-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtester.py
164 lines (132 loc) · 7.39 KB
/
tester.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
from time import time
import torch
from utils.utils import generate_piwivos_distance_matrix, probability_to_prediction, resize_tensor, \
compare_two_frames_k_avg
from utils.input_output import save_mask_test, load_model
from utils.evaluation_metrics import eval_metrics
class Tester:
def __init__(self, device, model, test_loader, args):
self.device = device
self.model = model
self.test_loader = test_loader
self.args = args
checkpoint_path = args.checkpoint_path
model_state_dict = load_model(checkpoint_path)
self.model.load_state_dict(model_state_dict)
self.model = self.model.to(self.device)
def test(self):
self.model.eval()
with torch.no_grad():
print(f'Testing model using checkpoint {self.args.checkpoint_path}...')
since = time()
test_J = []
test_F = []
all_time = 0
all_frames = 0
all_fps = []
distance_matrix = generate_piwivos_distance_matrix(self.args.model_name).to(self.device)
for ii, (frames, masks, info) in enumerate(self.test_loader):
seq_name = info['name'][0]
n_frames = info['n_frames'][0].item()
n_objects = info['n_objects'][0].item()
original_shape = tuple([x.item() for x in info['original_shape']])
has_gt = info['has_gt'][0]
palette = [x.item() for x in info['palette'][0]]
sequence_time = 0
since_frame_0 = time()
# Move frame 0 to GPU and forward pass it
frame_0 = frames[:, 0]
_, ch, h, w = frame_0.shape
frame_0 = frame_0.to(self.device)
frame_0 = self.model(frame_0) # (1, ch, h, w)
_, n_features, low_res_h, low_res_w = frame_0.shape
# Reduce frame_0 masks and move to GPU
# (batch, ch=1, h, w) -> (batch, ch=1, h', w')
masks_0 = resize_tensor(masks[:, 0], low_res_h, low_res_w)
masks_0 = masks_0.to(self.device)
# frame_prev is frame_0 for first iteration
frame_prev = frame_0
masks_prev = masks_0
sequence_time += time() - since_frame_0
# Sequence metrics
seq_dav_j = torch.empty(0)
seq_dav_f = torch.empty(0)
for t in range(1, n_frames):
since_frame_t_forward = time()
# Select frame_t, move to GPU and forward pass
frame_t = frames[:, t]
frame_t = frame_t.to(self.device)
frame_t = self.model(frame_t)
# Obtain scores vs frame_0 and frame_prev
scores_0, has_data_0 = compare_two_frames_k_avg(frame_0, frame_t, masks_0, n_objects,
self.args.k[0], self.args.lambd[0],
distance_matrix)
scores_prev, has_data_prev = compare_two_frames_k_avg(frame_prev, frame_t, masks_prev,
n_objects, self.args.k[1],
self.args.lambd[1], distance_matrix)
sequence_time += time() - since_frame_t_forward
# Generate low_res_mask, which will be used as masks_prev for following iteration
since_frame_t_low = time()
probabilities_low = torch.cat((scores_0, scores_prev), dim=1)
predicted_masks_low = probability_to_prediction(probabilities_low, n_objects)
sequence_time += time() - since_frame_t_low
# ## Prediction and metrics ##
since_frame_t_pred = time()
# Upscale score volumes to original dimensions
scores_0 = resize_tensor(scores_0, original_shape[1], original_shape[0], mode='bilinear',
align_corners=True)
scores_prev = resize_tensor(scores_prev, original_shape[1], original_shape[0], mode='bilinear',
align_corners=True)
# Merge both scores
probabilities = torch.cat((scores_0, scores_prev), dim=1)
predicted_masks = probability_to_prediction(probabilities, n_objects)
sequence_time += time() - since_frame_t_pred
if has_gt:
# Move gt masks to GPU to compute metrics
masks_t = masks[:, t]
gt_masks = resize_tensor(masks_t, original_shape[1], original_shape[0])
gt_masks = gt_masks.to(self.device)
# Compute metrics
frame_J, _, frame_F = eval_metrics(predicted_masks, gt_masks, n_objects)
seq_dav_j = torch.cat((seq_dav_j, frame_J), dim=0)
seq_dav_f = torch.cat((seq_dav_f, frame_F), dim=0)
if self.args.export:
save_mask_test(predicted_masks, seq_name, t, palette, self.args.checkpoint_path,
self.args.image_set)
frame_prev = frame_t
masks_prev = predicted_masks_low
all_time += sequence_time
all_frames += n_frames
seq_fps = n_frames / sequence_time
all_fps.append(seq_fps)
if has_gt:
per_object_j = seq_dav_j.mean(0)
per_object_f = seq_dav_f.mean(0)
seq_dav_j = seq_dav_j.mean().item()
seq_dav_f = seq_dav_f.mean().item()
test_J.append(seq_dav_j)
test_F.append(seq_dav_f)
print(f'{seq_name:<20} | '
f'J: {100 * seq_dav_j:>5.2f} % | '
f'F: {100 * seq_dav_f:>5.2f} % | '
f'G: {50 * seq_dav_j + 50 * seq_dav_f:>5.2f} % | FPS: {seq_fps:5.2f}')
for obj in range(per_object_j.shape[0]):
print(f'\t\tObject #{obj:2} | '
f'J: {100 * per_object_j[obj]:>5.2f} % | '
f'F: {100 * per_object_f[obj]:>5.2f} % | '
f'G: {50 * per_object_j[obj] + 50 * per_object_f[obj]:>5.2f} %')
print()
else:
print(f'{seq_name:<20} | FPS: {seq_fps:5.2f}')
fps_mean = sum(all_fps) / len(all_fps)
fps_real = all_frames / all_time
print()
if has_gt:
test_J = sum(test_J) / len(test_J)
test_F = sum(test_F) / len(test_F)
print(f"Testing complted. Elapsed time: {int(time() - since) // 60}'")
print(f"J: {100 * test_J:.3f} % | F: {100 * test_F:.3f} % | G: {50 * test_J + 50 * test_F:.3f} %")
print(f"FPS real: {fps_real:.3f} | FPS mean: {fps_mean:.3f}")
else:
print(f"Testing complted. Elapsed time: {int(time() - since) // 60}'")
print(f"FPS real: {fps_real:.3f} | FPS mean: {fps_mean:.3f}")