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import os
import numpy as np
import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
from .ocr import SpatialOCR_Module, SpatialGather_Module
from .resnetv1b import BasicBlockV1b, BottleneckV1b
relu_inplace = True
class HighResolutionModule(nn.Module):
def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
num_channels, fuse_method,multi_scale_output=True,
norm_layer=nn.BatchNorm2d, align_corners=True):
super(HighResolutionModule, self).__init__()
self._check_branches(num_branches, num_blocks, num_inchannels, num_channels)
self.num_inchannels = num_inchannels
self.fuse_method = fuse_method
self.num_branches = num_branches
self.norm_layer = norm_layer
self.align_corners = align_corners
self.multi_scale_output = multi_scale_output
self.branches = self._make_branches(
num_branches, blocks, num_blocks, num_channels)
self.fuse_layers = self._make_fuse_layers()
self.relu = nn.ReLU(inplace=relu_inplace)
def _check_branches(self, num_branches, num_blocks, num_inchannels, num_channels):
if num_branches != len(num_blocks):
error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(
num_branches, len(num_blocks))
raise ValueError(error_msg)
if num_branches != len(num_channels):
error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(
num_branches, len(num_channels))
raise ValueError(error_msg)
if num_branches != len(num_inchannels):
error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(
num_branches, len(num_inchannels))
raise ValueError(error_msg)
def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
stride=1):
downsample = None
if stride != 1 or \
self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.num_inchannels[branch_index],
num_channels[branch_index] * block.expansion,
kernel_size=1, stride=stride, bias=False),
self.norm_layer(num_channels[branch_index] * block.expansion),
)
layers = []
layers.append(block(self.num_inchannels[branch_index],
num_channels[branch_index], stride,
downsample=downsample, norm_layer=self.norm_layer))
self.num_inchannels[branch_index] = \
num_channels[branch_index] * block.expansion
for i in range(1, num_blocks[branch_index]):
layers.append(block(self.num_inchannels[branch_index],
num_channels[branch_index],
norm_layer=self.norm_layer))
return nn.Sequential(*layers)
def _make_branches(self, num_branches, block, num_blocks, num_channels):
branches = []
for i in range(num_branches):
branches.append(
self._make_one_branch(i, block, num_blocks, num_channels))
return nn.ModuleList(branches)
def _make_fuse_layers(self):
if self.num_branches == 1:
return None
num_branches = self.num_branches
num_inchannels = self.num_inchannels
fuse_layers = []
for i in range(num_branches if self.multi_scale_output else 1):
fuse_layer = []
for j in range(num_branches):
if j > i:
fuse_layer.append(nn.Sequential(
nn.Conv2d(in_channels=num_inchannels[j],
out_channels=num_inchannels[i],
kernel_size=1,
bias=False),
self.norm_layer(num_inchannels[i])))
elif j == i:
fuse_layer.append(None)
else:
conv3x3s = []
for k in range(i - j):
if k == i - j - 1:
num_outchannels_conv3x3 = num_inchannels[i]
conv3x3s.append(nn.Sequential(
nn.Conv2d(num_inchannels[j],
num_outchannels_conv3x3,
kernel_size=3, stride=2, padding=1, bias=False),
self.norm_layer(num_outchannels_conv3x3)))
else:
num_outchannels_conv3x3 = num_inchannels[j]
conv3x3s.append(nn.Sequential(
nn.Conv2d(num_inchannels[j],
num_outchannels_conv3x3,
kernel_size=3, stride=2, padding=1, bias=False),
self.norm_layer(num_outchannels_conv3x3),
nn.ReLU(inplace=relu_inplace)))
fuse_layer.append(nn.Sequential(*conv3x3s))
fuse_layers.append(nn.ModuleList(fuse_layer))
return nn.ModuleList(fuse_layers)
def get_num_inchannels(self):
return self.num_inchannels
def forward(self, x):
if self.num_branches == 1:
return [self.branches[0](x[0])]
for i in range(self.num_branches):
x[i] = self.branches[i](x[i])
x_fuse = []
for i in range(len(self.fuse_layers)):
y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
for j in range(1, self.num_branches):
if i == j:
y = y + x[j]
elif j > i:
width_output = x[i].shape[-1]
height_output = x[i].shape[-2]
y = y + F.interpolate(
self.fuse_layers[i][j](x[j]),
size=[height_output, width_output],
mode='bilinear', align_corners=self.align_corners)
else:
y = y + self.fuse_layers[i][j](x[j])
x_fuse.append(self.relu(y))
return x_fuse
class HighResolutionNet(nn.Module):
def __init__(self, width, num_classes, ocr_width=256, small=False,
norm_layer=nn.BatchNorm2d, align_corners=True):
super(HighResolutionNet, self).__init__()
self.norm_layer = norm_layer
self.width = width
self.ocr_width = ocr_width
self.align_corners = align_corners
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.bn1 = norm_layer(64)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.bn2 = norm_layer(64)
self.relu = nn.ReLU(inplace=relu_inplace)
num_blocks = 2 if small else 4
stage1_num_channels = 64
self.layer1 = self._make_layer(BottleneckV1b, 64, stage1_num_channels, blocks=num_blocks)
stage1_out_channel = BottleneckV1b.expansion * stage1_num_channels
self.stage2_num_branches = 2
num_channels = [width, 2 * width]
num_inchannels = [
num_channels[i] * BasicBlockV1b.expansion for i in range(len(num_channels))]
self.transition1 = self._make_transition_layer(
[stage1_out_channel], num_inchannels)
self.stage2, pre_stage_channels = self._make_stage(
BasicBlockV1b, num_inchannels=num_inchannels, num_modules=1, num_branches=self.stage2_num_branches,
num_blocks=2 * [num_blocks], num_channels=num_channels)
self.stage3_num_branches = 3
num_channels = [width, 2 * width, 4 * width]
num_inchannels = [
num_channels[i] * BasicBlockV1b.expansion for i in range(len(num_channels))]
self.transition2 = self._make_transition_layer(
pre_stage_channels, num_inchannels)
self.stage3, pre_stage_channels = self._make_stage(
BasicBlockV1b, num_inchannels=num_inchannels,
num_modules=3 if small else 4, num_branches=self.stage3_num_branches,
num_blocks=3 * [num_blocks], num_channels=num_channels)
self.stage4_num_branches = 4
num_channels = [width, 2 * width, 4 * width, 8 * width]
num_inchannels = [
num_channels[i] * BasicBlockV1b.expansion for i in range(len(num_channels))]
self.transition3 = self._make_transition_layer(
pre_stage_channels, num_inchannels)
self.stage4, pre_stage_channels = self._make_stage(
BasicBlockV1b, num_inchannels=num_inchannels, num_modules=2 if small else 3,
num_branches=self.stage4_num_branches,
num_blocks=4 * [num_blocks], num_channels=num_channels)
last_inp_channels = np.int(np.sum(pre_stage_channels))
if self.ocr_width > 0:
ocr_mid_channels = 2 * self.ocr_width
ocr_key_channels = self.ocr_width
self.conv3x3_ocr = nn.Sequential(
nn.Conv2d(last_inp_channels, ocr_mid_channels,
kernel_size=3, stride=1, padding=1),
norm_layer(ocr_mid_channels),
nn.ReLU(inplace=relu_inplace),
)
self.ocr_gather_head = SpatialGather_Module(num_classes)
self.ocr_distri_head = SpatialOCR_Module(in_channels=ocr_mid_channels,
key_channels=ocr_key_channels,
out_channels=ocr_mid_channels,
scale=1,
dropout=0.05,
norm_layer=norm_layer,
align_corners=align_corners)
self.cls_head = nn.Conv2d(
ocr_mid_channels, num_classes, kernel_size=1, stride=1, padding=0, bias=True)
self.aux_head = nn.Sequential(
nn.Conv2d(last_inp_channels, last_inp_channels,
kernel_size=1, stride=1, padding=0),
norm_layer(last_inp_channels),
nn.ReLU(inplace=relu_inplace),
nn.Conv2d(last_inp_channels, num_classes,
kernel_size=1, stride=1, padding=0, bias=True)
)
else:
self.cls_head = nn.Sequential(
nn.Conv2d(last_inp_channels, last_inp_channels,
kernel_size=3, stride=1, padding=1),
norm_layer(last_inp_channels),
nn.ReLU(inplace=relu_inplace),
nn.Conv2d(last_inp_channels, num_classes,
kernel_size=1, stride=1, padding=0, bias=True)
)
def _make_transition_layer(
self, num_channels_pre_layer, num_channels_cur_layer):
num_branches_cur = len(num_channels_cur_layer)
num_branches_pre = len(num_channels_pre_layer)
transition_layers = []
for i in range(num_branches_cur):
if i < num_branches_pre:
if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
transition_layers.append(nn.Sequential(
nn.Conv2d(num_channels_pre_layer[i],
num_channels_cur_layer[i],
kernel_size=3,
stride=1,
padding=1,
bias=False),
self.norm_layer(num_channels_cur_layer[i]),
nn.ReLU(inplace=relu_inplace)))
else:
transition_layers.append(None)
else:
conv3x3s = []
for j in range(i + 1 - num_branches_pre):
inchannels = num_channels_pre_layer[-1]
outchannels = num_channels_cur_layer[i] \
if j == i - num_branches_pre else inchannels
conv3x3s.append(nn.Sequential(
nn.Conv2d(inchannels, outchannels,
kernel_size=3, stride=2, padding=1, bias=False),
self.norm_layer(outchannels),
nn.ReLU(inplace=relu_inplace)))
transition_layers.append(nn.Sequential(*conv3x3s))
return nn.ModuleList(transition_layers)
def _make_layer(self, block, inplanes, planes, blocks, stride=1):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
self.norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(inplanes, planes, stride,
downsample=downsample, norm_layer=self.norm_layer))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(inplanes, planes, norm_layer=self.norm_layer))
return nn.Sequential(*layers)
def _make_stage(self, block, num_inchannels,
num_modules, num_branches, num_blocks, num_channels,
fuse_method='SUM',
multi_scale_output=True):
modules = []
for i in range(num_modules):
# multi_scale_output is only used last module
if not multi_scale_output and i == num_modules - 1:
reset_multi_scale_output = False
else:
reset_multi_scale_output = True
modules.append(
HighResolutionModule(num_branches,
block,
num_blocks,
num_inchannels,
num_channels,
fuse_method,
reset_multi_scale_output,
norm_layer=self.norm_layer,
align_corners=self.align_corners)
)
num_inchannels = modules[-1].get_num_inchannels()
return nn.Sequential(*modules), num_inchannels
def forward(self, x, additional_features=None):
feats = self.compute_hrnet_feats(x, additional_features)
if self.ocr_width > 0:
out_aux = self.aux_head(feats)
feats = self.conv3x3_ocr(feats)
context = self.ocr_gather_head(feats, out_aux)
feats = self.ocr_distri_head(feats, context)
out = self.cls_head(feats)
return [out, out_aux]
else:
return [self.cls_head(feats), None]
def compute_hrnet_feats(self, x, additional_features):
x = self.compute_pre_stage_features(x, additional_features)
x = self.layer1(x)
x_list = []
for i in range(self.stage2_num_branches):
if self.transition1[i] is not None:
x_list.append(self.transition1[i](x))
else:
x_list.append(x)
y_list = self.stage2(x_list)
x_list = []
for i in range(self.stage3_num_branches):
if self.transition2[i] is not None:
if i < self.stage2_num_branches:
x_list.append(self.transition2[i](y_list[i]))
else:
x_list.append(self.transition2[i](y_list[-1]))
else:
x_list.append(y_list[i])
y_list = self.stage3(x_list)
x_list = []
for i in range(self.stage4_num_branches):
if self.transition3[i] is not None:
if i < self.stage3_num_branches:
x_list.append(self.transition3[i](y_list[i]))
else:
x_list.append(self.transition3[i](y_list[-1]))
else:
x_list.append(y_list[i])
x = self.stage4(x_list)
return self.aggregate_hrnet_features(x)
def compute_pre_stage_features(self, x, additional_features):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
if additional_features is not None:
x = x + additional_features
x = self.conv2(x)
x = self.bn2(x)
return self.relu(x)
def aggregate_hrnet_features(self, x):
# Upsampling
x0_h, x0_w = x[0].size(2), x[0].size(3)
x1 = F.interpolate(x[1], size=(x0_h, x0_w),
mode='bilinear', align_corners=self.align_corners)
x2 = F.interpolate(x[2], size=(x0_h, x0_w),
mode='bilinear', align_corners=self.align_corners)
x3 = F.interpolate(x[3], size=(x0_h, x0_w),
mode='bilinear', align_corners=self.align_corners)
return torch.cat([x[0], x1, x2, x3], 1)
def load_pretrained_weights(self, pretrained_path=''):
model_dict = self.state_dict()
if not os.path.exists(pretrained_path):
print(f'\nFile "{pretrained_path}" does not exist.')
print('You need to specify the correct path to the pre-trained weights.\n'
'You can download the weights for HRNet from the repository:\n'
'https://github.com/HRNet/HRNet-Image-Classification')
exit(1)
pretrained_dict = torch.load(pretrained_path, map_location={'cuda:0': 'cpu'})
pretrained_dict = {k.replace('last_layer', 'aux_head').replace('model.', ''): v for k, v in
pretrained_dict.items()}
pretrained_dict = {k: v for k, v in pretrained_dict.items()
if k in model_dict.keys()}
model_dict.update(pretrained_dict)
self.load_state_dict(model_dict)
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