From e89f06031f47d2c53e35d4261809c06798c87665 Mon Sep 17 00:00:00 2001 From: sammlapp Date: Tue, 21 May 2024 15:19:32 -0400 Subject: [PATCH 1/4] only use internal pytorch architectures switch efficientnet to internal pytorch version. This will not be backwards compatible with efficientnets trained in previous opso versions. to use models from previous versions, you should use older opso version or specifically import the class that was used in previous versions. See this thread: https://github.com/kitzeslab/opensoundscape/issues/998 --- opensoundscape/ml/cnn_architectures.py | 137 ++++--------------------- 1 file changed, 18 insertions(+), 119 deletions(-) diff --git a/opensoundscape/ml/cnn_architectures.py b/opensoundscape/ml/cnn_architectures.py index 705373d0..63d2142a 100644 --- a/opensoundscape/ml/cnn_architectures.py +++ b/opensoundscape/ml/cnn_architectures.py @@ -29,6 +29,7 @@ architectures - the easiest way is to simply use the InceptionV3 class in opensoundscape.ml.cnn. """ + import warnings import torch @@ -490,32 +491,28 @@ def efficientnet_b0( num_channels: specify channels in input sample, eg [channels h,w] sample shape + Note: in v0.10.2, changed from using NVIDIA/DeepLearningExamples:torchhub repo + implementatiuon to native pytorch implementation + """ - torch.hub._validate_not_a_forked_repo = lambda a, b, c: True - architecture_ft = torch.hub.load( - "NVIDIA/DeepLearningExamples:torchhub", - "nvidia_efficientnet_b0", - pretrained=weights, - ) + architecture_ft = torchvision.models.efficientnet_b0(weights=weights) # prevent weights of feature extractor from being trained, if desired if freeze_feature_extractor: freeze_params(architecture_ft) # change number of output nodes - architecture_ft.classifier.fc = change_fc_output_size( - architecture_ft.classifier.fc, num_classes + architecture_ft.classifier[1] = change_fc_output_size( + architecture_ft.classifier[1], num_classes=num_classes ) # change input shape num_channels - architecture_ft.stem.conv = change_conv2d_channels( - architecture_ft.stem.conv, num_channels + architecture_ft.features[0][0] = change_conv2d_channels( + architecture_ft.features[0][0], num_channels ) # default target layers for activation maps like GradCAM and guided backpropagation - architecture_ft.cam_target_layers = [architecture_ft.layers[-1][-1]] - - return architecture_ft + architecture_ft.cam_target_layers = [architecture_ft.features[-1]] @register_arch @@ -537,126 +534,28 @@ def efficientnet_b4( num_channels: specify channels in input sample, eg [channels h,w] sample shape - """ - torch.hub._validate_not_a_forked_repo = lambda a, b, c: True - architecture_ft = torch.hub.load( - "NVIDIA/DeepLearningExamples:torchhub", - "nvidia_efficientnet_b4", - pretrained=weights, - ) - - # prevent weights of feature extractor from being trained, if desired - if freeze_feature_extractor: - freeze_params(architecture_ft) - - # change number of output nodes - architecture_ft.classifier.fc = change_fc_output_size( - architecture_ft.classifier.fc, num_classes - ) - - # change input shape num_channels - architecture_ft.stem.conv = change_conv2d_channels( - architecture_ft.stem.conv, num_channels - ) - - # default target layers for activation maps like GradCAM and guided backpropagation - architecture_ft.cam_target_layers = [architecture_ft.layers[-1][-1]] - - return architecture_ft - - -@register_arch -def efficientnet_widese_b0( - num_classes, freeze_feature_extractor=False, weights="DEFAULT", num_channels=3 -): - """Wrapper for efficientnet_widese_b0 architecture - - Args: - num_classes: - number of output nodes for the final layer - freeze_feature_extractor: - if False (default), entire network will have gradients and can train - if True, feature block is frozen and only final layer is trained - weights: - string containing version name of the pre-trained classification weights to use for this architecture. - if 'DEFAULT', model is loaded with best available weights (note that these may change across versions). - Pre-trained weights available for each architecture are listed at https://pytorch.org/vision/stable/models.html - num_channels: - specify channels in input sample, eg [channels h,w] sample shape + Note: in v0.10.2, changed from using NVIDIA/DeepLearningExamples:torchhub repo + implementatiuon to native pytorch implementation """ - torch.hub._validate_not_a_forked_repo = lambda a, b, c: True - architecture_ft = torch.hub.load( - "NVIDIA/DeepLearningExamples:torchhub", - "nvidia_efficientnet_widese_b0", - pretrained=weights, - ) + architecture_ft = torchvision.models.efficientnet_b4(weights=weights) # prevent weights of feature extractor from being trained, if desired if freeze_feature_extractor: freeze_params(architecture_ft) # change number of output nodes - architecture_ft.classifier.fc = change_fc_output_size( - architecture_ft.classifier.fc, num_classes + architecture_ft.classifier[1] = change_fc_output_size( + architecture_ft.classifier[1], num_classes=num_classes ) # change input shape num_channels - architecture_ft.stem.conv = change_conv2d_channels( - architecture_ft.stem.conv, num_channels + architecture_ft.features[0][0] = change_conv2d_channels( + architecture_ft.features[0][0], num_channels ) # default target layers for activation maps like GradCAM and guided backpropagation - architecture_ft.cam_target_layers = [architecture_ft.layers[-1][-1]] - - return architecture_ft - - -@register_arch -def efficientnet_widese_b4( - num_classes, freeze_feature_extractor=False, weights="DEFAULT", num_channels=3 -): - """Wrapper for efficientnet_widese_b4 architecture - - Args: - num_classes: - number of output nodes for the final layer - freeze_feature_extractor: - if False (default), entire network will have gradients and can train - if True, feature block is frozen and only final layer is trained - weights: - string containing version name of the pre-trained classification weights to use for this architecture. - if 'DEFAULT', model is loaded with best available weights (note that these may change across versions). - Pre-trained weights available for each architecture are listed at https://pytorch.org/vision/stable/models.html - num_channels: - specify channels in input sample, eg [channels h,w] sample shape - - """ - torch.hub._validate_not_a_forked_repo = lambda a, b, c: True - architecture_ft = torch.hub.load( - "NVIDIA/DeepLearningExamples:torchhub", - "nvidia_efficientnet_widese_b4", - pretrained=weights, - ) - - # prevent weights of feature extractor from being trained, if desired - if freeze_feature_extractor: - freeze_params(architecture_ft) - - # change number of output nodes - architecture_ft.classifier.fc = change_fc_output_size( - architecture_ft.classifier.fc, num_classes - ) - - # change input shape num_channels - architecture_ft.stem.conv = change_conv2d_channels( - architecture_ft.stem.conv, num_channels - ) - - # default target layers for activation maps like GradCAM and guided backpropagation - architecture_ft.cam_target_layers = [architecture_ft.layers[-1][-1]] - - return architecture_ft + architecture_ft.cam_target_layers = [architecture_ft.features[-1]] def change_conv2d_channels( From 422ad3ca9d65fea1e36089f3087d4d0898bdfe58 Mon Sep 17 00:00:00 2001 From: sammlapp Date: Thu, 23 May 2024 10:21:11 -0400 Subject: [PATCH 2/4] upgrade to pytoroch 2.1.1 resolves some issues with efficientnet hashes. Also, update cnn_architecture tests to assert type. Fix bug where architecture was not returned after initializing. --- opensoundscape/ml/cnn.py | 6 +- opensoundscape/ml/cnn_architectures.py | 4 + poetry.lock | 4096 ++++++++++++------------ pyproject.toml | 2 +- tests/test_cnn_architectures.py | 24 +- 5 files changed, 2148 insertions(+), 1984 deletions(-) diff --git a/opensoundscape/ml/cnn.py b/opensoundscape/ml/cnn.py index bb0ce47b..1a27dd8b 100644 --- a/opensoundscape/ml/cnn.py +++ b/opensoundscape/ml/cnn.py @@ -481,9 +481,9 @@ def __init__( len(classes), num_channels=num_channels ) else: - assert issubclass( - type(architecture), torch.nn.Module - ), "architecture must be a string or an instance of a subclass of torch.nn.Module" + assert isinstance( + architecture, torch.nn.Module + ), "architecture must be a string or an instance of (a subclass of) torch.nn.Module" if num_channels != 3: warnings.warn( f"Make sure your architecture expects the number of channels in " diff --git a/opensoundscape/ml/cnn_architectures.py b/opensoundscape/ml/cnn_architectures.py index 63d2142a..1180752f 100644 --- a/opensoundscape/ml/cnn_architectures.py +++ b/opensoundscape/ml/cnn_architectures.py @@ -514,6 +514,8 @@ def efficientnet_b0( # default target layers for activation maps like GradCAM and guided backpropagation architecture_ft.cam_target_layers = [architecture_ft.features[-1]] + return architecture_ft + @register_arch def efficientnet_b4( @@ -557,6 +559,8 @@ def efficientnet_b4( # default target layers for activation maps like GradCAM and guided backpropagation architecture_ft.cam_target_layers = [architecture_ft.features[-1]] + return architecture_ft + def change_conv2d_channels( conv2d, diff --git a/poetry.lock b/poetry.lock index 3195602f..388a6d97 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,47 +1,48 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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index b07f0ab6..0880d69b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,7 @@ include = ["opensoundscape/**/*.py"] python = ">=3.9,<3.12" docopt = ">=0.6.2" librosa = ">=0.10.0" -torch = ">=2.1.0" +torch = ">=2.1.1" torchvision = ">=0.15.1" ipykernel = ">=5.2.0" pandas = ">=1.3" diff --git a/tests/test_cnn_architectures.py b/tests/test_cnn_architectures.py index 53ec660f..65466cac 100644 --- a/tests/test_cnn_architectures.py +++ b/tests/test_cnn_architectures.py @@ -1,5 +1,6 @@ from opensoundscape.ml import cnn_architectures import pytest +import torch # test_cnn.py tests that all registered architectures are able to # predict on a sample (with modified input channels and output size) @@ -28,70 +29,77 @@ def test_freeze_params(): def test_resnet18(): arch = cnn_architectures.resnet18(0, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_resnet34(): arch = cnn_architectures.resnet34(10, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_resnet50(): arch = cnn_architectures.resnet50(2000, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_resnet101(): arch = cnn_architectures.resnet101(4, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_resnet152(): arch = cnn_architectures.resnet152(3, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_alexnet(): arch = cnn_architectures.alexnet(2, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_vgg11_bn(): arch = cnn_architectures.vgg11_bn(2, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_squeezenet1_0(): arch = cnn_architectures.squeezenet1_0(10, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_densenet121(): arch = cnn_architectures.densenet121(111, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_inception_v3(): arch = cnn_architectures.inception_v3(1, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_use_no_weights(): arch = cnn_architectures.resnet50(2000, weights=None) + assert isinstance(arch, torch.nn.Module) def test_load_specific_weights(): arch = cnn_architectures.resnet101(4, weights="IMAGENET1K_V2") + assert isinstance(arch, torch.nn.Module) def test_efficientnet_noweights(): arch = cnn_architectures.efficientnet_b0(2, weights=None) + assert isinstance(arch, torch.nn.Module) def test_efficientnet_b0(): arch = cnn_architectures.efficientnet_b0(2, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_efficientnet_b4(): arch = cnn_architectures.efficientnet_b4(2, weights="DEFAULT") - - -def test_efficientnet_widese_b0(): - arch = cnn_architectures.efficientnet_widese_b0(2, weights="DEFAULT") - - -def test_efficientnet_widese_b4(): - arch = cnn_architectures.efficientnet_widese_b4(2, weights="DEFAULT") + assert isinstance(arch, torch.nn.Module) def test_noninteger_output_nodes(): From 07475d1bf9d2eac21361db0781d8454a33298ad3 Mon Sep 17 00:00:00 2001 From: sammlapp Date: Thu, 23 May 2024 11:03:04 -0400 Subject: [PATCH 3/4] update to grad-cam >=1.5.0 device handling has changed, but now ScoreCAM is fixed and we can use any device instead of just boolean use_cuda flag --- opensoundscape/ml/cnn.py | 10 +++++--- opensoundscape/ml/utils.py | 52 +------------------------------------- pyproject.toml | 2 +- tests/test_cnn.py | 24 +++++++++++++++++- 4 files changed, 31 insertions(+), 57 deletions(-) diff --git a/opensoundscape/ml/cnn.py b/opensoundscape/ml/cnn.py index 1a27dd8b..f45b0fdb 100644 --- a/opensoundscape/ml/cnn.py +++ b/opensoundscape/ml/cnn.py @@ -1203,7 +1203,7 @@ def generate_cams( str can be any of the following: "gradcam": pytorch_grad_cam.GradCAM, "hirescam": pytorch_grad_cam.HiResCAM, - "scorecam": opensoundscape.ml.utils.ScoreCAM, #pytorch_grad_cam.ScoreCAM, + "scorecam": pytorch_grad_cam.ScoreCAM, "gradcam++": pytorch_grad_cam.GradCAMPlusPlus, "ablationcam": pytorch_grad_cam.AblationCAM, "xgradcam": pytorch_grad_cam.XGradCAM, @@ -1264,7 +1264,7 @@ def generate_cams( methods_dict = { "gradcam": pytorch_grad_cam.GradCAM, "hirescam": pytorch_grad_cam.HiResCAM, - "scorecam": opensoundscape.ml.utils.ScoreCAM, # pytorch_grad_cam.ScoreCAM, + "scorecam": pytorch_grad_cam.ScoreCAM, "gradcam++": pytorch_grad_cam.GradCAMPlusPlus, "ablationcam": pytorch_grad_cam.AblationCAM, "xgradcam": pytorch_grad_cam.XGradCAM, @@ -1277,11 +1277,13 @@ def generate_cams( if isinstance(method, str) and method in methods_dict: # get cam clsas based on string name and create instance cam = methods_dict[method](model=self.network, target_layers=target_layers) + cam.device = self.device elif method is None: cam = None elif issubclass(method, pytorch_grad_cam.base_cam.BaseCAM): # generate instance of cam from class cam = method(model=self.network, target_layers=target_layers) + cam.device = self.device else: raise ValueError( f"`method` {method} not supported. " @@ -1292,8 +1294,8 @@ def generate_cams( # initialize guided back propagation object if guided_backprop: gb_model = pytorch_grad_cam.GuidedBackpropReLUModel( - model=self.network, use_cuda=False - ) # TODO cuda usage - expose? use model setting? + model=self.network, device=self.device + ) # create dataloader to generate batches of AudioSamples dataloader = self.inference_dataloader_cls( diff --git a/opensoundscape/ml/utils.py b/opensoundscape/ml/utils.py index 1321ccca..599c0e15 100644 --- a/opensoundscape/ml/utils.py +++ b/opensoundscape/ml/utils.py @@ -1,4 +1,5 @@ """Utilties for .ml""" + import warnings import pandas as pd import numpy as np @@ -131,57 +132,6 @@ def apply_activation_layer(x, activation_layer=None): return x -# override pytorch_grad_cam's score cam class because it has a bug -# with device mismatch of upsampled (cpu) vs input_tensor (may be mps, cuda, etc) -class ScoreCAM(pytorch_grad_cam.base_cam.BaseCAM): - def __init__(self, model, target_layers, use_cuda=False, reshape_transform=None): - super(ScoreCAM, self).__init__( - model, - target_layers, - use_cuda, - reshape_transform=reshape_transform, - uses_gradients=False, - ) - - def get_cam_weights(self, input_tensor, target_layer, targets, activations, grads): - with torch.no_grad(): - upsample = torch.nn.UpsamplingBilinear2d(size=input_tensor.shape[-2:]) - activation_tensor = torch.from_numpy(activations) - if self.cuda: - activation_tensor = activation_tensor.cuda() - - upsampled = upsample(activation_tensor) - - maxs = upsampled.view(upsampled.size(0), upsampled.size(1), -1).max(dim=-1)[ - 0 - ] - mins = upsampled.view(upsampled.size(0), upsampled.size(1), -1).min(dim=-1)[ - 0 - ] - - maxs, mins = maxs[:, :, None, None], mins[:, :, None, None] - upsampled = (upsampled - mins) / (maxs - mins) - - upsampled = upsampled.to(input_tensor.device) - input_tensors = input_tensor[:, None, :, :] * upsampled[:, :, None, :, :] - - if hasattr(self, "batch_size"): - BATCH_SIZE = self.batch_size - else: - BATCH_SIZE = 16 - - scores = [] - for target, tensor in zip(targets, input_tensors): - for i in tqdm.tqdm(range(0, tensor.size(0), BATCH_SIZE)): - batch = tensor[i : i + BATCH_SIZE, :] - outputs = [target(o).cpu().item() for o in self.model(batch)] - scores.extend(outputs) - scores = torch.Tensor(scores) - scores = scores.view(activations.shape[0], activations.shape[1]) - weights = torch.nn.Softmax(dim=-1)(scores).numpy() - return weights - - def collate_audio_samples_to_tensors(batch): """ takes a list of AudioSample objects, returns batched tensors diff --git a/pyproject.toml b/pyproject.toml index 0880d69b..f6ea5df8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,7 +41,7 @@ jupyter-server = ">=2.7.2" protobuf = ">=4.21.6" joblib = ">=1.2.0" certifi = ">=2023.7.22" -grad-cam = ">=1.4.6" +grad-cam = ">=1.5.0" sentry-sdk = ">=1.14.0" aru-metadata-parser = ">=0.1.0" tqdm = ">=4.66.1" diff --git a/tests/test_cnn.py b/tests/test_cnn.py index a5a8a2ee..a186df1e 100644 --- a/tests/test_cnn.py +++ b/tests/test_cnn.py @@ -495,6 +495,28 @@ def test_generate_cams_num_workers(test_df): _ = model.generate_cams(test_df, num_workers=2) +def test_generate_cams_scorecam_devices(test_df): + """In pytorch_grad_cam <1.5.0 scorecam had device mismatch""" + + model = cnn.CNN("resnet18", classes=[0, 1], sample_duration=5.0) + import pytorch_grad_cam + + _ = model.generate_cams( + test_df, + method=pytorch_grad_cam.ScoreCAM, + ) + + # very slow on cpu - but can uncomment to check + # model = cnn.CNN("resnet18", classes=[0, 1], sample_duration=5.0) + # model.device = "cpu" + # import pytorch_grad_cam + + # _ = model.generate_cams( + # test_df, + # method=pytorch_grad_cam.ScoreCAM, + # ) + + def test_generate_cams_methods(test_df): """test each supported method both by passing class and string name""" @@ -504,7 +526,7 @@ def test_generate_cams_methods(test_df): methods_dict = { "gradcam": pytorch_grad_cam.GradCAM, "hirescam": pytorch_grad_cam.HiResCAM, - "scorecam": opensoundscape.ml.utils.ScoreCAM, # pytorch_grad_cam.ScoreCAM, + "scorecam": pytorch_grad_cam.ScoreCAM, "gradcam++": pytorch_grad_cam.GradCAMPlusPlus, "ablationcam": pytorch_grad_cam.AblationCAM, "xgradcam": pytorch_grad_cam.XGradCAM, From cd2b4bf775330cbfcb2699b6a3a7bb1c3ef6fc2b Mon Sep 17 00:00:00 2001 From: sammlapp Date: Thu, 23 May 2024 12:03:23 -0400 Subject: [PATCH 4/4] poetry lock --- poetry.lock | 86 ++++++++++++++++++++++++++--------------------------- 1 file changed, 43 insertions(+), 43 deletions(-) diff --git a/poetry.lock b/poetry.lock index 388a6d97..cfc4f387 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1504,13 +1504,13 @@ referencing = ">=0.31.0" [[package]] name = "jupyter-client" -version = "8.6.1" +version = "8.6.2" description = "Jupyter protocol implementation and client libraries" optional = false python-versions = ">=3.8" files = [ - 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