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README.md

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----
-title: Generative Augmented Classifiers
-emoji: 🚀
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 4.36.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Generative Augmented Classifiers
+emoji: 💻
+colorFrom: gray
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.36.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import random
+
+import gradio as gr
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from PIL import Image
+from torch import nn
+from torchvision.models import mobilenet_v2, resnet18
+from torchvision.transforms.functional import InterpolationMode
+
+datasets_n_classes = {
+    "Imagenette": 10,
+    "Imagewoof": 10,
+    "Stanford_dogs": 120,
+}
+
+datasets_model_types = {
+    "Imagenette": [
+        "base_200",
+        "base_200+100",
+        "synthetic_200",
+        "augment_noisy_200",
+        "augment_noisy_200+100",
+        "augment_clean_200",
+    ],
+    "Imagewoof": [
+        "base_200",
+        "base_200+100",
+        "synthetic_200",
+        "augment_noisy_200",
+        "augment_noisy_200+100",
+        "augment_clean_200",
+    ],
+    "Stanford_dogs": [
+        "base_200",
+        "base_200+100",
+        "synthetic_200",
+        "augment_noisy_200",
+        "augment_noisy_200+100",
+    ],
+}
+
+model_arch = ["resnet18", "mobilenet_v2"]
+
+list_200 = [
+    "Original",
+    "Synthetic",
+    "Original + Synthetic (Noisy)",
+    "Original + Synthetic (Clean)",
+]
+
+list_200_100 = ["Base+100", "AugmentNoisy+100"]
+
+methods_map = {
+    "200 Epochs": list_200,
+    "200 Epochs on Original + 100": list_200_100,
+}
+
+label_map = dict()
+label_map["Imagenette (10 classes)"] = "Imagenette"
+label_map["Imagewoof (10 classes)"] = "Imagewoof"
+label_map["Stanford Dogs (120 classes)"] = "Stanford_dogs"
+label_map["ResNet-18"] = "resnet18"
+label_map["MobileNetV2"] = "mobilenet_v2"
+label_map["200 Epochs"] = "200"
+label_map["200 Epochs on Original + 100"] = "200+100"
+label_map["Original"] = "base"
+label_map["Synthetic"] = "synthetic"
+label_map["Original + Synthetic (Noisy)"] = "augment_noisy"
+label_map["Original + Synthetic (Clean)"] = "augment_clean"
+label_map["Base+100"] = "base"
+label_map["AugmentNoisy+100"] = "augment_noisy"
+
+dataset_models = dict()
+for dataset, n_classes in datasets_n_classes.items():
+    models = dict()
+    for model_type in datasets_model_types[dataset]:
+        for arch in model_arch:
+            if arch == "resnet18":
+                model = resnet18(weights=None, num_classes=n_classes)
+                models[f"{arch}_{model_type}"] = (
+                    model,
+                    f"./models/{arch}/{dataset}/{dataset}_{model_type}.pth",
+                )
+            elif arch == "mobilenet_v2":
+                model = mobilenet_v2(weights=None, num_classes=n_classes)
+                models[f"{arch}_{model_type}"] = (
+                    model,
+                    f"./models/{arch}/{dataset}/{dataset}_{model_type}.pth",
+                )
+            else:
+                raise ValueError(f"Model architecture unavailable: {arch}")
+    dataset_models[dataset] = models
+
+
+def get_random_image(dataset, label_map=label_map) -> Image:
+    dataset_root = f"./data/{label_map[dataset]}/val"
+    dataset_img = torchvision.datasets.ImageFolder(
+        dataset_root,
+        transforms.Compose([transforms.PILToTensor()]),
+    )
+    random_idx = random.randint(0, len(dataset_img) - 1)
+    image, _ = dataset_img[random_idx]
+    image = transforms.ToPILImage()(image)
+    image = image.resize(
+        (256, 256),
+    )
+    return image
+
+
+def load_model(model_dict, model_name: str) -> nn.Module:
+    model_name_lower = model_name.lower()
+    if model_name_lower in model_dict:
+        model = model_dict[model_name_lower][0]
+        model_path = model_dict[model_name_lower][1]
+        checkpoint = torch.load(model_path)
+        if "setup" in checkpoint:
+            if checkpoint["setup"]["distributed"]:
+                torch.nn.modules.utils.consume_prefix_in_state_dict_if_present(
+                    checkpoint["model"], "module."
+                )
+            model.load_state_dict(checkpoint["model"])
+        else:
+            model.load_state_dict(checkpoint)
+        return model
+    else:
+        raise ValueError(
+            f"Model {model_name} is not available for image prediction. Please choose from {[name.capitalize() for name in model_dict.keys()]}."
+        )
+
+
+def postprocess_default(labels, output) -> dict:
+    probabilities = nn.functional.softmax(output[0], dim=0)
+    top_prob, top_catid = torch.topk(probabilities, 5)
+    confidences = {
+        labels[top_catid.tolist()[i]]: top_prob.tolist()[i]
+        for i in range(top_prob.shape[0])
+    }
+    return confidences
+
+
+def classify(
+    input_image: Image,
+    dataset_type: str,
+    arch_type: str,
+    methods: str,
+    training_ds: str,
+    dataset_models=dataset_models,
+    label_map=label_map,
+) -> dict:
+    for i in [dataset_type, arch_type, methods, training_ds]:
+        if i is None:
+            raise ValueError("Please select all options.")
+    dataset_type = label_map[dataset_type]
+    arch_type = label_map[arch_type]
+    methods = label_map[methods]
+    training_ds = label_map[training_ds]
+    preprocess_input = transforms.Compose(
+        [
+            transforms.Resize(
+                256,
+                interpolation=InterpolationMode.BILINEAR,
+                antialias=True,
+            ),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ]
+    )
+    if input_image is None:
+        raise ValueError("No image was provided.")
+    input_tensor: torch.Tensor = preprocess_input(input_image)
+    input_batch = input_tensor.unsqueeze(0)
+    model = load_model(
+        dataset_models[dataset_type], f"{arch_type}_{training_ds}_{methods}"
+    )
+
+    if torch.cuda.is_available():
+        input_batch = input_batch.to("cuda")
+        model.to("cuda")
+
+    model.eval()
+    with torch.inference_mode():
+        output: torch.Tensor = model(input_batch)
+    with open(f"./data/{dataset_type}.txt", "r") as f:
+        labels = {i: line.strip() for i, line in enumerate(f.readlines())}
+    return postprocess_default(labels, output)
+
+
+def update_methods(method, ds_type):
+    if ds_type == "Stanford Dogs (120 classes)" and method == "200 Epochs":
+        methods = list_200[:-1]
+    else:
+        methods = methods_map[method]
+    return gr.update(choices=methods, value=None)
+
+
+def downloadModel(
+    dataset_type, arch_type, methods, training_ds, dataset_models=dataset_models
+):
+    for i in [dataset_type, arch_type, methods, training_ds]:
+        if i is None:
+            return gr.update(label="Select Model", value=None)
+    dataset_type = label_map[dataset_type]
+    arch_type = label_map[arch_type]
+    methods = label_map[methods]
+    training_ds = label_map[training_ds]
+    if f"{arch_type}_{training_ds}_{methods}" not in dataset_models[dataset_type]:
+        return gr.update(label="Select Model", value=None)
+    model_path = dataset_models[dataset_type][f"{arch_type}_{training_ds}_{methods}"][1]
+    return gr.update(
+        label=f"Download Model: '{dataset_type}_{arch_type}_{training_ds}_{methods}'",
+        value=model_path,
+    )
+
+
+if __name__ == "__main__":
+    with gr.Blocks(title="Generative Augmented Image Classifiers") as demo:
+        gr.Markdown(
+            """
+# Generative Augmented Image Classifiers
+This demo showcases the performance of image classifiers trained on various datasets.
+"""
+        )
+        with gr.Row():
+            with gr.Column():
+                dataset_type = gr.Radio(
+                    choices=[
+                        "Imagenette (10 classes)",
+                        "Imagewoof (10 classes)",
+                        "Stanford Dogs (120 classes)",
+                    ],
+                    label="Dataset",
+                    value="Imagenette (10 classes)",
+                )
+                arch_type = gr.Radio(
+                    choices=["ResNet-18", "MobileNetV2"],
+                    label="Model Architecture",
+                    value="ResNet-18",
+                    interactive=True,
+                )
+                methods = gr.Radio(
+                    label="Methods",
+                    choices=["200 Epochs", "200 Epochs on Original + 100"],
+                    interactive=True,
+                    value="200 Epochs",
+                )
+                training_ds = gr.Radio(
+                    label="Training Dataset",
+                    choices=methods_map["200 Epochs"],
+                    interactive=True,
+                    value="Original",
+                )
+                dataset_type.change(
+                    fn=update_methods,
+                    inputs=[methods, dataset_type],
+                    outputs=[training_ds],
+                )
+                methods.change(
+                    fn=update_methods,
+                    inputs=[methods, dataset_type],
+                    outputs=[training_ds],
+                )
+                generate_button = gr.Button("Sample Random Image")
+                random_image_output = gr.Image(
+                    type="pil", label="Random Image from Validation Set"
+                )
+                classify_button_random = gr.Button("Classify")
+            with gr.Column():
+                output_label_random = gr.Label(num_top_classes=5)
+                download_model = gr.DownloadButton(
+                    label=f"Download Model: '{label_map[dataset_type.value]}_{label_map[arch_type.value]}_{label_map[training_ds.value]}_{label_map[methods.value]}'",
+                    value=dataset_models[label_map[dataset_type.value]][
+                        f"{label_map[arch_type.value]}_{label_map[training_ds.value]}_{label_map[methods.value]}"
+                    ][1],
+                )
+                dataset_type.change(
+                    fn=downloadModel,
+                    inputs=[dataset_type, arch_type, methods, training_ds],
+                    outputs=[download_model],
+                )
+                arch_type.change(
+                    fn=downloadModel,
+                    inputs=[dataset_type, arch_type, methods, training_ds],
+                    outputs=[download_model],
+                )
+                methods.change(
+                    fn=downloadModel,
+                    inputs=[dataset_type, arch_type, methods, training_ds],
+                    outputs=[download_model],
+                )
+                training_ds.change(
+                    fn=downloadModel,
+                    inputs=[dataset_type, arch_type, methods, training_ds],
+                    outputs=[download_model],
+                )
+
+        generate_button.click(
+            get_random_image,
+            inputs=[dataset_type],
+            outputs=random_image_output,
+        )
+        classify_button_random.click(
+            classify,
+            inputs=[random_image_output, dataset_type, arch_type, methods, training_ds],
+            outputs=output_label_random,
+        )
+    demo.launch(show_error=True)

data/Imagenette.txt

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+tench, Tinca tinca
+English springer, English springer spaniel
+cassette player
+chain saw, chainsaw
+church, church building
+French horn, horn
+garbage truck, dustcart
+gas pump, gasoline pump, petrol pump, island dispenser
+golf ball
+parachute, chute

data/Imagewoof.txt

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+Shih-Tzu
+Rhodesian ridgeback
+beagle
+English foxhound
+Border terrier
+Australian terrier
+golden retriever
+Old English sheepdog, bobtail
+Samoyed, Samoyede
+dingo, warrigal, warragal, Canis dingo

data/Stanford_dogs.txt

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+Chihuahua
+Japanese spaniel
+Maltese dog
+Pekinese
+Shih Tzu
+Blenheim spaniel
+papillon
+toy terrier
+Rhodesian ridgeback
+Afghan hound
+basset
+beagle
+bloodhound
+bluetick
+black and tan coonhound
+Walker hound
+English foxhound
+redbone
+borzoi
+Irish wolfhound
+Italian greyhound
+whippet
+Ibizan hound
+Norwegian elkhound
+otterhound
+Saluki
+Scottish deerhound
+Weimaraner
+Staffordshire bullterrier
+American Staffordshire terrier
+Bedlington terrier
+Border terrier
+Kerry blue terrier
+Irish terrier
+Norfolk terrier
+Norwich terrier
+Yorkshire terrier
+wire haired fox terrier
+Lakeland terrier
+Sealyham terrier
+Airedale
+cairn
+Australian terrier
+Dandie Dinmont
+Boston bull
+miniature schnauzer
+giant schnauzer
+standard schnauzer
+Scotch terrier
+Tibetan terrier
+silky terrier
+soft coated wheaten terrier
+West Highland white terrier
+Lhasa
+flat coated retriever
+curly coated retriever
+golden retriever
+Labrador retriever
+Chesapeake Bay retriever
+German short haired pointer
+vizsla
+English setter
+Irish setter
+Gordon setter
+Brittany spaniel
+clumber
+English springer
+Welsh springer spaniel
+cocker spaniel
+Sussex spaniel
+Irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+Old English sheepdog
+Shetland sheepdog
+collie
+Border collie
+Bouvier des Flandres
+Rottweiler
+German shepherd
+Doberman
+miniature pinscher
+Greater Swiss Mountain dog
+Bernese mountain dog
+Appenzeller
+EntleBucher
+boxer
+bull mastiff
+Tibetan mastiff
+French bulldog
+Great Dane
+Saint Bernard
+Eskimo dog
+malamute
+Siberian husky
+affenpinscher
+basenji
+pug
+Leonberg
+Newfoundland
+Great Pyrenees
+Samoyed
+Pomeranian
+chow
+keeshond
+Brabancon griffon
+Pembroke
+Cardigan
+toy poodle
+miniature poodle
+standard poodle
+Mexican hairless
+dingo
+dhole
+African hunting dog