#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# MIT License
#
# Copyright (c) 2018 Kim Seonghyeon
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# ------------------------------------------------------------------------------
#
# Copyright (C) IBM Corporation 2018
#
# 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.
"""
s_read_unit.py:
- Implementation of the :py:class:`ReadUnit` for the ``S-MAC`` network (simplified MAC).
- Cf https://arxiv.org/abs/1803.03067 for the reference MAC paper (Hudson and Manning, ICLR 2018).
"""
__author__ = "Vincent Marois & T.S. Jayram"
import torch
from torch.nn import Module
from miprometheus.models.mac.utils_mac import linear
[docs]class ReadUnit(Module):
"""
Implementation of the :py:class:`ReadUnit` for the ``S-MAC`` model.
.. note::
This implementation is part of a simplified version of the MAC network, where modifications regarding \
the different units have been done to reduce the number of linear layers (and thus number of parameters).
This is part of a submission to the ViGIL workshop for NIPS 2018. Feel free to use this model and refer to it \
with the following BibTex:
::
@article{marois2018transfer,
title={On transfer learning using a MAC model variant},
author={Marois, Vincent and Jayram, TS and Albouy, Vincent and Kornuta, Tomasz and Bouhadjar, Younes and Ozcan, Ahmet S},
journal={arXiv preprint arXiv:1811.06529},
year={2018}
}
"""
[docs] def __init__(self, dim):
"""
Constructor for the :py:class:`ReadUnit` of the ``S-MAC`` model.
:param dim: global 'd' hidden dimension.
:type dim: int
"""
# call base constructor
super(ReadUnit, self).__init__()
# linear layer to define I'(i,h,w) elements (r2 equation)
self.concat_layer = linear(dim, dim, bias=True)
# linear layer to compute attention weights
self.attn = linear(dim, 1, bias=False)
[docs] def forward(self, memory_state, ctrl_state, kb_proj):
"""
Forward pass of the :py:class:`ReadUnit`. Assuming 1 scalar attention weight per knowledge base elements.
:param memory_state: Memory state, shape `[batch_size x mem_dim]`.
:type memory_state: :py:class:`torch.Tensor`
:param ctrl_state: Control state, shape `[batch_size x ctrl_dim]`.
:type ctrl_state: :py:class:`torch.Tensor`
:param kb_proj: Linear projection of the image representation (output of CNN), \
shape `[batch_size x dim x (feat_H * feat_W)]`.
:type kb_proj: :py:class:`torch.Tensor`
:return: current read vector, shape `[batch_size x read_dim]` (:py:class:`torch.Tensor`)
"""
# assume mem_dim = ctrl_dim = nb_kernels = dim
memory_state = memory_state.unsqueeze(2)
# memory_state: [batch_size x dim x 1]
# compute I(i,h,w) elements (r1 equation)
# [batch_size x dim x 1] * [batch_size x dim x (H*W)] -> [batch_size x dim x (H*W)]
I_elements = memory_state * kb_proj
# compute I' elements (r2 equation)
concat = self.concat_layer(I_elements.permute(0, 2, 1)) # [batch_size x (H*W) x dim]
concat = concat + kb_proj.permute(0, 2, 1) # [batch_size x (H*W) x dim]
# compute the attention weights
rai = self.attn(concat * ctrl_state.unsqueeze(1)).squeeze(2) # [batch_size x (H*W)]
# This is for the time plot
self.rvi = torch.nn.functional.softmax(rai, 1).unsqueeze(1) # [batch_size x 1 x (H*W)]
# apply attn weights on knowledge base elements & sum on (H*W)
read_vector = (self.rvi * kb_proj).sum(2) # [batch_size x dim]
return read_vector