Delete app.py
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app.py
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import torch, torchvision
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from torchvision import transforms
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import numpy as np
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import gradio as gr
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from PIL import Image
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from pytorch_grad_cam import GradCAM
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from pytorch_grad_cam.utils.image import show_cam_on_image
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from model.network import ResNet18
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import matplotlib.pyplot as plt
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import PIL
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import io
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from PIL import Image
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from model.network import *
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from utils.gradio_utils import *
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from augment.augment import *
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from dataset.dataset import *
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model = ResNet18(20, None)
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model = model.load_from_checkpoint("resnet18.ckpt", map_location=torch.device("cpu"))
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dataloader_args = dict(shuffle=True, batch_size=64)
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_, test_transforms = get_transforms(mu, std)
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test = CIFAR10Dataset(transform=test_transforms, train=False)
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test_loader = torch.utils.data.DataLoader(test, **dataloader_args)
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target_layers = [model.res_block2.conv[-1]]
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targets = None
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device = torch.device("cpu")
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examples = get_examples()
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def upload_image_inference(input_img, n_top_classes, transparency):
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org_img = input_img.copy()
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input_img = test_transforms(image=org_img)['image']
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input_img = input_img.unsqueeze(0)
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outputs = model(input_img)
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softmax = torch.nn.Softmax(dim=0)
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o = softmax(outputs.flatten())
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confidences = {classes[i]: float(o[i]) for i in range(n_top_classes)}
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_, prediction = torch.max(outputs, 1)
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cam = GradCAM(model=model, target_layers=target_layers)
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grayscale_cam = cam(input_tensor=input_img, targets=None)
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grayscale_cam = grayscale_cam[0, :]
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img = input_img.squeeze(0)
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img = inv_normalize(img)
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rgb_img = np.transpose(img.cpu(), (1, 2, 0))
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rgb_img = rgb_img.numpy()
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visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
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return([confidences, [org_img, grayscale_cam, visualization]])
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def misclass_gr(num_images, layer_val, transparency):
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images_list = misclassified_data[:num_images]
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images_list = [image_to_array(img, layer_val, transparency) for img in images_list]
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return(images_list)
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def class_gr(num_images, layer_val, transparency):
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images_list = classified_data[:num_images]
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images_list = [image_to_array(img, layer_val, transparency) for img in images_list]
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return(images_list)
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def image_to_array(input_img, layer_val, transparency=0.6):
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input_tensor = input_img[0]
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cam = GradCAM(model=model, target_layers=[model.res_block2.conv[-layer_val]])
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grayscale_cam = cam(input_tensor=input_tensor, targets=targets)
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grayscale_cam = grayscale_cam[0, :]
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img = input_tensor.squeeze(0)
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img = inv_normalize(img)
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rgb_img = np.transpose(img, (1, 2, 0))
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rgb_img = rgb_img.numpy()
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visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True,
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image_weight=transparency)
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plt.imshow(visualization)
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plt.title(r"Correct: " + classes[input_img[1].item()] + '\n' + 'Output: ' + classes[input_img[2].item()])
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with io.BytesIO() as buffer:
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plt.savefig(buffer, format = "png")
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buffer.seek(0)
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image = Image.open(buffer)
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ar = np.asarray(image)
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return(ar)
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def get_misclassified_data(model, device, test_loader):
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"""
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Function to run the model on test set and return misclassified images
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:param model: Network Architecture
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:param device: CPU/GPU
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:param test_loader: DataLoader for test set
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"""
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mis_count = 0
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correct_count = 0
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# Prepare the model for evaluation i.e. drop the dropout layer
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model.eval()
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# List to store misclassified Images
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misclassified_data, classified_data = [], []
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# Reset the gradients
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with torch.no_grad():
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# Extract images, labels in a batch
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for data, target in test_loader:
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# Migrate the data to the device
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data, target = data.to(device), target.to(device)
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# Extract single image, label from the batch
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for image, label in zip(data, target):
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# Add batch dimension to the image
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image = image.unsqueeze(0)
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# Get the model prediction on the image
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output = model(image)
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# Convert the output from one-hot encoding to a value
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pred = output.argmax(dim=1, keepdim=True)
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# If prediction is incorrect, append the data
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if pred != label:
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misclassified_data.append((image, label, pred))
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mis_count += 1
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else:
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classified_data.append((image, label, pred))
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correct_count += 1
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if ((mis_count>=20) and (correct_count>=20)):
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return ((classified_data, misclassified_data))
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title = "CIFAR10 trained on ResNet18 (Pytorch Lightning) Model with GradCAM"
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description = "A simple Gradio interface to infer on ResNet model, get GradCAM results for existing & new Images"
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with gr.Blocks() as gradcam:
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classified_data, misclassified_data = get_misclassified_data(model, device, test_loader)
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gr.Markdown("Make Grad-Cam of uploaded image, or existing images.")
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with gr.Tab("Upload New Image"):
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upload_input = [gr.Image(shape=(32, 32)),
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gr.Number(minimum=0, maximum=10, label='n Top Classes', value=3, precision=0),
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gr.Slider(0, 1, label='Transparency', value=0.6)]
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upload_output = [gr.Label(label='Top Classes'),
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gr.Gallery(label="Image | CAM | Image+CAM",
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show_label=True, min_width=80).style(columns=[3],
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rows=[1],
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object_fit="contain",
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height="auto")]
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button1 = gr.Button("Perform Inference")
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gr.Examples(
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examples=examples,
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inputs=upload_input,
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outputs=upload_output,
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fn=upload_image_inference,
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cache_examples=True,
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)
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with gr.Tab("View Class Activate Maps"):
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with gr.Row():
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with gr.Column():
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cam_input21 = [gr.Number(minimum=1, maximum=20, precision=0, value=3, label='View Correctly Classified CAM | Num Images'),
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gr.Number(minimum=1, maximum=3, precision=0, value=1, label='(-) Target Layer'),
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gr.Slider(0, 1, value=0.6, label='Transparency')]
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image_output21 = gr.Gallery(label="Images - Grad-CAM (correct)",
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show_label=True, min_width=80)
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button21 = gr.Button("View Images")
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with gr.Column():
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cam_input22 = [gr.Number(minimum=1, maximum=20, precision=0, value=3, label='View Misclassified CAM | Num Images'),
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gr.Number(minimum=1, maximum=3, precision=0, value=1, label='(-) Target Layer'),
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gr.Slider(0, 1, value=0.6, label='Transparency')]
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image_output22 = gr.Gallery(label="Images - Grad-CAM (Misclassified)",
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show_label=True, min_width=80)
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button22 = gr.Button("View Images")
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button1.click(upload_image_inference, inputs=upload_input, outputs=upload_output)
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button21.click(class_gr, inputs=cam_input21, outputs=image_output21)
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button22.click(misclass_gr, inputs=cam_input22, outputs=image_output22)
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gradcam.launch()
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