解释代码 if input_channels!=3: model.conv1 = torch.nn.Conv2d(input_channels, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
时间: 2023-06-09 14:05:21 浏览: 43
这段代码是用来判断输入图像的通道数是否为3,如果通道数不为3,则将模型的第一层卷积层替换为一个3通道到64通道的卷积层,该卷积层使用7x7的卷积核、步幅为2、边缘填充为3,且不使用偏置项。这个操作通常用来适应不同通道数的输入,以使模型能够适应更广泛的输入数据。
相关问题
def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.conv1 = nn.Conv2d(input_nc, ndf, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 def forward(self, input): input = self.conv1(input) offset1 = self.conv_offset1(input) mask1 = torch.sigmoid(self.conv_mask1(input)) sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=(1, 1)) ] sequence2 = sequence1 + self.sequence2 self.model = nn.Sequential(*sequence2) nn.LeakyReLU(0.2, True) return self.model(input),上述代码出现问题:RuntimeError: Given groups=1, weight of size [18, 512, 3, 3], expected input[1, 64, 512, 512] to have 512 channels, but got 64 channels instead,如何修改
这个错误是由于conv_offset1和conv_mask1的输入通道数与conv1的输出通道数不匹配所引起的。你可以尝试修改conv_offset1和conv_mask1的输入通道数以匹配conv1的输出通道数,或者将conv1的输出通道数修改为匹配conv_offset1和conv_mask1的输入通道数。你可以使用print语句检查conv1的输出通道数和conv_offset1、conv_mask1的输入通道数是否匹配。
根据报错信息,可以看到输入的张量input的shape为[1, 64, 512, 512],而conv_offset1和conv_mask1的输入通道数为512,但conv1的输出通道数为ndf,即64。因此,你需要将conv_offset1和conv_mask1的输入通道数修改为ndf,即64。修改后的代码如下:
```
def __init__(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True):
super(NLayerDiscriminator, self).__init__()
self.use_parallel = use_parallel
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
self.conv1 = nn.Conv2d(input_nc, ndf, kernel_size=3, padding=1)
self.conv_offset1 = nn.Conv2d(ndf, 18, kernel_size=3, stride=1, padding=1)
init_offset1 = torch.Tensor(np.zeros([18, ndf, 3, 3]))
self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0
self.conv_mask1 = nn.Conv2d(ndf, 9, kernel_size=3, stride=1, padding=1)
init_mask1 = torch.Tensor(np.zeros([9, ndf, 3, 3]) + np.array([0.5]))
self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5
def forward(self, input):
input = self.conv1(input)
offset1 = self.conv_offset1(input)
mask1 = torch.sigmoid(self.conv_mask1(input))
sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=(1, 1)) ]
sequence2 = sequence1 + self.sequence2
self.model = nn.Sequential(*sequence2)
nn.LeakyReLU(0.2, True)
return self.model(input)
```
class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw - 1) / 2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2 ** n, 8) if n == 1: num_filters = ndf * nf_mult self.conv1 = nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) sequence += [ torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2 ** n_layers, 8) sequence += [ torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw) ] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): offset1 = self.conv_offset1(input) mask1 = self.conv_mask1(input) input = torch.cat([input, offset1, mask1], dim=1) return self.model(input),运行上述代码出现错误:RuntimeError: Given groups=1, weight of size [18, 512, 3, 3], expected input[1, 3, 512, 512] to have 512 channels, but got 3 channels instead,如何修改,给出代码
根据错误提示,可以发现是因为 `conv_offset1` 的输入通道数为 512,但是 `input` 的通道数为 3。因此需要将 `conv_offset1` 的输入通道数修改为 3。同时,根据代码中的注释,`num_filters` 应该为 `ndf * nf_mult_prev`,因此需要将 `num_filters` 的计算公式修改为 `ndf * nf_mult_prev * 4`。修改后的代码如下:
```
class NLayerDiscriminator(nn.Module):
def __init__(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True):
super(NLayerDiscriminator, self).__init__()
self.use_parallel = use_parallel
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
kw = 4
padw = int(np.ceil((kw - 1) / 2))
sequence = [
nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
nn.LeakyReLU(0.2, True)
]
nf_mult = 1
for n in range(1, n_layers):
nf_mult_prev = nf_mult
nf_mult = min(2 ** n, 8)
if n == 1:
num_filters = ndf * nf_mult_prev * 4
self.conv1 = nn.Conv2d(num_filters, num_filters, kernel_size=3, padding=1)
self.conv_offset1 = nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)
init_offset1 = torch.Tensor(np.zeros([18, 3, 3, 3]))
self.conv_offset1.weight = torch.nn.Parameter(init_offset1)
self.conv_mask1 = nn.Conv2d(3, 9, kernel_size=3, stride=1, padding=1)
init_mask1 = torch.Tensor(np.zeros([9, 3, 3, 3]) + np.array([0.5]))
self.conv_mask1.weight = torch.nn.Parameter(init_mask1)
sequence += [
torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
torchvision.ops.DeformConv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
]
if use_sigmoid:
sequence += [nn.Sigmoid()]
self.model = nn.Sequential(*sequence)
def forward(self, input):
offset1 = self.conv_offset1(input)
mask1 = self.conv_mask1(input)
input = torch.cat([input, offset1, mask1], dim=1)
return self.model(input)
```
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)