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