MiaoDX

"""
KITTI stereo disparity 3 pixel error, corrected

REFERENCES:
    * [PMSNet code](https://github.com/JiaRenChang/PSMNet/blob/b6520ff9b77168e3dfe3485f8bf6a2a798361d85/finetune.py)
    * [type converter](https://github.com/traveller59/torchplus/blob/master/tools.py)

MATLAB code:
```
% disp_error.m

diaoenmao

def extract_patches_2d(img,patch_shape,step=[1.0,1.0],batch_first=False):
    patch_H, patch_W = patch_shape[0], patch_shape[1]
    if(img.size(2)<patch_H):
        num_padded_H_Top = (patch_H - img.size(2))//2
        num_padded_H_Bottom = patch_H - img.size(2) - num_padded_H_Top
        padding_H = nn.ConstantPad2d((0,0,num_padded_H_Top,num_padded_H_Bottom),0)
        img = padding_H(img)
    if(img.size(3)<patch_W):
        num_padded_W_Left = (patch_W - img.size(3))//2
        num_padded_W_Right = patch_W - img.size(3) - num_padded_W_Left

felixgwu

import torch
from torch import nn

__all__ = ['FCDenseNet', 'fcdensenet_tiny', 'fcdensenet56_nodrop',
           'fcdensenet56', 'fcdensenet67', 'fcdensenet103',
           'fcdensenet103_nodrop']


class DenseBlock(nn.Module):

shagunsodhani

Fractional Max-Pooling (FMP)

Introduction

• Link to Paper
• Spatial pooling layers are building blocks for Convolutional Neural Networks (CNNs).
• Input to pooling operation is a N_in x N_in matrix and output is a smaller matrix N_out x N_out.
• Pooling operation divides N_in x N_in square into N²_out pooling regions P_{i, j}.
• P_{i, j} ⊂ {1, 2, . . . , N_in} ∀ (i, j) ∈ {1, . . . , N_out}²