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import torch
from torch import nn
from torchvision import transforms
import gradio as gr
title = "PyTorch Cat vs Dog"
description = "Classifying cats and dogs with Pytorch"
article = "<p style='text-align: center'><a href='https://github.com/TirendazAcademy'>Github Repo</a></p>"
# The model architecture
class ImageClassifier(nn.Module):
def __init__(self):
super().__init__()
self.conv_layer_1 = nn.Sequential(
nn.Conv2d(3, 64, 3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(2))
self.conv_layer_2 = nn.Sequential(
nn.Conv2d(64, 512, 3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(512),
nn.MaxPool2d(2))
self.conv_layer_3 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(512),
nn.MaxPool2d(2))
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(in_features=512*3*3, out_features=2)
)
def forward(self, x: torch.Tensor):
x = self.conv_layer_1(x)
x = self.conv_layer_2(x)
x = self.conv_layer_3(x)
x = self.conv_layer_3(x)
x = self.conv_layer_3(x)
x = self.conv_layer_3(x)
x = self.classifier(x)
return x
model = ImageClassifier()
model.load_state_dict(torch.load('image_classifier.pth'))
def predict(inp):
image_transform = transforms.Compose([ transforms.Resize(size=(224,224)), transforms.ToTensor()])
labels = ['cat', 'dog']
inp = image_transform(inp).unsqueeze(dim=0)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(inp))
confidences = {labels[i]: float(prediction.squeeze()[i]) for i in range(len(labels))}
return confidences
gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=2),
title=title,
description=description,
article=article,
examples=['cat.jpg', 'dog.jpg']).launch() |