#!/usr/bin/env python3
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"""
image_encoding.py: Implementation of the image processing done by the input unit in the MAC network. See \
https://arxiv.org/pdf/1803.03067.pdf for the reference paper.
"""
__author__ = "Vincent Marois"
import torch
from torch.nn import Module
[docs]class ImageProcessing(Module):
"""
Image encoding using a 2-layers CNN assuming the images have been already \
preprocessed by `ResNet101`.
"""
[docs] def __init__(self, dim):
"""
Constructor for the 2-layers CNN.
:param dim: global 'd' hidden dimension
:type dim: int
"""
# call base constructor
super(ImageProcessing, self).__init__()
self.conv = torch.nn.Sequential(torch.nn.Conv2d(1024, dim, 3, padding=1),
torch.nn.ELU(),
torch.nn.Conv2d(dim, dim, 3, padding=1),
torch.nn.ELU())
# specify weights initialization
torch.nn.init.kaiming_uniform_(self.conv[0].weight)
self.conv[0].bias.data.zero_()
torch.nn.init.kaiming_uniform_(self.conv[2].weight)
self.conv[2].bias.data.zero_()
[docs] def forward(self, feature_maps):
"""
Apply the constructed CNN model on the feature maps (coming from \
`ResNet101`).
:param feature_maps: [batch_size x nb_kernels x feat_H x feat_W] coming from `ResNet101`. \
Should have [nb_kernels x feat_H x feat_W] = [1024 x 14 x 14].
:type feature_maps: torch.tensor
:return feature_maps: feature map, shape [batch_size, dim, new_height, new_width]
"""
new_feature_maps = self.conv(feature_maps)
return new_feature_maps