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f8b9cae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | import torch
import gradio as gr
from PIL import Image
# setup Device to CUDA
device='cuda' if torch.cuda.is_available() else 'cpu'
device
class CustomBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(CustomBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
self.bn1 = nn.BatchNorm2d(out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.conv3 = nn.Conv2d(out_channels, out_channels , kernel_size=1)
self.bn3 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
self.identity_conv=nn.Conv2d(in_channels,out_channels,kernel_size=1, stride=stride, padding=1)
def forward(self, x):
identity = x
#print(identity.shape)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.conv3(x)
x = self.bn3(x)
#print(x.shape)
if self.identity_conv is not None:
identity = self.identity_conv(identity)
if x.shape != identity.shape:
identity=nn.functional.interpolate(identity,size=(x.shape[2],x.shape[3]),mode='nearest')
x += identity
x = self.relu(x)
return x
class SimpleResNet(nn.Module):
def __init__(self, num_classes=13):
super(SimpleResNet, self).__init__()
self.conv1 = nn.Conv2d(3,16, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(16)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=1, padding=1)
self.block1 = CustomBlock(16, 32)
self.block2 = CustomBlock(32,64)
self.block3 = CustomBlock(64,128)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.flatten=nn.Flatten()
self.fc = nn.Linear(128 ,128)
self.fc2=nn.Linear(128,256)
self.drop=nn.Dropout(p=0.5)
self.fc3=nn.Linear(256,num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.avgpool(x)
x = self.flatten(x)
x = self.fc(x)
x=self.fc2(x)
x=self.drop(x)
x=self.fc3(x)
return x
model=SimpleResNet(num_classes=21).to(device)
model.load_state_dict(torch.load('model_with_info_path_final.pt',map_location=device))
model.eval()
classes=['206','207','405','Dena','L90','Mazda-vanet','Naisan','Pars','Paykan-Vanet','Pride','Pride_vanet','Quiek',
'Saina','Tiba','Truck-Benz','Truck-Renault','Unknown','Volvo-FH-FM','Volvo-N10','Volvo-NH','samand']
transform=transforms.Compose([transforms.Resize((224,224)),
transforms.ToTensor(),
transforms.Normalize((.5),(.5))])
def classify_image(img1):
model.eval()
with torch.inference_mode():
#img1=Image.open(img1).convert("RGB")
img1=transform(img1).unsqueeze(0).to(device)
y_logits=model(img1)
y_pred=torch.softmax(y_logits,dim=1)#.argmax(dim=1)
conf,pred_class=torch.max(y_pred,dim=1)
if conf.item()<0.55:
return f"I'm not sure what this is and confidence:{conf.item():.2f}"
else:
return f'Car: {classes[pred_class]} confidence:{conf.item():.2f}'
#img1=Image.open(img1).convert("RGB")
#img1=transform(img1).unsqueeze(0).to(device)
#print(img.shape)
# y_logits=model(img1)
#y_pred=torch.softmax(y_logits,dim=1).argmax(dim=1)
#confidence = {classes[i]: float(y_pred[i]) for i in range(len(classes))}
#return classes[pred_class]
interface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=21),
title="Iranian Car Classifier")
interface.launch(share=True) |