clip

Author: jacobgil

usage_examples/clip_example.py

@@ -0,0 +1,167 @@
+import argparse
+
+import cv2
+import numpy as np
+import torch
+from torch import nn
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+try:
+    from transformers import CLIPProcessor, CLIPModel
+except ImportError:
+    print("The transformers package is not installed. Please install it to use CLIP.")
+    exit(1)
+
+
+from pytorch_grad_cam import GradCAM, \
+    ScoreCAM, \
+    GradCAMPlusPlus, \
+    AblationCAM, \
+    XGradCAM, \
+    EigenCAM, \
+    EigenGradCAM, \
+    LayerCAM, \
+    FullGrad
+
+from pytorch_grad_cam.utils.image import show_cam_on_image, \
+    preprocess_image
+from pytorch_grad_cam.ablation_layer import AblationLayerVit
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cpu',
+                        help='Torch device to use')
+    parser.add_argument(
+        '--image-path',
+        type=str,
+        default='./examples/both.png',
+        help='Input image path')
+    parser.add_argument(
+        '--labels',
+        type=str,
+        nargs='+',
+        default=["a cat", "a dog", "a car", "a person", "a shoe"],
+        help='need recognition labels'
+    )
+
+    parser.add_argument('--aug_smooth', action='store_true',
+                        help='Apply test time augmentation to smooth the CAM')
+    parser.add_argument(
+        '--eigen_smooth',
+        action='store_true',
+        help='Reduce noise by taking the first principle componenet'
+             'of cam_weights*activations')
+
+    parser.add_argument(
+        '--method',
+        type=str,
+        default='gradcam',
+        help='Can be gradcam/gradcam++/scorecam/xgradcam/ablationcam')
+
+    args = parser.parse_args()
+    if args.device:
+        print(f'Using device "{args.device}" for acceleration')
+    else:
+        print('Using CPU for computation')
+
+    return args
+
+
+def reshape_transform(tensor, height=16, width=16):
+    result = tensor[:, 1:, :].reshape(tensor.size(0),
+                                      height, width, tensor.size(2))
+
+    # Bring the channels to the first dimension,
+    # like in CNNs.
+    result = result.transpose(2, 3).transpose(1, 2)
+    return result
+
+
+class ImageClassifier(nn.Module):
+    def __init__(self, labels):
+        super(ImageClassifier, self).__init__()
+        self.clip = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+        self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+        self.labels = labels
+
+    def forward(self, x):
+        text_inputs = self.processor(text=self.labels, return_tensors="pt", padding=True)
+
+        outputs = self.clip(pixel_values=x, input_ids=text_inputs['input_ids'].to(self.clip.device),
+                            attention_mask=text_inputs['attention_mask'].to(self.clip.device))
+
+        logits_per_image = outputs.logits_per_image
+        probs = logits_per_image.softmax(dim=1)
+
+        for label, prob in zip(self.labels, probs[0]):
+            print(f"{label}: {prob:.4f}")
+        return probs
+
+
+if __name__ == '__main__':
+    """ python vit_gradcam.py --image-path <path_to_image>
+    Example usage of using cam-methods on a VIT network.
+
+    """
+
+    args = get_args()
+    methods = \
+        {"gradcam": GradCAM,
+         "scorecam": ScoreCAM,
+         "gradcam++": GradCAMPlusPlus,
+         "ablationcam": AblationCAM,
+         "xgradcam": XGradCAM,
+         "eigencam": EigenCAM,
+         "eigengradcam": EigenGradCAM,
+         "layercam": LayerCAM,
+         "fullgrad": FullGrad}
+
+    if args.method not in list(methods.keys()):
+        raise Exception(f"method should be one of {list(methods.keys())}")
+
+    labels = args.labels
+    model = ImageClassifier(labels).to(torch.device(args.device)).eval()
+    print(model)
+
+    target_layers = [model.clip.vision_model.encoder.layers[-1].layer_norm1]
+
+    if args.method not in methods:
+        raise Exception(f"Method {args.method} not implemented")
+
+    rgb_img = cv2.imread(args.image_path, 1)[:, :, ::-1]
+    rgb_img = cv2.resize(rgb_img, (224, 224))
+    rgb_img = np.float32(rgb_img) / 255
+    input_tensor = preprocess_image(rgb_img, mean=[0.5, 0.5, 0.5],
+                                    std=[0.5, 0.5, 0.5]).to(args.device)
+
+    if args.method == "ablationcam":
+        cam = methods[args.method](model=model,
+                                   target_layers=target_layers,
+                                   reshape_transform=reshape_transform,
+                                   ablation_layer=AblationLayerVit())
+    else:
+        cam = methods[args.method](model=model,
+                                   target_layers=target_layers,
+                                   reshape_transform=reshape_transform)
+
+
+
+    # If None, returns the map for the highest scoring category.
+    # Otherwise, targets the requested category.
+    #targets = [ClassifierOutputTarget(1)]
+    targets = None
+
+    # AblationCAM and ScoreCAM have batched implementations.
+    # You can override the internal batch size for faster computation.
+    cam.batch_size = 32
+
+    grayscale_cam = cam(input_tensor=input_tensor,
+                        targets=targets,
+                        eigen_smooth=args.eigen_smooth,
+                        aug_smooth=args.aug_smooth)
+
+    # Here grayscale_cam has only one image in the batch
+    grayscale_cam = grayscale_cam[0, :]
+
+    cam_image = show_cam_on_image(rgb_img, grayscale_cam)
+    cv2.imwrite(f'{args.method}_cam.jpg', cam_image)