88 строки
4.0 KiB
Python
88 строки
4.0 KiB
Python
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT license.
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import torch
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import torch.nn as nn
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import sys
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import logging
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sys.path.append('../')
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from settings import LossOperationType
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from torch.nn import CrossEntropyLoss, L1Loss, MSELoss, NLLLoss, PoissonNLLLoss, NLLLoss2d, KLDivLoss, BCELoss, BCEWithLogitsLoss, MarginRankingLoss, HingeEmbeddingLoss, MultiLabelMarginLoss, SmoothL1Loss, SoftMarginLoss, MultiLabelSoftMarginLoss, CosineEmbeddingLoss, MultiMarginLoss, TripletMarginLoss
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class Loss(nn.Module):
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'''
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For support multi_task or multi_output, the loss type changes to list.
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Using class Loss for parsing and constructing the loss list.
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Args:
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loss_conf: the loss for multi_task or multi_output.
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multi_loss_op: the operation for multi_loss
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losses: list type. Each element is single loss.
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eg: "loss": {
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"multi_loss_op": "weighted_sum",
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"losses": [
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{
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"type": "CrossEntropyLoss",
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"conf": {
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"gamma": 0,
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"alpha": 0.5,
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"size_average": true
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},
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"inputs": ["start_output", "start_label"]
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},
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{
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"type": "CrossEntropyLoss",
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"conf": {
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"gamma": 0,
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"alpha": 0.5,
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"size_average": true
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},
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"inputs": ["end_output", "end_label"]
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}
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],
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"weights": [0.5, 0.5]
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}
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'''
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def __init__(self, **kwargs):
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super(Loss, self).__init__()
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self.loss_fn = nn.ModuleList()
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self.loss_input = []
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self.weights = kwargs['weights'] if 'weights' in kwargs else None
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support_loss_op = set(LossOperationType.__members__.keys())
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if kwargs['multiLoss']:
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# check multi_loss_op
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if not kwargs['multi_loss_op'].lower() in support_loss_op:
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raise Exception("The multi_loss_op %s is not supported. Supported multi_loss_op are: %s"
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% (kwargs['multi_loss_op'], support_loss_op))
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self.multi_loss_op = kwargs['multi_loss_op']
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# check single loss inputs
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for single_loss in kwargs['losses']:
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if (not single_loss['inputs'][0] in kwargs['output_layer_id']) or (not single_loss['inputs'][1] in kwargs['answer_column_name']):
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raise Exception("The loss inputs are excepted to be part of output_layer_id and targets!")
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self.loss_fn.append(eval(single_loss['type'])(**single_loss['conf']))
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self.loss_input.append(single_loss['inputs'])
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def forward(self, model_outputs, targets):
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'''
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compute multi_loss according to multi_loss_op
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:param model_outputs: the representation of model output layer
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:type: dict {output_layer_id: output layer data}
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:param targets: the label of raw data
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:type: dict {target: data}
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:return:
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'''
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all_losses = []
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result_loss = 0.0
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for index, single_loss_fn in enumerate(self.loss_fn):
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all_losses.append(single_loss_fn(model_outputs[self.loss_input[index][0]], targets[self.loss_input[index][1]]))
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if hasattr(self, 'multi_loss_op'):
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if LossOperationType[self.multi_loss_op.lower()] == LossOperationType.weighted_sum:
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for index, single_loss in enumerate(all_losses):
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result_loss += (self.weights[index]*single_loss)
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else:
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result_loss = all_losses[0]
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return result_loss
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