各位大佬求解答 謝謝！！！1
最近在進行模型構建的時候，把一個模塊添加到原模型中時，恢復出的圖片顏色發生改變，以下是圖片結果和部分代碼：
原圖片：
恢復出的圖片
感覺圖片變得灰蒙蒙的 沒有綠色
代碼：
x_out_branch, x_spsr, x_grad = self.spsr(xspsr)#加進去的模塊
x = F.relu(self.conv_merge( torch.cat((x, x_spsr), dim=1) ))#64
x = self.conv_tail1(x)
x = self.conv_tail2(x)
其中：
self.spsr = SPSR.SPSRNet(in_nc=3, out_nc=3, nf=64,
nb=23, gc=32, upscale=4, norm_type=None,
act_type='leakyrelu', mode='CNA', upsample_mode='upconv')
self.conv_merge = conv3x3(n_feats*2, n_feats)
self.conv_tail1 = conv3x3(n_feats, n_feats//2)
self.conv_tail2 = conv1x1(n_feats//2, 3)
class SPSRNet(nn.Module):
def __init__(self, in_nc, out_nc, nf, nb, gc=32, upscale=4, norm_type=None, \
act_type='leakyrelu', mode='CNA', upsample_mode='upconv'):
super(SPSRNet, self).__init__()
n_upscale = int(math.log(upscale, 2))
if upscale == 3:
n_upscale = 1
fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None)
rb_blocks = [B.RRDB(nf, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
norm_type=norm_type, act_type=act_type, mode='CNA') for _ in range(nb)]
LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode)
if upsample_mode == 'upconv':
upsample_block = B.upconv_blcok
elif upsample_mode == 'pixelshuffle':
upsample_block = B.pixelshuffle_block
else:
raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
if upscale == 3:
upsampler = upsample_block(nf, nf, 3, act_type=act_type)
else:
upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)]
self.HR_conv0_new = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
self.HR_conv1_new = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=None)
self.model = B.sequential(fea_conv, B.ShortcutBlock(B.sequential(*rb_blocks, LR_conv)),\
*upsampler, self.HR_conv0_new)
self.get_g_nopadding = Get_gradient_nopadding()
self.b_fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None)
self.b_concat_1 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None)
self.b_block_1 = B.RRDB(nf*2, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
norm_type=norm_type, act_type=act_type, mode='CNA')
self.b_concat_2 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None)
self.b_block_2 = B.RRDB(nf*2, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
norm_type=norm_type, act_type=act_type, mode='CNA')
self.b_concat_3 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None)
self.b_block_3 = B.RRDB(nf*2, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
norm_type=norm_type, act_type=act_type, mode='CNA')
self.b_LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode)
if upsample_mode == 'upconv':
upsample_block = B.upconv_blcok
elif upsample_mode == 'pixelshuffle':
upsample_block = B.pixelshuffle_block
else:
raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
if upscale == 3:
b_upsampler = upsample_block(nf, nf, 3, act_type=act_type)
else:
b_upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)]
b_HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
b_HR_conv1 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=None)
self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1)
self.conv_w = B.conv_block(nf, out_nc, kernel_size=1, norm_type=None, act_type=None)
self.f_concat = B.conv_block(nf*2, nf, kernel_size=3, norm_type=None, act_type=None)
self.f_block = B.RRDB(nf*2, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
norm_type=norm_type, act_type=act_type, mode='CNA')
self.f_HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
def forward(self, x):
x_grad = self.get_g_nopadding(x)
x = self.model[0](x)
x, block_list = self.model[1](x)
x_ori = x
for i in range(5):
x = block_list[i](x)
x_fea1 = x
for i in range(5):
x = block_list[i+5](x)
x_fea2 = x
for i in range(5):
x = block_list[i+10](x)
x_fea3 = x
x = block_list[15:](x)
#short cut
x = x_ori+x
x= self.model[2:](x)
x = self.HR_conv1_new(x)
x_b_fea = self.b_fea_conv(x_grad)
x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1)
x_cat_1 = self.b_block_1(x_cat_1)
x_cat_1 = self.b_concat_1(x_cat_1)
x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1)
x_cat_2 = self.b_block_2(x_cat_2)
x_cat_2 = self.b_concat_2(x_cat_2)
x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1)
x_cat_3 = self.b_block_3(x_cat_3)
x_cat_3 = self.b_concat_3(x_cat_3)
x_cat_3 = self.b_LR_conv(x_cat_3)
#short cut
x_cat_3 = x_cat_3+x_b_fea
x_branch = self.b_module(x_cat_3)
x_out_branch = self.conv_w(x_branch)
########
x_branch_d = x_branch
x_f_cat = torch.cat([x_branch_d, x], dim=1)
x_f_cat = self.f_block(x_f_cat)
x_out = self.f_concat(x_f_cat)#64
x_out = self.f_HR_conv0(x_f_cat)
#########
return x_out_branch, x_out, x_grad
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