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import numpy as np
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_grad_cam.utils.image import show_cam_on_image
import matplotlib.pyplot as plt
def generate_gradcam(model, target_layers, images, labels, rgb_imgs):
results = []
cam = GradCAM(model=model, target_layers=target_layers, use_cuda=True)
for image, label, np_image in zip(images, labels, rgb_imgs):
targets = [ClassifierOutputTarget(label.item())]
# You can also pass aug_smooth=True and eigen_smooth=True, to apply smoothing.
grayscale_cam = cam(
input_tensor=image.unsqueeze(0), targets=targets, aug_smooth=True
)
# In this example grayscale_cam has only one image in the batch:
grayscale_cam = grayscale_cam[0, :]
visualization = show_cam_on_image(
np_image / np_image.max(), grayscale_cam, use_rgb=True
)
results.append(visualization)
return results
def visualize_gradcam(misimgs, mistgts, mispreds, classes):
fig, axes = plt.subplots(len(misimgs) // 2, 2)
fig.tight_layout()
for ax, img, tgt, pred in zip(axes.ravel(), misimgs, mistgts, mispreds):
ax.imshow(img)
ax.set_title(f"{classes[tgt]} | {classes[pred]}")
ax.grid(False)
ax.set_axis_off()
plt.show()
def plot_gradcam(model, data, classes, target_layers, number_of_samples, inv_normalize=None, targets=None, transparency = 0.60, figsize=(10,10), rows=2, cols=5):
fig = plt.figure(figsize=figsize)
cam = GradCAM(model=model, target_layers=target_layers, use_cuda=True)
for i in range(number_of_samples):
plt.subplot(rows, cols, i + 1)
input_tensor = data[i][0]
# Get the activations of the layer for the images
grayscale_cam = cam(input_tensor=input_tensor, targets=targets)
grayscale_cam = grayscale_cam[0, :]
# Get back the original image
img = input_tensor.squeeze(0).to('cpu')
if inv_normalize is not None:
img = inv_normalize(img)
rgb_img = np.transpose(img, (1, 2, 0))
rgb_img = rgb_img.numpy()
# Mix the activations on the original image
visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True, image_weight=transparency)
# Display the images on the plot
plt.imshow(visualization)
plt.title(f"Label: {classes[data[i][1].item()]} \n Prediction: {classes[data[i][2].item()]}")
plt.xticks([])
plt.yticks([])
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