IBM/pytorchpipe

# Copyright (C) tkornuta, IBM Corporation 2019
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

__author__ = "Tomasz Kornuta"

import torch

from ptp.components.component import Component
from ptp.components.mixins.word_mappings import WordMappings
from ptp.data_types.data_definition import DataDefinition


class SentenceOneHotEncoder(Component, WordMappings):
    """
    Class responsible for encoding of samples being sequences of words using 1-hot encoding.
    """
    def __init__(self, name, config):
        """
        Initializes the component.

        :param name: Component name (read from configuration file).
        :type name: str

        :param config: Dictionary of parameters (read from the configuration ``.yaml`` file).
        :type config: :py:class:`ptp.configuration.ConfigInterface`

        """
        # Call constructor(s) of parent class(es) - in the right order!
        Component.__init__(self, name, SentenceOneHotEncoder, config)
        WordMappings.__init__(self)

        # Set key mappings.
        self.key_inputs = self.stream_keys["inputs"]
        self.key_outputs = self.stream_keys["outputs"]


    def input_data_definitions(self):
        """ 
        Function returns a dictionary with definitions of input data that are required by the component.

        :return: dictionary containing input data definitions (each of type :py:class:`ptp.utils.DataDefinition`).
        """
        return {
            self.key_inputs: DataDefinition([-1, -1, 1], [list, list, str], "Batch of sentences, each represented as a list of words [BATCH_SIZE] x [SEQ_LENGTH] x [string]"),
            }

    def output_data_definitions(self):
        """ 
        Function returns a dictionary with definitions of output data produced the component.

        :return: dictionary containing output data definitions (each of type :py:class:`ptp.utils.DataDefinition`).
        """
        return {
            self.key_outputs: DataDefinition([-1, -1, len(self.word_to_ix)], [list, list, torch.Tensor], "Batch of sentences, each represented as a list of vectors [BATCH_SIZE] x [SEQ_LENGTH] x [VOCABULARY_SIZE]"),
            }

    def __call__(self, data_streams):
        """
        Encodes "inputs" in the format of list of tokens (for a single sample)
        Stores result in "encoded_inputs" field of in data_streams.

        :param data_streams: :py:class:`ptp.utils.DataStreams` object containing (among others):

            - "inputs": expected input field containing list of words [BATCH_SIZE] x [SEQ_SIZE] x [string]

            - "encoded_targets": added output field containing list of indices [BATCH_SIZE] x [SEQ_SIZE] x [VOCABULARY_SIZE1] 
        """
        # Get inputs to be encoded.
        inputs = data_streams[self.key_inputs]
        outputs_list = []
        # Process samples 1 by one.
        for sample in inputs:
            assert isinstance(sample, (list,)), 'This encoder requires input sample to contain a list of words'
            # Process list.
            output_sample = []
            # Encode sample (list of words)
            for token in sample:
                # Create empty vector.
                output_token = torch.zeros(len(self.word_to_ix)).type(self.app_state.FloatTensor)
                # Add one for given word
                output_token[self.word_to_ix[token]] += 1
                # Add to outputs.
                output_sample.append( output_token )

            outputs_list.append(output_sample)
        # Create the returned dict.
        data_streams.publish({self.key_outputs: outputs_list})