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import torch
import gradio as gr
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import torchvision.transforms as transforms
from PIL import Image
# MobileNetV3 Model Definition (keep this exactly as in your original code)
class hswish(nn.Module):
def forward(self, x):
return x * F.relu6(x + 3) / 6
class hsigmoid(nn.Module):
def forward(self, x):
return F.relu6(x + 3) / 6
class SeModule(nn.Module):
def __init__(self, in_size, reduction=4):
super().__init__()
self.se = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_size, in_size//reduction, 1, bias=False),
nn.BatchNorm2d(in_size//reduction),
nn.ReLU(inplace=True),
nn.Conv2d(in_size//reduction, in_size, 1, bias=False),
nn.BatchNorm2d(in_size),
hsigmoid()
)
def forward(self, x):
return x * self.se(x)
class Block(nn.Module):
def __init__(self, kernel_size, in_size, expand_size, out_size, nolinear, semodule, stride):
super().__init__()
self.stride = stride
self.se = semodule
self.conv1 = nn.Conv2d(in_size, expand_size, 1, 1, 0, bias=False)
self.bn1 = nn.BatchNorm2d(expand_size)
self.nolinear1 = nolinear
self.conv2 = nn.Conv2d(expand_size, expand_size, kernel_size, stride, 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, 1, 1, 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, 1, 1, 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: out = self.se(out)
return out + self.shortcut(x) if self.stride==1 else out
class MobileNetV3_Small(nn.Module):
def __init__(self, num_classes=30):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, 3, 2, 1, bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.hs1 = hswish()
self.bneck = nn.Sequential(
Block(3, 16, 16, 16, nn.ReLU(), SeModule(16), 2),
Block(3, 16, 72, 24, nn.ReLU(), None, 2),
Block(3, 24, 88, 24, nn.ReLU(), 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, 1, 1, 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)
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):
x = self.hs1(self.bn1(self.conv1(x)))
x = self.bneck(x)
x = self.hs2(self.bn2(self.conv2(x)))
x = F.avg_pool2d(x, x.size()[2:])
x = x.view(x.size(0), -1)
x = self.hs3(self.bn3(self.linear3(x)))
return self.linear4(x)
# Initialize Model
model = MobileNetV3_Small().cpu()
model.load_state_dict(torch.load("MobileNet3_small_StateDictionary.pth", map_location='cpu'))
model.eval()
# Class Labels
classes = [
'antelope', 'buffalo', 'chimpanzee', 'cow', 'deer', 'dolphin',
'elephant', 'fox', 'giantpanda', 'giraffe', 'gorilla', 'grizzlybear',
'hamster', 'hippopotamus', 'horse', 'humpbackwhale', 'leopard', 'lion',
'moose', 'otter', 'ox', 'pig', 'polarbear', 'rabbit', 'rhinoceros',
'seal', 'sheep', 'squirrel', 'tiger', 'zebra'
]
# Preprocessing
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def predict(images):
"""Process multiple images and return predictions"""
predictions = []
# Batch processing
batch = torch.stack([preprocess(Image.open(img).convert('RGB')) for img in images])
with torch.inference_mode():
outputs = model(batch)
_, preds = torch.max(outputs, 1)
return ", ".join([classes[p] for p in preds.cpu().numpy()])
# Gradio Interface
with gr.Blocks(title="Animal Classifier") as demo:
gr.Markdown("## 🐾 Animal Classifier")
gr.Markdown("Upload multiple animal images to get predictions!")
gr.Markdown("Detectable Classes: antelope, buffalo, chimpanzee, cow, deer, dolphin, elephant, fox, giantpanda, giraffe, gorilla, grizzlybear, hamster, hippopotamus, horse, humpbackwhale, leopard, lion, moose, otter, ox, pig, polarbear, rabbit, rhinoceros, seal, sheep, squirrel, tiger, zebra")
with gr.Row():
inputs = gr.File(
file_count="multiple",
file_types=["image"],
label="Upload Animal Images"
)
submit = gr.Button("Classify 🚀", variant="primary")
with gr.Row():
gallery = gr.Gallery(label="Upload Preview", columns=4)
outputs = gr.Textbox(label="Predictions", lines=5)
submit.click(
fn=lambda files: (
[f.name for f in files], # Update gallery
predict([f.name for f in files]) # Get predictions
),
inputs=inputs,
outputs=[gallery, outputs]
)
if __name__ == "__main__":
demo.launch(show_error=True) |