slahmr-test / lib /python3.9 /site-packages /caffe2 /python /layers /sparse_dropout_with_replacement.py
| from caffe2.python import schema | |
| from caffe2.python.layers.layers import ( | |
| IdList, | |
| ModelLayer, | |
| ) | |
| # Model layer for implementing probabilistic replacement of elements in | |
| # IdLists. Takes probabilities for train, eval and predict nets as input, as | |
| # well as the replacement value when dropout happens. For features we may have | |
| # available to us in train net but not in predict net, we'd set dropout | |
| # probability for predict net to be 1.0 and set the feature to the replacement | |
| # value given here. This way, the value is tied to the particular model and not | |
| # to any specific logic in feature processing in serving. | |
| # Consider the following example where X is the values in the IdList and Lengths | |
| # is the number of values corresponding to each example. | |
| # X: [1, 2, 3, 4, 5] | |
| # Lengths: [2, 3] | |
| # This IdList contains 2 items of lengths 2, 3. Let's assume we used a ratio of | |
| # 0.5 and ended up dropping out 2nd example, and used a replacement value of -1. | |
| # We will end up with the following IdList. | |
| # | |
| # Y: [1, 2, -1] | |
| # OutputLengths: [2, 1] | |
| # where the 2nd item values [3,4,5] were replaced with [-1] and the length got | |
| # set to 1. | |
| class SparseDropoutWithReplacement(ModelLayer): | |
| def __init__( | |
| self, | |
| model, | |
| input_record, | |
| dropout_prob_train, | |
| dropout_prob_eval, | |
| dropout_prob_predict, | |
| replacement_value, | |
| name='sparse_dropout', | |
| **kwargs): | |
| super(SparseDropoutWithReplacement, self).__init__(model, name, input_record, **kwargs) | |
| assert schema.equal_schemas(input_record, IdList), "Incorrect input type" | |
| self.dropout_prob_train = float(dropout_prob_train) | |
| self.dropout_prob_eval = float(dropout_prob_eval) | |
| self.dropout_prob_predict = float(dropout_prob_predict) | |
| self.replacement_value = int(replacement_value) | |
| assert (self.dropout_prob_train >= 0 and | |
| self.dropout_prob_train <= 1.0), \ | |
| "Expected 0 <= dropout_prob_train <= 1, but got %s" \ | |
| % self.dropout_prob_train | |
| assert (self.dropout_prob_eval >= 0 and | |
| self.dropout_prob_eval <= 1.0), \ | |
| "Expected 0 <= dropout_prob_eval <= 1, but got %s" \ | |
| % dropout_prob_eval | |
| assert (self.dropout_prob_predict >= 0 and | |
| self.dropout_prob_predict <= 1.0), \ | |
| "Expected 0 <= dropout_prob_predict <= 1, but got %s" \ | |
| % dropout_prob_predict | |
| assert(self.dropout_prob_train > 0 or | |
| self.dropout_prob_eval > 0 or | |
| self.dropout_prob_predict > 0), \ | |
| "Ratios all set to 0.0 for train, eval and predict" | |
| self.output_schema = schema.NewRecord(model.net, IdList) | |
| if input_record.lengths.metadata: | |
| self.output_schema.lengths.set_metadata( | |
| input_record.lengths.metadata) | |
| if input_record.items.metadata: | |
| self.output_schema.items.set_metadata( | |
| input_record.items.metadata) | |
| def _add_ops(self, net, ratio): | |
| input_values_blob = self.input_record.items() | |
| input_lengths_blob = self.input_record.lengths() | |
| output_lengths_blob = self.output_schema.lengths() | |
| output_values_blob = self.output_schema.items() | |
| net.SparseDropoutWithReplacement([input_values_blob, | |
| input_lengths_blob], | |
| [output_values_blob, | |
| output_lengths_blob], | |
| ratio=ratio, | |
| replacement_value=self.replacement_value) | |
| def add_train_ops(self, net): | |
| self._add_ops(net, self.dropout_prob_train) | |
| def add_eval_ops(self, net): | |
| self._add_ops(net, self.dropout_prob_eval) | |
| def add_ops(self, net): | |
| self._add_ops(net, self.dropout_prob_predict) | |