app.py

import torch
import gradio as gd
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import torchvision.transforms as transforms

'''MobileNetV3 in PyTorch.

See the paper "Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation" for more details.
'''



class hswish(nn.Module):
    def forward(self, x):
        out = x * F.relu6(x + 3, inplace=True) / 6
        return out


class hsigmoid(nn.Module):
    def forward(self, x):
        out = F.relu6(x + 3, inplace=True) / 6
        return out


class SeModule(nn.Module):
    def __init__(self, in_size, reduction=4):
        super(SeModule, self).__init__()
        self.se = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(in_size, in_size // reduction, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(in_size // reduction),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_size // reduction, in_size, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(in_size),
            hsigmoid()
        )

    def forward(self, x):
        return x * self.se(x)


class Block(nn.Module):
    '''expand + depthwise + pointwise'''
    def __init__(self, kernel_size, in_size, expand_size, out_size, nolinear, semodule, stride):
        super(Block, self).__init__()
        self.stride = stride
        self.se = semodule

        self.conv1 = nn.Conv2d(in_size, expand_size, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn1 = nn.BatchNorm2d(expand_size)
        self.nolinear1 = nolinear
        self.conv2 = nn.Conv2d(expand_size, expand_size, kernel_size=kernel_size, stride=stride, padding=kernel_size//2, groups=expand_size, bias=False)
        self.bn2 = nn.BatchNorm2d(expand_size)
        self.nolinear2 = nolinear
        self.conv3 = nn.Conv2d(expand_size, out_size, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn3 = nn.BatchNorm2d(out_size)

        self.shortcut = nn.Sequential()
        if stride == 1 and in_size != out_size:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_size, out_size, kernel_size=1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(out_size),
            )

    def forward(self, x):
        out = self.nolinear1(self.bn1(self.conv1(x)))
        out = self.nolinear2(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        if self.se != None:
            out = self.se(out)
        out = out + self.shortcut(x) if self.stride==1 else out
        return out

class MobileNetV3_Small(nn.Module):
    def __init__(self, num_classes= 30):
        super(MobileNetV3_Small, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.hs1 = hswish()

        self.bneck = nn.Sequential(
            Block(3, 16, 16, 16, nn.ReLU(inplace=True), SeModule(16), 2),
            Block(3, 16, 72, 24, nn.ReLU(inplace=True), None, 2),
            Block(3, 24, 88, 24, nn.ReLU(inplace=True), None, 1),
            Block(5, 24, 96, 40, hswish(), SeModule(40), 2),
            Block(5, 40, 240, 40, hswish(), SeModule(40), 1),
            Block(5, 40, 240, 40, hswish(), SeModule(40), 1),
            Block(5, 40, 120, 48, hswish(), SeModule(48), 1),
            Block(5, 48, 144, 48, hswish(), SeModule(48), 1),
            Block(5, 48, 288, 96, hswish(), SeModule(96), 2),
            Block(5, 96, 576, 96, hswish(), SeModule(96), 1),
            Block(5, 96, 576, 96, hswish(), SeModule(96), 1),
        )


        self.conv2 = nn.Conv2d(96, 576, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn2 = nn.BatchNorm2d(576)
        self.hs2 = hswish()
        self.linear3 = nn.Linear(576, 1280)
        self.bn3 = nn.BatchNorm1d(1280)
        self.hs3 = hswish()
        self.linear4 = nn.Linear(1280, num_classes)
        self.init_params()

    def init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def forward(self, x):
        out = self.hs1(self.bn1(self.conv1(x)))
        out = self.bneck(out)
        out = self.hs2(self.bn2(self.conv2(out)))
        out = F.avg_pool2d(out, 7)
        out = out.view(out.size(0), -1)
        out = self.hs3(self.bn3(self.linear3(out)))
        out = self.linear4(out)
        return out



"""creating modelinstance"""
model = MobileNetV3_Small().to("cpu")
model.load_state_dict( torch.load("MobileNet3_small_full.pth"))
classes = ['antelope',
 'buffalo',
 'chimpanzee',
 'cow',
 'deer',
 'dolphin',
 'elephant',
 'fox',
 'giant+panda',
 'giraffe',
 'gorilla',
 'grizzly+bear',
 'hamster',
 'hippopotamus',
 'horse',
 'humpback+whale',
 'leopard',
 'lion',
 'moose',
 'otter',
 'ox',
 'pig',
 'polar+bear',
 'rabbit',
 'rhinoceros',
 'seal',
 'sheep',
 'squirrel',
 'tiger',
 'zebra']

def predicts(img):
    model.eval()
    with torch.inference_mode:
        logits = model(img.unsqueez(dim=0))
        preds = logits.argmax(dim=1)
        return classes[preds]

"""gradio inteface"""
demo = gd.Interface(predicts ,gd.Image("Image", width=244, height=244, image_mode="RGB", ))


"""launch interface"""
if __name__ == "__main__":
    demo.launch()