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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 | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| # 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=example_list, | |
| title="CIFAR-10 Image Classifier", | |
| description="A computer Vision Model to Classify images 10 classes from CIFAR10 Dataset.", | |
| allow_flagging="never") | |
| demo.launch() |