def forward(self, x):
global glob_gau
global glob_blur
if args.print == 1:
npimg = np.array(x, dtype=float)
npimg = npimg.squeeze(0)
scipy.misc.toimage(npimg).save("img0.png")
#Noise generation part
if (glob_gau == 0) & (glob_blur == 0):
#no noise
pass
elif (glob_blur == 0) & (glob_gau == 1):
#gaussian noise add
gau_kernel = torch.randn(x.size()) * args.gau
x = Variable(gau_kernel.cuda()) + x
elif (glob_gau == 0) & (glob_blur == 1):
#blur noise add
blur_kernel_partial = torch.FloatTensor(
utils.genblurkernel(args.blur))
blur_kernel_partial = torch.matmul(
blur_kernel_partial.unsqueeze(1),
torch.transpose(blur_kernel_partial.unsqueeze(1), 0, 1))
kernel_size = blur_kernel_partial.size()[0]
zeros = torch.zeros(kernel_size, kernel_size)
blur_kernel = torch.cat(
(blur_kernel_partial, zeros, zeros, zeros, blur_kernel_partial,
zeros, zeros, zeros, blur_kernel_partial), 0)
blur_kernel = blur_kernel.view(3, 3, kernel_size, kernel_size)
blur_padding = int((blur_kernel_partial.size()[0] - 1) / 2)
#x = torch.nn.functional.conv2d(x, weight=blur_kernel.cuda(), padding=blur_padding)
x = torch.nn.functional.conv2d(x,
weight=Variable(blur_kernel.cuda()),
padding=blur_padding)
elif (glob_gau == 1) & (glob_blur == 1):
#both gaussian and blur noise added
blur_kernel_partial = torch.FloatTensor(
utils.genblurkernel(args.blur))
blur_kernel_partial = torch.matmul(
blur_kernel_partial.unsqueeze(1),
torch.transpose(blur_kernel_partial.unsqueeze(1), 0, 1))
kernel_size = blur_kernel_partial.size()[0]
zeros = torch.zeros(kernel_size, kernel_size)
blur_kernel = torch.cat(
(blur_kernel_partial, zeros, zeros, zeros, blur_kernel_partial,
zeros, zeros, zeros, blur_kernel_partial), 0)
blur_kernel = blur_kernel.view(3, 3, kernel_size, kernel_size)
blur_padding = int((blur_kernel_partial.size()[0] - 1) / 2)
x = torch.nn.functional.conv2d(x,
weight=Variable(blur_kernel.cuda()),
padding=blur_padding)
gau_kernel = torch.randn(x.size()) * args.gau
x = Variable(gau_kernel.cuda()) + x
else:
print("Something is wrong in noise adding part")
exit()
if args.print == 1:
npimg = np.array(x, dtype=float)
npimg = npimg.squeeze(0)
scipy.misc.toimage(npimg).save("img1.png")
exit()
fixed = 0
if fixed:
x = quant(x)
x = roundmax(x)
out = self.conv1(x)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv2(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv3(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv4(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv5(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv6(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv7(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv8(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv9(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv10(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv11(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv12(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv13(out)
out = out.view(out.size(0), -1)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.fc1(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.fc2(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.fc3(out)
if fixed:
out = quant(out)
out = roundmax(out)
return out
def forward(self, x):
if (args.gau == 0) & (args.blur == 0):
#no noise
pass
elif (args.blur == 0) & (args.gau != 0):
#gaussian noise add
gau_kernel = torch.randn(x.size()) * args.gau
x = Variable(gau_kernel.cuda()) + x
elif (args.gau == 0) & (args.blur != 0):
#blur noise add
blur_kernel_partial = torch.FloatTensor(
utils.genblurkernel(args.blur))
blur_kernel_partial = torch.matmul(
blur_kernel_partial.unsqueeze(1),
torch.transpose(blur_kernel_partial.unsqueeze(1), 0, 1))
kernel_size = blur_kernel_partial.size()[0]
zeros = torch.zeros(kernel_size, kernel_size)
blur_kernel = torch.cat(
(blur_kernel_partial, zeros, zeros, zeros, blur_kernel_partial,
zeros, zeros, zeros, blur_kernel_partial), 0)
blur_kernel = blur_kernel.view(3, 3, kernel_size, kernel_size)
blur_padding = int((blur_kernel_partial.size()[0] - 1) / 2)
#x = torch.nn.functional.conv2d(x, weight=blur_kernel.cuda(), padding=blur_padding)
x = torch.nn.functional.conv2d(x,
weight=Variable(blur_kernel.cuda()),
padding=blur_padding)
elif (args.gau != 0) & (args.blur != 0):
#both gaussian and blur noise added
blur_kernel_partial = torch.FloatTensor(
utils.genblurkernel(args.blur))
blur_kernel_partial = torch.matmul(
blur_kernel_partial.unsqueeze(1),
torch.transpose(blur_kernel_partial.unsqueeze(1), 0, 1))
kernel_size = blur_kernel_partial.size()[0]
zeros = torch.zeros(kernel_size, kernel_size)
blur_kernel = torch.cat(
(blur_kernel_partial, zeros, zeros, zeros, blur_kernel_partial,
zeros, zeros, zeros, blur_kernel_partial), 0)
blur_kernel = blur_kernel.view(3, 3, kernel_size, kernel_size)
blur_padding = int((blur_kernel_partial.size()[0] - 1) / 2)
x = torch.nn.functional.conv2d(x,
weight=Variable(blur_kernel.cuda()),
padding=blur_padding)
gau_kernel = torch.randn(x.size()) * args.gau
x = Variable(gau_kernel.cuda()) + x
else:
print("Something is wrong in noise adding part")
exit()
fixed = 0
if fixed:
x = quant(x)
x = roundmax(x)
out = self.conv1(x)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv2(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv3(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv4(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv5(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv6(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv7(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv8(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv9(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv10(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv11(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv12(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.conv13(out)
out = out.view(out.size(0), -1)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.fc1(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.fc2(out)
if fixed:
out = quant(out)
out = roundmax(out)
out = self.fc3(out)
if fixed:
out = quant(out)
out = roundmax(out)
return out
def forward(self, x):
if args.print == 1:
npimg = np.array(tensor, dtype=float)
npimg = npimg.squeeze(0)
scipy.misc.toimage(npimg).save("img0.png")
#Noise generation part
if (args.gau == 0) & (args.blur == 0):
#no noise
pass
elif args.blur == 0:
#gaussian noise add
gau_kernel = torch.randn(x.size())
x = Variable(gau_kernel.cuda()) + x
elif args.gau == 0:
#blur noise add
blur_kernel_partial = torch.FloatTensor(
utils.genblurkernel(args.blur))
blur_kernel_partial = torch.matmul(
blur_kernel_partial.unsqueeze(1),
torch.transpose(blur_kernel_partial.unsqueeze(1), 0, 1))
kernel_size = blur_kernel_partial.size()[0]
zeros = torch.zeros(kernel_size, kernel_size)
blur_kernel = torch.cat(
(blur_kernel_partial, zeros, zeros, zeros, blur_kernel_partial,
zeros, zeros, zeros, blur_kernel_partial), 0)
blur_kernel = blur_kernel.view(3, 3, kernel_size, kernel_size)
blur_padding = int((blur_kernel_partial.size()[0] - 1) / 2)
#x = torch.nn.functional.conv2d(x, weight=blur_kernel.cuda(), padding=blur_padding)
x = torch.nn.functional.conv2d(x,
weight=Variable(blur_kernel.cuda()),
padding=blur_padding)
elif (not (args.gau == 0)) & (not (args.blur == 0)):
#both gaussian and blur noise added
blur_kernel_partial = torch.FloatTensor(
utils.genblurkernel(args.blur))
blur_kernel_partial = torch.matmul(
blur_kernel_partial.unsqueeze(1),
torch.transpose(blur_kernel_partial.unsqueeze(1), 0, 1))
kernel_size = blur_kernel_partial.size()[0]
zeros = torch.zeros(kernel_size, kernel_size)
blur_kernel = torch.cat(
(blur_kernel_partial, zeros, zeros, zeros, blur_kernel_partial,
zeros, zeros, zeros, blur_kernel_partial), 0)
blur_kernel = blur_kernel.view(3, 3, kernel_size, kernel_size)
blur_padding = int((blur_kernel_partial.size()[0] - 1) / 2)
x = torch.nn.functional.conv2d(x,
weight=Variable(blur_kernel.cuda()),
padding=blur_padding)
gau_kernel = torch.randn(x.size())
x = Variable(gau_kernel.cuda()) + x
else:
print("Something is wrong in noise adding part")
exit()
if args.print == 1:
npimg = np.array(tensor, dtype=float)
npimg = npimg.squeeze(0)
scipy.misc.toimage(npimg).save("img1.png")
exit()
if args.fixed:
x = roundmax(x)
x = quant(x)
out1 = self.conv1(x) # 1250*64*32*32
if args.fixed:
out1 = quant(out1)
out1 = roundmax(out1)
out2 = self.conv2(out1) # 1250*64*32*32
if args.fixed:
out2 = quant(out2)
out2 = roundmax(out2)
out3 = self.maxpool1(out2)
out4 = self.conv3(out3) # 1250*128*16*16
if args.fixed:
out4 = quant(out4)
out4 = roundmax(out4)
out5 = self.conv4(out4) # 1250*128*16*16
if args.fixed:
out5 = quant(out5)
out5 = roundmax(out5)
out6 = self.maxpool2(out5)
out7 = self.conv5(out6) # 1250*256*8*8
if args.fixed:
out7 = quant(out7)
out7 = roundmax(out7)
out8 = self.conv6(out7) # 1250*256*8*8
if args.fixed:
out8 = quant(out8)
out8 = roundmax(out8)
out9 = self.conv7(out8) # 1250*256*8*8
if args.fixed:
out9 = quant(out9)
out9 = roundmax(out9)
out10 = self.maxpool3(out9)
out11 = self.conv8(out10) # 1250*512*4*4
if args.fixed:
out11 = quant(out11)
out11 = roundmax(out11)
out12 = self.conv9(out11) # 1250*512*4*4
if args.fixed:
out12 = quant(out12)
out12 = roundmax(out12)
out13 = self.conv10(out12) # 1250*512*4*
if args.fixed:
out13 = quant(out13)
out13 = roundmax(out13)
out14 = self.maxpool4(out13)
out15 = self.conv11(out14) # 1250*512*2*
if args.fixed:
out15 = quant(out15)
out15 = roundmax(out15)
out16 = self.conv12(out15) # 1250*512*2*
if args.fixed:
out16 = quant(out16)
out16 = roundmax(out16)
out17 = self.conv13(out16) # 1250*512*2*
if args.fixed:
out17 = quant(out17)
out17 = roundmax(out17)
out18 = self.maxpool5(out17)
out19 = out18.view(out18.size(0), -1)
out20 = self.fc1(out19) # 1250*512
if args.fixed:
out20 = quant(out20)
out20 = roundmax(out20)
out21 = self.fc2(out20) # 1250*512
if args.fixed:
out21 = quant(out21)
out21 = roundmax(out21)
out22 = self.fc3(out21) # 1250*10
if args.fixed:
out22 = quant(out22)
out22 = roundmax(out22)
return out22
def forward(self, x):
if (args.gau == 0) & (args.blur == 0):
#no noise
pass
elif (args.blur == 0) & (args.gau != 0):
#gaussian noise add
gau_kernel = torch.randn(x.size()) * args.gau
x = Variable(gau_kernel.cuda()) + x
elif (args.gau == 0) & (args.blur != 0):
#blur noise add
blur_kernel_partial = torch.FloatTensor(
utils.genblurkernel(args.blur))
blur_kernel_partial = torch.matmul(
blur_kernel_partial.unsqueeze(1),
torch.transpose(blur_kernel_partial.unsqueeze(1), 0, 1))
kernel_size = blur_kernel_partial.size()[0]
zeros = torch.zeros(kernel_size, kernel_size)
blur_kernel = torch.cat(
(blur_kernel_partial, zeros, zeros, zeros, blur_kernel_partial,
zeros, zeros, zeros, blur_kernel_partial), 0)
blur_kernel = blur_kernel.view(3, 3, kernel_size, kernel_size)
blur_padding = int((blur_kernel_partial.size()[0] - 1) / 2)
#x = torch.nn.functional.conv2d(x, weight=blur_kernel.cuda(), padding=blur_padding)
x = torch.nn.functional.conv2d(x,
weight=Variable(blur_kernel.cuda()),
padding=blur_padding)
elif (args.gau != 0) & (args.blur != 0):
#both gaussian and blur noise added
blur_kernel_partial = torch.FloatTensor(
utils.genblurkernel(args.blur))
blur_kernel_partial = torch.matmul(
blur_kernel_partial.unsqueeze(1),
torch.transpose(blur_kernel_partial.unsqueeze(1), 0, 1))
kernel_size = blur_kernel_partial.size()[0]
zeros = torch.zeros(kernel_size, kernel_size)
blur_kernel = torch.cat(
(blur_kernel_partial, zeros, zeros, zeros, blur_kernel_partial,
zeros, zeros, zeros, blur_kernel_partial), 0)
blur_kernel = blur_kernel.view(3, 3, kernel_size, kernel_size)
blur_padding = int((blur_kernel_partial.size()[0] - 1) / 2)
x = torch.nn.functional.conv2d(x,
weight=Variable(blur_kernel.cuda()),
padding=blur_padding)
gau_kernel = torch.randn(x.size()) * args.gau
x = Variable(gau_kernel.cuda()) + x
else:
print("Something is wrong in noise adding part")
exit()
tmp = Variable(torch.zeros(1, 3, 224, 224).cuda())
f = fft.Fft2d()
fft_rout, fft_iout = f(x, tmp)
mag = torch.sqrt(
torch.mul(fft_rout, fft_rout) + torch.mul(fft_iout, fft_iout))
tmp = torch.zeros(1, 1, 224, 224).cuda()
tmp = torch.add(torch.add(mag[:, 0, :, :], mag[:, 1, :, :]),
mag[:, 2, :, :])
tmp = torch.abs(tmp)
PFSUM = 0
for i in range(0, 224):
for j in range(0, 224):
if (i + j) < 167:
print_value = 0
elif (i - j) > 56:
print_value = 0
elif (j - i) > 56:
print_value = 0
elif (i + j) > 279:
print_value = 0
else:
PFSUM = PFSUM + tmp[0, i, j]
f = open(args.outputfile, 'a+')
print(PFSUM.item(), file=f)
f.close()
#f = open(args.outputfile,'a+')
#try:
# print(PFSUM.item(),file=f)
#except:
# print(PFSUM,file=f)
#f.close()
'''
f = open(args.outputfile,'a+')
for i in range(0,224):
for j in range(0,224):
print(tmp[0,i,j].item()/3,file = f)
f.close()
exit()
'''
"""
if args.fixed:
x = quant(x)
x = roundmax(x)
out = self.conv1(x)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv2(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv3(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv4(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv5(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv6(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv7(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv8(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv9(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv10(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv11(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv12(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.conv13(out)
out = out.view(out.size(0), -1)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.linear1(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.linear2(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
out = self.linear3(out)
if args.fixed:
out = quant(out)
out = roundmax(out)
"""
out = torch.zeros(1000)
return out
