turns-00020.parquet:21746
0d585eb5728f6754f44a5bcedegenerate_repetitionAbsentFinal dense release
USER
这是我们的双模态双分支结构的forward函数： def forward_features(self, z, x, event_z, event_x,
mask_z=None, mask_x=None,
ce_template_mask=None, ce_keep_rate=None,
return_last_attn=False
):
# 分支1 处理流程
B, H, W = x.shape[0], x.shape[2], x.shape[3]
x = self.patch_embed(x)
z = self.patch_embed(z)
z += self.pos_embed_z
x += self.pos_embed_x
if mask_z is not None and mask_x is not None:
mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]
mask_z = mask_z.flatten(1).unsqueeze(-1)
mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]
mask_x = mask_x.flatten(1).unsqueeze(-1)
mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode)
mask_x = mask_x.squeeze(-1)
if self.add_cls_token:
cls_tokens = self.cls_token.expand(B, -1, -1)
cls_tokens = cls_tokens + self.cls_pos_embed
if self.add_sep_seg:
x += self.search_segment_pos_embed
z += self.template_segment_pos_embed
x = combine_tokens(z, x, mode=self.cat_mode)
if self.add_cls_token:
x = torch.cat([cls_tokens, x], dim=1)
x = self.pos_drop(x)
lens_z = self.pos_embed_z.shape[1]
lens_x = self.pos_embed_x.shape[1]
global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device)
global_index_t = global_index_t.repeat(B, 1)
global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device)
global_index_s = global_index_s.repeat(B, 1)
removed_indexes_s = []
for i, blk in enumerate(self.blocks):
x, global_index_t, global_index_s, removed_index_s, attn = \
blk(x, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate)
if self.ce_loc is not None and i in self.ce_loc:
removed_indexes_s.append(removed_index_s)
# x = self.norm(x) # # [bs, n_patch, dim] = [bs, 320, 768] 320 = 64 + 256
# # 分支2 处理流程
event_x = self.pos_embed_event(event_x)
event_z = self.pos_embed_event(event_z)
event_x += self.pos_embed_x
event_z += self.pos_embed_z
event_x = combine_tokens(event_z, event_x, mode=self.cat_mode)
if self.add_cls_token:
event_x = torch.cat([cls_tokens, event_x], dim=1)
lens_z = self.pos_embed_z.shape[1]
lens_x = self.pos_embed_x.shape[1]
global_index_t1 = torch.linspace(0, lens_z - 1, lens_z).to(event_x.device)
global_index_t1 = global_index_t1.repeat(B, 1)
global_index_s1 = torch.linspace(0, lens_x - 1, lens_x).to(event_x.device)
global_index_s1 = global_index_s1.repeat(B, 1)
removed_indexes_s1 = []
for i, blk in enumerate(self.blocks):
event_x, global_index_t1, global_index_s1, removed_index_s1, attn = \
blk(event_x, global_index_t1, global_index_s1, mask_x, ce_template_mask, ce_keep_rate)
if self.ce_loc is not None and i in self.ce_loc:
removed_indexes_s1.append(removed_index_s1)
# 在所有blocks处理完，引入counter_guide进行模态间交互
x_inter, event_x_inter = self.counter_guide(x,event_x)
# 将交互后的特征增强原始特征
x_enhenced = x + x_inter
event_x_enhenced = event_x + event_x_inter
x = torch.cat([x_enhenced, event_x_enhenced], dim=1)
，现在将import torch,os
import torch.nn as nn
from torch.nn.parameter import Parameter
class Multi_Context(nn.Module):
def __init__(self, inchannels):
super(Multi_Context, self).__init__()
self.conv2_1 = nn.Sequential(
nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(inchannels),
nn.ReLU(inplace=True))
self.conv2_2 = nn.Sequential(
nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(inchannels),
nn.ReLU(inplace=True))
self.conv2_3 = nn.Sequential(
nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(inchannels),
nn.ReLU(inplace=True))
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=inchannels * 3, out_channels=inchannels, kernel_size=3, padding=1),
nn.BatchNorm2d(inchannels))
def forward(self, x):
x1 = self.conv2_1(x)
x2 = self.conv2_2(x)
x3 = self.conv2_3(x)
x = torch.cat([x1,x2,x3], dim=1)
x = self.conv2(x)
return x
class Adaptive_Weight(nn.Module):
def __init__(self, inchannels):
super(Adaptive_Weight, self).__init__()
self.avg = nn.AdaptiveAvgPool2d(1)
self.inchannels = inchannels
self.fc1 = nn.Conv2d(inchannels, inchannels//4, kernel_size=1, bias=False)
self.relu1 = nn.ReLU()
self.fc2 = nn.Conv2d(inchannels//4, 1, kernel_size=1, bias=False)
self.relu2 = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x_avg = self.avg(x)
weight = self.relu1(self.fc1(x_avg))
weight = self.relu2(self.fc2(weight))
weight = self.sigmoid(weight)
out = x * weight
return out
class Counter_attention(nn.Module):
def __init__(self, inchannels):
super(Counter_attention, self).__init__()
self.conv1 = nn.Sequential(nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=3, padding=1),
nn.BatchNorm2d(inchannels))
self.conv2 = nn.Sequential(nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=3, padding=1),
nn.BatchNorm2d(inchannels))
# self.conv3 = nn.Sequential(nn.Conv2d(in_channels=inchannels*2, out_channels=inchannels, kernel_size=1),
# nn.BatchNorm2d(inchannels))
self.sig = nn.Sigmoid()
self.mc1 = Multi_Context(inchannels)
self.mc2 = Multi_Context(inchannels)
self.ada_w1 = Adaptive_Weight(inchannels)
self.ada_w2 = Adaptive_Weight(inchannels)
def forward(self, assistant, present):
mc1 = self.mc1(assistant)
pr1 = present * self.sig(mc1)
pr2 = self.conv1(present)
pr2 = present * self.sig(pr2)
out1 = pr1 + pr2 + present
mc2 = self.mc2(present)
as1 = assistant * self.sig(mc2)
as2 = self.conv2(assistant)
as2 = assistant * self.sig(as2)
out2 = as1 + as2 + assistant
out1 = self.ada_w1(out1)
out2 = self.ada_w2(out2)
out = out1 + out2
# out = torch.cat([out1, out2], dim=1)
# out = self.conv3(out)
return out
class Counter_Guide(nn.Module):
def __init__(self):
super(Counter_Guide, self).__init__()
self.counter_atten1 = Counter_attention(128)
self.counter_atten2 = Counter_attention(256)
def forward(self, frame1, frame2, event1, event2):
out1 = self.counter_atten1(frame1, event1)
out2 = self.counter_atten2(frame2, event2)
return out1, out2
if __name__ == '__main__':
net = Counter_Guide()
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
net = net.cuda()
var1 = torch.FloatTensor(10, 128, 36, 36).cuda()
var2 = torch.FloatTensor(10, 256, 18, 18).cuda()
var3 = torch.FloatTensor(10, 128, 36, 36).cuda()
var4 = torch.FloatTensor(10, 256, 18, 18).cuda()
# var = Variable(var)
out1, out2 = net(var1, var2, var3, var4)
print('*************')
print(out1.shape, out2.shape)引入，实现双分支模态特征交互，现在需要在输入counterguide之前将x和event_x维度转换，以适应counterguide模块ASSISTANT
要将`x`和`event_x`的维度转换以适应`Counter_Guide`模块，首要任务是将这两组特征的维度转换为`Counter_Guide`模块所接受的输入格式。根据您给出的`Counter_Guide`模块定义，输入的特征维度应当是`[batch_size, channels, height, width]`。假设`x`和`event_x`初始化时的格式为`[batch_size, n_patches, channels]`，然后假设我们需要将这些特征转换为`Counter_Guide`能处理的2D空间特征图格式。
然而，`forward_features`的具体细节并没有直接显示`x`和`event_x`的原始维度以及它们在经过transformer blocks处理后的确切维度状态，因此需要根据常规的transformer输出以及您提供的网络层定义来进行一定的假定。
一种常见的方法是，在Transformer模型最后输出特征之后，添加一个reshape的步骤，以及可能的升维操作（比如通过一些卷积层），使其适配`Counter_Guide`期待的维度。假定`x`和`event_x`最后形状为`[batch_size, n_patches, channels]`，其中`n_patches`可能会因为前面的patch embedding和可能进行的任何池化操作而有所变化。
这里提供一个示例过程，用于将`x`和`event_x`转换为`Counter_Guide`所需要的格式：
1. **反序列化特征图到2D空间格式**: 假设`x`和`event_x`在经过transformer blocks后的形状是`[batch_size, n_patches, channels]`，其中`n_patches`可能等于`H' * W'`，`H'`和`W'`是转换后的特征图的高度和宽度。这一步，我们需要将其转换（reshape）回对应的2D空间结构。
```python
# 假设 height 和 width 是我们希望恢复到的特征图的高宽
x = x.permute(0, 2, 1).reshape(batch_size, channels, height, width) # 从 [B, N, C] 转换到 [B, C, H, W]
event_x = event_x.permute(0, 2, 1).reshape(batch_size, channels, height, width)
```
**注意:** 你需要基于你的特征图的实际大小来调整`height`和`width`。这里的`height`和`width`应当与模型架构设计中预期的输入空间尺寸相吻合。
2. **对特征图进行必要的调整**: 如果`Counter_Guide`模块期望特定的通道数量而`x`和`event_x`的维度不符，你可能需要添加一个`1x1`卷积层来调整通道的数量。
```python
adjust_conv = nn.Conv2d(channels_src, channels_tgt, kernel_size=1).cuda() # channels_src是原始channels，channels_tgt是目标channels
x = adjust_conv(x)
event_x = adjust_conv(event_x)
```
这里的`channels_tgt`应该根据`Counter_Guide`的期望输入通道数进行设置。
3. **将调整后的特征传递给`Counter_Guide`**: 现在`x`和`event_x`已经具备适合`Counter_Guide`输入的格式，可以将它们送入该模块进行处理。
```python
# 假设有frame1, frame2是对应于x的两种不同尺度的特征，event1, event2是对应于event_x的两种不同尺度的特征
x_inter, event_x_inter = net(frame1, frame2, event1, event2)
```
在实际应用中，如何选择`height`、`width`和调整卷积层的细节，将依赖于你的具体模型架构和输入数据的维度。务必确保维度的匹配和数据的一致性，以免运行时错误。