Hello, I have some questions about the YOLOv5 code. Could you please help me answer them?

[enjoynny]

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Question

Here are my questions:
In dataloader.py, why does the following occur:

if rect and shuffle: LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') shuffle = False
self.rect = False if image_weights else rect
In these codes, why must the use of the rect strategy be prohibited when using either the shuffle or image_weights strategies?

In train.py, there are three questions regarding the following code:
if RANK != -1:
loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.
It's unclear where it specifies that the losses from all GPUs should be aggregated onto the primary GPU to form the total loss.
What is the significance of loss *= WORLD_SIZE?
Even if opt.quad is true, isn't loss already the total loss? Why multiply it by 4 instead of directly using the total loss for backpropagation?
In val.py, there is this line of code: preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None). Here are my questions:

The model returns two values, but when I look at the return statement in yolo.py (return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)), it seems to return (torch.cat(z, 1), x). I understand that z represents various confidence scores for the bounding boxes, but why do we need torch.cat(z, 1)? Additionally, x is the output from line 53 of yolo.py, which corresponds to the CNN layers. However, this model is not the complete model; why is x considered the training output and used for calculating errors?

Additional

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[glenn-jocher]

Hello! Thanks for your detailed questions. Let's dive into them. 😊

1. Addressing dataloader.py Query:
   The --rect training strategy optimizes inference times by using rectangular images, reducing padding. However, it requires keeping the batch images the same dimension, which conflicts with the random nature of shuffling, hence the incompatibility. The image_weights strategy, aiming to balance dataset classes during training, inherently requires randomness, which again clashes with --rect's deterministic approach.

2. Insights into train.py Parts:

• Loss Aggregation in DDP mode: In Distributed Data Parallel (DDP) mode, each GPU processes a subset of the data. To ensure consistent optimization, the loss computed per GPU is scaled by the total number of GPUs (WORLD_SIZE) before being averaged across all GPUs during the backward pass. This ensures the gradient descent step reflects the total dataset's gradient.
• Significance of loss *= WORLD_SIZE: It scales the loss according to the number of GPUs, as explained above, ensuring all devices contribute equally to the model's learning.
• Regarding opt.quad: This option quadruples the loss for a specific experimental setting that requires this adjustment. It's context-specific and not a general practice.

3. Unraveling the val.py Line:

• The model in 'inference' mode (self.training == False) returns the final detections concatenated (torch.cat(z, 1)) and optionally the training outputs (x) if self.export == False. torch.cat(z, 1) merges the detections from different scales (z) for final output. The second return value, x, represents intermediate layer outputs used for auxiliary tasks, e.g., computing loss during training. These intermediate outputs provide a richer understanding of model performance across its depth, which can be crucial for certain analyses or enhancements.

I hope this clarifies your queries. Happy coding with YOLOv5! 🚀