sedna/examples/lifelong_learning/robot_dog_delivery/RFNet/models/util.py

import torch
import torch.nn as nn
import torch.nn.functional as F

upsample = lambda x, size: F.interpolate(x, size, mode='bilinear', align_corners=False)
batchnorm_momentum = 0.01 / 2


def get_n_params(parameters):
    pp = 0
    for p in parameters:
        nn = 1
        for s in list(p.size()):
            nn = nn * s
        pp += nn
    return pp


class _BNReluConv(nn.Sequential):
    def __init__(self, num_maps_in, num_maps_out, k=3, batch_norm=True, bn_momentum=0.1, bias=False, dilation=1):
        super(_BNReluConv, self).__init__()
        if batch_norm:
            self.add_module('norm', nn.BatchNorm2d(num_maps_in, momentum=bn_momentum))
        self.add_module('relu', nn.ReLU(inplace=batch_norm is True))
        padding = k // 2  # same conv
        self.add_module('conv', nn.Conv2d(num_maps_in, num_maps_out,
                                          kernel_size=k, padding=padding, bias=bias, dilation=dilation))


class _Upsample(nn.Module):
    def __init__(self, num_maps_in, skip_maps_in, num_maps_out, use_bn=True, k=3):
        super(_Upsample, self).__init__()
        self.bottleneck = _BNReluConv(skip_maps_in, num_maps_in, k=1, batch_norm=use_bn)
        self.blend_conv = _BNReluConv(num_maps_in, num_maps_out, k=k, batch_norm=use_bn)

    def forward(self, x, skip):
        skip = self.bottleneck.forward(skip)
        skip_size = skip.size()[2:4]
        x = upsample(x, skip_size)
        x = x + skip
        x = self.blend_conv.forward(x)
        return x


class SpatialPyramidPooling(nn.Module):
    def __init__(self, num_maps_in, num_levels, bt_size=512, level_size=128, out_size=128,
                 grids=(6, 3, 2, 1), square_grid=False, bn_momentum=0.1, use_bn=True):
        super(SpatialPyramidPooling, self).__init__()
        self.grids = grids
        self.square_grid = square_grid
        self.spp = nn.Sequential()
        self.spp.add_module('spp_bn',
                            _BNReluConv(num_maps_in, bt_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn))
        num_features = bt_size
        final_size = num_features
        for i in range(num_levels):
            final_size += level_size
            self.spp.add_module('spp' + str(i),
                                _BNReluConv(num_features, level_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn))
        self.spp.add_module('spp_fuse',
                            _BNReluConv(final_size, out_size, k=1, bn_momentum=bn_momentum, batch_norm=use_bn))

    def forward(self, x):
        levels = []
        target_size = x.size()[2:4]

        ar = target_size[1] / target_size[0]

        x = self.spp[0].forward(x)
        levels.append(x)
        num = len(self.spp) - 1

        for i in range(1, num):
            if not self.square_grid:
                grid_size = (self.grids[i - 1], max(1, round(ar * self.grids[i - 1])))
                x_pooled = F.adaptive_avg_pool2d(x, grid_size)
            else:
               x_pooled = F.adaptive_avg_pool2d(x, self.grids[i - 1])
            level = self.spp[i].forward(x_pooled)

            level = upsample(level, target_size)
            levels.append(level)
        x = torch.cat(levels, 1)
        x = self.spp[-1].forward(x)
        return x


class _UpsampleBlend(nn.Module):
    def __init__(self, num_features, use_bn=True):
        super(_UpsampleBlend, self).__init__()
        self.blend_conv = _BNReluConv(num_features, num_features, k=3, batch_norm=use_bn)

    def forward(self, x, skip):
        skip_size = skip.size()[2:4]
        x = upsample(x, skip_size)
        x = x + skip
        x = self.blend_conv.forward(x)
        return x