app.py

import torch
import os
import gradio as gr
import numpy as np
from torchvision.transforms import transforms
from typing import Optional
import torch.nn as nn
from utils import page_utils

class BasicBlock(nn.Module):
    """
    ResNet Basic Block.

    This class defines a basic building block for ResNet architectures. It consists of two convolutional
    layers with batch normalization and a ReLU activation function. Optionally, it can include an
    identity downsample layer to match the dimensions of the input and output when the stride is not 1.

    Parameters
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    stride : int, optional
        Convolution stride size, by default 1.
    identity_downsample : Optional[torch.nn.Module], optional
        Downsampling layer, by default None.

    Methods
    -------
    forward(x: torch.Tensor) -> torch.Tensor:
        Apply forward computation.
    """

    def __init__(self,
                in_channels: int,
                out_channels: int,
                stride: int = 1,
                identity_downsample: Optional[torch.nn.Module] = None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels,
                              out_channels,
                              kernel_size = 3,
                              stride = stride,
                              padding = 1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(out_channels,
                              out_channels,
                              kernel_size = 3,
                              stride = 1,
                              padding = 1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.identity_downsample = identity_downsample

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Apply forward computation."""
        identity = x
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)

        # Apply an operation to the identity output.
        # Useful to reduce the layer size and match from conv2 output
        if self.identity_downsample is not None:
            identity = self.identity_downsample(identity)
        x += identity
        x = self.relu(x)
        return x

class ResNet18(nn.Module):
    """
    Construct ResNet-18 Model.

    This class defines the ResNet-18 architecture, including convolutional layers, basic blocks, and
    fully connected layers for classification.

    Parameters
    ----------
    input_channels : int
        Number of input channels.
    num_classes : int
        Number of class outputs.

    Methods
    -------
    forward(x: torch.Tensor) -> torch.Tensor:
        Apply forward computation.
    """

    def __init__(self, input_channels, num_classes):

        super(ResNet18, self).__init__()
        self.conv1 = nn.Conv2d(input_channels,
                               64, kernel_size = 7,
                              stride = 2, padding=3)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size = 3,
                                   stride = 2,
                                   padding = 1)

        self.layer1 = self._make_layer(64, 64, stride = 1)
        self.layer2 = self._make_layer(64, 128, stride = 2)
        self.layer3 = self._make_layer(128, 256, stride = 2)
        self.layer4 = self._make_layer(256, 512, stride = 2)

        # Last layers
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def identity_downsample(self, in_channels: int, out_channels: int) -> nn.Module:
        """Downsampling block to reduce the feature sizes."""
        return nn.Sequential(
             nn.Conv2d(in_channels,
                       out_channels,
                       kernel_size = 3,
                       stride = 2,
                       padding = 1),
            nn.BatchNorm2d(out_channels)
        )

    def _make_layer(self, in_channels: int, out_channels: int, stride: int) -> nn.Module:
        """Create sequential basic block."""
        identity_downsample = None

        # Add downsampling function
        if stride != 1:
            identity_downsample = self.identity_downsample(in_channels, out_channels)

        return nn.Sequential(
                    BasicBlock(in_channels, out_channels, identity_downsample=identity_downsample, stride=stride),
                    BasicBlock(out_channels, out_channels)
                    )
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.shape[0], -1)
        x = self.fc(x)
        return x

model = ResNet18(3, 3)
checkpoint = torch.load('epoch=49-step=1750.ckpt', map_location=torch.device('cpu'))

state_dict = checkpoint['state_dict']
for key in list(state_dict.keys()):
    if 'net.' in key:
        state_dict[key.replace('net.', '')] = state_dict[key]
        del state_dict[key]
        
model.load_state_dict(state_dict)
model.eval()


class_names = ['benign', 'malignant', 'normal']
class_names.sort()

example_dir = "SAMPLES"

transformation_pipeline = transforms.Compose([
        transforms.ToPILImage(),
        transforms.Grayscale(num_output_channels=3),
        transforms.Resize((256, 256)),
        transforms.RandomRotation(20),
        transforms.ToTensor(), 
        transforms.Normalize(mean=[0.233827, 0.2338219, 0.23378967], std=[0.2016421162328173, 0.20164345656093885, 0.20160390432148026])
])

def preprocess_image(image: np.ndarray):
    """Preprocess the input image.

    Note that the input image is in RGB mode.

    Parameters
    ----------
    image: np.ndarray
        Input image from callback.
    """

    image = transformation_pipeline(image)
    image = torch.unsqueeze(image, 0)

    return image

def image_classifier(inp):
    """Image Classifier Function.

    Parameters
    ----------
    inp: Optional[np.ndarray] = None
        Input image from callback

    Returns
    -------
    Dict
        A dictionary class names and its probability
    """

    # If input not valid, return dummy data or raise error
    if inp is None:
      return {gr.Error()}

    # preprocess
    image = preprocess_image(inp)
    image = image.to(dtype=torch.float32)

    # inference
    result = model(image)

    # postprocess
    result = torch.nn.functional.softmax(result, dim=1) # apply softmax
    result = result[0].detach().numpy().tolist() # take the first batch
    labeled_result = {name:score for name, score in zip(class_names, result)}

    return labeled_result

with open('index.html', encoding="utf-8") as f:
    description = f.read()

with open('author.html', encoding="utf-8") as author:
    author_info = author.read()

with gr.Blocks(
    theme=gr.themes.Default(primary_hue=page_utils.KALBE_THEME_COLOR, secondary_hue=page_utils.KALBE_THEME_COLOR).set(
    button_primary_background_fill="*primary_600",
    button_primary_background_fill_hover="*primary_500",
    button_primary_text_color="white",)
    ) as demo:
    
    with gr.Column():
        gr.HTML(description)
        
    with gr.Row(): # Build a row
        with gr.Column(): # build a column section as the first item
            inp = gr.Image(label="image", image_mode="RGB") # build an image as the first column item
            with gr.Row(): # build a row section as the second item
                clear_btn = gr.Button("Clear")
                submit_btn = gr.Button("Submit")

        # build a label as the second item
        out = gr.Label(label="prediction", num_top_classes=3)

        # Define buttons functionalities
        submit_btn.click(fn=image_classifier, inputs=inp, outputs=out)
        clear_btn.click(
            lambda: (
                gr.update(value=None),
                gr.update(value=None),
            ),
            inputs=None,
            outputs=[inp, out]
        )

    # Add examples
    gr.Markdown("## Image Examples")
    gr.Examples(
        example_dir, 
        inputs=[inp], 
        label="Image Examples",
        cache_examples=False
        )
    
    with gr.Column():
        gr.HTML(author_info)
        
demo.launch(share=True)