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[zhangsanliisi]

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ASPP
模块可以添加到yolov5 7.0 里边吗，效果怎么样？

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[glenn-jocher]

@zhangsanliisi hello!

Thank you for your question and for checking the existing issues and discussions before posting. Yes, you can integrate an Atrous Spatial Pyramid Pooling (ASPP) module into YOLOv5. The ASPP module is often used to capture multi-scale information, which can be beneficial for object detection tasks.

To add an ASPP module to YOLOv5, you would need to modify the model architecture. Here’s a high-level approach to get you started:

1. Define the ASPP Module: Create a new class for the ASPP module in the models/common.py file.
2. Integrate ASPP into YOLOv5: Modify the models/yolov5s.yaml (or the specific model configuration file you are using) to include the ASPP module in the appropriate place.

Here’s a simple example of how you might define an ASPP module:

import torch
import torch.nn as nn

class ASPP(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ASPP, self).__init__()
        self.aspp1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1)
        self.aspp2 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=6, dilation=6)
        self.aspp3 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=12, dilation=12)
        self.aspp4 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=18, dilation=18)
        self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1)
        self.conv2 = nn.Conv2d(out_channels * 5, out_channels, kernel_size=1, stride=1)
        self.relu = nn.ReLU()

    def forward(self, x):
        x1 = self.aspp1(x)
        x2 = self.aspp2(x)
        x3 = self.aspp3(x)
        x4 = self.aspp4(x)
        x5 = self.global_avg_pool(x)
        x5 = self.conv1(x5)
        x5 = nn.Upsample((x.shape[2], x.shape[3]), mode='bilinear', align_corners=True)(x5)
        x = torch.cat((x1, x2, x3, x4, x5), dim=1)
        x = self.conv2(x)
        return self.relu(x)

After defining the ASPP module, you can integrate it into the YOLOv5 model by modifying the configuration file and the model definition.

Regarding the effectiveness, the impact of adding an ASPP module can vary depending on the specific use case and dataset. It’s recommended to experiment with and without the ASPP module and compare the results to see if it improves performance for your particular application.

If you encounter any issues during the integration, please provide a minimum reproducible example so we can assist you better. You can refer to our minimum reproducible example guide for more details.

Also, ensure you are using the latest versions of torch and YOLOv5 to avoid any compatibility issues.

Best of luck with your implementation! If you have any further questions, feel free to ask. 😊

[zhangsanliisi]

when i run “yolo.py”:
from n params module arguments
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
Traceback (most recent call last):
File "D:/YOLO/yolov5-7.0/models/yolo.py", line 386, in
model = Model(opt.cfg).to(device)
File "D:/YOLO/yolov5-7.0/models/yolo.py", line 185, in init
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
File "D:/YOLO/yolov5-7.0/models/yolo.py", line 354, in parse_model
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
TypeError: init() takes 3 positional arguments but 4 were given

[glenn-jocher]

Hello @zhangsanliisi,

Thank you for reaching out and providing detailed information about the issue you're encountering. It looks like there's a TypeError related to the __init__ method in your model definition.

To assist you better, could you please provide a minimum reproducible example of your code? This will help us understand the context and reproduce the issue on our end. You can refer to our minimum reproducible example guide for more details on how to create one. Reproducing the issue is crucial for us to investigate and provide a solution effectively.

Additionally, please ensure that you are using the latest versions of torch and the YOLOv5 repository. Sometimes, issues can be resolved by updating to the latest versions. You can update YOLOv5 by running the following commands:

git pull
pip install -U -r requirements.txt

If the issue persists after updating, please share the reproducible code example, and we will look into it promptly.

Thank you for your cooperation and understanding. We look forward to resolving this issue for you! 😊