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)"] 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): # try: # image_tensor = preprocess_image(image) # with torch.no_grad(): # output = model(image_tensor) # if isinstance(output, tuple): # logits = output[0] # else: # logits = output # # 取得預測的top k結果 # probabilities = F.softmax(logits, dim=1) # topk_probs, topk_indices = torch.topk(probabilities, k=3) # # 檢查最高的預測機率 # top1_prob = topk_probs[0][0].item() # if top1_prob >= 0.5: # # 正確辨識時，返回該品種資訊 # predicted = topk_indices[0][0] # breed = dog_breeds[predicted.item()] # description = get_dog_description(breed) # akc_link = get_akc_breeds_link() # if isinstance(description, dict): # description_str = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()]) # else: # description_str = description # # 添加AKC連結 # description_str += 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." # # 添加免責聲明 # 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.*") # description_str += disclaimer # return description_str # else: # # 不確定時，返回top 3的預測結果 # 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]] # # 用粗體返回品種和機率 # topk_results = "\n\n".join([f"**{i+1}. {breed}** ({prob} confidence)" for i, (breed, prob) in enumerate(zip(topk_breeds, topk_probs_percent))]) # # 提供說明 # explanation = ( # f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n{topk_results}\n\n" # "This can happen if the image quality is low or the breed is rare in the dataset. " # "Please try uploading a clearer image or a different angle of the dog. " # "For more accurate results, ensure the dog is the main subject of the photo." # ) # return explanation # except Exception as e: # return f"An error occurred: {e}" # iface = gr.Interface( # fn=predict, # inputs=gr.Image(label="Upload a dog image", type="numpy"), # outputs=gr.Markdown(label="Prediction Results"), # title="

🐶 Dog Breed Classifier 🔍

", # article= 'For more details on this project and other work, feel free to visit my GitHub [Dog Breed Classifier](https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/Dog%20Breed%20Classifier)', # description="

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

", # examples=['Border_Collie.jpg', # 'Golden_Retriever.jpeg', # 'Saint_Bernard.jpeg', # 'French_Bulldog.jpeg', # 'Samoyed.jpg'], # 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; # } # """, # theme='default') # # Launch the app # if __name__ == "__main__": # iface.launch() def predict(image): 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: # High confidence prediction 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: # Very low confidence prediction 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: # Medium confidence prediction explanation = ( f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\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): description_str = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()]) else: description_str = description akc_link = get_akc_breeds_link() description_str += 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.*") description_str += disclaimer return description_str def show_details(breed): description = get_dog_description(breed) return format_description(description, breed) 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()