|
import gradio as gr |
|
import torch, torchvision |
|
from torchvision import transforms |
|
from resnet import ResNet18 |
|
from resnet import ResBlocks |
|
from PIL import Image |
|
import numpy as np |
|
from pytorch_grad_cam import GradCAM |
|
from pytorch_grad_cam.utils.image import show_cam_on_image |
|
from pl_bolts.transforms.dataset_normalizations import cifar10_normalization |
|
|
|
model = ResNet18(0.00333) |
|
|
|
state_model = torch.load("final_model.pkl", map_location=torch.device('cpu')) |
|
state_dict = state_model.state_dict() |
|
|
|
model.load_state_dict(state_dict, strict=False) |
|
|
|
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') |
|
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 resize_image_pil(image, new_width, new_height): |
|
|
|
img = Image.fromarray(np.array(image)) |
|
|
|
width, height = img.size |
|
|
|
width_scale = new_width / width |
|
height_scale = new_height / height |
|
scale = min(width_scale, height_scale) |
|
|
|
resized = img.resize((int(width*scale), int(height*scale)), Image.NEAREST) |
|
|
|
resized = resized.crop((0, 0, new_width, new_height)) |
|
return np.array(resized) |
|
|
|
def inference(input_img, transparency = 0.5, target_layer_number = -1): |
|
|
|
input_img = resize_image_pil(input_img, 32, 32) |
|
org_img = input_img |
|
|
|
input_img = input_img.reshape((32, 32, 3)) |
|
transform = transforms.ToTensor() |
|
|
|
input_img = transform(input_img) |
|
input_img = input_img.unsqueeze(0) |
|
input_img = cifar10_normalization()(input_img) |
|
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) |
|
|
|
target_layers = [model.res_layers[2][target_layer_number]] |
|
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) |
|
print(transparency) |
|
|
|
visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency) |
|
return classes[prediction[0].item()], visualization, confidences |
|
|
|
title = "CIFAR10 trained on ResNet18 Model with GradCAM" |
|
description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results" |
|
|
|
iface = gr.Interface( |
|
inference, |
|
inputs = [ |
|
gr.Image(width=256, height=256, label="Input Image"), |
|
gr.Slider(0, 1, value = 0.5, label="Overall Opacity of Image"), |
|
gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?") |
|
], |
|
outputs = [ |
|
"text", |
|
gr.Image(width=256, height=256, label="Output"), |
|
gr.Label(num_top_classes=3) |
|
], |
|
title = title, |
|
description = description, |
|
) |
|
|
|
iface.launch() |
