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-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 diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..ba88edf63ce6ddad4aec6d9db6e405c39a07f921 --- /dev/null +++ b/app.py @@ -0,0 +1,309 @@ +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) diff --git a/data/Imagenette.txt b/data/Imagenette.txt new file mode 100644 index 0000000000000000000000000000000000000000..705744284f4bafe7e737a5a0f2db674e0c3e1db9 --- /dev/null +++ b/data/Imagenette.txt @@ -0,0 +1,10 @@ +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 \ No newline at end of file diff --git a/data/Imagenette/val/n01440764/ILSVRC2012_val_00009111.JPEG b/data/Imagenette/val/n01440764/ILSVRC2012_val_00009111.JPEG new file mode 100644 index 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