update requirements

app.py

@@ -11,7 +11,6 @@ from pytorch_grad_cam.utils.image import show_cam_on_image
 from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
 
 from models.custom_resnet import Model
-
 from utils import get_device
 
 DEVICE = get_device()

requirements.txt

@@ -1,15 +1,7 @@
 torch
 torchvision
-torchinfo
-tqdm
-matplotlib
-albumentations
 numpy
-opencv-python
-torch-lr-finder
 grad-cam
-pytorch-lightning
-torchmetrics
 pandas
 gradio
 Pillow

utils/misc.py

@@ -1,9 +1,4 @@
 import torch
-import torchinfo
-from matplotlib import pyplot as plt
-from pytorch_grad_cam import GradCAM
-from pytorch_grad_cam.utils.image import show_cam_on_image
-from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
 
 SEED = 42
 DEVICE = None
@@ -22,48 +17,3 @@ def get_device():
         DEVICE = "cpu"
     print("Device Selected:", DEVICE)
     return DEVICE
-
-
-def set_seed(seed=SEED):
-    torch.manual_seed(seed)
-    if get_device() == 'cuda':
-        torch.cuda.manual_seed(seed)
-
-
-def plot_examples(images, labels, figsize=None, n=20):
-    _ = plt.figure(figsize=figsize)
-
-    for i in range(n):
-        plt.subplot(4, n//4, i + 1)
-        plt.tight_layout()
-        image = images[i]
-        plt.imshow(image, cmap='gray')
-        label = labels[i]
-        plt.title(str(label))
-        plt.xticks([])
-        plt.yticks([])
-
-
-def get_incorrect_preds(prediction, labels):
-    prediction = prediction.argmax(dim=1)
-    indices = prediction.ne(labels).nonzero().reshape(-1).tolist()
-    return indices, prediction[indices].tolist(), labels[indices].tolist()
-
-
-def get_cam_visualisation(model, dataset, input_tensor, label, target_layer, use_cuda=False):
-    grad_cam = GradCAM(model=model, target_layers=[target_layer], use_cuda=use_cuda)
-
-    targets = [ClassifierOutputTarget(label)]
-
-    grayscale_cam = grad_cam(input_tensor=input_tensor.unsqueeze(0), targets=targets)
-    # In this example grayscale_cam has only one image in the batch:
-    grayscale_cam = grayscale_cam[0, :]
-
-    output = show_cam_on_image(dataset.show_transform(input_tensor).cpu().numpy(), grayscale_cam,
-                               use_rgb=True)
-    return output
-
-
-def model_summary(model, input_size=None):
-    return torchinfo.summary(model, input_size=input_size, depth=5,
-                             col_names=["input_size", "output_size", "num_params", "params_percent"])