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Commit 6d73a6e

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adam9500370meetps
adam9500370
authored and
meetps
committed
Make scripts clean
1 parent 54aa674 commit 6d73a6e
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‎test.py

Copy file name to clipboardExpand all lines: test.py
+6-8Lines changed: 6 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -39,7 +39,7 @@ def test(args):
3939
resized_img = misc.imresize(img, (loader.img_size[0], loader.img_size[1]), interp='bicubic')
4040

4141
orig_size = img.shape[:-1]
42-
if model_name == 'pspnet':
42+
if model_name in ['pspnet', 'icnet', 'icnetBN']:
4343
img = misc.imresize(img, (orig_size[0]//2*2+1, orig_size[1]//2*2+1)) # uint8 with RGB mode, resize width and height which are odd numbers
4444
else:
4545
img = misc.imresize(img, (loader.img_size[0], loader.img_size[1]))
@@ -65,10 +65,7 @@ def test(args):
6565
else:
6666
images = Variable(img, volatile=True)
6767

68-
if model_name == 'pspnet':
69-
outputs = model(images)[-1]
70-
else:
71-
outputs = model(images)
68+
outputs = model(images)
7269
#outputs = F.softmax(outputs, dim=1)
7370

7471
if args.dcrf:
@@ -93,9 +90,10 @@ def test(args):
9390
misc.imsave(dcrf_path, decoded_crf)
9491
print("Dense CRF Processed Mask Saved at: {}".format(dcrf_path))
9592

96-
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0).astype(np.uint8)
97-
if model_name == 'pspnet':
98-
pred = misc.imresize(pred, orig_size, 'nearest') # uint8 with RGB mode
93+
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
94+
if model_name in ['pspnet', 'icnet', 'icnetBN']:
95+
pred = pred.astype(np.float32)
96+
pred = misc.imresize(pred, orig_size, 'nearest', mode='F') # float32 with F mode, resize back to orig_size
9997
decoded = loader.decode_segmap(pred)
10098
print('Classes found: ', np.unique(pred))
10199
misc.imsave(args.out_path, decoded)

‎train.py

Copy file name to clipboardExpand all lines: train.py
+1-10Lines changed: 1 addition & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -86,16 +86,7 @@ def train(args):
8686
optimizer.zero_grad()
8787
outputs = model(images)
8888

89-
if args.arch == 'pspnet':
90-
aux_cls, final_cls = outputs
91-
92-
aux_loss = loss_fn(input=aux_cls, target=labels)
93-
final_loss = loss_fn(input=final_cls, target=labels)
94-
95-
LAMBDA1, LAMBDA2 = 0.4, 1.0
96-
loss = LAMBDA1 * aux_loss + LAMBDA2 * final_loss
97-
else:
98-
loss = loss_fn(input=outputs, target=labels)
89+
loss = loss_fn(input=outputs, target=labels)
9990

10091
loss.backward()
10192
optimizer.step()

‎validate.py

Copy file name to clipboardExpand all lines: validate.py
+17-22Lines changed: 17 additions & 22 deletions
Original file line numberDiff line numberDiff line change
@@ -49,27 +49,22 @@ def validate(args):
4949
images = Variable(images.cuda(), volatile=True)
5050
#labels = Variable(labels.cuda(), volatile=True)
5151

52-
if model_name == 'pspnet':
53-
outputs = model(images)[-1]
54-
if args.include_flip_mode:
55-
outputs = outputs.data.cpu().numpy()
56-
flipped_images = np.copy(images.data.cpu().numpy()[:, :, :, ::-1])
57-
flipped_images = Variable(torch.from_numpy( flipped_images ).float().cuda(), volatile=True)
58-
outputs_flipped = model( flipped_images )[-1].data.cpu().numpy()
59-
outputs = (outputs + outputs_flipped[:, :, :, ::-1]) / 2.0
60-
else:
52+
if args.eval_flip:
6153
outputs = model(images)
62-
if args.include_flip_mode:
63-
outputs = outputs.data.cpu().numpy()
64-
flipped_images = np.copy(images.data.cpu().numpy()[:, :, :, ::-1])
65-
flipped_images = Variable(torch.from_numpy( flipped_images ).float().cuda(), volatile=True)
66-
outputs_flipped = model( flipped_images ).data.cpu().numpy()
67-
outputs = (outputs + outputs_flipped[:, :, :, ::-1]) / 2.0
68-
69-
if args.include_flip_mode:
70-
pred = np.argmax(outputs, axis=1).astype(np.uint8)
54+
55+
# Flip images in numpy (not support in tensor)
56+
outputs = outputs.data.cpu().numpy()
57+
flipped_images = np.copy(images.data.cpu().numpy()[:, :, :, ::-1])
58+
flipped_images = Variable(torch.from_numpy( flipped_images ).float().cuda(), volatile=True)
59+
outputs_flipped = model( flipped_images )
60+
outputs_flipped = outputs_flipped.data.cpu().numpy()
61+
outputs = (outputs + outputs_flipped[:, :, :, ::-1]) / 2.0
62+
63+
pred = np.argmax(outputs, axis=1)
7164
else:
72-
pred = outputs.data.max(1)[1].cpu().numpy().astype(np.uint8)
65+
outputs = model(images)
66+
pred = outputs.data.max(1)[1].cpu().numpy()
67+
7368
#gt = labels.data.cpu().numpy()
7469
gt = labels.numpy()
7570

@@ -103,11 +98,11 @@ def validate(args):
10398
help='Disable input image scales normalization [0, 1] | True by default')
10499
parser.set_defaults(img_norm=True)
105100

106-
parser.add_argument('--include_flip_mode', dest='include_flip_mode', action='store_true',
101+
parser.add_argument('--eval_flip', dest='eval_flip', action='store_true',
107102
help='Enable evaluation with flipped image | True by default')
108-
parser.add_argument('--no-include_flip_mode', dest='include_flip_mode', action='store_false',
103+
parser.add_argument('--no-eval_flip', dest='eval_flip', action='store_false',
109104
help='Disable evaluation with flipped image | True by default')
110-
parser.set_defaults(include_flip_mode=True)
105+
parser.set_defaults(eval_flip=True)
111106

112107
parser.add_argument('--batch_size', nargs='?', type=int, default=1,
113108
help='Batch Size')

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