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import torch
import torch.nn as nn
from torchvision import models,transforms
from PIL import Image
import torch.nn.functional as f
import gradio as gr
from torchvision.transforms import transforms
# model=models.resnet18(pretrained=True)
# model.fc=nn.Linear(model.fc.in_features,10)
t=transforms.Compose([ transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),
transforms.RandomHorizontalFlip(0.5),
transforms.RandomRotation(10),
])
class_name=["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship","truck"]
class CIFAR_Module(nn.Module):
def __init__(self,in_channel):
self.in_channel=in_channel
super(CIFAR_Module,self).__init__()
self.con1=nn.Conv2d(in_channel,6*in_channel,5)
self.pool1=nn.MaxPool2d(5,stride=2)
self.con2=nn.Conv2d(6*in_channel,16*in_channel,5)
self.pool2=nn.MaxPool2d(5,stride=2)
self.flat=nn.Flatten()
self.fc1=nn.Linear(192,100*in_channel)
self.fc2=nn.Linear(100*in_channel,40*in_channel)
self.fc3=nn.Linear(40*in_channel,10)
def forward(self,x):
x=self.con1(x)
x=f.relu(x)
x=self.pool1(x)
x=f.relu(x)
x=self.con2(x)
x=f.relu(x)
x=self.pool2(x)
x=self.flat(x)
x=self.fc1(x)
x=f.relu(x)
x=self.fc2(x)
x=f.relu(x)
x=self.fc3(x)
return x
model=CIFAR_Module(3)
model.load_state_dict(torch.load("model.pth",weights_only=True))
model.eval()
print(model)
def predict(image):
image=image.resize((32,32))
image=t(image).unsqueeze(0)
with torch.no_grad():
output=model(image)
_,predicted=torch.max(output,1)
print(output)
predicted_class=class_name[predicted.item()-1]
return predicted_class
interface=gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs="text",
title="cifar dataset prediction",
description="upload an image to get its class"
)
interface.launch(share=True) |