padding mask代码
时间: 2023-06-13 10:05:46 浏览: 47
下面是一个简单的示例,展示如何使用PyTorch实现padding mask:
```python
import torch
# 创建输入张量
input_tensor = torch.tensor([[1, 2, 3, 0, 0], [4, 5, 0, 0, 0]])
# 创建padding mask
padding_mask = (input_tensor == 0)
# 将padding mask转换为浮点数张量
padding_mask = padding_mask.float()
print(padding_mask)
```
在这个示例中,我们首先创建了一个大小为2x5的输入张量,其中包含两个序列,每个序列都由数字组成。其中,每个序列的末尾都被填充了0,以使其与最长的序列相同长度。接下来,我们使用等于0的条件创建了一个padding mask,即将每个填充位置标记为True,否则为False。最后,我们将padding mask转换为浮点数张量,以便在计算中使用。
相关问题
key_padding_mask用python实现
key_padding_mask在PyTorch中可以使用torch.nn.functional.pad函数实现。下面是一个示例代码:
```python
import torch
# 假设输入的序列长度为seq_len,padding的长度为pad_len
seq_len = 10
pad_len = 5
# 创建一个随机的输入序列
input_seq = torch.randn(seq_len)
# 创建一个padding mask
padding_mask = torch.zeros(seq_len, dtype=torch.bool)
padding_mask[-pad_len:] = True
# 使用pad函数将padding mask应用到输入序列上
padded_seq = torch.nn.functional.pad(input_seq, (0, pad_len))
print("原始序列:", input_seq)
print("padding mask:", padding_mask)
print("padding后的序列:", padded_seq)
```
在上述代码中,我们首先创建一个长度为seq_len的随机输入序列。然后,我们创建一个长度为seq_len的全零tensor,并将最后pad_len个元素设置为True,以表示需要进行padding的位置。最后,我们使用torch.nn.functional.pad函数将padding mask应用到输入序列上,得到一个长度为seq_len+pad_len的padding后的序列。
请注意,padding mask的长度应该与输入序列的长度相同,并且使用布尔类型(bool)表示padding位置。
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
```