--- a
+++ b/src/new_grad_cam.py
@@ -0,0 +1,84 @@
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
+import numpy as np
+import torch
+from torch.nn import functional as F
+import os
+from torchvision.models import resnet50
+import cv2
+
+def load_images(images):
+    return preprocess(images)
+
+
+def preprocess(images):
+    inverse_norm = 255 * (0.5 * images + 0.5)
+    raw_images = (inverse_norm).numpy().transpose(0, 2, 3, 1)[..., ::-1]
+    return images, raw_images
+
+def gc(model, dataset, results_dir, classes, device):
+    """
+    Visualize model responses given multiple images
+    """
+    target_layer = 'conv1'
+    topk = 1
+    output_dir = results_dir
+    from shutil import rmtree
+    if os.path.exists(output_dir): rmtree(output_dir)
+    os.makedirs(output_dir)
+    model.to(device)
+    model.eval()
+
+    for idx in range(len(dataset)):
+        image, image_path = dataset[idx]
+        image_name, ext = os.path.split(image_path)[1].split('.')
+        images = torch.unsqueeze(image, 0)
+        inverse_norm = 255 * (0.5 * images + 0.5)
+        raw_images = (inverse_norm).numpy().transpose(0, 2, 3, 1)[..., ::-1]
+        images = images.to(device)
+        logits = model(images)
+        probs = F.softmax(logits, dim=1)
+        IH = classes[probs.argmax().item()]
+
+        image = images[0]
+        raw_image = raw_images[0]
+        IH_Prob = probs[0][1]
+
+        # =====================================================================
+        #model = resnet50(pretrained=True)
+        target_layer = model.resnet.layer4[-1]
+        #target_layer = model.layer4[-1]
+        input_tensor = images# Create an input tensor image for your model..
+        # Note: input_tensor can be a batch tensor with several images!
+
+        # Construct the CAM object once, and then re-use it on many images:
+        cam = GradCAM(model=model, target_layer=target_layer, use_cuda=True)
+
+        # If target_category is None, the highest scoring category
+        # will be used for every image in the batch.
+        # target_category can also be an integer, or a list of different integers
+        # for every image in the batch.
+        target_category = 1
+
+        # You can also pass aug_smooth=True and eigen_smooth=True, to apply smoothing.
+        grayscale_cam = cam(input_tensor=input_tensor, target_category=target_category, aug_smooth=True,eigen_smooth=True)
+
+        # In this example grayscale_cam has only one image in the batch:
+        grayscale_cam = grayscale_cam[0, :]
+        #visualization = show_cam_on_image(rgb_img, grayscale_cam)
+        gcam = show_cam_on_image(raw_images[0] / 255.0, grayscale_cam)
+
+        result_path = os.path.join(output_dir,
+                                   f'{image_name}-ProbIH:{IH_Prob:.4f}-{IH}.{ext}')
+
+        c0 = raw_image[..., 0]
+        c0 = np.stack((c0, c0, c0), axis=-1)
+        c1 = raw_image[..., 1]
+        c1 = np.stack((c1, c1, c1), axis=-1)
+        c2 = raw_image[..., 0]
+        c2 = np.stack((c2, c2, c2), axis=-1)
+        stack = np.concatenate((gcam, c0, c1, c2, raw_image), axis=1)
+
+
+        cv2.imwrite(f'{result_path}', stack)
+