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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() |