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test_AMP_Net_B.py
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from dataset import dataset_full,dataset
import os
import numpy as np
import glob
from utils import *
from scipy import io
import torch
from torch.nn import Module
from torch import nn
from torch.autograd import Variable
from skimage.io import imsave
"""
No mask training, no deblocking
"""
class Denoiser(Module):
def __init__(self):
super().__init__()
self.D = nn.Sequential(nn.Conv2d(1, 32, 3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, 3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, 3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 1, 3, padding=1,bias=False))
def forward(self, inputs):
inputs = torch.unsqueeze(torch.reshape(torch.transpose(inputs,0,1),[-1,33,33]),dim=1)
output = self.D(inputs)
# output=inputs-output
output = torch.transpose(torch.reshape(torch.squeeze(output),[-1,33*33]),0,1)
return output
class Deblocker(Module):
def __init__(self):
super().__init__()
self.D = nn.Sequential(nn.Conv2d(1, 32, 3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, 3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, 3, padding=1),
nn.ReLU(),
nn.Conv2d(32, 1, 3, padding=1,bias=False))
def forward(self, inputs):
inputs = torch.unsqueeze(inputs,dim=1)
output = self.D(inputs)
output = torch.squeeze(output,dim=1)
return output
class AMP_net_Deblock(Module):
def __init__(self,layer_num, A):
super().__init__()
self.layer_num = layer_num
self.denoisers = []
self.deblocks = []
self.steps = []
self.register_parameter("A",nn.Parameter(torch.from_numpy(A).float(),requires_grad=False))
self.register_parameter("Q", nn.Parameter(torch.from_numpy(np.transpose(A)).float(), requires_grad=True))
for n in range(layer_num):
self.denoisers.append(Denoiser())
self.deblocks.append(Deblocker())
self.register_parameter("step_" + str(n + 1), nn.Parameter(torch.tensor(1.0),requires_grad=True))
self.steps.append(eval("self.step_" + str(n + 1)))
for n,denoiser in enumerate(self.denoisers):
self.add_module("denoiser_"+str(n+1),denoiser)
for n,deblock in enumerate(self.deblocks):
self.add_module("deblock_"+str(n+1),deblock)
def forward(self, inputs, output_layers):
H = int(inputs.shape[2]/33)
L = int(inputs.shape[3]/33)
S = inputs.shape[0]
y = self.sampling(inputs)
X = torch.matmul(self.Q,y)
for n in range(output_layers):
step = self.steps[n]
denoiser = self.denoisers[n]
deblocker = self.deblocks[n]
z = self.block1(X, y,step)
noise = denoiser(X)
X = z - torch.matmul(
(step * torch.matmul(torch.transpose(self.A,0,1), self.A)) - torch.eye(33 * 33).float().cuda(), noise)
X = self.together(X,S,H,L)
X = X - deblocker(X)
X = torch.cat(torch.split(X, split_size_or_sections=33, dim=1), dim=0)
X = torch.cat(torch.split(X, split_size_or_sections=33, dim=2), dim=0)
X = torch.transpose(torch.reshape(X, [-1, 33 * 33]), 0, 1)
X = self.together(X, S, H, L)
return torch.unsqueeze(X, dim=1)
def sampling(self,inputs):
inputs = torch.squeeze(inputs,dim=1)
inputs = torch.cat(torch.split(inputs, split_size_or_sections=33, dim=1), dim=0)
inputs = torch.cat(torch.split(inputs, split_size_or_sections=33, dim=2), dim=0)
inputs = torch.transpose(torch.reshape(inputs, [-1, 33*33]),0,1)
outputs = torch.matmul(self.A, inputs)
return outputs
def block1(self,X,y,step):
outputs = torch.matmul(torch.transpose(self.A,0,1),y-torch.matmul(self.A,X))
outputs = step * outputs + X
return outputs
def together(self,inputs,S,H,L):
inputs = torch.reshape(torch.transpose(inputs,0,1),[-1,33,33])
inputs = torch.cat(torch.split(inputs, split_size_or_sections=H*S, dim=0), dim=2)
inputs = torch.cat(torch.split(inputs, split_size_or_sections=S, dim=0), dim=1)
return inputs
def compute_loss(outputs, target):
loss = []
for output in outputs:
loss.append(torch.mean((output - target) ** 2))
return loss
def get_final_loss(loss_all):
output = 0
for loss in loss_all:
output += loss
return output
def get_loss(outputs, noise_all, Xs, H, target, sigma=0.01):
loss1 = torch.mean((outputs[-1] - target) ** 2)
loss2 = torch.mean(torch.abs(outputs[-1] - target))
num = 0
for n in range(len(noise_all)):
num += 1
X = Xs[n]
noise = noise_all[n]
loss2 += torch.mean((noise - torch.matmul(H, target - X)) ** 2)
return loss1, loss2
def train(model, opt, train_loader, epoch, batch_size, CS_ratio,PhaseNum):
model.train()
n = 0
for data in train_loader:
n = n + 1
opt.zero_grad()
data = torch.unsqueeze(data,dim=1)
data = Variable(data.float().cuda())
outputs= model(data,PhaseNum)
loss = torch.mean((outputs-data)**2)
loss.backward()
opt.step()
if n % 25 == 0:
output = "CS_ratio: %d [%02d/%02d] loss: %.4f " % (
CS_ratio, epoch, batch_size * n, loss.data.item())
print(output)
def get_val_result(model,PhaseNum,save_path, is_cuda=True):
model.eval()
with torch.no_grad():
# test_set_path = "./dataset/Set11"
test_set_path = "./dataset/bsds500/test"
test_set_path = glob.glob(test_set_path + '/*.tif')
ImgNum = len(test_set_path)
PSNR_All = np.zeros([1, ImgNum], dtype=np.float32)
SSIM_All = np.zeros([1, ImgNum], dtype=np.float32)
model.eval()
for img_no in range(ImgNum):
imgName = test_set_path[img_no]
[Iorg, row, col, Ipad, row_new, col_new] = imread_CS_py(imgName)
Icol = img2col_py(Ipad, 33) / 255.0
Ipad /= 255.0
if is_cuda:
inputs = Variable(torch.from_numpy(Ipad.astype('float32')).cuda())
else:
inputs = Variable(torch.from_numpy(Ipad.astype('float32')))
inputs = torch.unsqueeze(torch.unsqueeze(inputs,dim=0),dim=0)
outputs = model(inputs, PhaseNum)
outputs = torch.squeeze(outputs)
if is_cuda:
outputs = outputs.cpu().data.numpy()
else:
outputs = outputs.data.numpy()
images_recovered = outputs[0:row, 0:col] * 255
aaa = images_recovered.astype(int)
bbb = aaa < 0
aaa[bbb] = 0
bbb = aaa > 255
aaa[bbb] = 255
rec_PSNR = psnr(aaa, Iorg)
PSNR_All[0, img_no] = rec_PSNR
rec_SSIM = compute_ssim(aaa, Iorg)
SSIM_All[0, img_no] = rec_SSIM
imgname_for_save = (imgName.split('/')[-1]).split('.')[0]
imsave(os.path.join(save_path,imgname_for_save+'_'+str(rec_PSNR)+'_'+str(rec_SSIM)+'.jpg'),aaa)
return np.mean(PSNR_All), np.mean(SSIM_All)
def load_sampling_matrix(CS_ratio):
path = "./dataset/sampling_matrix"
data = io.loadmat(os.path.join(path, str(CS_ratio) + '.mat'))['sampling_matrix']
return data
if __name__ == "__main__":
model_name = "AMP_Net_K_B"
CS_ratios = [30, 10, 4]
phase = 6
save_path = "./results/generated_images"
for CS_ratio in CS_ratios:
if not os.path.exists(save_path):
os.mkdir(save_path)
sub_save_path = os.path.join(results_saving_path, str(CS_ratio))
if not os.path.exists(sub_save_path):
os.mkdir(sub_save_path)
sub_save_path = os.path.join(results_saving_path, str(phase))
if not os.path.exists(sub_save_path):
os.mkdir(sub_save_path)
path = os.path.join("./results", model_name, str(CS_ratio), str(phase), "best_model.pkl")
A = load_sampling_matrix(CS_ratio)
model = AMP_net_Deblock(phase, A)
model.cuda()
model.load_state_dict(torch.load(path))
print("Start")
one_psnr, one_ssim = get_val_result(model, phase,sub_save_path, is_cuda=True) # test AMP_net
print(one_psnr, "dB", one_ssim)