How can I process the features during inference?

[Yangchen-nudt]

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Question

So much thank if developers can see my question and chat with me :) I use yolov5 project with ByteTrack(which is a two stage method: detect, then associate) to achieve multi-object tracking. But I found that there existing some missed detection: As shown in the pic, the car in the Bottom Right side cannot be detected (maybe due to the shadow cast on the car) However, i can inform the yolov5 algorithm the probable position of the undetected car, because it's detected in the previous tracking. So i think maybe i can enhance the three feature maps before the detect head. Specifically speaking, I generate one Gaussian distribution heatmap(the probable position is the peak point), and element-wise multiply the heatmap with the feature map. In this case, I want to let the yolov5 pay more attention to the probable position. Then when it comes to the pratical coding, I meet some problems cause I'm not that familiar with pytorch. I don't know how to extract the features before the Detect Head during inference, process them and them feed them back to the final Detect Head. I notice before the non_max_suppression, the detected result is given by: # Inference with dt[1]: visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False if model.xml and im.shape[0] > 1: pred = None for image in ims: if pred is None: pred = model(image, augment=augment, visualize=visualize).unsqueeze(0) else: pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0) pred = [pred, None] else: pred = model(im, augment=augment, visualize=visualize) and the model is loaded with my trained weight. What should I do if i want to extract the feature map and then feed it back to the final Detect head?

I'll appreciate it for any instructions given to me. Long for your reply

Additional

No response

[github-actions[bot]]

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[glenn-jocher]

@Yangchen-nudt hello,

Thank you for your detailed question and for providing context on your use case with ByteTrack and YOLOv5. Enhancing feature maps during inference is an interesting approach to address missed detections.

To achieve this, you will need to modify the YOLOv5 model to extract and manipulate the feature maps before they are passed to the detection head. Here’s a step-by-step guide to help you get started:

1. Modify the YOLOv5 Model: You will need to modify the model.py file to extract the feature maps. Specifically, you can hook into the forward pass of the model to access the intermediate feature maps.

2. Extract Feature Maps: You can use PyTorch hooks to extract the feature maps. Here’s an example of how you can do this:

   import torch
   from models.yolo import Model
   
   # Load your model
   model = Model('path/to/your/yolov5.yaml', ch=3, nc=80)
   model.load_state_dict(torch.load('path/to/your/weights.pt')['model'])
   
   # Register hooks to extract feature maps
   feature_maps = []
   
   def hook_fn(module, input, output):
      feature_maps.append(output)
   
   hooks = []
   for layer in model.model:
      if isinstance(layer, torch.nn.Conv2d):
          hooks.append(layer.register_forward_hook(hook_fn))
   
   # Perform inference
   img = torch.randn(1, 3, 640, 640)  # Example input
   with torch.no_grad():
      pred = model(img)
   
   # Remove hooks
   for hook in hooks:
      hook.remove()
   
   # Now feature_maps contains the intermediate feature maps

3. Enhance Feature Maps: Once you have the feature maps, you can enhance them using your Gaussian heatmap. Here’s an example of how you might do this:

   import torch.nn.functional as F
   
   # Generate Gaussian heatmap
   heatmap = torch.zeros_like(feature_maps[0])
   center = (320, 320)  # Example center
   sigma = 10
   for i in range(heatmap.shape[2]):
      for j in range(heatmap.shape[3]):
          heatmap[0, 0, i, j] = torch.exp(-((i - center[0]) ** 2 + (j - center[1]) ** 2) / (2 * sigma ** 2))
   
   # Enhance feature maps
   enhanced_feature_maps = [fm * heatmap for fm in feature_maps]

4. Feed Enhanced Feature Maps to Detection Head: Finally, you need to modify the forward pass of the model to use the enhanced feature maps. This will require deeper changes to the model’s code to ensure the enhanced feature maps are used in the detection head.

Please ensure you are using the latest versions of torch and https://github.com/ultralytics/yolov5 to avoid any compatibility issues. If you encounter any specific errors or need further assistance, please provide a minimum reproducible code example as outlined in our documentation.

I hope this helps! If you have any further questions, feel free to ask.

[glenn-jocher]

Hello @aybukesakaci,

Thank you for reaching out with your interesting project on unsupervised domain adaptation using YOLOv5x. Here’s a step-by-step guide to help you integrate an attention module into YOLOv5x:

1. Extract Features with YOLOv5x

To extract features from an intermediate layer of YOLOv5x, you can use PyTorch hooks. Here’s an example:

import torch
from models.yolo import Model

# Load your model
model = Model('path/to/your/yolov5x.yaml', ch=3, nc=80)
model.load_state_dict(torch.load('path/to/your/weights.pt')['model'])

# Register hooks to extract feature maps
feature_maps = []

def hook_fn(module, input, output):
    feature_maps.append(output)

hooks = []
for layer in model.model:
    if isinstance(layer, torch.nn.Conv2d):
        hooks.append(layer.register_forward_hook(hook_fn))

# Perform inference
img = torch.randn(1, 3, 640, 640)  # Example input
with torch.no_grad():
    pred = model(img)

# Remove hooks
for hook in hooks:
    hook.remove()

# Now feature_maps contains the intermediate feature maps

2. Pass Through GRL and Discriminator

You will need to implement a Gradient Reversal Layer (GRL) and a discriminator. Here’s a basic implementation:

import torch.nn as nn
import torch.autograd as autograd

class GRL(autograd.Function):
    @staticmethod
    def forward(ctx, x):
        return x.view_as(x)

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output.neg()

class Discriminator(nn.Module):
    def __init__(self, input_dim):
        super(Discriminator, self).__init__()
        self.fc = nn.Sequential(
            nn.Linear(input_dim, 1024),
            nn.ReLU(),
            nn.Linear(1024, 1024),
            nn.ReLU(),
            nn.Linear(1024, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = GRL.apply(x)
        return self.fc(x)

3. Modulate Features with Attention Weights

Pass the extracted features through the GRL and discriminator to get attention weights, then modulate the features:

# Assuming feature_maps[0] is the extracted feature map
features = feature_maps[0]
discriminator = Discriminator(features.shape[1])
attention_weights = discriminator(features.view(features.size(0), -1))
attention_weights = attention_weights.view_as(features)

# Modulate features
modulated_features = features * attention_weights

4. Feed Modulated Features Back to YOLOv5x

To feed the modulated features back into YOLOv5x, you will need to modify the forward pass of the model to accept these features. This requires deeper changes to the model’s code.

Additional Steps

1. Ensure Compatibility: Verify that you are using the latest versions of torch and https://github.com/ultralytics/yolov5.
2. Minimum Reproducible Example: If you encounter any issues, please provide a minimum reproducible code example as outlined in our documentation. This will help us investigate and provide a solution more effectively.

I hope this helps! If you have any further questions or run into any issues, feel free to ask. Good luck with your project! 🚀

[glenn-jocher]

Hello @aybukesakaci,

Great to hear that you've successfully completed the first three steps! Integrating the modulated features back into the YOLOv5x model can indeed be done without changing the backbone. Here’s how you can proceed:

1. Integrate Modulated Features

You can integrate the modulated features by modifying the forward pass of the YOLOv5 model to use these features. Here’s an example of how you can do this:

1. Modify the Model Class: Update the forward method in the Model class to accept the modulated features and integrate them into the backbone.

import torch
import torch.nn as nn
from models.yolo import Model

class CustomYOLOv5(Model):
    def forward(self, x, modulated_features=None, augment=False, profile=False, visualize=False):
        # Original forward pass
        y, dt = [], []
        for m in self.model:
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]
            if modulated_features is not None and isinstance(m, nn.Conv2d):
                x = x + modulated_features  # Integrate modulated features
            x = m(x)
            y.append(x if m.i in self.save else None)
        return x

2. Use the Custom Model: Replace the original model with the custom model in your inference script.

# Load your custom model
model = CustomYOLOv5('path/to/your/yolov5x.yaml', ch=3, nc=80)
model.load_state_dict(torch.load('path/to/your/weights.pt')['model'])

# Perform inference with modulated features
img = torch.randn(1, 3, 640, 640)  # Example input
with torch.no_grad():
    pred = model(img, modulated_features=modulated_features)

2. Ensure Compatibility

Make sure you are using the latest versions of torch and https://github.com/ultralytics/yolov5 to avoid any compatibility issues.

3. Testing and Validation

After integrating the modulated features, thoroughly test and validate the model to ensure it performs as expected.

If you encounter any specific issues or need further assistance, feel free to ask. The YOLO community and the Ultralytics team are here to help! 😊

Best of luck with your project!

[glenn-jocher]

Hello @aybukesakaci,

Thank you for providing additional context and the diagram. It looks like you're implementing a feedback loop where the modulated features from the discriminator are fed back into the model in subsequent cycles. This is a sophisticated approach and can indeed be challenging to implement.

Addressing the Runtime Error

The error you're encountering is due to attempting to backpropagate through the computation graph multiple times without retaining the graph. To resolve this, you can use the retain_graph=True argument in your backward() call. Here’s how you can adjust your training loop:

# Example training loop
for epoch in range(num_epochs):
    for imgs, targets in dataloader:
        imgs, targets = imgs.to(device), targets.to(device)

        # Forward pass
        if epoch == 0:
            outputs = model(imgs)
        else:
            outputs = model(imgs, modulated_features=modulated_features)

        # Compute loss
        loss = compute_loss(outputs, targets)

        # Backward pass
        optimizer.zero_grad()
        loss.backward(retain_graph=True)  # Retain the graph for subsequent backward passes
        optimizer.step()

        # Generate modulated features for the next cycle
        with torch.no_grad():
            features = extract_features(model, imgs)
            attention_weights = discriminator(features.view(features.size(0), -1))
            attention_weights = attention_weights.view_as(features)
            modulated_features = features * attention_weights

Extracting Features

Ensure you have a function to extract features from the model:

def extract_features(model, x):
    feature_maps = []
    hooks = []

    def hook_fn(module, input, output):
        feature_maps.append(output)

    for layer in model.model:
        if isinstance(layer, torch.nn.Conv2d):
            hooks.append(layer.register_forward_hook(hook_fn))

    with torch.no_grad():
        model(x)

    for hook in hooks:
        hook.remove()

    return feature_maps[-1]  # Return the desired feature map

Additional Tips

1. Verify Compatibility: Ensure you are using the latest versions of torch and https://github.com/ultralytics/yolov5 to avoid any compatibility issues.
2. Testing and Validation: After integrating the modulated features, thoroughly test and validate the model to ensure it performs as expected.

If you encounter any specific issues or need further assistance, feel free to ask. The YOLO community and the Ultralytics team are here to help! 😊

Best of luck with your project! 🚀

[aybukesakaci]

Hello again,

I have another question. I want to read target training data and source training data. But I can't read target data. I checked the training data, path etc. Everything is normal. I even shared the same file path for target and source data to try it, but it didn't read target data while reading source data. This is my yaml file:

This is the change I made in dataloaders:

def create_uda_dataloader( path_s, path_t, imgsz, batch_size, stride, single_cls=False, hyp=None, augment=False, cache=False, pad=0.0, rect=False, rank=-1, workers=8, image_weights=False, quad=False, prefix="", shuffle=False, seed=0, ): """Creates and returns a configured DataLoader instance for loading and processing image datasets.""" if rect and shuffle: LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False") shuffle = False with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP dataset_s = LoadImagesAndLabels( path_s, imgsz, batch_size, augment=augment, # augmentation hyp=hyp, # hyperparameters rect=rect, # rectangular batches cache_images=cache, single_cls=single_cls, stride=int(stride), pad=pad, image_weights=image_weights, prefix=prefix, rank=rank, )

    dataset_t = LoadImagesAndLabels(
        path_t,
        imgsz,
        batch_size,
        augment=augment,  # augmentation
        hyp=hyp,  # hyperparameters
        rect=rect,  # rectangular batches
        cache_images=cache,
        single_cls=single_cls,
        stride=int(stride),
        pad=pad,
        image_weights=image_weights,
        prefix=prefix,
        rank=rank,
    )

batch_size = min(batch_size, len(dataset))
nd = torch.cuda.device_count()  # number of CUDA devices
nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers
sampler_s = None if rank == -1 else SmartDistributedSampler(dataset_s, shuffle=shuffle)
sampler_t = None if rank == -1 else SmartDistributedSampler(dataset_t, shuffle=shuffle)
loader = DataLoader if image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates
generator = torch.Generator()
generator.manual_seed(6148914691236517205 + seed + RANK)

dataloder_s = loader(
    dataset_s,
    batch_size=batch_size,
    shuffle=shuffle and sampler_s is None,
    num_workers=nw,
    sampler=sampler_s,
    pin_memory=PIN_MEMORY,
    collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
    worker_init_fn=seed_worker,
    generator=generator,
)

dataloder_t = loader(
    dataset_t,
    batch_size=batch_size,
    shuffle=shuffle and sampler_t is None,
    num_workers=nw,
    sampler=sampler_t,
    pin_memory=PIN_MEMORY,
    collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
    worker_init_fn=seed_worker,
    generator=generator,
)

return dataloader_s, dataset_s, dataloader_t, dataset_t

This is the changes i made in train.py:

And always get this error:

Thanks in advance!

[glenn-jocher]

Hello @aybukesakaci,

Thank you for providing detailed information about your issue. It looks like you're encountering a problem with reading the target training data while the source training data is being read correctly. Let's try to troubleshoot this step-by-step.

Steps to Troubleshoot

1. Verify Data Paths and YAML Configuration: Ensure that the paths specified in your YAML file are correct and accessible. Double-check for any typos or incorrect directory structures.

2. Check Dataset Loading: Since the source data is being read correctly, the issue might be specific to how the target data is being handled. Ensure that the LoadImagesAndLabels class is correctly instantiated for the target data.

3. Debugging the DataLoader: Add some debug prints in your create_uda_dataloader function to verify that the paths and datasets are being correctly processed.

Example Debugging Steps

1. Print Dataset Paths: Add print statements to verify that the paths are being passed correctly.

   print(f"Source path: {path_s}")
   print(f"Target path: {path_t}")

2. Check Dataset Lengths: Verify that the datasets are being loaded correctly by printing their lengths.

   print(f"Source dataset length: {len(dataset_s)}")
   print(f"Target dataset length: {len(dataset_t)}")

3. Inspect DataLoader Initialization: Ensure that the DataLoader instances are being created without issues.

   print("Initializing source DataLoader...")
   dataloader_s = loader(
      dataset_s,
      batch_size=batch_size,
      shuffle=shuffle and sampler_s is None,
      num_workers=nw,
      sampler=sampler_s,
      pin_memory=PIN_MEMORY,
      collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
      worker_init_fn=seed_worker,
      generator=generator,
   )
   print("Source DataLoader initialized.")
   
   print("Initializing target DataLoader...")
   dataloader_t = loader(
      dataset_t,
      batch_size=batch_size,
      shuffle=shuffle and sampler_t is None,
      num_workers=nw,
      sampler=sampler_t,
      pin_memory=PIN_MEMORY,
      collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
      worker_init_fn=seed_worker,
      generator=generator,
   )
   print("Target DataLoader initialized.")

Additional Considerations

• Check for Errors in LoadImagesAndLabels: Ensure that the LoadImagesAndLabels class is not encountering any issues specific to the target data. You might want to add debug prints inside this class as well.

• Verify Data Format: Ensure that the target data is in the correct format expected by YOLOv5. This includes verifying annotations, image formats, and directory structures.

• Update to Latest Versions: If this is a bug, please verify that the issue is reproducible in the latest versions of the packages. Updating to the latest version of YOLOv5 and its dependencies might resolve the issue.

Example Code for Debugging

Here’s a snippet incorporating the debug prints:

def create_uda_dataloader(
    path_s,
    path_t,
    imgsz,
    batch_size,
    stride,
    single_cls=False,
    hyp=None,
    augment=False,
    cache=False,
    pad=0.0,
    rect=False,
    rank=-1,
    workers=8,
    image_weights=False,
    quad=False,
    prefix="",
    shuffle=False,
    seed=0,
):
    print(f"Source path: {path_s}")
    print(f"Target path: {path_t}")

    if rect and shuffle:
        LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False")
        shuffle = False
    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
        dataset_s = LoadImagesAndLabels(
            path_s,
            imgsz,
            batch_size,
            augment=augment,  # augmentation
            hyp=hyp,  # hyperparameters
            rect=rect,  # rectangular batches
            cache_images=cache,
            single_cls=single_cls,
            stride=int(stride),
            pad=pad,
            image_weights=image_weights,
            prefix=prefix,
            rank=rank,
        )

        dataset_t = LoadImagesAndLabels(
            path_t,
            imgsz,
            batch_size,
            augment=augment,  # augmentation
            hyp=hyp,  # hyperparameters
            rect=rect,  # rectangular batches
            cache_images=cache,
            single_cls=single_cls,
            stride=int(stride),
            pad=pad,
            image_weights=image_weights,
            prefix=prefix,
            rank=rank,
        )

    print(f"Source dataset length: {len(dataset_s)}")
    print(f"Target dataset length: {len(dataset_t)}")

    batch_size = min(batch_size, len(dataset_s))
    nd = torch.cuda.device_count()  # number of CUDA devices
    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers
    sampler_s = None if rank == -1 else SmartDistributedSampler(dataset_s, shuffle=shuffle)
    sampler_t = None if rank == -1 else SmartDistributedSampler(dataset_t, shuffle=shuffle)
    loader = DataLoader if image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates
    generator = torch.Generator()
    generator.manual_seed(6148914691236517205 + seed + RANK)

    print("Initializing source DataLoader...")
    dataloader_s = loader(
        dataset_s,
        batch_size=batch_size,
        shuffle=shuffle and sampler_s is None,
        num_workers=nw,
        sampler=sampler_s,
        pin_memory=PIN_MEMORY,
        collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
        worker_init_fn=seed_worker,
        generator=generator,
    )
    print("Source DataLoader initialized.")

    print("Initializing target DataLoader...")
    dataloader_t = loader(
        dataset_t,
        batch_size=batch_size,
        shuffle=shuffle and sampler_t is None,
        num_workers=nw,
        sampler=sampler_t,
        pin_memory=PIN_MEMORY,
        collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
        worker_init_fn=seed_worker,
        generator=generator,
    )
    print("Target DataLoader initialized.")

    return dataloader_s, dataset_s, dataloader_t, dataset_t

I hope this helps! If you continue to experience issues, please provide any additional error messages or logs that might help diagnose the problem further. The YOLO community and the Ultralytics team are here to support you! 😊