initial commit
Browse files- .ipynb_checkpoints/app-checkpoint.py +139 -0
- app.py +139 -0
- configs/resnet.yaml +20 -0
- models/robust_resnet50.pt +3 -0
.ipynb_checkpoints/app-checkpoint.py
ADDED
@@ -0,0 +1,139 @@
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from datasets import load_dataset
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from torchvision import transforms
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import torch
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from timm import create_model
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from omegaconf import OmegaConf
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import faiss
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import pickle
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import gradio as gr
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import os
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import joblib
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import torch.nn as nn
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from typing import Dict, Iterable, Callable
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from torch import Tensor
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import torchvision
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from PIL import Image
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def get_model(args,arch,load_from,arch_path):
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if load_from == 'timm':
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model = create_model(arch,pretrained = True).to(args.PARAMETERS.device)
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elif load_from == 'torchvision':
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if arch == 'resnet50':
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model = torchvision.models.resnet50(pretrained=False)
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if len(arch_path)>0:
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print("Loading pretrained Model")
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model.load_state_dict(torch.load(arch_path,map_location='cpu')['state_dict'],strict = True)
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model.eval()
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return model
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def get_transform(args):
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return transforms.Compose([transforms.Resize([args.PARAMETERS.img_resize,args.PARAMETERS.img_resize]),
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transforms.CenterCrop([args.PARAMETERS.img_crop,args.PARAMETERS.img_crop]),
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transforms.ToTensor()])
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class FeatureExtractor(nn.Module):
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def __init__(self, model: nn.Module, layers: Iterable[str]):
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super().__init__()
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self.model = model
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self.layers = layers
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self._features = {layer: torch.empty(0) for layer in layers}
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for layer_id in layers:
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layer = dict([*self.model.named_modules()])[layer_id]
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layer.register_forward_hook(self.save_outputs_hook(layer_id))
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def save_outputs_hook(self, layer_id: str) -> Callable:
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def fn(_, __, output):
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self._features[layer_id] = output
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return fn
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def forward(self, x: Tensor) -> Dict[str, Tensor]:
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_ = self.model(x)
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return self._features
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def _load_dataset(args):
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if args.PARAMETERS.metric == 'L2':
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faiss_metric = faiss.METRIC_L2
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dataset = load_dataset(args.PARAMETERS.dataset,split = 'train')
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dataset = dataset.add_faiss_index(column=args.ROBUST.embedding_col,metric_type = faiss_metric)
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dataset = dataset.add_faiss_index(column=args.NONROBUST.embedding_col,metric_type = faiss_metric)
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return dataset
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args = OmegaConf.load("configs/resnet.yaml")
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wiki_dataset = _load_dataset(args)
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TRANSFORMS = get_transform(args)
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robust_model = get_model(args,args.ROBUST.arch,args.ROBUST.load_from,args.ROBUST.arch_path)
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non_robust_model = get_model(args,args.NONROBUST.arch,args.NONROBUST.load_from,args.NONROBUST.arch_path)
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fe_robust_model = FeatureExtractor(robust_model,layers = [args.ROBUST.layer])
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fe_nonrobust_model = FeatureExtractor(non_robust_model,layers = [args.NONROBUST.layer])
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# +
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def retrieval_fn(image,radio):
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try:
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image = Image.fromarray(image)
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except:
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pass
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image = TRANSFORMS(image).unsqueeze(0)
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image = image.to(args.PARAMETERS.device)
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if radio == 'robust':
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emb = fe_robust_model(image)[args.ROBUST.layer]
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emb = emb.view(1,-1).detach().cpu().numpy()
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scores, retrieved_examples = wiki_dataset.get_nearest_examples(index_name = args.ROBUST.embedding_col,
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query = emb,
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k = 3)
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elif radio == 'standard':
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emb = fe_nonrobust_model(image)[args.NONROBUST.layer]
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emb = emb.view(1,-1).detach().cpu().numpy()
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scores, retrieved_examples = wiki_dataset.get_nearest_examples(index_name = args.NONROBUST.embedding_col,
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query = emb,
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k=3)
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return scores,retrieved_examples
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def gradio_fn(image,radio):
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scores,retrieved_examples = retrieval_fn(image,radio)
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m = []
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for description,image,score in zip(retrieved_examples['description'],
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retrieved_examples['image'],
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scores):
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m.append(description)
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m.append(image)
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return m
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# -
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if __name__ == '__main__':
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demo = gr.Blocks()
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with demo:
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gr.Markdown("# Robust vs Standard Image Retrieval")
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with gr.Tabs():
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with gr.TabItem("Upload your Image"):
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with gr.Row():
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with gr.Column():
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with gr.Row():
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image_input = gr.Image(label="Input Image")
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with gr.Row():
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radio_button = gr.Radio(["robust","standard"],
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value = "robust",
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label = "OD Model")
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with gr.Row():
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calculate_button = gr.Button("Compute")
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with gr.Column():
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textbox1 = gr.Textbox(label = "Artist / Title / Style / Genre / Date")
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output_image1 = gr.Image(label="1st Best match")
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textbox2 = gr.Textbox(label = "Artist / Title / Style / Genre / Date")
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output_image2 = gr.Image(label="2nd Best match")
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textbox3 = gr.Textbox(label = "Artist / Title / Style / Genre / Date")
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output_image3 = gr.Image(label="3rd Best match")
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calculate_button.click(fn = gradio_fn,
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inputs = [image_input,radio_button],
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outputs = [textbox1,output_image1,textbox2,output_image2,textbox3,output_image3])
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demo.launch(share = False,debug = True)
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app.py
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1 |
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from datasets import load_dataset
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2 |
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from torchvision import transforms
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3 |
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import torch
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4 |
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from timm import create_model
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5 |
+
from omegaconf import OmegaConf
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6 |
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import faiss
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7 |
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import pickle
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8 |
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import gradio as gr
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9 |
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import os
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10 |
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import joblib
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11 |
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import torch.nn as nn
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12 |
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from typing import Dict, Iterable, Callable
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13 |
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from torch import Tensor
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14 |
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import torchvision
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from PIL import Image
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def get_model(args,arch,load_from,arch_path):
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if load_from == 'timm':
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model = create_model(arch,pretrained = True).to(args.PARAMETERS.device)
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elif load_from == 'torchvision':
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if arch == 'resnet50':
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model = torchvision.models.resnet50(pretrained=False)
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if len(arch_path)>0:
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print("Loading pretrained Model")
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model.load_state_dict(torch.load(arch_path,map_location='cpu')['state_dict'],strict = True)
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model.eval()
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return model
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def get_transform(args):
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return transforms.Compose([transforms.Resize([args.PARAMETERS.img_resize,args.PARAMETERS.img_resize]),
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transforms.CenterCrop([args.PARAMETERS.img_crop,args.PARAMETERS.img_crop]),
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transforms.ToTensor()])
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class FeatureExtractor(nn.Module):
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def __init__(self, model: nn.Module, layers: Iterable[str]):
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super().__init__()
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self.model = model
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self.layers = layers
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self._features = {layer: torch.empty(0) for layer in layers}
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for layer_id in layers:
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layer = dict([*self.model.named_modules()])[layer_id]
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layer.register_forward_hook(self.save_outputs_hook(layer_id))
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def save_outputs_hook(self, layer_id: str) -> Callable:
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def fn(_, __, output):
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self._features[layer_id] = output
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return fn
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def forward(self, x: Tensor) -> Dict[str, Tensor]:
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_ = self.model(x)
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return self._features
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def _load_dataset(args):
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if args.PARAMETERS.metric == 'L2':
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faiss_metric = faiss.METRIC_L2
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dataset = load_dataset(args.PARAMETERS.dataset,split = 'train')
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dataset = dataset.add_faiss_index(column=args.ROBUST.embedding_col,metric_type = faiss_metric)
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dataset = dataset.add_faiss_index(column=args.NONROBUST.embedding_col,metric_type = faiss_metric)
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return dataset
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args = OmegaConf.load("configs/resnet.yaml")
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wiki_dataset = _load_dataset(args)
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TRANSFORMS = get_transform(args)
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robust_model = get_model(args,args.ROBUST.arch,args.ROBUST.load_from,args.ROBUST.arch_path)
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non_robust_model = get_model(args,args.NONROBUST.arch,args.NONROBUST.load_from,args.NONROBUST.arch_path)
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fe_robust_model = FeatureExtractor(robust_model,layers = [args.ROBUST.layer])
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fe_nonrobust_model = FeatureExtractor(non_robust_model,layers = [args.NONROBUST.layer])
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# +
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def retrieval_fn(image,radio):
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try:
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image = Image.fromarray(image)
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except:
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pass
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image = TRANSFORMS(image).unsqueeze(0)
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image = image.to(args.PARAMETERS.device)
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if radio == 'robust':
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emb = fe_robust_model(image)[args.ROBUST.layer]
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emb = emb.view(1,-1).detach().cpu().numpy()
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scores, retrieved_examples = wiki_dataset.get_nearest_examples(index_name = args.ROBUST.embedding_col,
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query = emb,
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k = 3)
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elif radio == 'standard':
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emb = fe_nonrobust_model(image)[args.NONROBUST.layer]
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emb = emb.view(1,-1).detach().cpu().numpy()
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scores, retrieved_examples = wiki_dataset.get_nearest_examples(index_name = args.NONROBUST.embedding_col,
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query = emb,
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k=3)
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return scores,retrieved_examples
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def gradio_fn(image,radio):
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scores,retrieved_examples = retrieval_fn(image,radio)
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m = []
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for description,image,score in zip(retrieved_examples['description'],
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retrieved_examples['image'],
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scores):
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m.append(description)
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m.append(image)
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return m
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# -
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if __name__ == '__main__':
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demo = gr.Blocks()
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with demo:
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gr.Markdown("# Robust vs Standard Image Retrieval")
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with gr.Tabs():
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with gr.TabItem("Upload your Image"):
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with gr.Row():
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with gr.Column():
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with gr.Row():
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image_input = gr.Image(label="Input Image")
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with gr.Row():
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radio_button = gr.Radio(["robust","standard"],
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value = "robust",
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label = "OD Model")
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with gr.Row():
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calculate_button = gr.Button("Compute")
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with gr.Column():
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textbox1 = gr.Textbox(label = "Artist / Title / Style / Genre / Date")
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output_image1 = gr.Image(label="1st Best match")
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textbox2 = gr.Textbox(label = "Artist / Title / Style / Genre / Date")
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output_image2 = gr.Image(label="2nd Best match")
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textbox3 = gr.Textbox(label = "Artist / Title / Style / Genre / Date")
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output_image3 = gr.Image(label="3rd Best match")
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calculate_button.click(fn = gradio_fn,
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inputs = [image_input,radio_button],
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outputs = [textbox1,output_image1,textbox2,output_image2,textbox3,output_image3])
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demo.launch(share = False,debug = True)
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configs/resnet.yaml
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@@ -0,0 +1,20 @@
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PARAMETERS:
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img_resize: 256
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img_crop: 256
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num_workers: 72
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device: "cpu"
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dataset: "Artificio/WikiArt_mini_demos"
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metric: "L2"
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8 |
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ROBUST:
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arch: "resnet50"
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arch_path: "models/robust_resnet50.pt"
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12 |
+
load_from: "torchvision"
|
13 |
+
layer: "avgpool"
|
14 |
+
embedding_col: "resnet50_robust_features_2048"
|
15 |
+
NONROBUST:
|
16 |
+
arch: "resnet50"
|
17 |
+
arch_path : ""
|
18 |
+
load_from: "timm"
|
19 |
+
layer: "global_pool"
|
20 |
+
embedding_col: "resnet50_non_robust_features_2048"
|
models/robust_resnet50.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9ee26195016452801a20de92d7d8d26f42249b5074301a4aeff342eb565b3c47
|
3 |
+
size 102544897
|