苗火 Nicholas

[DL]SRCNN:Super-resolution with CNN

2022-4-27 萧 写技术

import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image,ImageFilter
import matplotlib.pyplot as plt
import numpy as np
import torchvision
from torchvision import transforms
from torchvision.transforms import ToTensor
from torchvision.transforms import ToPILImage
from torch.autograd import Variable
import math
import random

means = [0.485, 0.456, 0.406]
stds = [ 0.229, 0.224, 0.225]

pic_height = 512
pic_width = 512


"""
# errors
def psnr(img1, img2):
    img1 = Variable(img1)
    img2 = Variable(img2)
    mse = (  (img1/1.0) - (img2/1.0)   ) ** 2 
    mse = np.mean( np.array(mse) )
    if mse < 1.0e-10:
        return 100
    return 10 * math.log10(255.0**2/mse)
"""

def psnr(img1, img2):
    img1 = Variable(img1)
    img2 = Variable(img2)
    mse = (  (img1/255.0) - (img2/255.0)   ) ** 2 
    mse = np.mean( np.array(mse) )
    if mse < 1.0e-10:
        return 100
    return 10 * math.log10(1/math.sqrt(mse))


def rgb2ycbcr(rgb_image):
    """convert rgb into ycbcr"""
    if len(rgb_image.shape)!=3 or rgb_image.shape[2]!=3:
        return rgb_image
        #raise ValueError("input image is not a rgb image")
    rgb_image = rgb_image.astype(np.float32)
    transform_matrix = np.array([[0.257, 0.564, 0.098],
                                 [-0.148, -0.291, 0.439],
                                 [0.439, -0.368, -0.071]])
    shift_matrix = np.array([16, 128, 128])
    ycbcr_image = np.zeros(shape=rgb_image.shape)
    w, h, _ = rgb_image.shape
    for i in range(w):
        for j in range(h):
            ycbcr_image[i, j, :] = np.dot(transform_matrix, rgb_image[i, j, :]) + shift_matrix
    return ycbcr_image

def ycbcr2rgb(ycbcr_image):
    """convert ycbcr into rgb"""
    if len(ycbcr_image.shape)!=3 or ycbcr_image.shape[2]!=3:
        return ycbcr_image
        #raise ValueError("input image is not a rgb image")
    ycbcr_image = ycbcr_image.astype(np.float32)
    transform_matrix = np.array([[0.257, 0.564, 0.098],
                                 [-0.148, -0.291, 0.439],
                                 [0.439, -0.368, -0.071]])
    transform_matrix_inv = np.linalg.inv(transform_matrix)
    shift_matrix = np.array([16, 128, 128])
    rgb_image = np.zeros(shape=ycbcr_image.shape)
    w, h, _ = ycbcr_image.shape
    for i in range(w):
        for j in range(h):
            rgb_image[i, j, :] = np.dot(transform_matrix_inv, ycbcr_image[i, j, :]) - np.dot(transform_matrix_inv, shift_matrix)
    return rgb_image.astype(np.uint8)

class dataloader:
    def __init__(self, path, batchs,  batch_size, test= False, test_offset = 0.9, order = False):
        self.test = test
        self.test_offset = test_offset
        self.batchs_cnt = 0
        self.batchs = batchs
        self.batch_size = batch_size
        self.path = path
        self.file_list = os.listdir(self.path)
        if order:
            self.file_list = sorted(self.file_list)
        else:
            random.shuffle(self.file_list)
        self.file_number = len(self.file_list)

        self.file_start = 0
        self.file_stop = self.file_number * self.test_offset - 1
        if self.test:
            self.file_start = self.file_number * self.test_offset
            self.file_stop = self.file_number - 1

        self.file_start = int(np.floor(self.file_start))
        self.file_stop = int(np.floor(self.file_stop))

        self.file_idx = self.file_start

        self.downsample = transforms.Compose([
            transforms.Resize( (int(pic_height/2),int(pic_width/2)), Image.BILINEAR),
            #transforms.RandomResizedCrop( pic_height, scale=(0.08,1) ),
            #transforms.Grayscale(num_output_channels = 3),
            #transforms.ToTensor()
            #transforms.Normalize(means, stds)
            ])

        self.transform = transforms.Compose([
            #transforms.Resize( (pic_height,pic_width), Image.BILINEAR),
            transforms.RandomResizedCrop( pic_height, scale=(1,1) ),
            #transforms.Grayscale(num_output_channels = 3),
            transforms.ToTensor()
            #transforms.Normalize(means, stds)
            ])

    def get_len(self):
        return self.file_stop  - self.file_start

    def __iter__(self):
        return self

    def __next__(self):
        if (self.batchs_cnt >= self.batchs) & (self.batchs > 0):
            self.batchs_cnt = 0
            raise StopIteration
        self.batchs_cnt += 1

        X = []
        Y = []
        for i in range( self.batch_size):
            X_, Y_ = self._next()
            X.append(X_)
            Y.append(Y_)

        X = torch.stack(X, 0)
        Y = torch.stack(Y, 0)
        return X, Y

    def _next(self):
        if self.file_idx >= self.file_stop:
            self.file_idx = self.file_start

        file_path = self.path + '/' + self.file_list[self.file_idx]
        self.file_idx += 1
        Y = Image.open(file_path)
        #print( "Y:", file_path, " (", Y.size )

        if len(Y.getbands()) == 1:
            Y = Y.convert("RGB")

        #X = self.downsample(Y)
        X = Y.filter(ImageFilter.BLUR)

        X = self.transform(X)
        Y = self.transform(Y)

        return X,Y

class SRCNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.patch_extraction = nn.Conv2d(1, 64, kernel_size = 9, stride = 1, padding=4, padding_mode='replicate')
        self.non_linear = nn.Conv2d(64, 32, kernel_size = 1, stride = 1, padding = 0)
        self.reconstruction = nn.Conv2d(32, 1, kernel_size = 5, stride = 1, padding = 2, padding_mode='replicate')

    def init_weights(self):
        print(" ****************************")
        self.patch_extraction.weight.data.normal_(mean=0.0, std=0.001)
        self.patch_extraction.bias.data.zero_()
        self.non_linear.weight.data.normal_(mean=0.0, std=0.001)
        self.non_linear.bias.data.zero_()
        self.reconstruction.weight.data.normal_(mean=0.0, std=0.001)
        self.reconstruction.bias.data.zero_()

    def forward(self, x):
        fm_1 = F.relu(self.patch_extraction(x))
        fm_2 = F.relu(self.non_linear(fm_1))
        #fm_3 = F.sigmoid(self.reconstruction(fm_2))
        fm_3 = self.reconstruction(fm_2)
        return fm_3



tensor_to_pil = ToPILImage()
'''
dataloader = dataloader('./dataset/Set14/image_SRF_2')
plt.figure("ddd")
plt.ion()
plt.cla()
for X,Y in dataloader:
    plt.imshow(X)
    plt.pause(0.1)
plt.ioff()

iii = tensor_to_pil(Y_[0])
plt.imshow(iii)
plt.show()


'''

model1 = SRCNN()
model2 = SRCNN()
model3 = SRCNN()


loss = nn.MSELoss()

"""
optimizer1 = torch.optim.SGD( [
    {"params":model1.patch_extraction.parameters(), "lr":0.0001},
    {"params":model1.non_linear.parameters(), "lr":0.0001},
    {"params":model1.reconstruction.parameters(), "lr":0.00001},
    ])
optimizer2 = torch.optim.SGD( [
    {"params":model2.patch_extraction.parameters(), "lr":0.0001},
    {"params":model2.non_linear.parameters(), "lr":0.0001},
    {"params":model2.reconstruction.parameters(), "lr":0.00001},
    ])
optimizer3 = torch.optim.SGD( [
    {"params":model3.patch_extraction.parameters(), "lr":0.0001},
    {"params":model3.non_linear.parameters(), "lr":0.0001},
    {"params":model3.reconstruction.parameters(), "lr":0.00001},
    ])
"""

optimizer1 = torch.optim.Adam( [
    {"params":model1.patch_extraction.parameters(), "lr":0.0001},
    {"params":model1.non_linear.parameters(), "lr":0.0001},
    {"params":model1.reconstruction.parameters(), "lr":0.00001},
    ])
optimizer2 = torch.optim.Adam( [
    {"params":model2.patch_extraction.parameters(), "lr":0.0001},
    {"params":model2.non_linear.parameters(), "lr":0.0001},
    {"params":model2.reconstruction.parameters(), "lr":0.00001},
    ])
optimizer3 = torch.optim.Adam( [
    {"params":model3.patch_extraction.parameters(), "lr":0.0001},
    {"params":model3.non_linear.parameters(), "lr":0.0001},
    {"params":model3.reconstruction.parameters(), "lr":0.00001},
    ])

"""
optimizer1 = torch.optim.Adam(model1.parameters(), lr = 0.001, betas = (0.9, 0.999))
optimizer2 = torch.optim.Adam(model2.parameters(), lr = 0.001, betas = (0.9, 0.999))
optimizer3 = torch.optim.Adam(model3.parameters(), lr = 0.001, betas = (0.9, 0.999))
"""

dataloader_train = dataloader('/home/nicholas/Documents/dataset/imageNet', 200, 2)
#dataloader_test = dataloader('/home/nicholas/Documents/dataset/imageNet', 1, 2)
dataloader_test = dataloader('./dataset/Set14/image_SRF_3', 1, 2)
new_training = 0

def train():

    if new_training == 1:
        print(" new training ...")
        model1.init_weights()
        model2.init_weights()
        model3.init_weights()
    else:
        print(" continue training ...")
        model1= torch.load("./model/srcnn_l1.m")
        model2= torch.load("./model/srcnn_l2.m")
        model3= torch.load("./model/srcnn_l3.m")


    running_loss = 0
    plt.ion()

    i = 0

    for X,Y in dataloader_train:
        #print(X.shape)

        '''
        Train
        '''
        model1.train()
        model2.train()
        model3.train()

        _,c,h,w = X.shape

        Y_1 = model1(X[:,0].reshape(-1,1,h,w))
        optimizer1.zero_grad()
        cost1 = loss(Y_1, Y[:,0].reshape(-1,1,h,w))
        cost1.backward()
        optimizer1.step()

        Y_2 = model2(X[:,1].reshape(-1,1,h,w))
        optimizer2.zero_grad()
        cost2 = loss(Y_2, Y[:,1].reshape(-1,1,h,w))
        cost2.backward()
        optimizer2.step()

        Y_3 = model3(X[:,2].reshape(-1,1,h,w))
        optimizer3.zero_grad()
        cost3 = loss(Y_3, Y[:,2].reshape(-1,1,h,w))
        cost3.backward()
        optimizer3.step()


        #running_loss += cost.item()
        print( "batch:{}, loss  is {:.4f} {:.4f} {:.4f} ".format(i, cost1, cost2, cost3) )

        i += 1
        if i%10 == 0:
            test(model1, model2, model3)

        '''
        Display images
        '''
        """
        Y_ = torch.stack( [Y_1[:,0], Y_2[:,0], Y_3[:,0] ], dim = 1)

        iii = tensor_to_pil(Y_[0])
        plt.subplot(1, 2, 1)
        plt.imshow(iii)
        
        iii = tensor_to_pil(X[0])
        plt.subplot(1, 2, 2)
        plt.imshow(iii)

        plt.show()
        plt.pause(0.1)
        """

    plt.ioff()
    torch.save(model1, "./model/srcnn_l1.m")
    torch.save(model2, "./model/srcnn_l2.m")
    torch.save(model3, "./model/srcnn_l3.m")

def test(model1, model2, model3):

    running_loss = 0
    #plt.ion()

    for X,Y in dataloader_test:

        #print(X.shape)
        model1.eval()
        model2.eval()
        model3.eval()

        _,c,h,w = X.shape

        Y_1 = model1(X[:,0].reshape(-1,1,h,w))
        Y_2 = model2(X[:,1].reshape(-1,1,h,w))
        Y_3 = model3(X[:,2].reshape(-1,1,h,w))

        print( c, h, w)
        Y_ = torch.stack( [Y_1[:,0], Y_2[:,0], Y_3[:,0] ], dim = 1)

        psnrv = psnr(Y_, Y)

        print(" Psnr = ", psnr(Y_,Y), "  psnr2 = ", psnr(X, Y) )

        '''
        Display images
        '''
        iii = tensor_to_pil(Y_[0])
        plt.subplot(1, 2, 1)
        plt.imshow(iii)
        
        iii = tensor_to_pil(X[0])
        plt.subplot(1, 2, 2)
        plt.imshow(iii)

        plt.show()
        plt.pause(0.1)

    #plt.ioff()



def run():
    model1= torch.load("./model/srcnn_l1.m")
    model2= torch.load("./model/srcnn_l2.m")
    model3= torch.load("./model/srcnn_l3.m")

    model1.eval()
    model2.eval()
    model3.eval()
    path = "./images"

    file_list = os.listdir(path)
    #plt.ion()

    transform = transforms.Compose([
        transforms.RandomResizedCrop( pic_height, scale=(1,1) ),
        transforms.ToTensor()
            ])
 
    for img_name in file_list:
        img = Image.open(path+'/'+img_name)
        if len(img.getbands()) == 1:
            img = img.convert("RGB")
        X = transform(img)

        c,h,w = X.shape
        X = X.reshape(1,c,h,w)

        _,c,h,w = X.shape

        Y_1 = model1(X[:,0].reshape(-1,1,h,w))
        Y_2 = model2(X[:,1].reshape(-1,1,h,w))
        Y_3 = model3(X[:,2].reshape(-1,1,h,w))

        Y_ = torch.stack( [Y_1[:,0], Y_2[:,0], Y_3[:,0] ], dim = 1)

        '''
        Display images
        '''

        iii = tensor_to_pil(Y_[0])
        plt.subplot(1, 2, 1)
        plt.imshow(iii)
        
        iii = tensor_to_pil(X[0])
        plt.subplot(1, 2, 2)
        plt.imshow(iii)

        plt.show()
        plt.pause(1)

    #plt.ioff()

"""

For training run this command:
python face_cnn.py train

For testing fun this command:
python face_cnn.py test

"""
if __name__ == '__main__':
    args = sys.argv[1:]
    print( args, len(args))
    if (len(args) == 1) & (args[0] == "train"):
        train()
    elif (len(args) == 1) & (args[0] == "run"):
        run()
    else:
        test()

标签: DL