Revision models.detection.yolo #851

redleaf-kim · 2022-08-03T13:02:42Z

What does this PR do?

Related to #839

pl_bolts.models.detection.yolo.yolo_config.py
pl_bolts.models.detection.yolo.yolo_module.py
pl_bolts.models.detection.yolo.yolo_layers.py

Added some unit tests & yolo_giou config

tests.models.yolo.test_yolo_config.py
tests.models.yolo.test_yolo_layers.py
tests.data.yolo_giou.cfg

Feedback is welcomed with open arms.

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Did you write any new necessary tests? [not needed for typos/docs]
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If you made a notable change (that affects users), did you update the CHANGELOG?

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redleaf-kim

remove unused import & variable

luca-medeiros

Very thoughtful tests! I would re-check all type hints once more and make sure tests comply with #844. Good stuff 👍

luca-medeiros · 2022-08-04T11:49:14Z

pl_bolts/models/detection/yolo/yolo_config.py

+    else:
+        assert False, "Unknown overlap loss: " + overlap_loss_name


We should check if that's the desired design. Maybe rather than raising an error, we could set IoU as default and raise a warning.

According to the code six lines above, the default should be mse

This was based on the original YOLO implementation, which I think uses MSE loss. That's why "mse" was the default. Darknet configuration files allow other variants of the iou loss (diou, ciou) that were not available in Torchvision at the time of writing this code, so I chose to use IoULoss in case something else than "mse" or "giou" is specified in the configuration file. I created another pull request a while ago that adds support for the ciou and diou losses, that are now available in Torchvision.

luca-medeiros · 2022-08-04T11:50:18Z

pl_bolts/models/detection/yolo/yolo_config.py

 def _create_shortcut(config, num_inputs):
    module = yolo_layers.ShortcutLayer(config["from"])
    return module, num_inputs[-1]


-@under_review()
 def _create_upsample(config, num_inputs):


Type hints. Would be nice to re-check all methods of this PR for type hints!

pl_bolts/models/detection/yolo/yolo_config.py

tests/models/test_detection.py

luca-medeiros · 2022-08-04T12:04:32Z

tests/models/yolo/unit/test_yolo_config.py

+    if config["batch_normalize"]:
+        assert len(conv) == 3


What is this part testing?

If the conv layer doesn't use batch normalize, the length of module is 2 which is consist of conv and act layers.

tests/models/yolo/unit/test_yolo_config.py

tests/models/yolo/unit/test_yolo_layers.py

luca-medeiros · 2022-08-04T12:47:16Z

Maybe we can consider moving with #817 too.

# Conflicts: # tests/models/yolo/unit/test_yolo_config.py

luca-medeiros

You can check #848 to have an idea of how to use the 'catch_warnings' on all your tests.

luca-medeiros · 2022-08-09T21:59:24Z

pl_bolts/models/detection/yolo/yolo_layers.py

@@ -500,5 +500,5 @@ def __init__(self, source_layer: int) -> None:
        super().__init__()
        self.source_layer = source_layer

-    def forward(self, x, outputs):
+    def forward(self, x, outputs: List[Union[Tensor, None]]) -> Tensor:


We could review why we are passing 'x' to the forward method and doing nothing with it. Seems to be just to keep with the format...

@luca-medeiros you're right. RouteLayer and ShortcutLayer do not use x (the output from the previous layer) and calling them is anyway handled as a special case, so the x can be dropped.

luca-medeiros · 2022-08-09T21:59:41Z

pl_bolts/models/detection/yolo/yolo_layers.py

@@ -483,7 +483,7 @@ def __init__(self, source_layers: List[int], num_chunks: int, chunk_idx: int) ->
        self.num_chunks = num_chunks
        self.chunk_idx = chunk_idx

-    def forward(self, x, outputs):
+    def forward(self, x, outputs: List[Union[Tensor, None]]) -> Tensor:


Same with this 'x' here!

otaj

Hi, @heimish-kyma, sorry for taking so long. The PR in general looks good, I agree with all of the luca-medeiros's comments. (redundant argument in couple places, not using catch_warnings fixture.

However, if I can ask for a bit more careful review of yolo_module.py file? First of all, I'd like to avoid using mutable objects as default arguments to methods as it can very easily come back and bite you. Another nitpick is that in infer() you are setting the model to eval() mode, but not back to the original mode after the method.

Plus, to be fair, I am not very big fan of the design of forward method (one big opaque ModuleList, that is always doing same checks such as which module is supposed to have which inputs, every single time in forward instead of doing this once in __init__ to have this constructed a bit more carefully. But, I absolutely understand you're not the original author of this implementation, so those complete redesigns might be a bit more difficult - and they're not the point of this review. But it's something I had to get off my chest 😂

tests/models/yolo/unit/test_yolo_layers.py

tests/models/yolo/unit/test_yolo_config.py

tests/models/test_detection.py

otaj

Btw, mypy is showing some serious errors in typing. We don't necessarily need to get typing to 100%, but we don't want to introduce wrong typing

otaj · 2022-09-15T08:49:58Z

Hi, @heimish-kyma, do you plan to continue on this PR? ⚡ Thank you! 🔩

redleaf-kim · 2022-09-16T00:15:44Z

Hi, @otaj sorry for being delayed first of all.

I will take care things mentioned above comments this weekends, Cheers!

otaj · 2022-09-16T08:12:33Z

Hi, @otaj sorry for being delayed first of all.

I will take care things mentioned above comments this weekends, Cheers!

@heimish-kyma, no need to apologize, I know things are taking a long time for me as well 😄 Thank you ⚡

senarvi · 2022-10-06T10:12:31Z

However, if I can ask for a bit more careful review of yolo_module.py file? First of all, I'd like to avoid using mutable objects as default arguments to methods as it can very easily come back and bite you. Another nitpick is that in infer() you are setting the model to eval() mode, but not back to the original mode after the method.

Plus, to be fair, I am not very big fan of the design of forward method (one big opaque ModuleList, that is always doing same checks such as which module is supposed to have which inputs, every single time in forward instead of doing this once in __init__ to have this constructed a bit more carefully. But, I absolutely understand you're not the original author of this implementation, so those complete redesigns might be a bit more difficult - and they're not the point of this review. But it's something I had to get off my chest 😂

Hi @otaj,
good comments. I'm the original author, so I guess other than the catch_warnings fixture, these comments are related to my code. I fixed those small things in my pull request, i.e.

removed unused module arguments,
avoided mutable objects as default arguments, and
infer() returns the model to the previous mode.

About the last comment: The design is ugly, because we read a Darknet configuration file and construct the network dynamically. My pull request adds support for using any typical PyTorch network that constructs the modules in __init__. That is, I have separated the Darknet network, which creates a ModuleList based on a Darknet configuration file, and the YOLO model, which can also accept traditional PyTorch networks.

If you have time to look at that pull request too, I would be interested to hear what you think.

otaj · 2022-10-11T09:25:51Z

@senarvi that is great to hear! If you could please merge your PR into this branch, that would be great! I would do it myself, but I have to say, I got a bit overwhelmed by the amount of changes in #817 and navigating them myself without knowing too much of the context felt like I could easily mess something up 😂 I believe that since you have more context about YOLO than I do, you'd be the perfect person to do so. Btw, nice tool you can use for it is gh (github-cli), that allows you to checkout a PR by number and registers branches and remotes for you.

The reason I'm asking you to do this is simple, I don't want to merge this PR without resolving these issues we brought up in review (i.e. mutable arguments, etc.) and since you're saying you've already solved them in your PR, that feels like the best course of action.

Thank you!

senarvi · 2022-10-11T11:06:37Z

@senarvi that is great to hear! If you could please merge your PR into this branch, that would be great!

@otaj sure, I can try to do that. But the source branch of this PR is in @heimish-kyma 's repository and presumably I don't have write access there. To which branch do you actually suggest that I write the merged code?

senarvi · 2022-10-12T06:37:40Z

@otaj I merged the changes, but as I guessed, I couldn't push them into this branch. I pushed them into my branch. So the merged changes appear now in this pull request. I don't know if that's what you meant. I'll now go through your comments there.

senarvi · 2022-10-12T08:00:27Z

@heimish-kyma should self.log(..., batch_size=images.size(0)) be used in test_step() too?

pl_bolts/models/detection/yolo/yolo_config.py

redleaf-kim and others added 4 commits August 2, 2022 23:58

remove under_review decorators

cf1646c

add yolo cfg with giou & update related test function

fecf88c

add serveral yolo config & layers function test

b5abc8f

Merge branch 'Lightning-AI:master' into yolo_review

198ebc1

github-actions bot added the model label Aug 3, 2022

redleaf-kim commented Aug 3, 2022

View reviewed changes

remove unused import & variable

08f17f7

redleaf-kim marked this pull request as ready for review August 3, 2022 13:14

redleaf-kim requested review from Borda, otaj, akihironitta, ethanwharris and awaelchli as code owners August 3, 2022 13:14

luca-medeiros reviewed Aug 4, 2022

View reviewed changes

redleaf-kim added 4 commits August 10, 2022 01:22

add type hints

db2601a

remove and merge duplicated test

fe38bb7

improve readability

7da9d4a

Merge remote-tracking branch 'origin/yolo_review' into yolo_review

8c3ed4e

# Conflicts: # tests/models/yolo/unit/test_yolo_config.py

luca-medeiros reviewed Aug 9, 2022

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Merge branch 'master' into yolo_review

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otaj reviewed Aug 12, 2022

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otaj mentioned this pull request Aug 12, 2022

Major revision of Bolts #839

Open

Merge branch 'master' into yolo_review

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otaj and others added 2 commits September 16, 2022 11:42

Merge branch 'master' into yolo_review

9ff86ab

add catch_warning fixture

17fab64

otaj added 2 commits October 11, 2022 11:14

add catch_warnings

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Merge branch 'master' into yolo_review

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Merge branch 'master' into yolo_review

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Borda force-pushed the master branch from e80f685 to 74f3369 Compare March 28, 2023 18:18

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Revision models.detection.yolo #851

Revision models.detection.yolo #851

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senarvi commented Oct 12, 2022

		else:
		assert False, "Unknown overlap loss: " + overlap_loss_name

Revision models.detection.yolo #851

Revision models.detection.yolo #851

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redleaf-kim commented Aug 3, 2022

What does this PR do?

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