Kevin Alberts
fb9ce46bd8
- Encoders: sparse, denoising, contractive and variational - Noise: gaussian
36 lines
1.4 KiB
Python
36 lines
1.4 KiB
Python
import logging
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import torch
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from typing import Optional
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from torch import Tensor
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from main import load_dotted_path
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from models.base_corruption import BaseCorruption, NoCorruption
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from models.base_encoder import BaseEncoder
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class DenoisingAutoEncoder(BaseEncoder):
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# Based on https://github.com/pranjaldatta/Denoising-Autoencoder-in-Pytorch/blob/master/DenoisingAutoencoder.ipynb
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name = "DenoisingAutoEncoder"
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def __init__(self, name: Optional[str] = None, input_shape: int = 0,
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input_corruption_model: BaseCorruption = NoCorruption):
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self.log = logging.getLogger(self.__class__.__name__)
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# Call superclass to initialize parameters.
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super(DenoisingAutoEncoder, self).__init__(name, input_shape)
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# Network, optimizer and loss function are the same as defined in the base encoder.
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# Corruption used for data corruption during training
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if isinstance(input_corruption_model, str):
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self.input_corruption_model = load_dotted_path(input_corruption_model)
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else:
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self.input_corruption_model = input_corruption_model
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# Need to corrupt features used for training, to add 'noise' to training data (comparison features are unchanged)
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def process_train_features(self, features: Tensor) -> Tensor:
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return torch.tensor([self.input_corruption_model.corrupt_image(x) for x in features], dtype=torch.float32)
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