import os import numpy as np import torch import torch.nn as nn import gradio as gr from torchvision.models import efficientnet_v2_m, EfficientNet_V2_M_Weights import torch.nn.functional as F from torchvision import transforms from PIL import Image from data_manager import get_dog_description from urllib.parse import quote # dog_breeds = ["Afghan_Hound(阿富汗獵犬)", "African_Hunting_Dog(非洲野犬)", "Airedale(艾爾谷犬)", # "American_Staffordshire_Terrier(美國斯塔福郡梗)", "Appenzeller(亞賓澤爾犬)", # "Australian_Terrier(澳大利亞梗)", "Bedlington_Terrier(貝德靈頓梗)", # "Bernese_Mountain_Dog(伯恩山犬)", "Blenheim_Spaniel(布萊尼姆獵犬)", # "Border_Collie(邊境牧羊犬)", "Border_Terrier(邊境梗)", "Boston_Bull(波士頓梗)", # "Bouvier_Des_Flandres(法蘭德斯牧羊犬)", "Brabancon_Griffon(布魯塞爾格里芬犬)", # "Brittany_Spaniel(布列塔尼獵犬)", "Cardigan(卡迪根威爾士柯基犬)", # "Chesapeake_Bay_Retriever(切薩皮克灣獵犬)", "Chihuahua(吉娃娃)", # "Dandie_Dinmont(丹第丁蒙梗)", "Doberman(杜賓犬)", "English_Foxhound(英國獵狐犬)", # "English_Setter(英國雪達犬)", "English_Springer(英國跳獵犬)", # "EntleBucher(恩特雷布赫山地犬)", "Eskimo_Dog(愛斯基摩犬)", "French_Bulldog(法國鬥牛犬)", # "German_Shepherd(德國牧羊犬)", "German_Short-Haired_Pointer(德國短毛指示犬)", # "Gordon_Setter(戈登雪達犬)", "Great_Dane(大丹犬)", "Great_Pyrenees(大白熊犬)", # "Greater_Swiss_Mountain_Dog(大瑞士山地犬)", "Ibizan_Hound(依比沙獵犬)", # "Irish_Setter(愛爾蘭雪達犬)", "Irish_Terrier(愛爾蘭梗)", # "Irish_Water_Spaniel(愛爾蘭水獵犬)", "Irish_Wolfhound(愛爾蘭獵狼犬)", # "Italian_Greyhound(義大利灰狗)", "Japanese_Spaniel(日本狆)", # "Kerry_Blue_Terrier(凱利藍梗)", "Labrador_Retriever(拉布拉多尋回犬)", # "Lakeland_Terrier(湖畔梗)", "Leonberg(獅毛狗)", "Lhasa(拉薩犬)", # "Maltese_Dog(馬爾濟斯犬)", "Mexican_Hairless(墨西哥無毛犬)", "Newfoundland(紐芬蘭犬)", # "Norfolk_Terrier(諾福克梗)", "Norwegian_Elkhound(挪威獵麋犬)", # "Norwich_Terrier(諾利治梗)", "Old_English_Sheepdog(古代英國牧羊犬)", # "Pekinese(北京犬)", "Pembroke(威爾士柯基犬)", "Pomeranian(博美犬)", # "Rhodesian_Ridgeback(羅得西亞脊背犬)", "Rottweiler(羅威納犬)", # "Saint_Bernard(聖伯納犬)", "Saluki(薩路基獵犬)", "Samoyed(薩摩耶犬)", # "Scotch_Terrier(蘇格蘭梗)", "Scottish_Deerhound(蘇格蘭獵鹿犬)", # "Sealyham_Terrier(錫利哈姆梗)", "Shetland_Sheepdog(設得蘭牧羊犬)", # "Shih-Tzu(西施犬)", "Siberian_Husky(西伯利亞哈士奇)", # "Staffordshire_Bullterrier(斯塔福郡鬥牛梗)", "Sussex_Spaniel(蘇塞克斯獵犬)", # "Tibetan_Mastiff(藏獒)", "Tibetan_Terrier(西藏梗)", "Walker_Hound(沃克獵犬)", # "Weimaraner(威瑪犬)", "Welsh_Springer_Spaniel(威爾士跳獵犬)", # "West_Highland_White_Terrier(西高地白梗)", "Yorkshire_Terrier(約克夏梗)", # "Affenpinscher(猴犬)", "Basenji(巴辛吉犬)", "Basset(巴吉度獵犬)", "Beagle(比格犬)", # "Black-and-Tan_Coonhound(黑褐獵浣熊犬)", "Bloodhound(尋血獵犬)", # "Bluetick(布魯提克獵犬)", "Borzoi(俄羅斯獵狼犬)", "Boxer(拳師犬)", "Briard(布里亞犬)", # "Bull_Mastiff(獒犬)", "Cairn(凱恩梗)", "Chow(鬆獅犬)", "Clumber(克倫伯獵犬)", # "Cocker_Spaniel(可卡獵犬)", "Collie(柯利牧羊犬)", "Curly-Coated_Retriever(捲毛尋回犬)", # "Dhole(豺)", "Dingo(澳洲野犬)", "Flat-Coated_Retriever(平毛尋回犬)", # "Giant_Schnauzer(大型雪納瑞犬)", "Golden_Retriever(黃金獵犬)", # "Groenendael(比利時牧羊犬)", "Keeshond(荷蘭毛獅犬)", "Kelpie(澳洲卡爾比犬)", # "Komondor(匈牙利牧羊犬)", "Kuvasz(庫瓦茲犬)", "Malamute(阿拉斯加雪橇犬)", # "Malinois(比利時瑪利諾犬)", "Miniature_Pinscher(迷你杜賓犬)", # "Miniature_Poodle(迷你貴賓犬)", "Miniature_Schnauzer(迷你雪納瑞犬)", # "Otterhound(水獺獵犬)", "Papillon(蝴蝶犬)", "Pug(巴哥犬)", "Redbone(紅骨獵浣熊犬)", # "Schipperke(舒柏奇犬)", "Silky_Terrier(絲毛梗)", # "Soft-Coated_Wheaten_Terrier(愛爾蘭軟毛梗)", "Standard_Poodle(標準貴賓犬)", # "Standard_Schnauzer(標準雪納瑞犬)", "Toy_Poodle(玩具貴賓犬)", "Toy_Terrier(玩具梗)", # "Vizsla(維茲拉犬)", "Whippet(惠比特犬)", "Wire-Haired_Fox_Terrier(硬毛獵狐梗)"] dog_breeds = ["Afghan_Hound", "African_Hunting_Dog", "Airedale", "American_Staffordshire_Terrier", "Appenzeller", "Australian_Terrier", "Bedlington_Terrier", "Bernese_Mountain_Dog", "Blenheim_Spaniel", "Border_Collie", "Border_Terrier", "Boston_Bull", "Bouvier_Des_Flandres", "Brabancon_Griffon", "Brittany_Spaniel", "Cardigan", "Chesapeake_Bay_Retriever", "Chihuahua", "Dandie_Dinmont", "Doberman", "English_Foxhound", "English_Setter", "English_Springer", "EntleBucher", "Eskimo_Dog", "French_Bulldog", "German_Shepherd", "German_Short-Haired_Pointer", "Gordon_Setter", "Great_Dane", "Great_Pyrenees", "Greater_Swiss_Mountain_Dog", "Ibizan_Hound", "Irish_Setter", "Irish_Terrier", "Irish_Water_Spaniel", "Irish_Wolfhound", "Italian_Greyhound", "Japanese_Spaniel", "Kerry_Blue_Terrier", "Labrador_Retriever", "Lakeland_Terrier", "Leonberg", "Lhasa", "Maltese_Dog", "Mexican_Hairless", "Newfoundland", "Norfolk_Terrier", "Norwegian_Elkhound", "Norwich_Terrier", "Old_English_Sheepdog", "Pekinese", "Pembroke", "Pomeranian", "Rhodesian_Ridgeback", "Rottweiler", "Saint_Bernard", "Saluki", "Samoyed", "Scotch_Terrier", "Scottish_Deerhound", "Sealyham_Terrier", "Shetland_Sheepdog", "Shih-Tzu", "Siberian_Husky", "Staffordshire_Bullterrier", "Sussex_Spaniel", "Tibetan_Mastiff", "Tibetan_Terrier", "Walker_Hound", "Weimaraner", "Welsh_Springer_Spaniel", "West_Highland_White_Terrier", "Yorkshire_Terrier", "Affenpinscher", "Basenji", "Basset", "Beagle", "Black-and-Tan_Coonhound", "Bloodhound", "Bluetick", "Borzoi", "Boxer", "Briard", "Bull_Mastiff", "Cairn", "Chow", "Clumber", "Cocker_Spaniel", "Collie", "Curly-Coated_Retriever", "Dhole", "Dingo", "Flat-Coated_Retriever", "Giant_Schnauzer", "Golden_Retriever", "Groenendael", "Keeshond", "Kelpie", "Komondor", "Kuvasz", "Malamute", "Malinois", "Miniature_Pinscher", "Miniature_Poodle", "Miniature_Schnauzer", "Otterhound", "Papillon", "Pug", "Redbone", "Schipperke", "Silky_Terrier", "Soft-Coated_Wheaten_Terrier", "Standard_Poodle", "Standard_Schnauzer", "Toy_Poodle", "Toy_Terrier", "Vizsla", "Whippet", "Wire-Haired_Fox_Terrier"] class MultiHeadAttention(nn.Module): def __init__(self, in_dim, num_heads=8): super().__init__() self.num_heads = num_heads self.head_dim = max(1, in_dim // num_heads) self.scaled_dim = self.head_dim * num_heads self.fc_in = nn.Linear(in_dim, self.scaled_dim) self.query = nn.Linear(self.scaled_dim, self.scaled_dim) self.key = nn.Linear(self.scaled_dim, self.scaled_dim) self.value = nn.Linear(self.scaled_dim, self.scaled_dim) self.fc_out = nn.Linear(self.scaled_dim, in_dim) def forward(self, x): N = x.shape[0] x = self.fc_in(x) q = self.query(x).view(N, self.num_heads, self.head_dim) k = self.key(x).view(N, self.num_heads, self.head_dim) v = self.value(x).view(N, self.num_heads, self.head_dim) energy = torch.einsum("nqd,nkd->nqk", [q, k]) attention = F.softmax(energy / (self.head_dim ** 0.5), dim=2) out = torch.einsum("nqk,nvd->nqd", [attention, v]) out = out.reshape(N, self.scaled_dim) out = self.fc_out(out) return out class BaseModel(nn.Module): def __init__(self, num_classes, device='cuda' if torch.cuda.is_available() else 'cpu'): super().__init__() self.device = device self.backbone = efficientnet_v2_m(weights=EfficientNet_V2_M_Weights.IMAGENET1K_V1) self.feature_dim = self.backbone.classifier[1].in_features self.backbone.classifier = nn.Identity() self.num_heads = max(1, min(8, self.feature_dim // 64)) self.attention = MultiHeadAttention(self.feature_dim, num_heads=self.num_heads) self.classifier = nn.Sequential( nn.LayerNorm(self.feature_dim), nn.Dropout(0.3), nn.Linear(self.feature_dim, num_classes) ) self.to(device) def forward(self, x): x = x.to(self.device) features = self.backbone(x) attended_features = self.attention(features) logits = self.classifier(attended_features) return logits, attended_features num_classes = 120 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = BaseModel(num_classes=num_classes, device=device) checkpoint = torch.load('best_model_81_dog.pth', map_location=torch.device('cpu')) model.load_state_dict(checkpoint['model_state_dict']) # evaluation mode model.eval() # Image preprocessing function def preprocess_image(image): # If the image is numpy.ndarray turn into PIL.Image if isinstance(image, np.ndarray): image = Image.fromarray(image) # Use torchvision.transforms to process images transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) return transform(image).unsqueeze(0) def get_akc_breeds_link(): return "https://www.akc.org/dog-breeds/" def predict(image): if image is None: return "Please upload an image to get started.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) try: image_tensor = preprocess_image(image) with torch.no_grad(): output = model(image_tensor) logits = output[0] if isinstance(output, tuple) else output probabilities = F.softmax(logits, dim=1) topk_probs, topk_indices = torch.topk(probabilities, k=3) top1_prob = topk_probs[0][0].item() topk_breeds = [dog_breeds[idx.item()] for idx in topk_indices[0]] topk_probs_percent = [f"{prob.item() * 100:.2f}%" for prob in topk_probs[0]] if top1_prob >= 0.5: breed = topk_breeds[0] description = get_dog_description(breed) return format_description(description, breed), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) elif top1_prob < 0.1: return ("The image is too unclear or the dog breed is not in the dataset. Please upload a clearer image of the dog.", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)) else: explanation = ( f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n" f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]} confidence)\n" f"2. **{topk_breeds[1]}** ({topk_probs_percent[1]} confidence)\n" f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)\n\n" "Click on a button to view more information about the breed." ) return explanation, gr.update(visible=True, value=f"More about {topk_breeds[0]}"), gr.update(visible=True, value=f"More about {topk_breeds[1]}"), gr.update(visible=True, value=f"More about {topk_breeds[2]}") except Exception as e: return f"An error occurred: {e}", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) def format_description(description, breed): if isinstance(description, dict): formatted_description = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()]) else: formatted_description = description akc_link = get_akc_breeds_link() formatted_description += f"\n\n**Want to learn more about dog breeds?** [Visit the AKC dog breeds page]({akc_link}) and search for {breed} to find detailed information." disclaimer = ("\n\n*Disclaimer: The external link provided leads to the American Kennel Club (AKC) dog breeds page. " "You may need to search for the specific breed on that page. " "I am not responsible for the content on external sites. " "Please refer to the AKC's terms of use and privacy policy.*") formatted_description += disclaimer return formatted_description def show_details(breed): breed_name = breed.split("More about ")[-1] description = get_dog_description(breed_name) return format_description(description, breed_name) with gr.Blocks(css=""" .container { max-width: 900px; margin: 0 auto; padding: 20px; background-color: rgba(255, 255, 255, 0.9); border-radius: 15px; box-shadow: 0 0 20px rgba(0, 0, 0, 0.1); } .gr-form { display: flex; flex-direction: column; align-items: center; } .gr-box { width: 100%; max-width: 500px; } .output-markdown, .output-image { margin-top: 20px; padding: 15px; background-color: #f5f5f5; border-radius: 10px; } .examples { display: flex; justify-content: center; flex-wrap: wrap; gap: 10px; margin-top: 20px; } .examples img { width: 100px; height: 100px; object-fit: cover; } """) as iface: gr.HTML("

🐶 Dog Breed Classifier 🔍

") gr.HTML("

Upload a picture of a dog, and the model will predict its breed, provide detailed information, and include an extra information link!

") with gr.Row(): input_image = gr.Image(label="Upload a dog image", type="numpy") output = gr.Markdown(label="Prediction Results") with gr.Row(): btn1 = gr.Button("View More 1", visible=False) btn2 = gr.Button("View More 2", visible=False) btn3 = gr.Button("View More 3", visible=False) input_image.change(predict, inputs=input_image, outputs=[output, btn1, btn2, btn3]) btn1.click(show_details, inputs=btn1, outputs=output) btn2.click(show_details, inputs=btn2, outputs=output) btn3.click(show_details, inputs=btn3, outputs=output) gr.Examples( examples=['Border_Collie.jpg', 'Golden_Retriever.jpeg', 'Saint_Bernard.jpeg', 'French_Bulldog.jpeg', 'Samoyed.jpg'], inputs=input_image ) gr.HTML('For more details on this project and other work, feel free to visit my GitHub Dog Breed Classifier') if __name__ == "__main__": iface.launch()