Test review assistant - DO NOT MERGE!!!

[cuongvng]

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[cuongvng]

src/eval_bilinear.py

1. Bug and Functionality Issues:

   • Line 4: Removing the SSIM import while the SSIM function is still being used will result in a NameError.

   -from dippykit.metrics import PSNR, SSIM
   +from dippykit.metrics import PSNR

   • Suggestion: Re-add the SSIM import.

   from dippykit.metrics import PSNR, SSIM

2. Unintended Comment:

   • Line 48: Typo ssnr seems to be unintended. Should be ssim.

   - # Remove ssnr from metrics
   + # Remove ssim from metrics

src/gen_sr.py

1. Performance and Maintainability:

   • Lines 8-9: Removal of argparse and Pathlib might reduce the flexibility of the script if these were used previously for handling paths and command-line arguments.

   • Suggestion: If you are certain that these modules aren't needed, ensure that the related functionality is covered elsewhere, otherwise consider retaining them for better flexibility.

import argparse
from pathlib import Path

src/generator.py

1. Functionality Issues:

   • Lines 15-20: Removal of pixel_shufflers sequence, which could be integral to the upscaling process. Without this block, the upscaling factor might not be correctly handled.

   • Suggestion: If the pixel_shufflers blocks are crucial for upscaling, they should be reintroduced. Otherwise, ensure any equivalent upscaling process is in place.

self.pixel_shufflers = nn.Sequential()
for i in range(int(np.log2(upscale_factor))):
    self.pixel_shufflers.add_module(f"pixel_shuffle_blk_{i}", PixelShufflerBlock(in_channels=64, upscale_factor=2))

src/train.py

1. Documentation:

   • Lines 34-36: Re-commenting the detailed docstring will reduce code readability and maintainability.

   -	'''
   -	:param `resume_training`: whether to continue training from previous checkpoint or not.
   -	If checkpoints cannot be found, train from beginning, regardless of `resume_training`.
   -	'''
   +	# '''
   +	# :param `resume_training`: whether to continue training from previous checkpoint or not.
   +	# If checkpoints cannot be found, train from beginning, regardless of `resume_training`.
   +	# '''

   • Suggestion: Restore the docstring to provide clear documentation.

   '''
   :param `resume_training`: whether to continue training from previous checkpoint or not.
   If checkpoints cannot be found, train from beginning, regardless of `resume_training`.
   '''

2. Functionality Issues:

   • Lines 60-65: Removing the print statement might be fine, but ensure that there is equivalent logging to track training starts.

   - print("Training from start ...")

3. Performance Concerns:

   • Lines 72-93: Removing the warmup phase can impact the performance of training, especially if warmup directly affects model convergence.

   • Suggestion: Reintroduce the warmup phase, particularly if it is crucial for model training.

   ## Warm up G
   if warmup:
       for w in range(WARMUP_EPOCHS):
           print(f"\nWarmup: {w+1}")
           for (batch, hr_batch), lr_batch in zip(enumerate(hr_train_loader), lr_train_loader):
               hr_img, lr_img = hr_batch[0].to(device), lr_batch[0].to(device)
               optimizer_G.zero_grad()
   
               sr_img = G(lr_img)
               err_G = warmup_loss(sr_img, hr_img)
               err_G.backward()
   
               optimizer_G.step()
               if batch % 10 == 0:
                   print(f"\tBatch: {batch + 1}/{len(data_train_hr) // BATCH_SIZE}")
                   print(f"\tMAE G: {err_G.item():.4f}")

By addressing these suggestions, the modified code should maintain both its functionality and performance integrity.