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162#################### 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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