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import torch |
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from configs.optim_params import EvaluatedDict |
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dataset_constants = {"CUB2011":{"num_classes":200}, |
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"TravelingBirds":{"num_classes":200}, |
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"ImageNet":{"num_classes":1000}, |
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"StanfordCars":{"num_classes":196}, |
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"FGVCAircraft": {"num_classes":100}} |
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normalize_params = {"CUB2011":{"mean": torch.tensor([0.4853, 0.4964, 0.4295]),"std":torch.tensor([0.2300, 0.2258, 0.2625])}, |
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"TravelingBirds":{"mean": torch.tensor([0.4584, 0.4369, 0.3957]),"std":torch.tensor([0.2610, 0.2569, 0.2722])}, |
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"ImageNet":{'mean': torch.tensor([0.485, 0.456, 0.406]),'std': torch.tensor([0.229, 0.224, 0.225])} , |
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"StanfordCars":{'mean': torch.tensor([0.4593, 0.4466, 0.4453]),'std': torch.tensor([0.2920, 0.2910, 0.2988])} , |
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"FGVCAircraft":{'mean': torch.tensor([0.4827, 0.5130, 0.5352]), |
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'std': torch.tensor([0.2236, 0.2170, 0.2478]),} |
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} |
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dense_batch_size = EvaluatedDict({False: 16,True: 1024,}, lambda x: x == "ImageNet") |
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ft_batch_size = EvaluatedDict({False: 16,True: 1024,}, lambda x: x == "ImageNet") |
