import torch in_channels, out_channels = 5, 10 width, height = 100, 100 kernel_size = 3 #默认转为3*3,最好用奇数正方形 #在pytorch中的数据处理都是通过batch来实现的 #因此对于C*W*H的三个维度图像,在代码中实际上是一个B(batch)*C*W*H的四个维度的图像 batch_size = 1 #生成一个四维的随机数 input = torch.randn(batch_size, in_channels, width, height) #Conv2d需要设定,输入输出的通道数以及卷积核尺寸 conv_layer = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size) output = conv_layer(input) print(input.shape) print(output.shape) print(conv_layer.weight.shape)
时间: 2024-04-18 22:24:26 浏览: 16
torch.nn.Conv2d是PyTorch中用于二维卷积操作的类。在你给出的代码中,首先定义了输入图像的通道数in_channels为5,输出图像的通道数out_channels为10。然后定义了输入图像的宽度width和高度height都为100。接下来定义了卷积核的尺寸kernel_size为3。
在PyTorch中,数据处理通常通过批处理(batch)来进行。因此,对于一个形状为C*W*H的图像,在代码中实际上表示为一个形状为B*C*W*H的四维图像,其中B表示批处理大小。在你的代码中,将批处理大小batch_size设置为1。
接下来,通过torch.randn函数生成一个四维的随机数作为输入input,其形状为1*5*100*100。然后使用torch.nn.Conv2d创建了一个卷积层conv_layer,输入通道数为5,输出通道数为10,卷积核尺寸为3*3。最后,将输入input传递给卷积层conv_layer进行卷积操作,得到输出output。
打印出input.shape可以看到输入图像的形状为torch.Size([1, 5, 100, 100]),即批处理大小为1,通道数为5,宽度和高度都为100。打印出output.shape可以看到输出图像的形状为torch.Size([1, 10, 98, 98]),即批处理大小为1,通道数为10,宽度和高度分别为98。打印出conv_layer.weight.shape可以看到卷积层的权重形状为torch.Size([10, 5, 3, 3]),即输出通道数为10,输入通道数为5,卷积核尺寸为3*3。
相关问题
import torch import torch.nn as nn class LeNetConvLSTM(nn.Module): def __init__(self, input_size, hidden_size, kernel_size): super(LeNetConvLSTM, self).__init__() # LeNet网络部分 self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5) self.pool1 = nn.MaxPool2d(kernel_size=2) self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5) self.pool2 = nn.MaxPool2d(kernel_size=2) self.fc1 = nn.Linear(in_features=16*5*5, out_features=120) self.fc2 = nn.Linear(in_features=120, out_features=84) # ConvLSTM部分 self.lstm = nn.LSTMCell(input_size, hidden_size) self.hidden_size = hidden_size self.kernel_size = kernel_size self.padding = kernel_size // 2 def forward(self, x): # LeNet网络部分 x = self.pool1(torch.relu(self.conv1(x))) x = self.pool2(torch.relu(self.conv2(x))) x = x.view(-1, 16*5*5) x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) # 将输出转换为ConvLSTM所需的格式 batch_size, channels, height, width = x.shape x = x.view(batch_size, channels, height*width) x = x.permute(0, 2, 1) # ConvLSTM部分 hx = torch.zeros(batch_size, self.hidden_size).to(x.device) cx = torch.zeros(batch_size, self.hidden_size).to(x.device) for i in range(height*width): hx, cx = self.lstm(x[:, i, :], (hx, cx)) hx = hx.view(batch_size, self.hidden_size, 1, 1) cx = cx.view(batch_size, self.hidden_size, 1, 1) if i == 0: output = hx else: output = torch.cat((output, hx), dim=1) # 将输出转换为正常的格式 output = output.permute(0, 2, 3, 1) output = output.view(batch_size, height, width, self.hidden_size) return output
这段代码定义了一个名为 LeNetConvLSTM 的 PyTorch 模型,它实现了一个结合了 LeNet 网络和 ConvLSTM 的神经网络。LeNet 网络用于提取输入数据的特征,ConvLSTM 用于处理时间序列数据。在 forward 方法中,该模型首先通过 LeNet 网络处理输入数据,然后将输出转换为 ConvLSTM 所需的格式,并对每个时间步运行一个 LSTM 单元,最后将 LSTM 单元的输出转换为正常的格式。
mask R_CNN实现各部分代码
Mask R-CNN 是一种基于 Faster R-CNN 的目标检测和实例分割的算法,主要由以下几部分组成:
1. Backbone 网络:通常采用 ResNet 或者 ResNeXt 等深度卷积神经网络作为基础网络,用于特征提取。
2. RPN 网络:Region Proposal Network,用于生成候选区域。
3. ROI Align 操作:将不同大小的候选区域映射到固定大小的特征图上。
4. Mask Head 网络:用于实例分割,生成每个物体实例的掩模。
下面是 Mask R-CNN 的主要代码实现:
1. Backbone 网络:使用 ResNet50 作为基础网络,代码如下:
```python
import torch.nn as nn
import torchvision.models.resnet as resnet
class ResNet50Backbone(nn.Module):
def __init__(self):
super(ResNet50Backbone, self).__init__()
resnet50 = resnet.resnet50(pretrained=True)
self.conv1 = resnet50.conv1
self.bn1 = resnet50.bn1
self.relu = resnet50.relu
self.maxpool = resnet50.maxpool
self.layer1 = resnet50.layer1
self.layer2 = resnet50.layer2
self.layer3 = resnet50.layer3
self.layer4 = resnet50.layer4
self.out_channels = 2048
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
c1 = self.layer1(x)
c2 = self.layer2(c1)
c3 = self.layer3(c2)
c4 = self.layer4(c3)
return [c1, c2, c3, c4]
```
2. RPN 网络:使用 Pytorch 内置的 Conv2d 和 nn.ModuleList 实现,代码如下:
```python
import torch.nn.functional as F
class RPN(nn.Module):
def __init__(self, in_channels, num_anchors):
super(RPN, self).__init__()
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1)
self.bbox_pred = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1, stride=1)
def forward(self, x):
x = F.relu(self.conv(x))
logits = self.cls_logits(x)
bbox_pred = self.bbox_pred(x)
return logits, bbox_pred
```
3. ROI Align 操作:使用 Pytorch 内置的 nn.AdaptiveMaxPool2d 实现,代码如下:
```python
import torch.nn.functional as F
class RoIAlign(nn.Module):
def __init__(self, output_size, spatial_scale):
super(RoIAlign, self).__init__()
self.output_size = output_size
self.spatial_scale = spatial_scale
def forward(self, features, rois):
x_min, y_min, x_max, y_max = rois.chunk(4, dim=1)
h = (y_max - y_min + 1) * self.spatial_scale
w = (x_max - x_min + 1) * self.spatial_scale
image_height, image_width = features.shape[-2:]
# Normalize coordinates to [0, h or w]
x_min = x_min / image_width * w
y_min = y_min / image_height * h
x_max = x_max / image_width * w
y_max = y_max / image_height * h
# Compute bin sizes
bin_size_h = h / self.output_size[0]
bin_size_w = w / self.output_size[1]
# Compute grid of bin indices
grid_y = torch.linspace(0, self.output_size[0] - 1, self.output_size[0]).to(rois.device)
grid_x = torch.linspace(0, self.output_size[1] - 1, self.output_size[1]).to(rois.device)
grid_y, grid_x = torch.meshgrid(grid_y, grid_x)
# Compute centers of each bin
grid_y = grid_y * bin_size_h + y_min
grid_x = grid_x * bin_size_w + x_min
# Reshape grid to (N, H*W, 2)
grid = torch.stack([grid_x, grid_y], dim=-1).reshape(-1, 2)
# Sample features using bilinear interpolation
features = F.grid_sample(features, grid.view(1, self.output_size[0] * self.output_size[1], 2), align_corners=False)
features = features.reshape(-1, *self.output_size, features.shape[-1])
return features
```
4. Mask Head 网络:使用 Pytorch 内置的 Conv2d 和 nn.ModuleList 实现,代码如下:
```python
class MaskHead(nn.Module):
def __init__(self, in_channels):
super(MaskHead, self).__init__()
self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv6 = nn.Conv2d(in_channels // 2, 1, kernel_size=1, stride=1)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
x = F.relu(self.conv4(x))
x = F.relu(self.conv5(x))
x = torch.sigmoid(self.conv6(x))
return x
```