def main():
# get the image path list for inference
image_dir = Path('./test_data/test_portrait_images/your_portrait_im/')
image_paths = list(image_dir.glob('*'))
print("Number of images: ", len(image_paths))
# indicate the output directory
out_dir = Path('./test_data/test_portrait_images/your_portrait_results')
out_dir.mkdir(exist_ok=True)
# Load the cascade face detection model
face_cascade = cv2.CascadeClassifier('./saved_models/face_detection_cv2/haarcascade_frontalface_default.xml')
# u2net_portrait path
model_dir = './saved_models/u2net_portrait/u2net_portrait.pth'
# load u2net_portrait model
net = U2NET(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# do the inference one-by-one
for i in trange(len(image_paths)):
# load each image
img = cv2.imread(str(image_paths[i]))[..., ::-1]
face = detect_single_face(face_cascade, img)
im_face = crop_face(img, face)
im_portrait = inference(net, im_face)
# save the output
cv2.imwrite(out_dir / (image_paths[i].stem + '.png'), (im_portrait * 255).astype(np.uint8))
def main():
# --------- 1. get image path and name ---------
model_name = 'u2net_portrait' #u2netp
image_dir = './test_data/test_portrait_images/portrait_im'
prediction_dir = './test_data/test_portrait_images/portrait_results'
if (not os.path.exists(prediction_dir)):
os.mkdir(prediction_dir)
model_dir = './saved_models/u2net_portrait/u2net_portrait.pth'
img_name_list = glob.glob(image_dir + '/*')
print("Number of images: ", len(img_name_list))
# --------- 2. dataloader ---------
#1. dataloader
test_salobj_dataset = SalObjDataset(
img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([RescaleT(512),
ToTensorLab(flag=0)]))
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. model define ---------
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
print("inferencing:", img_name_list[i_test].split(os.sep)[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
# normalization
pred = 1.0 - d1[:, 0, :, :]
pred = normPRED(pred)
# save results to test_results folder
save_output(img_name_list[i_test], pred, prediction_dir)
del d1, d2, d3, d4, d5, d6, d7
def __init__(self, checkpoint_path: str):
net = U2NET(3, 1)
net.load_state_dict(_torch.load(checkpoint_path))
if _torch.cuda.is_available():
net.cuda()
net.eval()
self.net = net
def __init__(self, model_name='u2net', cuda_mode=True, output_format='np'):
self.model_name = model_name
self.cuda_mode = cuda_mode and torch.cuda.is_available() # Fallback to CPU mode, if cuda is not available
self.trans = transforms.Compose([RescaleT(320), ToTensorLab(flag=0)])
self.output_format = output_format
# Validate
if output_format not in self.FORMATS:
raise AssertionError('Invalid "output_format"', 'Use "np" or "pil"')
if model_name not in self.MODEL_NAMES:
raise AssertionError('Invalid "model_name"', 'Use "u2net" or "u2netp"')
if model_name == 'u2net':
print("Model: U2NET (173.6 MB)")
self.net = U2NET(3, 1) # 173.6 MB
elif model_name == 'u2netp':
print("Model: U2NetP (4.7 MB)")
self.net = U2NETP(3, 1) # 4.7 MB
else:
raise AssertionError('Invalid "model_name"', 'Use "u2net" or "u2netp"')
# Load network
model_file = os.path.join(os.path.dirname(__file__), 'saved_models', model_name + '.pth')
print("model_file:", model_file)
if cuda_mode:
print("CUDA mode")
self.net.load_state_dict(torch.load(model_file))
self.net.cuda()
else:
print("CPU mode")
self.net.load_state_dict(torch.load(model_file, map_location=torch.device('cpu')))
self.net.eval()
def main():
# --------- 1. get image path and name ---------
model_name = 'u2net' #u2netp
image_dir = './save_images/images/'
prediction_dir = './save_images/' + model_name + '_result/'
model_dir = './saved_models/' + model_name + '/' + model_name + '.pth'
img_name_list = glob.glob(image_dir + '*')
print(img_name_list)
# --------- 2. dataloader ---------
#1. dataloader
test_salobj_dataset = SalObjDataset(
img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([RescaleT(320),
ToTensorLab(flag=0)]))
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. model define ---------
if (model_name == 'u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
elif (model_name == 'u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
print("inferencing:", img_name_list[i_test].split("/")[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
# normalization
pred = d1[:, 0, :, :]
pred = normPRED(pred)
# save results to test_results folder
save_output(img_name_list[i_test], pred, prediction_dir)
del d1, d2, d3, d4, d5, d6, d7
def __init__(self, model_dir, image_size):
print("Loading U-2-Net...")
self.image_size = int(image_size)
self.net = U2NET(3, 1)
if torch.cuda.is_available():
self.net.load_state_dict(torch.load(model_dir))
self.net.cuda()
else:
self.net.load_state_dict(
torch.load(model_dir, map_location=torch.device('cpu')))
self.net.eval()
def main():
model_name = 'u2net' # u2netp
model_dir = os.path.join(os.getcwd(), 'saved_models', model_name,
model_name + '.pth')
if (model_name == 'u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
elif (model_name == 'u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
cap = cv2.VideoCapture(0)
import time
while True:
ret, frame = cap.read()
if ret:
t0 = time.time()
img = cv2.resize(frame, (320, 320))
img = img.transpose((2, 0, 1))
img = img[None, ...] / 255.
inputs_test = torch.from_numpy(img)
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
pred = d1[:, 0, :, :]
predict = normalize(pred)
predict = predict.squeeze()
predict_np = predict.cpu().data.numpy()
h, w = frame.shape[:2]
pred_resized = cv2.resize(predict_np, (w, h))
img = (frame.astype(np.float32) * np.dstack(
(pred_resized, pred_resized, pred_resized))).astype(np.uint8)
cv2.imshow("out", img)
cv2.waitKey(1)
del d1, d2, d3, d4, d5, d6, d7
def main(colored=False, imagepath=''):
# --------- 1. get image path and name ---------
model_name='u2net'#u2netp
prediction_dir = './test_data/' + model_name + '_results/'
model_dir = './saved_models/'+ model_name + '/' + model_name + '.pth'
# --------- 2. dataloader ---------
#1. dataloader
test_salobj_dataset = SalObjDataset(img_name_list = [imagepath],
lbl_name_list = [],
transform=transforms.Compose([RescaleT(320),
ToTensorLab(flag=0)])
)
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. model define ---------
if(model_name=='u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3,1)
elif(model_name=='u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3,1)
net.load_state_dict(torch.load(model_dir, map_location=torch.device('cpu')))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for _, data_test in enumerate(test_salobj_dataloader):
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
d1,d2,d3,d4,d5,d6,d7= net(inputs_test)
# normalization
pred = d1[:,0,:,:]
pred = normPRED(pred)
del d1,d2,d3,d4,d5,d6,d7
# save results to test_results folder
return save_output(imagepath, pred, prediction_dir, colored=colored)
def main():
# --------- 1. get image path and name ---------
model_name = 'u2net'
cwd = Path(os.getcwd())
image_dir = cwd / 'test_data' / 'test_human_images'
prediction_dir = cwd / 'test_data' / 'test_human_images_results'
prediction_dir.mkdir(exist_ok=True)
model_dir = cwd / 'saved_models' / (model_name + '_human_seg') / (model_name + '_human_seg.pth')
img_name_list = list(image_dir.glob('*'))
print("Images in test:", len(img_name_list))
# --------- 2. dataloader ---------
# 1. dataloader
test_salobj_dataset = SalObjDataset(img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([RescaleT(320), ToTensorLab(flag=0)])
)
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. model define ---------
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net.load_state_dict(torch.load(model_dir, map_location=device)).to(device)
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
image_path = img_name_list[i_test]
print("inferencing:", image_path.name)
inputs_test = data_test['image']
inputs_test = inputs_test.to(next(net.parameters()))
with torch.no_grad():
d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
# normalization
pred = d1[:, 0, :, :]
pred = normPRED(pred)
# save results to test_results folder
save_output(image_path, pred, prediction_dir)
del d1, d2, d3, d4, d5, d6, d7
def main():
# --------- 1. get image path and name ---------
model_name = 'u2netp' # u2netp
image_dir = '../train2014'
prediction_dir = os.path.join(os.getcwd(), 'test_data', model_name + '_results' + os.sep)
model_dir = '../models/' + model_name + '.pth'
img_name_list = glob.glob(image_dir + os.sep + '*')
# --------- 2. dataloader ---------
# 1. dataloader
test_salobj_dataset = SalObjDataset(img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([RescaleT(320),
ToTensorLab(flag=0)])
)
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
if (model_name == 'u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
elif (model_name == 'u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
net.load_state_dict(torch.load(model_dir))
# if torch.cuda.is_available():
# net.cuda()
net.eval()
all_out = {}
for i_test, data_test in tqdm(enumerate(test_salobj_dataloader)):
sep_ = img_name_list[i_test].split(os.sep)[-1]
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
#
# if torch.cuda.is_available():
# inputs_test = Variable(inputs_test.cuda())
# else:
inputs_test = Variable(inputs_test)
d = net(inputs_test)
pred = normPRED(d)
all_out[sep_] = pred
pickle.dump(all_out, open("../data/coco_train_u2net.pik", "wb"), protocol=2)
def __init__(self, in_ch: int, out_ch: int, lr: float,
pytorch_pretrained_model: str):
super().__init__()
self.save_hyperparameters()
self.model = U2NET(in_ch, out_ch)
self.lr = lr
#self.bce_loss = nn.BCELoss(size_average=True)
self.bce_loss = nn.BCEWithLogitsLoss(size_average=True)
self.pretrained_path = pytorch_pretrained_model
# Validation Metrics
self.iou = JaccardIndex(num_classes=2)
def __init__(self, model_name):
self.model_dir = os.path.join(os.getcwd(), 'saved_models', model_name,
model_name + '.pth')
if model_name == 'u2net':
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
elif model_name == 'u2netp':
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
net.load_state_dict(
torch.load(self.model_dir, map_location=torch.device('cpu')))
if torch.cuda.is_available():
net.cuda()
net.eval()
self.net = net
def model(model_name='u2net'):
model_dir = os.path.join(os.getcwd(), 'saved_models', model_name,
model_name + '.pth')
if (model_name == 'u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
elif (model_name == 'u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
return net
def model(model_name="u2net"):
model_dir = os.path.join(os.getcwd(), "saved_models", model_name,
model_name + ".pth")
if model_name == "u2net":
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
elif model_name == "u2netp":
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
return net
def main():
# --------- 1. get image path and name ---------
model_name = 'u2net_portrait' #u2netp
image_dir = './test_data/test_portrait_images/portrait_im'
prediction_dir = './test_data/test_portrait_images/portrait_results'
if(not os.path.exists(prediction_dir)):
os.mkdir(prediction_dir)
model_dir = './saved_models/u2net_portrait/u2net_portrait.pth'
img_name_list = glob.glob(image_dir+'/*')
print("Number of images: ", len(img_name_list))
# --------- 2. dataloader ---------
test_salobj_dataset = sal_generator(batch_size=1,
img_name_list = img_name_list,
lbl_name_list = [],
transform=transforms.Compose([RescaleT(512),
ToTensorLab(flag=0)])
)
# --------- 3. model define ---------
print("...load U2NET---173.6 MB")
net = U2NET(3,1)
net.load(model_dir)
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataset):
print("inferencing:", img_name_list[i_test].split(os.sep)[-1])
d1,d2,d3,d4,d5,d6,d7= net(data_test, steps=1)
# normalization
pred = 1.0 - d1[:,0,:,:]
pred = normPRED(pred)
# save results to test_results folder
save_output(img_name_list[i_test],pred,prediction_dir)
del d1,d2,d3,d4,d5,d6,d7
def remove_bg(images):
dataset = SalObjDataset(img_name_list=images,
lbl_name_list=[],
transform=transforms.Compose(
[RescaleT(320),
ToTensorLab(flag=0)]))
dataloader = DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=1)
net = U2NET(3, 1)
net.load_state_dict(torch.load(model_dir, map_location='cpu'))
net.eval()
outputs = []
for i, data in enumerate(dataloader):
inputs = data['image']
inputs = inputs.type(torch.FloatTensor)
inputs = Variable(inputs)
d1, d2, d3, d4, d5, d6, d7 = net(inputs)
pred = d1[:, 0, :, :]
pred = normPRED(pred)
filename = save_output(images[i], pred, output_dir)
outputs.append(filename)
img = cv2.imread(images[i])
mask = cv2.imread(filename, 0)
rgba = cv2.cvtColor(img, cv2.COLOR_RGB2RGBA)
rgba[:, :, 3] = mask
cv2.imwrite(filename, rgba)
del d1, d2, d3, d4, d5, d6, d7
return outputs
def main():
# get the image path list for inference
im_list = glob('./test_data/test_portrait_images/your_portrait_im/*')
print("Number of images: ", len(im_list))
# indicate the output directory
out_dir = './test_data/test_portrait_images/your_portrait_results'
if (not os.path.exists(out_dir)):
os.mkdir(out_dir)
# Load the cascade face detection model
face_cascade = cv2.CascadeClassifier(
'./saved_models/face_detection_cv2/haarcascade_frontalface_default.xml'
)
# u2net_portrait path
model_dir = './saved_models/u2net_portrait/u2net_portrait.pth'
# load u2net_portrait model
net = U2NET(3, 1)
if torch.cuda.is_available():
net.load_state_dict(torch.load(model_dir))
else:
net.load_state_dict(
torch.load(model_dir, map_location=torch.device('cpu')))
if torch.cuda.is_available():
net.cuda()
net.eval()
# do the inference one-by-one
for i in range(0, len(im_list)):
print("--------------------------")
print("inferencing ", i, "/", len(im_list), im_list[i])
# load each image
img = cv2.imread(im_list[i])
height, width = img.shape[0:2]
face = detect_single_face(face_cascade, img)
im_face = crop_face(img, face)
im_portrait = inference(net, im_face)
# save the output
cv2.imwrite(out_dir + "/" + im_list[i].split('/')[-1][0:-4] + '.png',
(im_portrait * 255).astype(np.uint8))
def main():
model_name='u2netp'
model_dir = os.path.join(os.getcwd(), 'saved_models', model_name, model_name + '.pth')
image_dir = os.path.join(os.getcwd(), 'test_data', 'test_images')
img_name_list = glob.glob(image_dir + os.sep + '*')
test_salobj_dataset = SalObjDataset(img_name_list = img_name_list,
lbl_name_list = [],
transform=transforms.Compose([RescaleT(320),
ToTensorLab(flag=0)])
)
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
if(model_name=='u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3,1)
elif(model_name=='u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3,1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
for data_test in test_salobj_dataloader:
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
dummy_input = inputs_test
torch.onnx.export(net, dummy_input,"exported/onnx/{}.onnx".format(model_name),
opset_version=10)
break
def main():
# get the image path list for inference
im_list = glob('./facein/*')
print("Number of images: ", len(im_list))
# indicate the output directory
out_dir = './faceout'
if (not os.path.exists(out_dir)):
os.mkdir(out_dir)
# Load the cascade face detection model
face_cascade = cv2.CascadeClassifier(
'./model/haarcascade_frontalface_default.xml')
# u2net_portrait path
model_dir = './model/u2net_portrait.pth'
# load u2net_portrait model
net = U2NET(3, 1)
net.load_state_dict(torch.load(model_dir))
print('loaded model')
if torch.cuda.is_available():
net.cuda()
net.eval()
# do the inference one-by-one
for i in range(0, len(im_list)):
print("--------------------------")
print("inferencing ", i, "/", len(im_list), im_list[i])
# load each image
img = cv2.imread(im_list[i])
height, width = img.shape[0:2]
face = detect_single_face(face_cascade, img)
#print (face)
im_portrait = crop_face(img, face)
#im_face = crop_face(img, face)
#im_portrait = inference(net,im_face)
# save the output
cv2.imwrite(out_dir + "/" + im_list[i].split('/')[-1][0:-4] + '.png',
im_portrait)
def main():
# --------- 1. get image path and name ---------
model_name = 'u2net' # u2netp
image_dir = os.path.join(os.getcwd(), 'test_data', 'test_images')
prediction_dir = os.path.join(os.getcwd(), 'test_data',
model_name + '_results' + os.sep)
model_dir = os.path.join(os.getcwd(), 'saved_models', model_name,
model_name + '.pth')
img_name_list = glob.glob(image_dir + os.sep + '*')
print(img_name_list)
# --------- 2. dataloader ---------
test_salobj_dataset = SalObjDataset(
img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([RescaleT(320),
ToTensorLab(flag=0)]))
if (model_name == 'u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
elif (model_name == 'u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
for i_test, data_test in enumerate(len(img_name_list)):
print("inferencing:", img_name_list[i_test].split(os.sep)[-1])
d1, d2, d3, d4, d5, d6, d7 = net.predict(test_salobj_dataset, steps=1)
pred = d1[:, 0, :, :]
pred = normPRED(pred)
if not os.path.exists(prediction_dir):
os.makedirs(prediction_dir, exist_ok=True)
save_output(img_name_list[i_test], pred, prediction_dir)
del d1, d2, d3, d4, d5, d6, d7
def main():
# --------- 1. get image path and name ---------
model_name = 'rgbd_u2net' #u2netp
image_dir = os.path.join(os.getcwd(), 'test_data', 'test_images')
prediction_dir = os.path.join(os.getcwd(), 'test_data',
model_name + '_results' + os.sep)
# model_dir = os.path.join(os.getcwd(), 'saved_models', model_name, model_name + '_s6457_2021-02-18_12_36_53u2net_6457_bce_itr_38000_train_0.107382_tar_0.009159.pth')
model_dir = 'saved_models/rgbd_u2net/rgbd_u2net_s30857_2021-04-06_03_52_47/rgbd_u2net_30857_bce_itr_220000_train_0.089360_tar_0.008674.pth'
# path_files = Path('/pool/2021-03-31_22-11-41')
path_files = Path('/pool/2021-03-31_22-32-41')
img_name_list1 = sorted([str(x) for x in path_files.rglob('**/rgb/*.png')])
img_name_list1 = [
str(x) for x in img_name_list1
if '2021-03-31' in str(x) or '2021-04-01' in str(x)
]
# path_files2 = Path('/dataset')
# img_name_list2 = sorted([str(x) for x in path_files2.rglob('**/rgb/*.png') if not Path(str(x).replace('rgb','annotation')).exists()])
# img_name_list2 = [str(x) for x in img_name_list2 if '2021-03-10' in str(x)]
img_name_list = img_name_list1 #+ img_name_list2
print('img len', len(img_name_list))
depth_name_list = [
x.replace('rgb', 'aligned_depth') for x in img_name_list
]
# --------- 2. dataloader ---------
#1. dataloader
test_salobj_dataset = RGBD_SalObjDataset(img_name_list=img_name_list,
depth_name_list=depth_name_list,
lbl_name_list=[],
transform=transforms.Compose([
RGBD_RescaleT(320),
RGBD_ToTensorLab(flag=0)
]))
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. model define ---------
if (model_name == 'rgbd_u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(4, 1)
elif (model_name == 'u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(tqdm(test_salobj_dataloader)):
# print("inferencing:",img_name_list[i_test].split(os.sep)[-1])
inputs_test = data_test['image']
depth = data_test['depth']
inputs_test = torch.cat((inputs_test, torch.unsqueeze(depth, dim=1)),
dim=1) # H x W x 4
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
with torch.no_grad():
d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
# normalization
pred = d1[:, 0, :, :]
pred = normPRED(pred)
# save results to test_results folder
if not os.path.exists(prediction_dir):
os.makedirs(prediction_dir, exist_ok=True)
save_output(img_name_list[i_test], pred, prediction_dir, i_test)
del d1, d2, d3, d4, d5, d6, d7
salobj_dataset = SalObjDataset(
img_name_list=tra_img_name_list,
lbl_name_list=tra_lbl_name_list,
transform=transforms.Compose([
RescaleT(320),
RandomCrop(288),
ToTensorLab(flag=0)]))
salobj_dataloader = DataLoader(salobj_dataset, batch_size=batch_size_train, shuffle=True
#shuffle=False
, num_workers=0)
# ------- 3. define model --------
# define the net
#选择模型
if(model_name=='u2net'):
net = U2NET(3, 1)
elif(model_name=='u2netp'):
net = U2NETP(3,1)
if torch.cuda.is_available():
net.cuda()
# ------- 4. define optimizer --------
print("---define optimizer...")
optimizer = optim.Adam(net.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
# ------- 5. training process --------
print("---start training...")
ite_num = 0
running_loss = 0.0
running_tar_loss = 0.0
def main():
# --------- 1. get image path and name ---------
#model_name='u2net'
model_name = 'u2netp'
image_dir = os.path.join(os.getcwd(), 'test_data', 'test_images')
prediction_dir = os.path.join(os.getcwd(), 'test_data',
model_name + '_results' + os.sep)
model_dir = os.path.join(os.getcwd(), 'saved_models', model_name,
model_name + '.pth')
img_name_list = glob.glob(image_dir + os.sep + '*')
print(img_name_list)
# --------- 2. dataloader ---------
#1. dataloader
test_salobj_dataset = SalObjDataset(
img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([RescaleT(320),
ToTensorLab(flag=0)]))
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. model define ---------
if (model_name == 'u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
elif (model_name == 'u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
print("inferencing:", img_name_list[i_test].split(os.sep)[-1])
inputs_test = data_test['image']
#print("test", inputs_test.shape)
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
# normalization
pred = d1[:, 0, :, :]
pred = normPRED(pred)
#print("pred",pred.shape)
# save results to test_results folder
if not os.path.exists(prediction_dir):
os.makedirs(prediction_dir, exist_ok=True)
save_output(img_name_list[i_test], pred, prediction_dir)
del d1, d2, d3, d4, d5, d6, d7
def main():
# --------- 1. get image path and name ---------
model_name = 'u2net' #u2netp
image_dir = os.path.join(os.getcwd(), 'test_data', 'test_images')
prediction_dir = os.path.join(os.getcwd(), 'test_data',
model_name + '_results' + os.sep)
# image_dir = os.path.join('/nfs/project/huxiaoliang/data/white_or_not/white_bg_image')
# prediction_dir = os.path.join('/nfs/project/huxiaoliang/data/white_or_not/white_bg_image_pred'+ os.sep)
model_dir = os.path.join('/nfs/private/modelfiles/u2net-saved_models',
model_name, model_name + '.pth')
img_name_list = glob.glob(image_dir + os.sep + '*')
print(img_name_list)
# --------- 2. dataloader ---------
#1. dataloader
test_salobj_dataset = SalObjDataset(
img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([RescaleT(320),
ToTensorLab(flag=0)]))
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. model define ---------
if (model_name == 'u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
elif (model_name == 'u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
error = []
for i_test, data_test in enumerate(test_salobj_dataloader):
try:
print("inferencing:", img_name_list[i_test].split(os.sep)[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
# normalization
pred = d1[:, 0, :, :]
pred = normPRED(pred)
# save results to test_results folder
if not os.path.exists(prediction_dir):
os.makedirs(prediction_dir, exist_ok=True)
save_output(img_name_list[i_test], pred, prediction_dir)
del d1, d2, d3, d4, d5, d6, d7
except Exception as ex:
traceback.print_exc()
error.append(img_name_list[i_test].split(os.sep)[-1])
print('异常数据：', error)
def main():
# --------- 1. get image path and name ---------
model_name = 'u2net' #u2netp
image_dir = "/home/vybt/Downloads/U2_Net_Test"
prediction_dir = "/home/vybt/Downloads/u-2--bps-net-prediction"
model_dir = '/media/vybt/DATA/SmartFashion/deep-learning-projects/U-2-Net/saved_models/u2net/_bps_bce_itr_300000_train_0.107041_tar_0.011690.pth'
img_name_list = glob.glob(image_dir + os.sep + '*')
# print(img_name_list)
# --------- 2. dataloader ---------
#1. dataloader
test_salobj_dataset = SalObjDataset(
img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([RescaleT(320),
ToTensorLab(flag=0)]))
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. model define ---------
if model_name == 'u2net':
print("...load U2NET---173.6 MB")
net = U2NET(3, 8)
elif model_name == 'u2netp':
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 8)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
print("Inference: ", img_name_list[i_test].split(os.sep)[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
# print("inputs test: {}".format(inputs_test.shape))
d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
# normalization
pred = d1
pred = normPRED(pred)
# save results to test_results folder
if not os.path.exists(prediction_dir):
os.makedirs(prediction_dir, exist_ok=True)
save_output(img_name_list[i_test], pred, prediction_dir)
del d1, d2, d3, d4, d5, d6, d7
def main():
# --------- 1. get image path and name ---------
model_name = 'u2netp' # u2netp u2net
data_dir = '/data2/wangjiajie/datasets/scene_segment1023/u2data/'
image_dir = os.path.join(data_dir, 'test_imgs')
prediction_dir = os.path.join('./outputs/', model_name + '/')
if not os.path.exists(prediction_dir):
os.makedirs(prediction_dir, exist_ok=True)
# tra_label_dir = 'test_lbls/'
image_ext = '.jpg'
# label_ext = '.jpg' # '.png'
model_dir = os.path.join(os.getcwd(), 'saved_models', model_name,
model_name + '.pth')
img_name_list = glob.glob(image_dir + os.sep + '*')
print(f'test img numbers are: {len(img_name_list)}')
# --------- 2. dataloader ---------
#1. dataloader
test_salobj_dataset = SalObjDataset(img_name_list=img_name_list,
lbl_name_list=[],
transform=Compose([
SmallestMaxSize(max_size=320),
]))
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. model define ---------
if (model_name == 'u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
elif (model_name == 'u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
# net.load_state_dict(torch.load(model_dir))
checkpoint = torch.load(model_dir)
d = collections.OrderedDict()
for key, value in checkpoint.items():
tmp = key[7:]
d[tmp] = value
net.load_state_dict(d)
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
print("inferencing:", img_name_list[i_test].split(os.sep)[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
# normalization
pred = 1.0 - d1[:, 0, :, :]
pred = normPRED(pred)
# save results to test_results folder
save_output(img_name_list[i_test], pred, prediction_dir)
del d1, d2, d3, d4, d5, d6, d7
def main():
name = 'test'
# please input the height unit:M
body_height = 1.63
#"Input image" path
image_dir = os.path.join(os.getcwd(), 'input')
#"Output model" path
outbody_filenames = './output/{}.obj'.format(name)
#########################################################
#this code used for image segmentation to remove the background to get Silhouettes
# --------- 1. get image path and name ---------
model_name='u2net'#u2net or u2netp
#set orignal silhouette images path
prediction_dir1 = os.path.join(os.getcwd(), 'Silhouette' + os.sep)
#set the path of silhouette images after horizontal flippath
prediction_dir = os.path.join(os.getcwd(), 'test_data' + os.sep)
model_dir = os.path.join(os.getcwd(), 'saved_models', model_name, model_name + '.pth')
img_name_list = glob.glob(image_dir + os.sep + '*')
print(img_name_list)
# --------- 2. dataloader ---------
test_salobj_dataset = SalObjDataset(img_name_list = img_name_list,
lbl_name_list = [],
transform=transforms.Compose([RescaleT(320),
ToTensorLab(flag=0)])
)
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=1)
# --------- 3. silhouette cutting model define ---------
if(model_name=='u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3,1)
elif(model_name=='u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3,1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
print("inferencing:",img_name_list[i_test].split(os.sep)[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = Variable(inputs_test.cuda())
else:
inputs_test = Variable(inputs_test)
d1,d2,d3,d4,d5,d6,d7= net(inputs_test)
# normalization
pred = d1[:,0,:,:]
pred = normPRED(pred)
# save results to test_results folder
if not os.path.exists(prediction_dir):
os.makedirs(prediction_dir, exist_ok=True)
save_output(img_name_list[i_test],pred,prediction_dir,prediction_dir1)
del d1,d2,d3,d4,d5,d6,d7
###########################################################
#this code used for reconstruct 3d model:
#--------5.get the silhouette images --------
img_filenames = ['./test_data/front.png', './test_data/side.png']
# img = cv2.imread(img_filenames[1])
# cv2.flip(img,1)
# -----------6.load input data---------
sampling_num = 648
data = np.zeros([2, 2, sampling_num])
for i in np.arange(len(img_filenames)):
img = img_filenames[i]
im = getBinaryimage(img, 600) # deal with white-black image simply
sample_points = getSamplePoints(im, sampling_num, i)
center_p = np.mean(sample_points, axis=0)
sample_points = sample_points - center_p
data[i, :, :] = sample_points.T
data = repeat_data(data)
#--------7 load CNN model----reconstruct 3d body shape
print('==> begining...')
len_out = 22
model_name = './Models/model.ckpt'
ourModel = RegressionPCA(len_out)
ourModel.load_state_dict(torch.load(model_name))
ourModel.eval()
#----------8 output results--------------
save_obj(outbody_filenames, ourModel, body_height, data)
def main(model_name, img_dir, retrain, weight, model_dir):
model_name = 'u2net' #'u2netp'
#data_dir = os.path.join(os.getcwd(), 'train_data' + os.sep)
# tra_image_dir = os.path.join('DUTS', 'DUTS-TR', 'DUTS-TR', 'im_aug' + os.sep)
# tra_label_dir = os.path.join('DUTS', 'DUTS-TR', 'DUTS-TR', 'gt_aug' + os.sep)
tra_image_dir = os.path.join(img_dir, 'origin')
tra_label_dir = os.path.join(img_dir, 'mask')
# train_image_dir = os.path.join('')
image_ext = '.jpg'
label_ext = '.png'
model_dir = os.path.join(os.getcwd(), 'saved_models', model_name + os.sep)
epoch_start = 0
epoch_num = 500
batch_size_train = 20
batch_size_val = 1
train_num = 4000
val_num = 500
# tra_img_name_list = glob.glob(tra_image_dir + '*' + image_ext)
tra_img_name_list = os.listdir(tra_image_dir)
for i,item in enumerate(tra_img_name_list):
tra_img_name_list[i] = os.path.join(tra_image_dir, item)
tra_lbl_name_list = os.listdir(tra_label_dir)
for i,item in enumerate(tra_lbl_name_list):
tra_lbl_name_list[i] = os.path.join(tra_label_dir, item)
print(tra_img_name_list)
# for img_path in tra_img_name_list:
# img_name = img_path.split(os.sep)[-1]
# aaa = img_name.split(".")
# bbb = aaa[0:-1]
# imidx = bbb[0]
# for i in range(1,len(bbb)):
# imidx = imidx + "." + bbb[i]
# tra_lbl_name_list.append(tra_label_dir + imidx + label_ext)
print("---")
print("train images: ", len(tra_img_name_list))
print("train labels: ", len(tra_lbl_name_list))
print("---")
train_num = len(tra_img_name_list)
salobj_dataset = SalObjDataset(
img_name_list=tra_img_name_list,
lbl_name_list=tra_lbl_name_list,
transform=transforms.Compose([
RescaleT(320),
RandomCrop(288),
ToTensorLab(flag=0)]))
salobj_dataloader = DataLoader(salobj_dataset, batch_size=batch_size_train, shuffle=True, num_workers=4)
# ------- 3. define model --------
# define the net
if(model_name=='u2net'):
net = U2NET(3, 1)
elif(model_name=='u2netp'):
net = U2NETP(3,1)
if torch.cuda.is_available():
net.cuda()
# ------- 4. define optimizer --------
print("---define optimizer...")
optimizer = optim.Adam(net.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
# ------- 5. training process --------
print("---start training...")
if retrain == True:
checkpoint = torch.load(weight)
net.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']
# loss = checkpoint['loss']
ite_num = 0
running_loss = 0.0
running_tar_loss = 0.0
ite_num4val = 0
save_frq = 2000 # save the model every 2000 iterations
for epoch in range(0, epoch_num):
net.train()
for i, data in enumerate(salobj_dataloader):
ite_num = ite_num + 1
ite_num4val = ite_num4val + 1
# print(data)
inputs, labels = data['image'], data['label']
inputs = inputs.type(torch.FloatTensor)
labels = labels.type(torch.FloatTensor)
# wrap them in Variable
if torch.cuda.is_available():
inputs_v, labels_v = Variable(inputs.cuda(), requires_grad=False), Variable(labels.cuda(),
requires_grad=False)
else:
inputs_v, labels_v = Variable(inputs, requires_grad=False), Variable(labels, requires_grad=False)
# y zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
d0, d1, d2, d3, d4, d5, d6 = net(inputs_v)
loss2, loss = muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6, labels_v)
loss.backward()
optimizer.step()
# # print statistics
running_loss += loss.data.item()
running_tar_loss += loss2.data.item()
# del temporary outputs and loss
del d0, d1, d2, d3, d4, d5, d6, loss2, loss
print("[epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %3f, tar: %3f " % (
epoch + 1, epoch_num, (i + 1) * batch_size_train, train_num, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val))
# if ite_num % save_frq == 0:
# # torch.save(net.state_dict(), model_dir + model_name+"_bce_itr_%d_train_%3f_tar_%3f.pth" % (ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val))
# # torch.save({
# # 'epoch': epoch,
# # 'model_state_dict': net.state_dict(),
# # 'optimizer_state_dict': optimizer.state_dict(),
# # 'loss': loss,
# # }, model_dir + model_name + epoch)
# running_loss = 0.0
# running_tar_loss = 0.0
# net.train() # resume train
# ite_num4val = 0
torch.save(
{
'epoch': epoch,
'model_state_dict': net.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, os.path.join(model_dir, model_name + str(epoch))
)
def main():
# --------- 1. get image path and name ---------
model_name = 'u2netp' # u2net
# image_dir = os.path.join(os.getcwd(), 'test_data', 'test_images')
image_dir = '/home/hha/dataset/circle/circle'
prediction_dir = '/home/hha/dataset/circle/circle_pred'
model_dir = '/home/hha/pytorch_code/U-2-Net-master/saved_models/u2netp/u2netp.pthu2netp_bce_itr_2000_train_0.077763_tar_0.006976.pth'
# model_dir = os.path.join(os.getcwd(), 'saved_models', model_name, model_name + '.pth')
img_name_list = glob.glob(image_dir + os.sep + '*')
img_name_list = list(filter(lambda f: f.find('_mask') < 0, img_name_list))
# print(img_name_list)
# --------- 2. dataloader ---------
# 1. dataloader
test_salobj_dataset = SalObjDataset(
img_name_list=img_name_list,
lbl_name_list=[],
transform=transforms.Compose([
RescaleT(320), # 320
ToTensorLab(flag=0)
]))
test_salobj_dataloader = DataLoader(test_salobj_dataset,
batch_size=1,
shuffle=False,
num_workers=0)
# --------- 3. model define ---------
if (model_name == 'u2net'):
print("...load U2NET---173.6 MB")
net = U2NET(3, 1)
elif (model_name == 'u2netp'):
print("...load U2NEP---4.7 MB")
net = U2NETP(3, 1)
net.load_state_dict(torch.load(model_dir))
if torch.cuda.is_available():
net.cuda()
net.eval()
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
print("inferencing:", img_name_list[i_test].split(os.sep)[-1])
inputs_test = data_test['image']
inputs_test = inputs_test.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test = inputs_test.cuda()
else:
inputs_test = inputs_test
d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)
# normalization
pred = d1[:, 0, :, :]
# pred = normPRED(pred)
# save results to test_results folder
if not os.path.exists(prediction_dir):
os.makedirs(prediction_dir, exist_ok=True)
save_output(img_name_list[i_test], pred, prediction_dir)
del d1, d2, d3, d4, d5, d6, d7
def main():
# ------- 2. set the directory of training dataset --------
model_name = 'u2net' #'u2netp'
data_dir = './train_data/'
tra_image_dir = 'DUTS/DUTS-TR/DUTS-TR/im_aug/'
tra_label_dir = 'DUTS/DUTS-TR/DUTS-TR/gt_aug/'
image_ext = '.jpg'
label_ext = '.png'
model_dir = './saved_models/' + model_name + '/'
epoch_num = 100000
batch_size_train = 12
batch_size_val = 1
train_num = 0
val_num = 0
tra_img_name_list = glob.glob(data_dir + tra_image_dir + '*' + image_ext)
tra_lbl_name_list = []
for img_path in tra_img_name_list:
img_name = img_path.split("/")[-1]
aaa = img_name.split(".")
bbb = aaa[0:-1]
imidx = bbb[0]
for i in range(1, len(bbb)):
imidx = imidx + "." + bbb[i]
tra_lbl_name_list.append(data_dir + tra_label_dir + imidx + label_ext)
print("---")
print("train images: ", len(tra_img_name_list))
print("train labels: ", len(tra_lbl_name_list))
print("---")
train_num = len(tra_img_name_list)
salobj_dataset = SalObjDataset(img_name_list=tra_img_name_list,
lbl_name_list=tra_lbl_name_list,
transform=transforms.Compose([
RescaleT(320),
RandomCrop(288),
ToTensorLab(flag=0)
]))
salobj_dataloader = DataLoader(salobj_dataset,
batch_size=batch_size_train,
shuffle=True,
num_workers=1)
# ------- 3. define model --------
# define the net
if (model_name == 'u2net'):
net = U2NET(3, 1)
elif (model_name == 'u2netp'):
net = U2NETP(3, 1)
if torch.cuda.is_available():
net.cuda()
# ------- 4. define optimizer --------
print("---define optimizer...")
optimizer = optim.Adam(net.parameters(),
lr=0.001,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0)
# ------- 5. training process --------
print("---start training...")
ite_num = 0
running_loss = 0.0
running_tar_loss = 0.0
ite_num4val = 0
save_frq = 2000 # save the model every 2000 iterations
for epoch in range(0, epoch_num):
net.train()
for i, data in enumerate(salobj_dataloader):
ite_num = ite_num + 1
ite_num4val = ite_num4val + 1
inputs, labels = data['image'], data['label']
inputs = inputs.type(torch.FloatTensor)
labels = labels.type(torch.FloatTensor)
# wrap them in Variable
if torch.cuda.is_available():
inputs_v, labels_v = Variable(inputs.cuda(),
requires_grad=False), Variable(
labels.cuda(),
requires_grad=False)
else:
inputs_v, labels_v = Variable(
inputs, requires_grad=False), Variable(labels,
requires_grad=False)
# y zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
d0, d1, d2, d3, d4, d5, d6 = net(inputs_v)
loss2, loss = muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6,
labels_v)
loss.backward()
optimizer.step()
# # print statistics
running_loss += loss.data[0]
running_tar_loss += loss2.data[0]
# del temporary outputs and loss
del d0, d1, d2, d3, d4, d5, d6, loss2, loss
print(
"[epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %3f, tar: %3f "
% (epoch + 1, epoch_num,
(i + 1) * batch_size_train, train_num, ite_num,
running_loss / ite_num4val, running_tar_loss / ite_num4val))
if ite_num % save_frq == 0:
torch.save(
net.state_dict(), model_dir + model_name +
"_bce_itr_%d_train_%3f_tar_%3f.pth" %
(ite_num, running_loss / ite_num4val,
running_tar_loss / ite_num4val))
running_loss = 0.0
running_tar_loss = 0.0
net.train() # resume train
ite_num4val = 0
