Uploading Model, Config

alexnet_model_v1.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c5b4d32609c7c235550d9db88bbc70f9be1f9e96cbbce85e7d8ce93502636bf3
+size 228185434

app.py

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+import torch
+import gradio as gr
+from model import AlexNet
+from torchvision import transforms
+
+model_path = './alexnet_model_v1.pth'
+model = AlexNet()
+model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+model.eval()
+
+labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+
+
+def predict(inp):
+  inp = transforms.ToTensor()(inp).unsqueeze(0)
+  with torch.no_grad():
+    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
+    confidences = {labels[i]: float(prediction[i]) for i in range(10)}    
+  return confidences
+
+
+
+gr.Interface(fn=predict, 
+             inputs=gr.components.Image(type="pil"),
+             outputs=gr.components.Label(num_top_classes=5),
+             examples=["frog.jpeg", "car.jpeg"],
+             theme="default",
+             css=".footer{display:none !important}").launch()

model.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.transforms as transforms
+from torchvision.datasets import CIFAR10
+from torch.utils.data import DataLoader
+
+class AlexNet(nn.Module):
+    def __init__(self, num_classes=10):
+        super(AlexNet, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+            nn.Conv2d(64, 192, kernel_size=5, padding=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+            nn.Conv2d(192, 384, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(384, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+        )
+        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
+        self.classifier = nn.Sequential(
+            nn.Dropout(),
+            nn.Linear(256 * 6 * 6, 4096),
+            nn.ReLU(inplace=True),
+            nn.Dropout(),
+            nn.Linear(4096, 4096),
+            nn.ReLU(inplace=True),
+            nn.Linear(4096, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.classifier(x)
+        return x