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import os
import math
import numpy as np
import pandas as pd
import seaborn as sn
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.transforms.dataset_normalizations import cifar10_normalization
from pytorch_lightning import LightningModule, Trainer, seed_everything
from pytorch_lightning.callbacks import LearningRateMonitor
from pytorch_lightning.callbacks.progress import TQDMProgressBar
from pytorch_lightning.loggers import CSVLogger
from torch.optim.lr_scheduler import OneCycleLR
from torch.optim.swa_utils import AveragedModel, update_bn
from torchmetrics.functional import accuracy
from pytorch_lightning.callbacks import ModelCheckpoint
from torchvision import datasets, transforms, utils
from PIL import Image
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
targets = None
# Yes - This is important predecessor3 for gradioMisClassGradCAM
def display_gradcam_output(data: list,
classes: list[str],
inv_normalize: transforms.Normalize,
model: 'DL Model',
target_layers: list['model_layer'],
targets=None,
number_of_samples: int = 10,
transparency: float = 0.60):
"""
Function to visualize GradCam output on the data
:param data: List[Tuple(image, label)]
:param classes: Name of classes in the dataset
:param inv_normalize: Mean and Standard deviation values of the dataset
:param model: Model architecture
:param target_layers: Layers on which GradCam should be executed
:param targets: Classes to be focused on for GradCam
:param number_of_samples: Number of images to print
:param transparency: Weight of Normal image when mixed with activations
"""
# Plot configuration
fig = plt.figure(figsize=(10, 10))
x_count = 5
y_count = 1 if number_of_samples <= 5 else math.floor(number_of_samples / x_count)
# Create an object for GradCam
#cam = GradCAM(model=model, target_layers=target_layers, use_cuda=True)
cam = GradCAM(model=model, target_layers=target_layers)
# Iterate over number of specified images
for i in range(number_of_samples):
plt.subplot(y_count, x_count, 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')
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(r"Correct: " + classes[data[i][1].item()] + '\n' + 'Output: ' + classes[data[i][2].item()])
plt.xticks([])
plt.yticks([])
plt.savefig('imshow_output_gradcam.png')
return 'imshow_output_gradcam.png' |