=(h, w), mode='bilinear', align_corners=False)
return images