feat: Added original CAM implementation (#2)

* docs: Fixed usage instruction

* feat: Added original CAM implementation

* chore: Reorganized package

* test: Added CAM unittest

* refactor: Refactored CAM

* refactor: Refactored GradCAMs

* refactor: Refactored CAMs

* test: Updated unittests accordingly

* docs: Updated readme

* style: Removed extra blank line

* docs: Updated documentation

README.md

@@ -50,7 +50,8 @@ import matplotlib.pyplot as plt
 from torchvision.models import resnet50
 from torchvision.transforms import transforms
 from torchvision.transforms.functional import to_pil_image
-from gradcam import GradCAM, GradCAMpp, overlay_mask
+from torchcam.cams import CAM, GradCAM, GradCAMpp
+from torchcam.utils import overlay_mask
 
 
 # Pretrained imagenet model
@@ -81,7 +82,7 @@ classes = {int(key):value for (key, value)
 class_idx = 232
 
 # Use the hooked data to compute activation map
-activation_maps = gradcam.get_activation_maps(out, class_idx)
+activation_maps = gradcam(out, class_idx)
 # Convert it to PIL image
 # The indexing below means first image in batch
 heatmap = to_pil_image(activation_maps[0].cpu().numpy(), mode='F')
@@ -101,7 +102,7 @@ plt.imshow(result); plt.axis('off'); plt.title(classes.get(class_idx)); plt.tigh
 
 The project is currently under development, here are the objectives for the next releases:
 
-- [ ] Parallel CAMs: enable batch processing.
+- [x] Parallel CAMs: enable batch processing.
 - [ ] Benchmark: compare class activation map computations for different architectures.
 - [ ] Signature improvement: retrieve automatically the last convolutional layer.
 - [ ] Refine RPN: create a region proposal network using CAM.

docs/source/cams.rst

@@ -0,0 +1,24 @@
+torchcam.cams
+=============
+
+
+.. currentmodule:: torchcam.cams
+
+
+CAM
+--------
+Related to activation-based class activation maps.
+
+
+.. autoclass:: CAM
+
+
+Grad-CAM
+--------
+Related to gradient-based class activation maps.
+
+
+.. autoclass:: GradCAM
+
+
+.. autoclass:: GradCAMpp

docs/source/index.rst

@@ -7,7 +7,7 @@ The :mod:`torchcam` package gives PyTorch users the possibility to visualize the
    :maxdepth: 1
    :caption: Package Reference
 
-   torchcam
+   cams
    utils
 
 

test/test_cams.py

@@ -0,0 +1,82 @@
+import unittest
+import requests
+from io import BytesIO
+from PIL import Image
+import torch
+from torchvision.models import resnet18, mobilenet_v2
+from torchvision.transforms.functional import resize, to_tensor, normalize
+
+from torchcam import cams
+
+
+class Tester(unittest.TestCase):
+
+    def _verify_cam(self, cam):
+        # Simple verifications
+        self.assertIsInstance(cam, torch.Tensor)
+        self.assertEqual(cam.shape, (1, 7, 7))
+
+    @staticmethod
+    def _get_img_tensor():
+
+        # Get a dog image
+        URL = 'https://www.woopets.fr/assets/races/000/066/big-portrait/border-collie.jpg'
+        response = requests.get(URL)
+
+        # Forward an image
+        pil_img = Image.open(BytesIO(response.content), mode='r').convert('RGB')
+        img_tensor = normalize(to_tensor(resize(pil_img, (224, 224))),
+                               [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+
+        return img_tensor
+
+    def test_cam(self):
+        # Get a pretrained model
+        model = resnet18(pretrained=True).eval()
+        conv_layer = 'layer4'
+        fc_layer = 'fc'
+        # Border collie index in ImageNet
+        class_idx = 232
+
+        # Hook the corresponding layer in the model
+        extractor = cams.CAM(model, conv_layer, fc_layer)
+
+        # Get a dog image
+        img_tensor = self._get_img_tensor()
+        # Forward it
+        with torch.no_grad():
+            _ = model(img_tensor.unsqueeze(0))
+
+        # Use the hooked data to compute activation map
+        self._verify_cam(extractor(class_idx))
+
+    def _test_gradcam(self, name):
+
+        # Get a pretrained model
+        model = mobilenet_v2(pretrained=True)
+        conv_layer = 'features'
+        # Border collie index in ImageNet
+        class_idx = 232
+
+        # Hook the corresponding layer in the model
+        extractor = cams.__dict__[name](model, conv_layer)
+
+        # Get a dog image
+        img_tensor = self._get_img_tensor()
+
+        # Forward an image
+        out = model(img_tensor.unsqueeze(0))
+
+        # Use the hooked data to compute activation map
+        self._verify_cam(extractor(out, class_idx))
+
+
+for cam_extractor in ['GradCAM', 'GradCAMpp']:
+    def do_test(self, cam_extractor=cam_extractor):
+        self._test_gradcam(cam_extractor)
+
+    setattr(Tester, "test_" + cam_extractor.lower(), do_test)
+
+
+if __name__ == '__main__':
+    unittest.main()

torchcam/__init__.py

@@ -1,4 +1,4 @@
-from torchcam.gradcam import *
+from torchcam import cams
 from torchcam import utils
 
 

torchcam/cams/__init__.py

@@ -0,0 +1,5 @@
+from .cam import *
+from .gradcam import *
+
+del cam
+del gradcam

torchcam/cams/cam.py

@@ -0,0 +1,80 @@
+#!usr/bin/python
+# -*- coding: utf-8 -*-
+
+"""
+GradCAM
+"""
+
+import torch
+
+
+__all__ = ['CAM']
+
+
+class _CAM(object):
+    """Implements a class activation map extractor
+
+    Args:
+        model (torch.nn.Module): input model
+        conv_layer (str): name of the last convolutional layer
+    """
+
+    hook_a = None
+
+    def __init__(self, model, conv_layer):
+
+        if not hasattr(model, conv_layer):
+            raise ValueError(f"Unable to find submodule {conv_layer} in the model")
+        self.model = model
+        # Forward hook
+        self.model._modules.get(conv_layer).register_forward_hook(self._hook_a)
+
+    def _hook_a(self, module, input, output):
+        self.hook_a = output.data
+
+    @staticmethod
+    def _normalize(cams):
+        cams -= cams.flatten(start_dim=1).min().view(-1, 1, 1)
+        cams /= cams.flatten(start_dim=1).max().view(-1, 1, 1)
+
+        return cams
+
+    def _get_weights(self, class_idx):
+
+        raise NotImplementedError
+
+    def __call__(self, class_idx, normalized=True):
+
+        # Get map weight
+        weights = self._get_weights(class_idx)
+
+        # Perform the weighted combination to get the CAM
+        batch_cams = (weights.view(-1, 1, 1) * self.hook_a).sum(dim=1)
+
+        # Normalize the CAM
+        if normalized:
+            batch_cams = self._normalize(batch_cams)
+
+        return batch_cams
+
+
+class CAM(_CAM):
+    """Implements a class activation map extractor as described in https://arxiv.org/abs/1512.04150
+
+    Args:
+        model (torch.nn.Module): input model
+        conv_layer (str): name of the last convolutional layer
+    """
+
+    hook_a = None
+
+    def __init__(self, model, conv_layer, fc_layer):
+
+        super().__init__(model, conv_layer)
+        # Softmax weight
+        self._fc_weights = self.model._modules.get(fc_layer).weight.data
+
+    def _get_weights(self, class_idx):
+
+        # Take the FC weights of the target class
+        return self._fc_weights[class_idx, :]