Spaces:
Runtime error
Runtime error
File size: 5,481 Bytes
c7f097c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..net_util import *
class HourGlass(nn.Module):
def __init__(self, num_modules, depth, num_features, norm='batch'):
super(HourGlass, self).__init__()
self.num_modules = num_modules
self.depth = depth
self.features = num_features
self.norm = norm
self._generate_network(self.depth)
def _generate_network(self, level):
self.add_module('b1_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
self.add_module('b2_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
if level > 1:
self._generate_network(level - 1)
else:
self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
self.add_module('b3_' + str(level), ConvBlock(self.features, self.features, norm=self.norm))
def _forward(self, level, inp):
# Upper branch
up1 = inp
up1 = self._modules['b1_' + str(level)](up1)
# Lower branch
low1 = F.avg_pool2d(inp, 2, stride=2)
low1 = self._modules['b2_' + str(level)](low1)
if level > 1:
low2 = self._forward(level - 1, low1)
else:
low2 = low1
low2 = self._modules['b2_plus_' + str(level)](low2)
low3 = low2
low3 = self._modules['b3_' + str(level)](low3)
# NOTE: for newer PyTorch (1.3~), it seems that training results are degraded due to implementation diff in F.grid_sample
# if the pretrained model behaves weirdly, switch with the commented line.
# NOTE: I also found that "bicubic" works better.
up2 = F.interpolate(low3, scale_factor=2, mode='bicubic', align_corners=True)
# up2 = F.interpolate(low3, scale_factor=2, mode='nearest)
return up1 + up2
def forward(self, x):
return self._forward(self.depth, x)
class HGFilter(nn.Module):
def __init__(self, opt):
super(HGFilter, self).__init__()
self.num_modules = opt.num_stack
self.opt = opt
# Base part
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
if self.opt.norm == 'batch':
self.bn1 = nn.BatchNorm2d(64)
elif self.opt.norm == 'group':
self.bn1 = nn.GroupNorm(32, 64)
if self.opt.hg_down == 'conv64':
self.conv2 = ConvBlock(64, 64, self.opt.norm)
self.down_conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
elif self.opt.hg_down == 'conv128':
self.conv2 = ConvBlock(64, 128, self.opt.norm)
self.down_conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1)
elif self.opt.hg_down == 'ave_pool':
self.conv2 = ConvBlock(64, 128, self.opt.norm)
else:
raise NameError('Unknown Fan Filter setting!')
self.conv3 = ConvBlock(128, 128, self.opt.norm)
self.conv4 = ConvBlock(128, 256, self.opt.norm)
# Stacking part
for hg_module in range(self.num_modules):
self.add_module('m' + str(hg_module), HourGlass(1, opt.num_hourglass, 256, self.opt.norm))
self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256, self.opt.norm))
self.add_module('conv_last' + str(hg_module),
nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
if self.opt.norm == 'batch':
self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
elif self.opt.norm == 'group':
self.add_module('bn_end' + str(hg_module), nn.GroupNorm(32, 256))
self.add_module('l' + str(hg_module), nn.Conv2d(256,
opt.hourglass_dim, kernel_size=1, stride=1, padding=0))
if hg_module < self.num_modules - 1:
self.add_module(
'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
self.add_module('al' + str(hg_module), nn.Conv2d(opt.hourglass_dim,
256, kernel_size=1, stride=1, padding=0))
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)), True)
tmpx = x
if self.opt.hg_down == 'ave_pool':
x = F.avg_pool2d(self.conv2(x), 2, stride=2)
elif self.opt.hg_down in ['conv64', 'conv128']:
x = self.conv2(x)
x = self.down_conv2(x)
else:
raise NameError('Unknown Fan Filter setting!')
normx = x
x = self.conv3(x)
x = self.conv4(x)
previous = x
outputs = []
for i in range(self.num_modules):
hg = self._modules['m' + str(i)](previous)
ll = hg
ll = self._modules['top_m_' + str(i)](ll)
ll = F.relu(self._modules['bn_end' + str(i)]
(self._modules['conv_last' + str(i)](ll)), True)
# Predict heatmaps
tmp_out = self._modules['l' + str(i)](ll)
outputs.append(tmp_out)
if i < self.num_modules - 1:
ll = self._modules['bl' + str(i)](ll)
tmp_out_ = self._modules['al' + str(i)](tmp_out)
previous = previous + ll + tmp_out_
return outputs, tmpx.detach(), normx
|