new

README.md

@@ -1,29 +1,52 @@
 ## U^2-Net
 The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection, [Xuebin Qin](https://webdocs.cs.ualberta.ca/~xuebin/), [Zichen Zhang](https://webdocs.cs.ualberta.ca/~zichen2/), [Chenyang Huang](https://chenyangh.com/), [Masood Dehghan](https://sites.google.com/view/masooddehghan), [Osmar R. Zaiane](http://webdocs.cs.ualberta.ca/~zaiane/) and [Martin Jagersand](https://webdocs.cs.ualberta.ca/~jag/)."
 
-## (2020-May-04) The code will be released soon (before 2020-May-10)!
+## U^2-Net Results
+
+![U^2-Net Results](figures/u2netqual.png)
 
 __Contact__: xuebin[at]ualberta[dot]ca
 
-
 ## Our previous work: [BASNet (CVPR 2019)](https://github.com/NathanUA/BASNet)
 
+## Required libraries
+
+Python 3.6  
+numpy 1.15.2  
+scikit-image 0.14.0  
+PIL 5.2.0  
+PyTorch 0.4.0  
+torchvision 0.2.1  
+glob  
+
+## Usage
+1. Clone this repo
+```
+git clone https://github.com/NathanUA/U-2-Net.git
+```
+2. Download the pre-trained model [u2net.pth 173.6 MB](https://drive.google.com/file/d/1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ/view?usp=sharing) or [u2netp.pth 4.7 MB](https://drive.google.com/file/d/1rbSTGKAE-MTxBYHd-51l2hMOQPT_7EPy/view?usp=sharing) and put it into the dirctory './saved_models/u2net/' and './saved_models/u2netp/'
+
+3.  Cd to the directory 'U-2-Net', run the train or inference process by command: ```python u2net_train.py```
+or ```python u2net_test.py``` respectively. The 'model_name' in both files can be changed to 'u2net' or 'u2netp' for using different models.  
+
+ We also provide the predicted saliency maps ([u2net results](https://drive.google.com/file/d/1mZFWlS4WygWh1eVI8vK2Ad9LrPq4Hp5v/view?usp=sharing),[u2netp results](https://drive.google.com/file/d/1j2pU7vyhOO30C2S_FJuRdmAmMt3-xmjD/view?usp=sharing)) for datasets SOD, ECSSD, DUT-OMRON, PASCAL-S, HKU-IS and DUTS-TE.
+
 
 ## U^2-Net Architecture
 
-![U^2-Net architecture](U2NETPR.png)
+![U^2-Net architecture](figures/U2NETPR.png)
 
 
 ## Quantitative Comparison
 
-![Quantitative Comparison](quan_1.png)
+![Quantitative Comparison](figures/quan_1.png)
 
-![Quantitative Comparison](quan_2.png)
+![Quantitative Comparison](figures/quan_2.png)
 
 
 ## Qualitative Comparison
 
-![Qualitative Comparison](qual.png?raw=true)
+![Qualitative Comparison](figures/qual.png?raw=true)
 
 
 ## Citation

data_loader.py

@@ -0,0 +1,259 @@
+# data loader
+from __future__ import print_function, division
+import glob
+import torch
+from skimage import io, transform, color
+import numpy as np
+import random
+import math
+import matplotlib.pyplot as plt
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms, utils
+from PIL import Image
+#==========================dataset load==========================
+class RescaleT(object):
+
+	def __init__(self,output_size):
+		assert isinstance(output_size,(int,tuple))
+		self.output_size = output_size
+
+	def __call__(self,sample):
+		imidx, image, label = sample['imidx'], sample['image'],sample['label']
+
+		h, w = image.shape[:2]
+
+		if isinstance(self.output_size,int):
+			if h > w:
+				new_h, new_w = self.output_size*h/w,self.output_size
+			else:
+				new_h, new_w = self.output_size,self.output_size*w/h
+		else:
+			new_h, new_w = self.output_size
+
+		new_h, new_w = int(new_h), int(new_w)
+
+		# #resize the image to new_h x new_w and convert image from range [0,255] to [0,1]
+		# img = transform.resize(image,(new_h,new_w),mode='constant')
+		# lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True)
+
+		img = transform.resize(image,(self.output_size,self.output_size),mode='constant')
+		lbl = transform.resize(label,(self.output_size,self.output_size),mode='constant', order=0, preserve_range=True)
+
+		return {'imidx':imidx, 'image':img,'label':lbl}
+
+class Rescale(object):
+
+	def __init__(self,output_size):
+		assert isinstance(output_size,(int,tuple))
+		self.output_size = output_size
+
+	def __call__(self,sample):
+		imidx, image, label = sample['imidx'], sample['image'],sample['label']
+
+		h, w = image.shape[:2]
+
+		if isinstance(self.output_size,int):
+			if h > w:
+				new_h, new_w = self.output_size*h/w,self.output_size
+			else:
+				new_h, new_w = self.output_size,self.output_size*w/h
+		else:
+			new_h, new_w = self.output_size
+
+		new_h, new_w = int(new_h), int(new_w)
+
+		# #resize the image to new_h x new_w and convert image from range [0,255] to [0,1]
+		img = transform.resize(image,(new_h,new_w),mode='constant')
+		lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True)
+
+		return {'imidx':imidx, 'image':img,'label':lbl}
+
+class RandomCrop(object):
+
+	def __init__(self,output_size):
+		assert isinstance(output_size, (int, tuple))
+		if isinstance(output_size, int):
+			self.output_size = (output_size, output_size)
+		else:
+			assert len(output_size) == 2
+			self.output_size = output_size
+	def __call__(self,sample):
+		imidx, image, label = sample['imidx'], sample['image'], sample['label']
+
+		h, w = image.shape[:2]
+		new_h, new_w = self.output_size
+
+		top = np.random.randint(0, h - new_h)
+		left = np.random.randint(0, w - new_w)
+
+		image = image[top: top + new_h, left: left + new_w]
+		label = label[top: top + new_h, left: left + new_w]
+
+		return {'imidx':imidx,'image':image, 'label':label}
+
+class ToTensor(object):
+	"""Convert ndarrays in sample to Tensors."""
+
+	def __call__(self, sample):
+
+		imidx, image, label = sample['imidx'], sample['image'], sample['label']
+
+		tmpImg = np.zeros((image.shape[0],image.shape[1],3))
+		tmpLbl = np.zeros(label.shape)
+
+		image = image/np.max(image)
+		if(np.max(label)<1e-6):
+			label = label
+		else:
+			label = label/np.max(label)
+
+		if image.shape[2]==1:
+			tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
+			tmpImg[:,:,1] = (image[:,:,0]-0.485)/0.229
+			tmpImg[:,:,2] = (image[:,:,0]-0.485)/0.229
+		else:
+			tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
+			tmpImg[:,:,1] = (image[:,:,1]-0.456)/0.224
+			tmpImg[:,:,2] = (image[:,:,2]-0.406)/0.225
+
+		tmpLbl[:,:,0] = label[:,:,0]
+
+		# change the r,g,b to b,r,g from [0,255] to [0,1]
+		#transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225))
+		tmpImg = tmpImg.transpose((2, 0, 1))
+		tmpLbl = label.transpose((2, 0, 1))
+
+		return {'imidx':torch.from_numpy(imidx), 'image': torch.from_numpy(tmpImg), 'label': torch.from_numpy(tmpLbl)}
+
+class ToTensorLab(object):
+	"""Convert ndarrays in sample to Tensors."""
+	def __init__(self,flag=0):
+		self.flag = flag
+
+	def __call__(self, sample):
+
+		imidx, image, label =sample['imidx'], sample['image'], sample['label']
+
+		tmpLbl = np.zeros(label.shape)
+
+		if(np.max(label)<1e-6):
+			label = label
+		else:
+			label = label/np.max(label)
+
+		# change the color space
+		if self.flag == 2: # with rgb and Lab colors
+			tmpImg = np.zeros((image.shape[0],image.shape[1],6))
+			tmpImgt = np.zeros((image.shape[0],image.shape[1],3))
+			if image.shape[2]==1:
+				tmpImgt[:,:,0] = image[:,:,0]
+				tmpImgt[:,:,1] = image[:,:,0]
+				tmpImgt[:,:,2] = image[:,:,0]
+			else:
+				tmpImgt = image
+			tmpImgtl = color.rgb2lab(tmpImgt)
+
+			# nomalize image to range [0,1]
+			tmpImg[:,:,0] = (tmpImgt[:,:,0]-np.min(tmpImgt[:,:,0]))/(np.max(tmpImgt[:,:,0])-np.min(tmpImgt[:,:,0]))
+			tmpImg[:,:,1] = (tmpImgt[:,:,1]-np.min(tmpImgt[:,:,1]))/(np.max(tmpImgt[:,:,1])-np.min(tmpImgt[:,:,1]))
+			tmpImg[:,:,2] = (tmpImgt[:,:,2]-np.min(tmpImgt[:,:,2]))/(np.max(tmpImgt[:,:,2])-np.min(tmpImgt[:,:,2]))
+			tmpImg[:,:,3] = (tmpImgtl[:,:,0]-np.min(tmpImgtl[:,:,0]))/(np.max(tmpImgtl[:,:,0])-np.min(tmpImgtl[:,:,0]))
+			tmpImg[:,:,4] = (tmpImgtl[:,:,1]-np.min(tmpImgtl[:,:,1]))/(np.max(tmpImgtl[:,:,1])-np.min(tmpImgtl[:,:,1]))
+			tmpImg[:,:,5] = (tmpImgtl[:,:,2]-np.min(tmpImgtl[:,:,2]))/(np.max(tmpImgtl[:,:,2])-np.min(tmpImgtl[:,:,2]))
+
+			# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))
+
+			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.mean(tmpImg[:,:,0]))/np.std(tmpImg[:,:,0])
+			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.mean(tmpImg[:,:,1]))/np.std(tmpImg[:,:,1])
+			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.mean(tmpImg[:,:,2]))/np.std(tmpImg[:,:,2])
+			tmpImg[:,:,3] = (tmpImg[:,:,3]-np.mean(tmpImg[:,:,3]))/np.std(tmpImg[:,:,3])
+			tmpImg[:,:,4] = (tmpImg[:,:,4]-np.mean(tmpImg[:,:,4]))/np.std(tmpImg[:,:,4])
+			tmpImg[:,:,5] = (tmpImg[:,:,5]-np.mean(tmpImg[:,:,5]))/np.std(tmpImg[:,:,5])
+
+		elif self.flag == 1: #with Lab color
+			tmpImg = np.zeros((image.shape[0],image.shape[1],3))
+
+			if image.shape[2]==1:
+				tmpImg[:,:,0] = image[:,:,0]
+				tmpImg[:,:,1] = image[:,:,0]
+				tmpImg[:,:,2] = image[:,:,0]
+			else:
+				tmpImg = image
+
+			tmpImg = color.rgb2lab(tmpImg)
+
+			# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))
+
+			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.min(tmpImg[:,:,0]))/(np.max(tmpImg[:,:,0])-np.min(tmpImg[:,:,0]))
+			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.min(tmpImg[:,:,1]))/(np.max(tmpImg[:,:,1])-np.min(tmpImg[:,:,1]))
+			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.min(tmpImg[:,:,2]))/(np.max(tmpImg[:,:,2])-np.min(tmpImg[:,:,2]))
+
+			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.mean(tmpImg[:,:,0]))/np.std(tmpImg[:,:,0])
+			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.mean(tmpImg[:,:,1]))/np.std(tmpImg[:,:,1])
+			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.mean(tmpImg[:,:,2]))/np.std(tmpImg[:,:,2])
+
+		else: # with rgb color
+			tmpImg = np.zeros((image.shape[0],image.shape[1],3))
+			image = image/np.max(image)
+			if image.shape[2]==1:
+				tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
+				tmpImg[:,:,1] = (image[:,:,0]-0.485)/0.229
+				tmpImg[:,:,2] = (image[:,:,0]-0.485)/0.229
+			else:
+				tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
+				tmpImg[:,:,1] = (image[:,:,1]-0.456)/0.224
+				tmpImg[:,:,2] = (image[:,:,2]-0.406)/0.225
+
+		tmpLbl[:,:,0] = label[:,:,0]
+
+		# change the r,g,b to b,r,g from [0,255] to [0,1]
+		#transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225))
+		tmpImg = tmpImg.transpose((2, 0, 1))
+		tmpLbl = label.transpose((2, 0, 1))
+
+		return {'imidx':torch.from_numpy(imidx), 'image': torch.from_numpy(tmpImg), 'label': torch.from_numpy(tmpLbl)}
+
+class SalObjDataset(Dataset):
+	def __init__(self,img_name_list,lbl_name_list,transform=None):
+		# self.root_dir = root_dir
+		# self.image_name_list = glob.glob(image_dir+'*.png')
+		# self.label_name_list = glob.glob(label_dir+'*.png')
+		self.image_name_list = img_name_list
+		self.label_name_list = lbl_name_list
+		self.transform = transform
+
+	def __len__(self):
+		return len(self.image_name_list)
+
+	def __getitem__(self,idx):
+
+		# image = Image.open(self.image_name_list[idx])#io.imread(self.image_name_list[idx])
+		# label = Image.open(self.label_name_list[idx])#io.imread(self.label_name_list[idx])
+
+		image = io.imread(self.image_name_list[idx])
+		imname = self.image_name_list[idx]
+		imidx = np.array([idx])
+
+		if(0==len(self.label_name_list)):
+			label_3 = np.zeros(image.shape)
+		else:
+			label_3 = io.imread(self.label_name_list[idx])
+
+		label = np.zeros(label_3.shape[0:2])
+		if(3==len(label_3.shape)):
+			label = label_3[:,:,0]
+		elif(2==len(label_3.shape)):
+			label = label_3
+
+		if(3==len(image.shape) and 2==len(label.shape)):
+			label = label[:,:,np.newaxis]
+		elif(2==len(image.shape) and 2==len(label.shape)):
+			image = image[:,:,np.newaxis]
+			label = label[:,:,np.newaxis]
+
+		sample = {'imidx':imidx, 'image':image, 'label':label}
+
+		if self.transform:
+			sample = self.transform(sample)
+
+		return sample

model/__init__.py

@@ -0,0 +1,2 @@
+from .u2net import U2NET
+from .u2net import U2NETP

model/u2net.py

@@ -0,0 +1,541 @@
+import torch
+import torch.nn as nn
+from torchvision import models
+import torch.nn.functional as F
+
+class REBNCONV(nn.Module):
+    def __init__(self,in_ch=3,out_ch=3,dirate=1):
+        super(REBNCONV,self).__init__()
+
+        self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate)
+        self.bn_s1 = nn.BatchNorm2d(out_ch)
+        self.relu_s1 = nn.ReLU(inplace=True)
+
+    def forward(self,x):
+
+        hx = x
+        xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
+
+        return xout
+
+### RSU-7 ###
+class RSU7(nn.Module):#UNet07DRES(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU7,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+        self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear')
+
+    def forward(self,x):
+
+        hx = x
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+        hx = self.pool5(hx5)
+
+        hx6 = self.rebnconv6(hx)
+
+        hx7 = self.rebnconv7(hx6)
+
+        hx6d =  self.rebnconv6d(torch.cat((hx7,hx6),1))
+        hx6up = self.upscore2(hx6d)
+        # print(hx6up.shape,hx5.shape)
+        hx5d =  self.rebnconv5d(torch.cat((hx6up,hx5),1))
+        hx5dup = self.upscore2(hx5d)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = self.upscore2(hx4d)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = self.upscore2(hx3d)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = self.upscore2(hx2d)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-6 ###
+class RSU6(nn.Module):#UNet06DRES(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU6,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+        self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear')
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+
+        hx6 = self.rebnconv6(hx5)
+
+
+        hx5d =  self.rebnconv5d(torch.cat((hx6,hx5),1))
+        hx5dup = self.upscore2(hx5d)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = self.upscore2(hx4d)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = self.upscore2(hx3d)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = self.upscore2(hx2d)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-5 ###
+class RSU5(nn.Module):#UNet05DRES(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU5,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+        self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear')
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+
+        hx5 = self.rebnconv5(hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
+        hx4dup = self.upscore2(hx4d)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = self.upscore2(hx3d)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = self.upscore2(hx2d)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-4 ###
+class RSU4(nn.Module):#UNet04DRES(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+        self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear')
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
+        hx3dup = self.upscore2(hx3d)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = self.upscore2(hx2d)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-4F ###
+class RSU4F(nn.Module):#UNet04FRES(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4F,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)
+
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx2 = self.rebnconv2(hx1)
+        hx3 = self.rebnconv3(hx2)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
+        hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
+        hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
+
+        return hx1d + hxin
+
+
+##### U^2-Net ####
+class U2NET(nn.Module):
+
+    def __init__(self,in_ch=3,out_ch=1):
+        super(U2NET,self).__init__()
+
+        self.stage1 = RSU7(in_ch,32,64)
+        self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage2 = RSU6(64,32,128)
+        self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage3 = RSU5(128,64,256)
+        self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage4 = RSU4(256,128,512)
+        self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage5 = RSU4F(512,256,512)
+        self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage6 = RSU4F(512,256,512)
+
+        # decoder
+        self.stage5d = RSU4F(1024,256,512)
+        self.stage4d = RSU4(1024,128,256)
+        self.stage3d = RSU5(512,64,128)
+        self.stage2d = RSU6(256,32,64)
+        self.stage1d = RSU7(128,16,64)
+
+        self.side1 = nn.Conv2d(64,1,3,padding=1)
+        self.side2 = nn.Conv2d(64,1,3,padding=1)
+        self.side3 = nn.Conv2d(128,1,3,padding=1)
+        self.side4 = nn.Conv2d(256,1,3,padding=1)
+        self.side5 = nn.Conv2d(512,1,3,padding=1)
+        self.side6 = nn.Conv2d(512,1,3,padding=1)
+
+        self.upscore6 = nn.Upsample(scale_factor=32,mode='bilinear')
+        self.upscore5 = nn.Upsample(scale_factor=16,mode='bilinear')
+        self.upscore4 = nn.Upsample(scale_factor=8,mode='bilinear')
+        self.upscore3 = nn.Upsample(scale_factor=4,mode='bilinear')
+        self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear')
+
+        self.outconv = nn.Conv2d(6,1,1)
+
+    def forward(self,x):
+
+        hx = x
+
+        #stage 1
+        hx1 = self.stage1(hx)
+        hx = self.pool12(hx1)
+
+        #stage 2
+        hx2 = self.stage2(hx)
+        hx = self.pool23(hx2)
+
+
+
+        #stage 3
+        hx3 = self.stage3(hx)
+        hx = self.pool34(hx3)
+
+        #stage 4
+        hx4 = self.stage4(hx)
+        hx = self.pool45(hx4)
+
+        #stage 5
+        hx5 = self.stage5(hx)
+        hx = self.pool56(hx5)
+
+        #stage 6
+        hx6 = self.stage6(hx)
+        hx6up = self.upscore2(hx6)
+
+        #-------------------- decoder --------------------
+        hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
+        hx5dup = self.upscore2(hx5d)
+
+        hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = self.upscore2(hx4d)
+
+        hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = self.upscore2(hx3d)
+
+        hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = self.upscore2(hx2d)
+
+        hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
+
+
+        #side output
+        d1 = self.side1(hx1d)
+
+        d2 = self.side2(hx2d)
+        d2 = self.upscore2(d2)
+
+        d3 = self.side3(hx3d)
+        d3 = self.upscore3(d3)
+
+        d4 = self.side4(hx4d)
+        d4 = self.upscore4(d4)
+
+        d5 = self.side5(hx5d)
+        d5 = self.upscore5(d5)
+
+        d6 = self.side6(hx6)
+        d6 = self.upscore6(d6)
+
+        d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))
+
+        return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)
+
+### U^2-Net small ###
+class U2NETP(nn.Module):
+
+    def __init__(self,in_ch=3,out_ch=1):
+        super(U2NETP,self).__init__()
+
+        self.stage1 = RSU7(in_ch,16,64)
+        self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage2 = RSU6(64,16,64)
+        self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage3 = RSU5(64,16,64)
+        self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage4 = RSU4(64,16,64)
+        self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage5 = RSU4F(64,16,64)
+        self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage6 = RSU4F(64,16,64)
+
+        # decoder
+        self.stage5d = RSU4F(128,16,64)
+        self.stage4d = RSU4(128,16,64)
+        self.stage3d = RSU5(128,16,64)
+        self.stage2d = RSU6(128,16,64)
+        self.stage1d = RSU7(128,16,64)
+
+        self.side1 = nn.Conv2d(64,1,3,padding=1)
+        self.side2 = nn.Conv2d(64,1,3,padding=1)
+        self.side3 = nn.Conv2d(64,1,3,padding=1)
+        self.side4 = nn.Conv2d(64,1,3,padding=1)
+        self.side5 = nn.Conv2d(64,1,3,padding=1)
+        self.side6 = nn.Conv2d(64,1,3,padding=1)
+
+        self.upscore6 = nn.Upsample(scale_factor=32,mode='bilinear')
+        self.upscore5 = nn.Upsample(scale_factor=16,mode='bilinear')
+        self.upscore4 = nn.Upsample(scale_factor=8,mode='bilinear')
+        self.upscore3 = nn.Upsample(scale_factor=4,mode='bilinear')
+        self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear')
+
+        self.outconv = nn.Conv2d(6,1,1)
+
+    def forward(self,x):
+
+        hx = x
+
+        #stage 1
+        hx1 = self.stage1(hx)
+        hx = self.pool12(hx1)
+
+        #stage 2
+        hx2 = self.stage2(hx)
+        hx = self.pool23(hx2)
+
+        #stage 3
+        hx3 = self.stage3(hx)
+        hx = self.pool34(hx3)
+
+        #stage 4
+        hx4 = self.stage4(hx)
+        hx = self.pool45(hx4)
+
+        #stage 5
+        hx5 = self.stage5(hx)
+        hx = self.pool56(hx5)
+
+        #stage 6
+        hx6 = self.stage6(hx)
+        hx6up = self.upscore2(hx6)
+
+        #decoder
+        hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
+        hx5dup = self.upscore2(hx5d)
+
+        hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = self.upscore2(hx4d)
+
+        hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = self.upscore2(hx3d)
+
+        hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = self.upscore2(hx2d)
+
+        hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
+
+
+        #side output
+        d1 = self.side1(hx1d)
+
+        d2 = self.side2(hx2d)
+        d2 = self.upscore2(d2)
+
+        d3 = self.side3(hx3d)
+        d3 = self.upscore3(d3)
+
+        d4 = self.side4(hx4d)
+        d4 = self.upscore4(d4)
+
+        d5 = self.side5(hx5d)
+        d5 = self.upscore5(d5)
+
+        d6 = self.side6(hx6)
+        d6 = self.upscore6(d6)
+
+        d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))
+        # d00 = d0 + self.refconv(d0)
+
+        return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)

u2net_test.py

@@ -0,0 +1,116 @@
+import os
+from skimage import io, transform
+import torch
+import torchvision
+from torch.autograd import Variable
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms#, utils
+# import torch.optim as optim
+
+import numpy as np
+from PIL import Image
+import glob
+
+from data_loader import RescaleT
+from data_loader import ToTensor
+from data_loader import ToTensorLab
+from data_loader import SalObjDataset
+
+from model import U2NET # full size version 173.6 MB
+from model import U2NETP # small version u2net 4.7 MB
+
+# normalize the predicted SOD probability map
+def normPRED(d):
+    ma = torch.max(d)
+    mi = torch.min(d)
+
+    dn = (d-mi)/(ma-mi)
+
+    return dn
+
+def save_output(image_name,pred,d_dir):
+
+    predict = pred
+    predict = predict.squeeze()
+    predict_np = predict.cpu().data.numpy()
+
+    im = Image.fromarray(predict_np*255).convert('RGB')
+    img_name = image_name.split("/")[-1]
+    image = io.imread(image_name)
+    imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BILINEAR)
+
+    pb_np = np.array(imo)
+
+    aaa = img_name.split(".")
+    bbb = aaa[0:-1]
+    imidx = bbb[0]
+    for i in range(1,len(bbb)):
+        imidx = imidx + "." + bbb[i]
+
+    imo.save(d_dir+imidx+'.png')
+
+def main():
+
+    # --------- 1. get image path and name ---------
+    model_name='u2net'#u2netp
+
+
+    image_dir = './test_data/test_images/'
+    prediction_dir = './test_data/' + model_name + '_results/'
+    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
+
+if __name__ == "__main__":
+    main()

u2net_train.py

@@ -0,0 +1,164 @@
+import torch
+import torchvision
+from torch.autograd import Variable
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms, utils
+import torch.optim as optim
+import torchvision.transforms as standard_transforms
+
+import numpy as np
+import glob
+
+from data_loader import Rescale
+from data_loader import RescaleT
+from data_loader import RandomCrop
+from data_loader import ToTensor
+from data_loader import ToTensorLab
+from data_loader import SalObjDataset
+
+from model import U2NET
+from model import U2NETP
+
+# ------- 1. define loss function --------
+
+bce_loss = nn.BCELoss(size_average=True)
+
+def muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6, labels_v):
+
+	loss0 = bce_loss(d0,labels_v)
+	loss1 = bce_loss(d1,labels_v)
+	loss2 = bce_loss(d2,labels_v)
+	loss3 = bce_loss(d3,labels_v)
+	loss4 = bce_loss(d4,labels_v)
+	loss5 = bce_loss(d5,labels_v)
+	loss6 = bce_loss(d6,labels_v)
+
+	loss = loss0 + loss1 + loss2 + loss3 + loss4 + loss5 + loss6
+	print("l0: %3f, l1: %3f, l2: %3f, l3: %3f, l4: %3f, l5: %3f, l6: %3f\n"%(loss0.data[0],loss1.data[0],loss2.data[0],loss3.data[0],loss4.data[0],loss5.data[0],loss6.data[0]))
+
+	return loss0, loss
+
+
+# ------- 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 = 10 # 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
+
+if __name__ == "__main__":
+    main()