IBM/pytorchpipe

# -*- coding: utf-8 -*-
#
# 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.data_types.data_definition import DataDefinition


class ConcatenateTensor(Component):
    """
    Class responsible for concatenation of list of input tensors into a single tensor.

    """

    def __init__(self, name, config):
        """
        Initializes object.

        :param name: Loss name.
        :type name: str

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

        """
        # Call constructors of parent classes.
        Component.__init__(self, name, ConcatenateTensor, config)

        # Get key mappings.
        self.key_outputs = self.stream_keys["outputs"]

        # Load list of streams names (keys).
        self.input_stream_keys = self.config["input_streams"]
        if type(self.input_stream_keys) == str:
            self.input_stream_keys = self.input_stream_keys.replace(" ", "").split(",")
        

        # Get input shapes from configuration.
        # Assuming that it will be list of lists.
        self.input_stream_dims = [[int(x) for x in dims] for dims in self.config["input_dims"]]

        # Get output shape from configuration.
        self.output_dims = [int(x) for x in self.config["output_dims"]]

        # Get concatenation dimension.
        self.dim = self.config["dim"]

        # Set global variable - all dimensions ASIDE OF BATCH.
        self.globals["output_size"] = self.output_dims[1:]

    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 {
            keys: DataDefinition(dims, [torch.Tensor], "Batch of inputs [BATCH_SIZE x ...]")
                for (keys, dims) in zip(self.input_stream_keys, self.input_stream_dims)
            }

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

        :return: Empty dictionary.
        """
        return {
            self.key_outputs: DataDefinition(self.output_dims, [torch.Tensor], "Batch of outputs [BATCH_SIZE x ... ]"),
            }


    def __call__(self, data_streams):
        """
        Encodes "inputs" in the format of a single tensor.
        Stores reshaped tensor in "outputs" field of in data_streams.

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

            - "inputs": expected input field containing tensor [BATCH_SIZE x ...]

            - "outputs": added output field containing tensor [BATCH_SIZE x ...] 
        """
        # Get inputs to be concatentated.
        inputs = [data_streams[stream_key] for stream_key in self.input_stream_keys]

        #print("{}: input shape: {}, device: {}\n".format(self.name, [input.shape for input in inputs], [input.device for input in inputs]))


        # Concat.
        outputs = torch.cat(inputs, dim=self.dim)

        # Create the returned dict.
        data_streams.publish({self.key_outputs: outputs})