bodypartxr classifier

app.py

@@ -1,7 +1,85 @@
 import gradio as gr
+import torch
+from torchvision.transforms import transforms
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
+from resnet18 import ResNet18
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+model = ResNet18(1, 5)
+
+checkpoint = torch.load('C:\jason\semester 8\Magang\Hugging-face-bodypartxr\bodypartxr\acc=0.94.ckpt')
+
+# The state dict will contains net.layer_name
+# Our model doesn't contains `net.` so we have to rename it
+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 = ['abdominal', 'adult', 'others', 'pediatric', 'spine']
+class_names.sort()
+
+transformation_pipeline = transforms.Compose([
+    transforms.ToPILImage(),
+    transforms.Grayscale(num_output_channels=1),
+    transforms.CenterCrop((384, 384)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.50807575], std=[0.20823])
+])
+
+
+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 {'cat': 0.3, 'dog': 0.7}
+
+    # 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
+
+demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
+demo.launch()

resnet18.py

@@ -0,0 +1,129 @@
+from typing import Optional
+
+import torch.nn as nn
+import torch
+
+class BasicBlock(nn.Module):
+    """ResNet Basic Block.
+
+    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
+    """
+
+    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.
+
+    Parameters
+    ----------
+    input_channels : int
+        Number of input channels
+    num_classes : int
+        Number of class outputs
+    """
+
+    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