def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
config_path = os.path.join(os.path.dirname(args.restore_from), 'opts.yaml')
with open(config_path, 'r') as stream:
config = yaml.load(stream)
args.model = config['model']
print('ModelType:%s' % args.model)
print('NormType:%s' % config['norm_style'])
gpu0 = args.gpu
batchsize = args.batchsize
model_name = os.path.basename(os.path.dirname(args.restore_from))
#args.save += model_name
if not os.path.exists(args.save):
os.makedirs(args.save)
if args.model == 'DeepLab':
model = DeeplabMulti(num_classes=args.num_classes,
use_se=config['use_se'],
train_bn=False,
norm_style=config['norm_style'])
elif args.model == 'Oracle':
model = Res_Deeplab(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_ORC
elif args.model == 'DeeplabVGG':
model = DeeplabVGG(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_VGG
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
try:
model.load_state_dict(saved_state_dict)
except:
model = torch.nn.DataParallel(model)
model.load_state_dict(saved_state_dict)
model = torch.nn.DataParallel(model)
model.eval()
model.cuda(gpu0)
testloader = data.DataLoader(robotDataSet(args.data_dir,
args.data_list,
crop_size=(960, 1280),
resize_size=(1280, 960),
mean=IMG_MEAN,
scale=False,
mirror=False,
set=args.set),
batch_size=batchsize,
shuffle=False,
pin_memory=True,
num_workers=4)
scale = 1.25
testloader2 = data.DataLoader(robotDataSet(
args.data_dir,
args.data_list,
crop_size=(round(960 * scale), round(1280 * scale)),
resize_size=(round(1280 * scale), round(960 * scale)),
mean=IMG_MEAN,
scale=False,
mirror=False,
set=args.set),
batch_size=batchsize,
shuffle=False,
pin_memory=True,
num_workers=4)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(960, 1280),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(960, 1280), mode='bilinear')
sm = torch.nn.Softmax(dim=1)
for index, img_data in enumerate(zip(testloader, testloader2)):
batch, batch2 = img_data
image, _, _, name = batch
image2, _, _, name2 = batch2
print(image.shape)
inputs = image.cuda()
inputs2 = image2.cuda()
print('\r>>>>Extracting feature...%04d/%04d' %
(index * batchsize, NUM_STEPS),
end='')
if args.model == 'DeepLab':
with torch.no_grad():
output1, output2 = model(inputs)
output_batch = interp(sm(0.5 * output1 + output2))
output1, output2 = model(fliplr(inputs))
output1, output2 = fliplr(output1), fliplr(output2)
output_batch += interp(sm(0.5 * output1 + output2))
del output1, output2, inputs
output1, output2 = model(inputs2)
output_batch += interp(sm(0.5 * output1 + output2))
output1, output2 = model(fliplr(inputs2))
output1, output2 = fliplr(output1), fliplr(output2)
output_batch += interp(sm(0.5 * output1 + output2))
del output1, output2, inputs2
output_batch = output_batch.cpu().data.numpy()
elif args.model == 'DeeplabVGG' or args.model == 'Oracle':
output_batch = model(Variable(image).cuda())
output_batch = interp(output_batch).cpu().data.numpy()
output_batch = output_batch.transpose(0, 2, 3, 1)
score_batch = np.max(output_batch, axis=3)
output_batch = np.asarray(np.argmax(output_batch, axis=3),
dtype=np.uint8)
#output_batch[score_batch<3.6] = 255 #3.6 = 4*0.9
for i in range(output_batch.shape[0]):
output = output_batch[i, :, :]
output_col = colorize_mask(output)
output = Image.fromarray(output)
name_tmp = name[i].split('/')[-1]
dir_name = name[i].split('/')[-2]
save_path = args.save + '/' + dir_name
#save_path = re.replace(save_path, 'leftImg8bit', 'pseudo')
#print(save_path)
if not os.path.isdir(save_path):
os.mkdir(save_path)
output.save('%s/%s' % (save_path, name_tmp))
print('%s/%s' % (save_path, name_tmp))
output_col.save('%s/%s_color.png' %
(save_path, name_tmp.split('.')[0]))
return args.save
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
config_path = os.path.join(os.path.dirname(args.restore_from), 'opts.yaml')
with open(config_path, 'r') as stream:
config = yaml.load(stream)
args.model = config['model']
print('ModelType:%s' % args.model)
print('NormType:%s' % config['norm_style'])
gpu0 = args.gpu
batchsize = args.batchsize
model_name = os.path.basename(os.path.dirname(args.restore_from))
args.save += model_name
if not os.path.exists(args.save):
os.makedirs(args.save)
if args.model == 'DeepLab':
model = DeeplabMulti(num_classes=args.num_classes,
use_se=config['use_se'],
train_bn=False,
norm_style=config['norm_style'])
elif args.model == 'Oracle':
model = Res_Deeplab(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_ORC
elif args.model == 'DeeplabVGG':
model = DeeplabVGG(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_VGG
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
try:
model.load_state_dict(saved_state_dict)
except:
model = torch.nn.DataParallel(model)
model.load_state_dict(saved_state_dict)
#model = torch.nn.DataParallel(model)
model.eval()
model.cuda(gpu0)
testloader = data.DataLoader(cityscapesDataSet(args.data_dir,
args.data_list,
crop_size=(512, 1024),
resize_size=(1024, 512),
mean=IMG_MEAN,
scale=False,
mirror=False,
set=args.set),
batch_size=batchsize,
shuffle=False,
pin_memory=True,
num_workers=4)
scale = 1.25
testloader2 = data.DataLoader(cityscapesDataSet(
args.data_dir,
args.data_list,
crop_size=(round(512 * scale), round(1024 * scale)),
resize_size=(round(1024 * scale), round(512 * scale)),
mean=IMG_MEAN,
scale=False,
mirror=False,
set=args.set),
batch_size=batchsize,
shuffle=False,
pin_memory=True,
num_workers=4)
scale = 0.9
testloader3 = data.DataLoader(cityscapesDataSet(
args.data_dir,
args.data_list,
crop_size=(round(512 * scale), round(1024 * scale)),
resize_size=(round(1024 * scale), round(512 * scale)),
mean=IMG_MEAN,
scale=False,
mirror=False,
set=args.set),
batch_size=batchsize,
shuffle=False,
pin_memory=True,
num_workers=4)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(1024, 2048),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(1024, 2048), mode='bilinear')
sm = torch.nn.Softmax(dim=1)
log_sm = torch.nn.LogSoftmax(dim=1)
kl_distance = nn.KLDivLoss(reduction='none')
for index, img_data in enumerate(zip(testloader, testloader2,
testloader3)):
batch, batch2, batch3 = img_data
image, _, _, name = batch
image2, _, _, name2 = batch2
#image3, _, _, name3 = batch3
inputs = image.cuda()
inputs2 = image2.cuda()
#inputs3 = Variable(image3).cuda()
print('\r>>>>Extracting feature...%03d/%03d' %
(index * batchsize, NUM_STEPS),
end='')
if args.model == 'DeepLab':
with torch.no_grad():
output1, output2 = model(inputs)
output_batch = interp(sm(0.5 * output1 + output2))
heatmap_output1, heatmap_output2 = output1, output2
#output_batch = interp(sm(output1))
#output_batch = interp(sm(output2))
output1, output2 = model(fliplr(inputs))
output1, output2 = fliplr(output1), fliplr(output2)
output_batch += interp(sm(0.5 * output1 + output2))
heatmap_output1, heatmap_output2 = heatmap_output1 + output1, heatmap_output2 + output2
#output_batch += interp(sm(output1))
#output_batch += interp(sm(output2))
del output1, output2, inputs
output1, output2 = model(inputs2)
output_batch += interp(sm(0.5 * output1 + output2))
#output_batch += interp(sm(output1))
#output_batch += interp(sm(output2))
output1, output2 = model(fliplr(inputs2))
output1, output2 = fliplr(output1), fliplr(output2)
output_batch += interp(sm(0.5 * output1 + output2))
#output_batch += interp(sm(output1))
#output_batch += interp(sm(output2))
del output1, output2, inputs2
output_batch = output_batch.cpu().data.numpy()
heatmap_batch = torch.sum(kl_distance(log_sm(heatmap_output1),
sm(heatmap_output2)),
dim=1)
heatmap_batch = torch.log(
1 + 10 * heatmap_batch) # for visualization
heatmap_batch = heatmap_batch.cpu().data.numpy()
#output1, output2 = model(inputs3)
#output_batch += interp(sm(0.5* output1 + output2)).cpu().data.numpy()
#output1, output2 = model(fliplr(inputs3))
#output1, output2 = fliplr(output1), fliplr(output2)
#output_batch += interp(sm(0.5 * output1 + output2)).cpu().data.numpy()
#del output1, output2, inputs3
elif args.model == 'DeeplabVGG' or args.model == 'Oracle':
output_batch = model(Variable(image).cuda())
output_batch = interp(output_batch).cpu().data.numpy()
output_batch = output_batch.transpose(0, 2, 3, 1)
scoremap_batch = np.asarray(np.max(output_batch, axis=3))
output_batch = np.asarray(np.argmax(output_batch, axis=3),
dtype=np.uint8)
output_iterator = []
heatmap_iterator = []
scoremap_iterator = []
for i in range(output_batch.shape[0]):
output_iterator.append(output_batch[i, :, :])
heatmap_iterator.append(heatmap_batch[i, :, :] /
np.max(heatmap_batch[i, :, :]))
scoremap_iterator.append(1 - scoremap_batch[i, :, :] /
np.max(scoremap_batch[i, :, :]))
name_tmp = name[i].split('/')[-1]
name[i] = '%s/%s' % (args.save, name_tmp)
with Pool(4) as p:
p.map(save, zip(output_iterator, name))
p.map(save_heatmap, zip(heatmap_iterator, name))
p.map(save_scoremap, zip(scoremap_iterator, name))
del output_batch
return args.save
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
w, h = map(int, args.input_size.split(','))
config_path = os.path.join(os.path.dirname(args.restore_from), 'opts.yaml')
with open(config_path, 'r') as stream:
config = yaml.load(stream)
args.model = config['model']
print('ModelType:%s' % args.model)
print('NormType:%s' % config['norm_style'])
gpu0 = args.gpu
batchsize = args.batchsize
model_name = os.path.basename(os.path.dirname(args.restore_from))
#args.save += model_name
if not os.path.exists(args.save):
os.makedirs(args.save)
confidence_path = os.path.join(args.save, 'submit/confidence')
label_path = os.path.join(args.save, 'submit/labelTrainIds')
label_invalid_path = os.path.join(args.save,
'submit/labelTrainIds_invalid')
for path in [confidence_path, label_path, label_invalid_path]:
if not os.path.exists(path):
os.makedirs(path)
if args.model == 'DeepLab':
model = DeeplabMulti(num_classes=args.num_classes,
use_se=config['use_se'],
train_bn=False,
norm_style=config['norm_style'])
elif args.model == 'Oracle':
model = Res_Deeplab(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_ORC
elif args.model == 'DeeplabVGG':
model = DeeplabVGG(num_classes=args.num_classes)
if args.restore_from == RESTORE_FROM:
args.restore_from = RESTORE_FROM_VGG
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
try:
model.load_state_dict(saved_state_dict)
except:
model = torch.nn.DataParallel(model)
model.load_state_dict(saved_state_dict)
model.eval()
model.cuda(gpu0)
testloader = data.DataLoader(DarkZurichDataSet(args.data_dir,
args.data_list,
crop_size=(h, w),
resize_size=(w, h),
mean=IMG_MEAN,
scale=False,
mirror=False,
set=args.set),
batch_size=batchsize,
shuffle=False,
pin_memory=True,
num_workers=4)
scale = 1.25
testloader2 = data.DataLoader(DarkZurichDataSet(
args.data_dir,
args.data_list,
crop_size=(round(h * scale), round(w * scale)),
resize_size=(round(w * scale), round(h * scale)),
mean=IMG_MEAN,
scale=False,
mirror=False,
set=args.set),
batch_size=batchsize,
shuffle=False,
pin_memory=True,
num_workers=4)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(1080, 1920),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(1080, 1920), mode='bilinear')
sm = torch.nn.Softmax(dim=1)
log_sm = torch.nn.LogSoftmax(dim=1)
kl_distance = nn.KLDivLoss(reduction='none')
prior = np.load('./utils/prior_all.npy').transpose(
(2, 0, 1))[np.newaxis, :, :, :]
prior = torch.from_numpy(prior)
for index, img_data in enumerate(zip(testloader, testloader2)):
batch, batch2 = img_data
image, _, name = batch
image2, _, name2 = batch2
inputs = image.cuda()
inputs2 = image2.cuda()
print('\r>>>>Extracting feature...%04d/%04d' %
(index * batchsize, args.batchsize * len(testloader)),
end='')
if args.model == 'DeepLab':
with torch.no_grad():
output1, output2 = model(inputs)
output_batch = interp(sm(0.5 * output1 + output2))
heatmap_batch = torch.sum(kl_distance(log_sm(output1),
sm(output2)),
dim=1)
output1, output2 = model(fliplr(inputs))
output1, output2 = fliplr(output1), fliplr(output2)
output_batch += interp(sm(0.5 * output1 + output2))
del output1, output2, inputs
output1, output2 = model(inputs2)
output_batch += interp(sm(0.5 * output1 + output2))
output1, output2 = model(fliplr(inputs2))
output1, output2 = fliplr(output1), fliplr(output2)
output_batch += interp(sm(0.5 * output1 + output2))
del output1, output2, inputs2
ratio = 0.95
output_batch = output_batch.cpu() / 4
# output_batch = output_batch *(ratio + (1 - ratio) * prior)
output_batch = output_batch.data.numpy()
heatmap_batch = heatmap_batch.cpu().data.numpy()
elif args.model == 'DeeplabVGG' or args.model == 'Oracle':
output_batch = model(Variable(image).cuda())
output_batch = interp(output_batch).cpu().data.numpy()
output_batch = output_batch.transpose(0, 2, 3, 1)
score_batch = np.max(output_batch, axis=3)
output_batch = np.asarray(np.argmax(output_batch, axis=3),
dtype=np.uint8)
threshold = 0.3274
for i in range(output_batch.shape[0]):
output_single = output_batch[i, :, :]
output_col = colorize_mask(output_single)
output = Image.fromarray(output_single)
name_tmp = name[i].split('/')[-1]
dir_name = name[i].split('/')[-2]
save_path = args.save + '/' + dir_name
if not os.path.isdir(save_path):
os.mkdir(save_path)
output.save('%s/%s' % (save_path, name_tmp))
print('%s/%s' % (save_path, name_tmp))
output_col.save('%s/%s_color.png' %
(save_path, name_tmp.split('.')[0]))
# heatmap_tmp = heatmap_batch[i,:,:]/np.max(heatmap_batch[i,:,:])
# fig = plt.figure()
# plt.axis('off')
# heatmap = plt.imshow(heatmap_tmp, cmap='viridis')
# fig.colorbar(heatmap)
# fig.savefig('%s/%s_heatmap.png' % (save_path, name_tmp.split('.')[0]))
if args.set == 'test' or args.set == 'val':
# label
output.save('%s/%s' % (label_path, name_tmp))
# label invalid
output_single[score_batch[i, :, :] < threshold] = 255
output = Image.fromarray(output_single)
output.save('%s/%s' % (label_invalid_path, name_tmp))
# conficence
confidence = score_batch[i, :, :] * 65535
confidence = np.asarray(confidence, dtype=np.uint16)
print(confidence.min(), confidence.max())
iio.imwrite('%s/%s' % (confidence_path, name_tmp), confidence)
return args.save