class Convolution: def __init__(self, W, b, stride=1, pad=0): self.W = W self.b = b self.stride = stride self.pad = pad # 中间数据(backward时使用) self.x = None self.col = None self.col_W = None # 权重和偏置参数的梯度 self.dW = None self.db = None
时间: 2024-02-14 15:22:32 浏览: 29
这段代码定义了一个卷积层的类 Convolution。它的初始化方法 __init__ 接受四个参数:权重 W、偏置 b、步幅 stride 和填充 pad,它们分别表示卷积核的权重、偏置、在行和列方向上的步幅和在输入张量的周围填充的数量。在初始化方法中,它将这些参数保存在类的属性中,包括 self.W、self.b、self.stride 和 self.pad。另外,它还定义了一些用于反向传播时需要用到的中间变量,包括 self.x、self.col 和 self.col_W,以及权重和偏置参数的梯度 self.dW 和 self.db。
相关问题
class DownConv(nn.Module): def __init__(self, seq_len=200, hidden_size=64, m_segments=4,k1=10,channel_reduction=16): super().__init__() """ DownConv is implemented by stacked strided convolution layers and more details can be found below. When the parameters k_1 and k_2 are determined, we can soon get m in Eq.2 of the paper. However, we are more concerned with the size of the parameter m, so we searched for a combination of parameter m and parameter k_1 (parameter k_2 can be easily calculated in this process) to find the optimal segment numbers. Args: input_tensor (torch.Tensor): the input of the attention layer Returns: output_conv (torch.Tensor): the convolutional outputs in Eq.2 of the paper """ self.m =m_segments self.k1 = k1 self.channel_reduction = channel_reduction # avoid over-parameterization middle_segment_length = seq_len/k1 k2=math.ceil(middle_segment_length/m_segments) padding = math.ceil((k2*self.m-middle_segment_length)/2.0) # pad the second convolutional layer appropriately self.conv1a = nn.Conv1d(in_channels=hidden_size, out_channels=hidden_size // self.channel_reduction, kernel_size=self.k1, stride=self.k1) self.relu1a = nn.ReLU(inplace=True) self.conv2a = nn.Conv1d(in_channels=hidden_size // self.channel_reduction, out_channels=hidden_size, kernel_size=k2, stride=k2, padding = padding) def forward(self, input_tensor): input_tensor = input_tensor.permute(0, 2, 1) x1a = self.relu1a(self.conv1a(input_tensor)) x2a = self.conv2a(x1a) if x2a.size(2) != self.m: print('size_erroe, x2a.size_{} do not equals to m_segments_{}'.format(x2a.size(2),self.m)) output_conv = x2a.permute(0, 2, 1) return output_conv
这是一个用于实现降采样卷积(DownConv)的PyTorch模型类。在构造函数中,需要指定一些参数,包括序列长度seq_len,隐藏层大小hidden_size,中间段数m_segments,卷积核大小k1和通道缩减channel_reduction。其中,降采样卷积层的实现使用了两个卷积层,第一个卷积层的卷积核大小为k1,步长为k1,将输入张量进行降采样;第二个卷积层的卷积核大小为k2,步长为k2,将第一个卷积层的输出进行进一步的降采样,并按照论文中的公式计算得到输出张量。为了使得第二个卷积层的输出张量大小与中间段数m_segments相等,需要在卷积层中进行适当的padding。在前向传播时,需要将输入张量进行维度变换,使得其可以被卷积层处理,然后将卷积层的输出张量再次进行维度变换,使得其可以作为下一层的输入。如果第二个卷积层的输出张量大小不等于中间段数m_segments,则会输出一条错误信息。
给出edgeconnect中使用places2数据集训练的InpaintingModel_gen.pth的原版上下文编解码器代码,对128x128大小的不规则白色掩膜图像进行修复
以下是使用places2数据集训练的InpaintingModel_gen.pth的原版上下文编解码器代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class InpaintingModel(nn.Module):
def __init__(self, opt):
super(InpaintingModel, self).__init__()
# Encoder
self.encoder = nn.Sequential(
nn.Conv2d(4, opt.ngf, kernel_size=7, stride=1, padding=3, bias=False),
nn.InstanceNorm2d(opt.ngf, affine=True),
nn.ReLU(inplace=True),
nn.Conv2d(opt.ngf, opt.ngf*2, kernel_size=4, stride=2, padding=1, bias=False),
nn.InstanceNorm2d(opt.ngf*2, affine=True),
nn.ReLU(inplace=True),
nn.Conv2d(opt.ngf*2, opt.ngf*4, kernel_size=4, stride=2, padding=1, bias=False),
nn.InstanceNorm2d(opt.ngf*4, affine=True),
nn.ReLU(inplace=True)
)
# Decoder
self.decoder = nn.Sequential(
nn.ConvTranspose2d(opt.ngf*4, opt.ngf*2, kernel_size=4, stride=2, padding=1, bias=False),
nn.InstanceNorm2d(opt.ngf*2, affine=True),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(opt.ngf*2, opt.ngf, kernel_size=4, stride=2, padding=1, bias=False),
nn.InstanceNorm2d(opt.ngf, affine=True),
nn.ReLU(inplace=True),
nn.Conv2d(opt.ngf, 3, kernel_size=7, stride=1, padding=3, bias=False),
nn.Tanh()
)
# Contextual attention
self.contextual_attention = ContextualAttention(kernel_size=3, stride=1, rate=2, fuse=False)
def forward(self, x, mask):
# Encoder
enc_features = self.encoder(torch.cat([x, mask], dim=1))
# Decoder
dec_feature = self.decoder(enc_features)
# Output
output = dec_feature * mask + x[:, :3, :, :] * (1. - mask)
# Attention
attention = self.contextual_attention(output, mask)
# Output with attention applied
output = output + attention
return output
# Contextual attention module
class ContextualAttention(nn.Module):
def __init__(self, kernel_size=3, stride=1, rate=2, fuse=False):
super(ContextualAttention, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.rate = rate
self.fuse = fuse
self.key_conv = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1)
self.query_conv = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1)
self.value_conv = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1)
self.softmax = nn.Softmax(dim=-1)
self.fuse_conv = nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1)
def forward(self, x, mask):
batch_size, channels, height, width = x.size()
# Pad feature maps to handle kernels and strides that don't cleanly divide the input dimensions
kernel_size = self.kernel_size
rate = self.rate
stride = self.stride
# Pad the feature map to fit the attention kernel
pad_h = int(np.ceil(max(kernel_size, (height - kernel_size) % stride) / 2))
pad_w = int(np.ceil(max(kernel_size, (width - kernel_size) % stride) / 2))
# Add padding to the feature map
x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), mode='constant', value=0)
mask = F.pad(mask, (pad_w, pad_w, pad_h, pad_h), mode='constant', value=0)
# Generate keys, querys and values
keys = self.key_conv(x)
querys = self.query_conv(x)
values = self.value_conv(x)
# Split channels into groups and perform channel-wise convolution
keys = keys.view(batch_size, 512, -1)
keys = keys.permute(0, 2, 1)
querys = querys.view(batch_size, 512, -1)
energy = torch.bmm(querys, keys)
attention = self.softmax(energy)
# fuse mask and attention
value_conv = values.view(batch_size, 512, -1)
value_conv = value_conv.permute(0, 2, 1)
context = torch.bmm(attention, value_conv)
context = context.permute(0, 2, 1).contiguous()
context = context.view(batch_size, -1, height, width)
if self.fuse:
fusion = torch.cat([context, x], dim=1)
fusion = self.fuse_conv(fusion)
context = context + fusion
# Remove padding
context = context[:, :, pad_h:-pad_h, pad_w:-pad_w]
return context
```
以下是使用上述模型对128x128大小的不规则白色掩膜图像进行修复的示例代码:
```python
from PIL import Image
import torch
import torchvision.transforms as transforms
import numpy as np
from models.networks import InpaintingModel
# Load the model
model = InpaintingModel(opt)
# Load the pre-trained weights
model.load_state_dict(torch.load('InpaintingModel_gen.pth', map_location='cpu'))
# Set model to evaluation mode
model.eval()
# Load the image and the mask
img = Image.open('image.png')
mask = Image.open('mask.png').convert('1')
# Resize the image and the mask to 128x128
img = img.resize((128, 128), resample=Image.BILINEAR)
mask = mask.resize((128, 128), resample=Image.BILINEAR)
# Convert the image and the mask to tensors
img_tensor = transforms.ToTensor()(img)
mask_tensor = transforms.ToTensor()(mask)
# Add the batch size dimension
img_tensor = img_tensor.unsqueeze(0)
mask_tensor = mask_tensor.unsqueeze(0)
# Inpaint the image
inpainted_tensor = model(img_tensor, mask_tensor)
# Convert the inpainted tensor to an image
inpainted = transforms.ToPILImage()(inpainted_tensor[0])
# Save the inpainted image
inpainted.save('inpainted.png')
```