Initial commit

app.py

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+import gradio as gr
+import torch
+import os
+import torchvision.transforms as transforms
+from timeit import default_timer as timer
+
+# ResNet9 model definition
+def conv_block(in_channels, out_channels, pool=False):
+    layers = [torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
+              torch.nn.BatchNorm2d(out_channels),
+              torch.nn.ReLU(inplace=True)]
+    if pool: layers.append(torch.nn.MaxPool2d(2))
+    return torch.nn.Sequential(*layers)
+
+class ResNet9(torch.nn.Module):
+    def __init__(self, in_channels, num_classes):
+        super().__init__()
+
+        self.conv1 = conv_block(in_channels, 64)
+        self.conv2 = conv_block(64, 128, pool=True)
+        self.res1 = torch.nn.Sequential(conv_block(128, 128), conv_block(128, 128))
+
+        self.conv3 = conv_block(128, 256, pool=True)
+        self.conv4 = conv_block(256, 512, pool=True)
+        self.res2 = torch.nn.Sequential(conv_block(512, 512), conv_block(512, 512))
+
+        self.classifier = torch.nn.Sequential(torch.nn.MaxPool2d(4),
+                                              torch.nn.Flatten(),
+                                              torch.nn.Dropout(0.2),
+                                              torch.nn.Linear(512, num_classes))
+
+    def forward(self, xb):
+        out = self.conv1(xb)
+        out = self.conv2(out)
+        out = self.res1(out) + out
+        out = self.conv3(out)
+        out = self.conv4(out)
+        out = self.res2(out) + out
+        out = self.classifier(out)
+        return out
+
+# Load the trained model
+model = ResNet9(3, 10)
+model.load_state_dict(torch.load('cifar10-resnet9.pth', map_location=torch.device('cpu')))
+model.eval()
+
+# Define the CIFAR-10 classes
+class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+
+# Define the image transformations
+transform = transforms.Compose([
+    transforms.Resize((32, 32)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
+])
+
+def predict(img):
+    start_time = timer()  # Start the timer
+    img = transform(img).unsqueeze(0)  # Apply transforms and add batch dimension
+    with torch.no_grad():
+        preds = model(img)
+        probabilities = torch.nn.functional.softmax(preds, dim=1)
+        top_prob, top_catid = torch.topk(probabilities, 5)
+    end_time = timer()  # End the timer
+    
+    prediction_time = end_time - start_time
+    
+    # Ensure that we use the correct dimensions
+    top_prob = top_prob.squeeze().tolist()
+    top_catid = top_catid.squeeze().tolist()
+    
+    # Construct the prediction dictionary
+    prediction = {class_names[idx]: prob for idx, prob in zip(top_catid, top_prob)}
+    
+    return prediction, prediction_time
+
+
+# Example images for the Gradio interface
+examples = [
+    ["/content/data/cifar10/test/airplane/0001.png"],
+    ["/content/data/cifar10/test/bird/0007.png"],
+    ["/content/data/cifar10/test/dog/0004.png"],
+    ["/content/data/cifar10/test/ship/0009.png"]
+]
+
+# Create the Gradio interface
+demo = gr.Interface(fn=predict, 
+                    inputs=gr.Image(type="pil"), 
+                    outputs=[gr.Label(num_top_classes=5, label="Predictions"),
+                              gr.Number(label="Prediction time (s)")], 
+                    examples=examples,
+                    title="CIFAR-10 Image Classifier",
+                    description="A computer Vision Model to Classify images 10 classes from CIFAR10 Dataset.",
+                    allow_flagging="never")
+
+demo.launch()

cifar10-resnet9.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a4c8ba77746a6e0b511d97c32920e82d06b83e3f2bd6c6f6bf6ba79f4f82e818
+size 26339032

requirements.txt

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+torch 
+torchvision
+gradio
+Pillow