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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 torch.utils.data import DataLoader
import itertools
import matplotlib.pyplot as plt
import utils as utils
from model import Net
import os
model = Net()
model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False)
model.eval()
classes = ('plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck')
cifar_valid = utils.Cifar10SearchDataset('.', train=False, download=True, transform=utils.augmentation_custom_resnet())
inv_normalize = transforms.Normalize(
mean=[-0.50/0.23, -0.50/0.23, -0.50/0.23],
std=[1/0.23, 1/0.23, 1/0.23]
)
def inference(wants_gradcam, n_gradcam, target_layer_number, transparency, wants_misclassified, n_misclassified, input_img = None, n_top_classes=10):
if wants_gradcam:
outputs_inference_gc = []
cifar_valid_loader = DataLoader(cifar_valid, batch_size=1, shuffle = True)
count_gradcam = 1
for data, target in cifar_valid_loader:
data, target = data.to('cpu'), target.to('cpu')
if target_layer_number == '-2':
target_layers = [model.convblock31[0]]
else:
target_layers = [model.convblock21[0]]
cam = GradCAM(model=model, target_layers=target_layers)
grayscale_cam = cam(input_tensor=data, targets=None)
grayscale_cam = grayscale_cam[0, :]
org_img = inv_normalize(data).squeeze(0).numpy()
org_img = np.transpose(org_img, (1, 2, 0))
visualization = np.array(show_cam_on_image(org_img, grayscale_cam, use_rgb=True, image_weight=0.5))
outputs_inference_gc.append(visualization)
count_gradcam += 1
if count_gradcam > n_gradcam:
break
else:
outputs_inference_gc = None
if wants_misclassified:
outputs_inference_mis = []
cifar_valid_loader = DataLoader(cifar_valid, batch_size=1, shuffle = True)
count_mis = 1
for data, target in cifar_valid_loader:
data, target = data.to('cpu'), target.to('cpu')
outputs = model(data)
softmax = torch.nn.Softmax(dim=0)
o = softmax(outputs.flatten())
confidences = {classes[i]: float(o[i]) for i in range(10)}
_, prediction = torch.max(outputs, 1)
if target.numpy()[0] != prediction.numpy()[0]:
count_mis += 1
org_img = inv_normalize(data).squeeze(0).numpy()
org_img = np.transpose(org_img, (1, 2, 0))
fig = plt.figure()
fig.add_subplot(111)
plt.imshow(org_img)
plt.title(f'Target: {classes[target.numpy()[0]]}\nPred: {classes[prediction.numpy()[0]]}')
plt.axis('off')
fig.canvas.draw()
fig_img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
fig_img = fig_img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close(fig)
outputs_inference_mis.append(fig_img)
if count_mis > n_misclassified:
break
else:
outputs_inference_mis = None
if input_img is not None:
transform=utils.augmentation_custom_resnet('Valid')
org_img = input_img
input_img = transform(image=input_img)
input_img = input_img['image'].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(10)}
_, prediction = torch.max(outputs, 1)
confidences = {k: v for k, v in sorted(confidences.items(), key=lambda item: item[1], reverse=True)}
confidences = dict(itertools.islice(confidences.items(), n_top_classes))
else:
confidences = None
return outputs_inference_gc, outputs_inference_mis, confidences
title = "CIFAR10 trained on Custom ResNet Model with GradCAM"
description = "A Gradio interface to infer on Custom ResNet model, and to get GradCAM results"
examples = [[f'examples/{i}'] for i in os.listdir("examples")]
demo = gr.Interface(inference,
inputs = [gr.Checkbox(False, label='Do you want to see GradCAM outputs?'),
gr.Slider(1, 10, value = 1, step=1, label="How many?"),
gr.Dropdown([-2, -1], label="Which target layer?"),
gr.Slider(0.1, 1, value = 0.5, label="Opacity of GradCAM"),
gr.Checkbox(False, label='Do you want to see misclassified images?'),
gr.Slider(0, 10, value = 0, step=1, label="How many?"),
gr.Image(height=32, width=32, label="Input image"),
gr.Slider(0, 10, value = 0, step=1, label="How many top classes you want to see?")
],
outputs = [
gr.Gallery(label="GradCAM Outputs", show_label=True, elem_id="gallery",columns=[2], rows=[2], object_fit="contain", height="auto"),
gr.Gallery(label="Misclassified Images", show_label=True, elem_id="gallery",columns=[2], rows=[2], object_fit="contain", height="auto"),
gr.Label(num_top_classes=10, label = "Top classes")
],
title = title,
description = description,
examples = examples
)
demo.launch() |