####################
class stride_OctaveConv(nn.Module):
def __init__(self, in_nc, out_nc, kernel_size, alpha=0.5, stride=1, dilation=1, groups=1,
bias=True, pad_type='zero', norm_type=None, act_type='prelu', mode='CNA'):
super(stride_OctaveConv, self).__init__()
assert mode in ['CNA', 'NAC', 'CNAC'], 'Wong conv mode [{:s}]'.format(mode)
padding = get_valid_padding(kernel_size, dilation) if pad_type == 'zero' else 0
self.h2g_pool = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.stride = stride
self.l2l = nn.Conv2d(int(alpha * in_nc), int(alpha * out_nc),
kernel_size, 1, padding, dilation, groups, bias)
self.l2h = nn.Conv2d(int(alpha * in_nc), out_nc - int(alpha * out_nc),
kernel_size, 1, padding, dilation, groups, bias)
self.h2l = nn.Conv2d(in_nc - int(alpha * in_nc), int(alpha * out_nc),
kernel_size, 2, padding, dilation, groups, bias)
self.h2h = nn.Conv2d(in_nc - int(alpha * in_nc), out_nc - int(alpha * out_nc),
kernel_size, 1, padding, dilation, groups, bias)
self.a = act(act_type) if act_type else None
self.n_h = norm(norm_type, int(out_nc*(1 - alpha))) if norm_type else None
self.n_l = norm(norm_type, int(out_nc*alpha)) if norm_type else None
def forward(self, x):
X_h, X_l = x
if self.stride ==2:
X_h, X_l = self.h2g_pool(X_h), self.h2g_pool(X_l)
X_h2h = self.h2h(X_h)
X_l2h = self.upsample(self.l2h(X_l))
X_l2l = self.l2l(X_l)
X_h2l = self.h2l(X_h)
#print(X_l2h.shape,"~~~~",X_h2h.shape)
X_h = X_l2h + X_h2h
X_l = X_h2l + X_l2l
if self.n_h and self.n_l:
X_h = self.n_h(X_h)
X_l = self.n_l(X_l)
if self.a:
X_h = self.a(X_h)
X_l = self.a(X_l)
return X_h, X_l
class stride_FirstOctaveConv(nn.Module):
def __init__(self, in_nc, out_nc, kernel_size, alpha=0.5, stride=1, dilation=1, groups=1,
bias=True, pad_type='zero', norm_type=None, act_type='prelu', mode='CNA'):
super(stride_FirstOctaveConv, self).__init__()
assert mode in ['CNA', 'NAC', 'CNAC'], 'Wong conv mode [{:s}]'.format(mode)
padding = get_valid_padding(kernel_size, dilation) if pad_type == 'zero' else 0
stride2=2
#padding2 = get_valid_padding(kernel_size, dilation2) if pad_type == 'zero' else 0
#self.h2g_pool = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
self.stride = stride
self.h2l = nn.Conv2d(in_nc, int(alpha * out_nc),
kernel_size, 2, padding,dilation, groups, bias)
self.h2h = nn.Conv2d(in_nc, out_nc - int(alpha * out_nc),
kernel_size, 1, padding, dilation, groups, bias)
self.a = act(act_type) if act_type else None
self.n_h = norm(norm_type, int(out_nc*(1 - alpha))) if norm_type else None
self.n_l = norm(norm_type, int(out_nc*alpha)) if norm_type else None
def forward(self, x):
if self.stride ==2:
x = self.h2g_pool(x)
X_h = self.h2h(x)
#X_l = self.h2l(self.h2g_pool(x))
X_l = self.h2l(x)
# print (X_h.shape)
# print (X_l.shape)
# exit()
if self.n_h and self.n_l:
X_h = self.n_h(X_h)
X_l = self.n_l(X_l)
if self.a:
X_h = self.a(X_h)
X_l = self.a(X_l)
return X_h, X_l
class stride_LastOctaveConv(nn.Module):
def __init__(self, in_nc, out_nc, kernel_size, alpha=0.5, stride=1, dilation=1, groups=1,
bias=True, pad_type='zero', norm_type=None, act_type='prelu', mode='CNA'):
super(stride_LastOctaveConv, self).__init__()
assert mode in ['CNA', 'NAC', 'CNAC'], 'Wong conv mode [{:s}]'.format(mode)
padding = get_valid_padding(kernel_size, dilation) if pad_type == 'zero' else 0
self.h2g_pool = nn.AvgPool2d(kernel_size=(2,2), stride=2)
self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
self.stride = stride
self.l2h = nn.Conv2d(int(alpha * in_nc), out_nc,
kernel_size, 1, padding, dilation, groups, bias)
self.h2h = nn.Conv2d(in_nc - int(alpha * in_nc), out_nc,
kernel_size, 1, padding, dilation, groups, bias)
self.a = act(act_type) if act_type else None
self.n_h = norm(norm_type, out_nc) if norm_type else None
def forward(self, x):
X_h, X_l = x
if self.stride ==2:
X_h, X_l = self.h2g_pool(X_h), self.h2g_pool(X_l)
X_h2h = self.h2h(X_h)
X_l2h = self.upsample(self.l2h(X_l))
X_h = X_h2h + X_l2h
if self.n_h:
X_h = self.n_h(X_h)
if self.a:
X_h = self.a(X_h)
return X_h
class stride_OctaveResBlock(nn.Module):
'''
ResNet Block, 3-3 style
with extra residual scaling used in EDSR
(Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17)
'''
def __init__(self, in_nc, mid_nc, out_nc, kernel_size=3, alpha=0.75, stride=1, dilation=1, groups=1,
bias=True, pad_type='zero', norm_type=None, act_type='prelu', mode='CNA', res_scale=1):
super(stride_OctaveResBlock, self).__init__()
conv0 = OctaveConv(in_nc, mid_nc, kernel_size, alpha, stride, dilation, groups, bias, pad_type,
norm_type, act_type, mode)
if mode == 'CNA':
act_type = None
if mode == 'CNAC': # Residual path: |-CNAC-|
act_type = None
norm_type = None
conv1 = stride_OctaveConv(mid_nc, out_nc, kernel_size, alpha, stride, dilation, groups, bias, pad_type,
norm_type, act_type, mode)
self.res = sequential(conv0, conv1)
self.res_scale = res_scale
def forward(self, x):
#if(len(x)>2):
#print(x[0].shape," ",x[1].shape," ",x[2].shape," ",x[3].shape)
#print(len(x))
res = self.res(x)
res = (res[0].mul(self.res_scale), res[1].mul(self.res_scale))
x = (x[0] + res[0], x[1] + res[1])
#print(len(x),"~~~",len(res),"~~~",len(x + res))
#return (x[0] + res[0], x[1]+res[1])
return x
最后
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