-
Notifications
You must be signed in to change notification settings - Fork 3
/
source_triple_multitask_tester.py
205 lines (159 loc) · 8.05 KB
/
source_triple_multitask_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
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
import argparse
import os
from pprint import pprint
import numpy as np
import torch
from PIL import Image
from torch.autograd import Variable
from torch.utils import data
from tqdm import tqdm
from argmyparse import add_additional_params_to_args
from datasets import get_dataset, AVAILABLE_DATASET_LIST
from loss import CrossEntropyLoss2d
from models.model_util import get_triple_multitask_models
from transform import get_img_transform, get_lbl_transform, unnormalize
from util import mkdir_if_not_exist, save_dic_to_json, check_if_done, save_colorized_lbl, exec_eval, calc_entropy, \
get_class_weight_from_file, set_debugger_org_frc
set_debugger_org_frc()
parser = argparse.ArgumentParser(description='Adapt tester for validation data')
parser.add_argument('tgt_dataset', type=str, choices=AVAILABLE_DATASET_LIST)
parser.add_argument('--split', type=str, default='val', help="'val' or 'test') is used")
parser.add_argument('trained_checkpoint', type=str, metavar="PTH")
parser.add_argument('--outdir', type=str, default="test_output",
help='output directory')
parser.add_argument('--test_img_shape', default=None, nargs=2,
help="W H, FOR Valid(2048, 1024) Test(1280, 720)")
parser.add_argument("--saves_prob", action="store_true",
help='whether you save probability tensors')
args = parser.parse_args()
args = add_additional_params_to_args(args)
if not os.path.exists(args.trained_checkpoint):
raise OSError("%s does not exist!" % args.resume)
checkpoint = torch.load(args.trained_checkpoint)
train_args = checkpoint['args'] # Load args!
weight = get_class_weight_from_file(n_class=train_args.n_class, weight_filename=train_args.loss_weights_file,
add_bg_loss=train_args.add_bg_loss)
if torch.cuda.is_available():
weight = weight.cuda()
criterion = CrossEntropyLoss2d(weight)
if "use_seg2bd_conv" not in train_args.__dict__.keys():
train_args.use_seg2bd_conv = False
model_enc, model_dec = get_triple_multitask_models(net_name=train_args.net, input_ch=train_args.input_ch,
n_class=train_args.n_class,
is_data_parallel=train_args.is_data_parallel,
semseg_criterion=criterion,
depth_shortcut=train_args.depth_shortcut,
semseg_shortcut=train_args.semseg_shortcut,
add_pred_seg_boundary_loss=train_args.add_pred_seg_boundary_loss,
is_src_only=True, use_seg2bd_conv=train_args.use_seg2bd_conv)
model_enc.load_state_dict(checkpoint['enc_state_dict'])
model_dec.load_state_dict(checkpoint['dec_state_dict'])
print ("----- train args ------")
pprint(train_args.__dict__, indent=4)
print ("-" * 50)
args.train_img_shape = train_args.train_img_shape
print("=> loaded checkpoint '{}'".format(args.trained_checkpoint))
indir, infn = os.path.split(args.trained_checkpoint)
trained_mode = indir.split(os.path.sep)[-2]
args.mode = "%s---%s-%s" % (trained_mode, args.tgt_dataset, args.split)
model_name = infn.replace(".pth", "")
base_outdir = os.path.join(args.outdir, args.mode, model_name)
mkdir_if_not_exist(base_outdir)
json_fn = os.path.join(base_outdir, "param.json")
check_if_done(json_fn)
args.machine = os.uname()[1]
save_dic_to_json(args.__dict__, json_fn)
train_img_shape = tuple([int(x) for x in args.train_img_shape])
test_img_shape = tuple([int(x) for x in args.test_img_shape])
if "crop_size" in train_args.__dict__.keys() and train_args.crop_size > 0:
train_img_shape = test_img_shape
print ("train_img_shape was set to the same as test_img_shape")
if "normalize_way" in train_args.__dict__.keys():
img_transform = get_img_transform(img_shape=train_img_shape, normalize_way=train_args.normalize_way)
else:
img_transform = get_img_transform(img_shape=train_img_shape)
if "background_id" in train_args.__dict__.keys():
label_transform = get_lbl_transform(img_shape=train_img_shape, n_class=train_args.n_class,
background_id=train_args.background_id)
else:
label_transform = get_lbl_transform(img_shape=train_img_shape, n_class=train_args.n_class)
tgt_dataset = get_dataset(dataset_name=args.tgt_dataset, split=args.split, img_transform=img_transform,
label_transform=label_transform, test=True, input_ch=6)
target_loader = data.DataLoader(tgt_dataset, batch_size=1, pin_memory=True)
if torch.cuda.is_available():
model_enc.cuda()
model_dec.cuda()
model_enc.eval()
model_dec.eval()
def add_subdir_if_necessary(outdir, subdir, tgt_dataset):
if tgt_dataset == "suncg":
outdir = os.path.join(outdir, subdir)
return outdir
data_list_fn = os.path.join(base_outdir, "data_list.txt")
with open(data_list_fn, "w") as f:
pass
total_ent = 0.
for index, (imgs, labels, paths) in tqdm(enumerate(target_loader)):
path = paths[0]
imgs = Variable(imgs)
if torch.cuda.is_available():
imgs = imgs.cuda()
rgbs = imgs[:, :3, :, :]
# depths = imgs[:, 3:-1, :, :]
# boundaries = imgs[:, -1:, :, :]
fets = model_enc(rgbs)
pred_semseg, pred_depth, pred_boundary = model_dec(fets)
total_ent += calc_entropy(pred_semseg).data.cpu().numpy()[0]
if args.saves_prob:
# Save probability tensors
prob_outdir = os.path.join(base_outdir, "prob")
mkdir_if_not_exist(prob_outdir)
prob_outfn = os.path.join(prob_outdir, path.split('/')[-1].replace('png', 'npy'))
np.save(prob_outfn, pred_semseg[0].data.cpu().numpy())
# Save predicted pixel labels(pngs)
if train_args.add_bg_loss:
pred = pred_semseg[0, :args.n_class].data.max(0)[1].cpu()
else:
pred = pred_semseg[0, :args.n_class - 1].data.max(0)[1].cpu()
img = Image.fromarray(np.uint8(pred.numpy()))
img = img.resize(test_img_shape, Image.NEAREST)
label_outdir = os.path.join(base_outdir, "label")
subdir = path.split('/')[-2]
label_outdir = add_subdir_if_necessary(label_outdir, subdir, args.tgt_dataset)
mkdir_if_not_exist(label_outdir)
label_fn = os.path.join(label_outdir, path.split('/')[-1])
img.save(label_fn)
# Save visualized predicted pixel labels(pngs)
vis_outdir = os.path.join(base_outdir, "vis")
vis_outdir = add_subdir_if_necessary(vis_outdir, subdir, args.tgt_dataset)
mkdir_if_not_exist(vis_outdir)
vis_fn = os.path.join(vis_outdir, path.split('/')[-1])
save_colorized_lbl(img, vis_fn, args.tgt_dataset)
# Save Predicted Depth Image
depth_outdir = os.path.join(base_outdir, "depth")
depth_outdir = add_subdir_if_necessary(depth_outdir, subdir, args.tgt_dataset)
mkdir_if_not_exist(depth_outdir)
depth_fn = os.path.join(depth_outdir, path.split('/')[-1])
depth_im = pred_depth.data.cpu().numpy()[0]
depth_im = depth_im.transpose([1, 2, 0])
depth_im = unnormalize(depth_im)
depth_im = depth_im.resize(test_img_shape, Image.BILINEAR)
depth_im.save(depth_fn)
# Save Predicted Boundary Image
boundary_outdir = os.path.join(base_outdir, "boundary")
boundary_outdir = add_subdir_if_necessary(boundary_outdir, subdir, args.tgt_dataset)
mkdir_if_not_exist(boundary_outdir)
boundary_fn = os.path.join(boundary_outdir, path.split('/')[-1])
boundary_im = pred_boundary.data.cpu().numpy()[0]
boundary_im = boundary_im.transpose([1, 2, 0])[:, :, 0]
boundary_im = Image.fromarray(np.uint8(boundary_im * 255))
# boundary_im = replace_lbl_id(boundary_im, before_id=1, after_id=255)
boundary_im = boundary_im.resize(test_img_shape, Image.BILINEAR)
boundary_im.save(boundary_fn)
with open(data_list_fn, "a") as f:
f.write(path.split('/')[-2] + os.path.sep + path.split('/')[-1].split("_")[0] + "\n")
exec_eval(args.tgt_dataset, label_outdir)
ave_ent = total_ent / len(target_loader)
print ("average entropy: %s" % ave_ent)
with open(os.path.join(base_outdir, "ave_ent_%s.txt" % ave_ent), "w") as f:
f.write(str(ave_ent))