Added init file

utils.py

@@ -0,0 +1,100 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import matplotlib.cm
+from PIL import Image
+
+# Adapted from: https://colab.research.google.com/github/kevinzakka/clip_playground/blob/main/CLIP_GradCAM_Visualization.ipynb
+class Hook:
+    """Attaches to a module and records its activations and gradients."""
+
+    def __init__(self, module: nn.Module):
+        self.data = None
+        self.hook = module.register_forward_hook(self.save_grad)
+        
+    def save_grad(self, module, input, output):
+        self.data = output
+        output.requires_grad_(True)
+        output.retain_grad()
+        
+    def __enter__(self):
+        return self
+    
+    def __exit__(self, exc_type, exc_value, exc_traceback):
+        self.hook.remove()
+        
+    @property
+    def activation(self) -> torch.Tensor:
+        return self.data
+    
+    @property
+    def gradient(self) -> torch.Tensor:
+        return self.data.grad
+
+
+# Reference: https://arxiv.org/abs/1610.02391
+def gradCAM(
+    model: nn.Module,
+    input: torch.Tensor,
+    target: torch.Tensor,
+    layer: nn.Module
+) -> torch.Tensor:
+    # Zero out any gradients at the input.
+    if input.grad is not None:
+        input.grad.data.zero_()
+        
+    # Disable gradient settings.
+    requires_grad = {}
+    for name, param in model.named_parameters():
+        requires_grad[name] = param.requires_grad
+        param.requires_grad_(False)
+        
+    # Attach a hook to the model at the desired layer.
+    assert isinstance(layer, nn.Module)
+    with Hook(layer) as hook:        
+        # Do a forward and backward pass.
+        output = model(input)
+        output.backward(target)
+
+        grad = hook.gradient.float()
+        act = hook.activation.float()
+    
+        # Global average pool gradient across spatial dimension
+        # to obtain importance weights.
+        alpha = grad.mean(dim=(2, 3), keepdim=True)
+        # Weighted combination of activation maps over channel
+        # dimension.
+        gradcam = torch.sum(act * alpha, dim=1, keepdim=True)
+        # We only want neurons with positive influence so we
+        # clamp any negative ones.
+        gradcam = torch.clamp(gradcam, min=0)
+
+    # Resize gradcam to input resolution.
+    gradcam = F.interpolate(
+        gradcam,
+        input.shape[2:],
+        mode='bicubic',
+        align_corners=False)
+    
+    # Restore gradient settings.
+    for name, param in model.named_parameters():
+        param.requires_grad_(requires_grad[name])
+        
+    return gradcam
+
+
+# Modified from: https://github.com/salesforce/ALBEF/blob/main/visualization.ipynb
+def getAttMap(img, attn_map):
+    # Normalize attention map
+    attn_map = attn_map - attn_map.min()
+    if attn_map.max() > 0:
+        attn_map = attn_map / attn_map.max()
+
+    H = matplotlib.cm.jet(attn_map)
+    H = (H * 255).astype(np.uint8)[:, :, :3]
+    img_heatmap = Image.fromarray(H)
+    img_heatmap = img_heatmap.resize((256, 256))
+    
+    return Image.blend(
+        img.resize((256, 256)), img_heatmap, 0.4)