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