def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None,
plugins=None):
"""Bottleneck block for ResNet.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(Bottleneck, self).__init__()
assert style in ['pytorch', 'caffe']
assert dcn is None or isinstance(dcn, dict)
assert plugins is None or isinstance(plugins, list)
if plugins is not None:
allowed_position = ['after_conv1', 'after_conv2', 'after_conv3']
assert all(p['position'] in allowed_position for p in plugins)
self.inplanes = inplanes
self.planes = planes
self.stride = stride
self.dilation = dilation
self.style = style
self.with_cp = with_cp
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.dcn = dcn
self.with_dcn = dcn is not None
self.plugins = plugins
self.with_plugins = plugins is not None
if self.with_plugins:
# collect plugins for conv1/conv2/conv3
self.after_conv1_plugins = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_conv1'
]
self.after_conv2_plugins = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_conv2'
]
self.after_conv3_plugins = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_conv3'
]
if self.style == 'pytorch':
self.conv1_stride = 1
self.conv2_stride = stride
else:
self.conv1_stride = stride
self.conv2_stride = 1
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.norm3_name, norm3 = build_norm_layer(norm_cfg,
planes * self.expansion,
postfix=3)
self.conv1 = build_conv_layer(conv_cfg,
inplanes,
planes,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
fallback_on_stride = False
if self.with_dcn:
fallback_on_stride = dcn.pop('fallback_on_stride', False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(conv_cfg,
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
bias=False)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
self.conv2 = build_conv_layer(dcn,
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(conv_cfg,
planes,
planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
if self.with_plugins:
self.after_conv1_plugin_names = self.make_block_plugins(
planes, self.after_conv1_plugins)
self.after_conv2_plugin_names = self.make_block_plugins(
planes, self.after_conv2_plugins)
self.after_conv3_plugin_names = self.make_block_plugins(
planes * self.expansion, self.after_conv3_plugins)
def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None,
gcb=None,
gen_attention=None):
"""Bottleneck block for ResNet.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(BottleneckV1s, self).__init__()
assert style in ['pytorch', 'caffe']
assert dcn is None or isinstance(dcn, dict)
assert gcb is None or isinstance(gcb, dict)
assert gen_attention is None or isinstance(gen_attention, dict)
self.inplanes = inplanes
self.planes = planes
self.stride = stride
self.dilation = dilation
self.style = style
self.with_cp = with_cp
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.dcn = dcn
self.with_dcn = dcn is not None
self.gcb = gcb
self.with_gcb = gcb is not None
self.gen_attention = gen_attention
self.with_gen_attention = gen_attention is not None
if self.style == 'pytorch':
self.conv1_stride = 1
self.conv2_stride = stride
else:
self.conv1_stride = stride
self.conv2_stride = 1
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
norm_cfg, planes * self.expansion, postfix=3)
self.conv1 = build_conv_layer(
conv_cfg,
inplanes,
planes,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
fallback_on_stride = False
self.with_modulated_dcn = False
if self.with_dcn:
fallback_on_stride = dcn.get('fallback_on_stride', False)
self.with_modulated_dcn = dcn.get('modulated', False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(
conv_cfg,
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
bias=False)
else:
assert conv_cfg is None, 'conv_cfg must be None for DCN'
self.deformable_groups = dcn.get('deformable_groups', 1)
if not self.with_modulated_dcn:
conv_op = DeformConv
offset_channels = 18
else:
conv_op = ModulatedDeformConv
offset_channels = 27
self.conv2_offset = nn.Conv2d(
planes,
self.deformable_groups * offset_channels,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation)
self.conv2 = conv_op(
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
deformable_groups=self.deformable_groups,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
conv_cfg,
planes,
planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
if self.with_gcb:
gcb_inplanes = planes * self.expansion
self.context_block = ContextBlock(inplanes=gcb_inplanes, **gcb)
# gen_attention
if self.with_gen_attention:
self.gen_attention_block = GeneralizedAttention(
planes, **gen_attention)
def __init__(self,
in_channels,
out_channels,
expansion=4,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN')):
# Protect mutable default arguments
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
assert style in ['pytorch', 'caffe']
self.in_channels = in_channels
self.out_channels = out_channels
self.expansion = expansion
assert out_channels % expansion == 0
self.mid_channels = out_channels // expansion
self.stride = stride
self.dilation = dilation
self.style = style
self.with_cp = with_cp
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
if self.style == 'pytorch':
self.conv1_stride = 1
self.conv2_stride = stride
else:
self.conv1_stride = stride
self.conv2_stride = 1
self.norm1_name, norm1 = build_norm_layer(
norm_cfg, self.mid_channels, postfix=1)
self.norm2_name, norm2 = build_norm_layer(
norm_cfg, self.mid_channels, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
norm_cfg, out_channels, postfix=3)
self.conv1 = build_conv_layer(
conv_cfg,
in_channels,
self.mid_channels,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(
conv_cfg,
self.mid_channels,
self.mid_channels,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
conv_cfg,
self.mid_channels,
out_channels,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
def __init__(self,
in_channels=3,
out_channels=17,
num_deconv_layers=3,
num_deconv_filters=(256, 256, 256),
num_deconv_kernels=(4, 4, 4),
extra=None,
in_index=0,
input_transform=None,
align_corners=False,
loss_keypoint=None):
super().__init__()
self.in_channels = in_channels
self.loss = build_loss(loss_keypoint)
self._init_inputs(in_channels, in_index, input_transform)
self.in_index = in_index
self.align_corners = align_corners
if extra is not None and not isinstance(extra, dict):
raise TypeError('extra should be dict or None.')
if num_deconv_layers > 0:
self.deconv_layers = self._make_deconv_layer(
num_deconv_layers,
num_deconv_filters,
num_deconv_kernels,
)
elif num_deconv_layers == 0:
self.deconv_layers = nn.Identity()
else:
raise ValueError(
f'num_deconv_layers ({num_deconv_layers}) should >= 0.')
identity_final_layer = False
if extra is not None and 'final_conv_kernel' in extra:
assert extra['final_conv_kernel'] in [0, 1, 3]
if extra['final_conv_kernel'] == 3:
padding = 1
elif extra['final_conv_kernel'] == 1:
padding = 0
else:
# 0 for Identity mapping.
identity_final_layer = True
kernel_size = extra['final_conv_kernel']
else:
kernel_size = 1
padding = 0
if identity_final_layer:
self.final_layer = nn.Identity()
else:
conv_channels = num_deconv_filters[
-1] if num_deconv_layers > 0 else self.in_channels
layers = []
if extra is not None:
num_conv_layers = extra.get('num_conv_layers', 0)
num_conv_kernels = extra.get('num_conv_kernels',
[1] * num_conv_layers)
for i in range(num_conv_layers):
layers.append(
build_conv_layer(dict(type='Conv2d'),
in_channels=conv_channels,
out_channels=conv_channels,
kernel_size=num_conv_kernels[i],
stride=1,
padding=(num_conv_kernels[i] - 1) //
2))
layers.append(
build_norm_layer(dict(type='BN'), conv_channels)[1])
layers.append(nn.ReLU(inplace=True))
layers.append(
build_conv_layer(cfg=dict(type='Conv2d'),
in_channels=conv_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=1,
padding=padding))
if len(layers) > 1:
self.final_layer = nn.Sequential(*layers)
else:
self.final_layer = layers[0]
def __init__(self,
extra,
in_channels=3,
conv_cfg=None,
norm_cfg=dict(type='BN'),
norm_eval=True,
with_cp=False,
zero_init_residual=False,
multiscale_output=True,
pretrained=None,
init_cfg=None):
super(HRNet, self).__init__(init_cfg)
self.pretrained = pretrained
assert not (init_cfg and pretrained), \
'init_cfg and pretrained cannot be setting at the same time'
if isinstance(pretrained, str):
warnings.warn('DeprecationWarning: pretrained is deprecated, '
'please use "init_cfg" instead')
self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
elif pretrained is None:
if init_cfg is None:
self.init_cfg = [
dict(type='Kaiming', layer='Conv2d'),
dict(type='Constant',
val=1,
layer=['_BatchNorm', 'GroupNorm'])
]
else:
raise TypeError('pretrained must be a str or None')
# Assert configurations of 4 stages are in extra
assert 'stage1' in extra and 'stage2' in extra \
and 'stage3' in extra and 'stage4' in extra
# Assert whether the length of `num_blocks` and `num_channels` are
# equal to `num_branches`
for i in range(4):
cfg = extra[f'stage{i + 1}']
assert len(cfg['num_blocks']) == cfg['num_branches'] and \
len(cfg['num_channels']) == cfg['num_branches']
self.extra = extra
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.norm_eval = norm_eval
self.with_cp = with_cp
self.zero_init_residual = zero_init_residual
# stem net
self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
self.conv1 = build_conv_layer(self.conv_cfg,
in_channels,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(self.conv_cfg,
64,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
# stage 1
self.stage1_cfg = self.extra['stage1']
num_channels = self.stage1_cfg['num_channels'][0]
block_type = self.stage1_cfg['block']
num_blocks = self.stage1_cfg['num_blocks'][0]
block = self.blocks_dict[block_type]
stage1_out_channels = num_channels * block.expansion
self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
# stage 2
self.stage2_cfg = self.extra['stage2']
num_channels = self.stage2_cfg['num_channels']
block_type = self.stage2_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition1 = self._make_transition_layer([stage1_out_channels],
num_channels)
self.stage2, pre_stage_channels = self._make_stage(
self.stage2_cfg, num_channels)
# stage 3
self.stage3_cfg = self.extra['stage3']
num_channels = self.stage3_cfg['num_channels']
block_type = self.stage3_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition2 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage3, pre_stage_channels = self._make_stage(
self.stage3_cfg, num_channels)
# stage 4
self.stage4_cfg = self.extra['stage4']
num_channels = self.stage4_cfg['num_channels']
block_type = self.stage4_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition3 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage4, pre_stage_channels = self._make_stage(
self.stage4_cfg, num_channels, multiscale_output=multiscale_output)
def __init__(self,
in_channels,
base_channels=64,
num_conv=3,
norm_cfg=dict(type='BN'),
init_cfg=dict(type='normal', gain=0.02)):
super().__init__()
assert isinstance(norm_cfg, dict), ("'norm_cfg' should be dict, but"
f'got {type(norm_cfg)}')
assert 'type' in norm_cfg, "'norm_cfg' must have key 'type'"
# We use norm layers in the patch discriminator.
# Only for IN, use bias since it does not have affine parameters.
use_bias = norm_cfg['type'] == 'IN'
kernel_size = 4
padding = 1
# input layer
sequence = [
ConvModule(in_channels=in_channels,
out_channels=base_channels,
kernel_size=kernel_size,
stride=2,
padding=padding,
bias=True,
norm_cfg=None,
act_cfg=dict(type='LeakyReLU', negative_slope=0.2))
]
# stacked intermediate layers,
# gradually increasing the number of filters
multiple_now = 1
multiple_prev = 1
for n in range(1, num_conv):
multiple_prev = multiple_now
multiple_now = min(2**n, 8)
sequence += [
ConvModule(in_channels=base_channels * multiple_prev,
out_channels=base_channels * multiple_now,
kernel_size=kernel_size,
stride=2,
padding=padding,
bias=use_bias,
norm_cfg=norm_cfg,
act_cfg=dict(type='LeakyReLU', negative_slope=0.2))
]
multiple_prev = multiple_now
multiple_now = min(2**num_conv, 8)
sequence += [
ConvModule(in_channels=base_channels * multiple_prev,
out_channels=base_channels * multiple_now,
kernel_size=kernel_size,
stride=1,
padding=padding,
bias=use_bias,
norm_cfg=norm_cfg,
act_cfg=dict(type='LeakyReLU', negative_slope=0.2))
]
# output one-channel prediction map
sequence += [
build_conv_layer(dict(type='Conv2d'),
base_channels * multiple_now,
1,
kernel_size=kernel_size,
stride=1,
padding=padding)
]
self.model = nn.Sequential(*sequence)
self.init_type = 'normal' if init_cfg is None else init_cfg.get(
'type', 'normal')
self.init_gain = 0.02 if init_cfg is None else init_cfg.get(
'gain', 0.02)
def __init__(self,
#in_channels,
#out_channels,
inplanes,
planes,
base_channels=64,
groups=32,
width_per_group=4,
**kwargs):
in_channels = inplanes
out_channels = planes * self.expansion
self.in_channels = in_channels
self.out_channels = out_channels
self.mid_channels = planes
super(Bottleneck, self).__init__(in_channels, out_channels, **kwargs)
self.groups = groups
self.width_per_group = width_per_group
# For ResNet bottleneck, middle channels are determined by expansion
# and out_channels, but for ResNeXt bottleneck, it is determined by
# groups and width_per_group and the stage it is located in.
if groups != 1:
assert self.mid_channels % base_channels == 0
self.mid_channels = (
groups * width_per_group * self.mid_channels // base_channels)
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, self.mid_channels, postfix=1)
self.norm2_name, norm2 = build_norm_layer(
self.norm_cfg, self.mid_channels, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
self.norm_cfg, self.out_channels, postfix=3)
self.conv1 = build_conv_layer(
self.conv_cfg,
self.in_channels,
self.mid_channels,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(
self.conv_cfg,
self.mid_channels,
self.mid_channels,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
self.conv_cfg,
self.mid_channels,
self.out_channels,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
def __init__(self,
block,
inplanes,
planes,
num_blocks,
stride=1,
dilation=1,
avg_down=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
downsample_first=True,
**kwargs):
self.block = block
if stride != 1 or inplanes != planes * block.expansion:
if stride == 1 and dilation == 1:
kernel_size = 1
dd = 1
padding = 0
else:
kernel_size = 3
if dilation > 1:
dd = dilation // 2
padding = dd
else:
dd = 1
padding = 0
downsample = nn.Sequential(
build_conv_layer(conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dd,
bias=False),
build_norm_layer(norm_cfg, planes * block.expansion)[1])
layers = []
if downsample_first:
layers.append(
block(inplanes=inplanes,
planes=planes,
stride=stride,
dilation=dilation,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
inplanes = planes * block.expansion
for _ in range(1, num_blocks):
layers.append(
block(inplanes=inplanes,
planes=planes,
stride=1,
dilation=dilation,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
else: # downsample_first=False is for HourglassModule
for _ in range(num_blocks - 1):
layers.append(
block(inplanes=inplanes,
planes=inplanes,
stride=1,
dilation=dilation,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
layers.append(
block(inplanes=inplanes,
planes=planes,
stride=stride,
dilation=dilation,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
super(SOTResLayer, self).__init__(*layers)
def __init__(
self,
in_channels=3,
embed_dims=768,
conv_type='Conv2d',
kernel_size=16,
stride=16,
padding='corner',
dilation=1,
bias=True,
norm_cfg=None,
input_size=None,
init_cfg=None,
):
super(PatchEmbed, self).__init__(init_cfg=init_cfg)
self.embed_dims = embed_dims
if stride is None:
stride = kernel_size
kernel_size = to_2tuple(kernel_size)
stride = to_2tuple(stride)
dilation = to_2tuple(dilation)
if isinstance(padding, str):
self.adap_padding = AdaptivePadding(kernel_size=kernel_size,
stride=stride,
dilation=dilation,
padding=padding)
# disable the padding of conv
padding = 0
else:
self.adap_padding = None
padding = to_2tuple(padding)
self.projection = build_conv_layer(dict(type=conv_type),
in_channels=in_channels,
out_channels=embed_dims,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=bias)
if norm_cfg is not None:
self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
else:
self.norm = None
if input_size:
input_size = to_2tuple(input_size)
# `init_out_size` would be used outside to
# calculate the num_patches
# when `use_abs_pos_embed` outside
self.init_input_size = input_size
if self.adap_padding:
pad_h, pad_w = self.adap_padding.get_pad_shape(input_size)
input_h, input_w = input_size
input_h = input_h + pad_h
input_w = input_w + pad_w
input_size = (input_h, input_w)
# https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
h_out = (input_size[0] + 2 * padding[0] - dilation[0] *
(kernel_size[0] - 1) - 1) // stride[0] + 1
w_out = (input_size[1] + 2 * padding[1] - dilation[1] *
(kernel_size[1] - 1) - 1) // stride[1] + 1
self.init_out_size = (h_out, w_out)
else:
self.init_input_size = None
self.init_out_size = None
def __init__(self,
inplanes,
planes,
eta=8,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None,
plugins=None, **kwargs):
"""Bottleneck block for ResNet.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(Bottleneck, self).__init__()
assert style in ['pytorch', 'caffe']
assert dcn is None or isinstance(dcn, dict)
self.inplanes = inplanes
self.planes = planes
self.stride = stride
self.dilation = dilation
self.style = style
self.with_cp = with_cp
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.dcn = dcn
self.with_dcn = dcn is not None
# mask
self.mask_s = Mask_s( inplanes, eta, **kwargs)
self.mask_c1 = Mask_c( inplanes, planes, **kwargs)
self.mask_c2 = Mask_c( planes, planes, **kwargs)
if self.style == 'pytorch':
self.conv1_stride = 1
self.conv2_stride = stride
else:
self.conv1_stride = stride
self.conv2_stride = 1
self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
norm_cfg, planes * self.expansion, postfix=3)
self.conv1 = build_conv_layer(
conv_cfg,
inplanes,
planes,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
fallback_on_stride = False
if self.with_dcn:
fallback_on_stride = dcn.pop('fallback_on_stride', False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(
conv_cfg,
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
bias=False)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
self.conv2 = build_conv_layer(
dcn,
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
conv_cfg,
planes,
planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
# mask flops
flops_mask_c1 = self.mask_c1.get_flops()
flops_mask_c2 = self.mask_c2.get_flops()
self.flops_mask = torch.Tensor([flops_mask_c1 + flops_mask_c2])
def __init__(self,
block,
inplanes,
planes,
num_blocks,
stride=1,
dilation=1,
avg_down=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
multi_grid=None,
contract_dilation=False,
**kwargs):
self.block = block
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = []
conv_stride = stride
if avg_down:
conv_stride = 1
downsample.append(
nn.AvgPool2d(kernel_size=stride,
stride=stride,
ceil_mode=True,
count_include_pad=False))
downsample.extend([
build_conv_layer(conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
stride=conv_stride,
bias=False),
build_norm_layer(norm_cfg, planes * block.expansion)[1]
])
downsample = nn.Sequential(*downsample)
layers = []
if multi_grid is None:
if dilation > 1 and contract_dilation:
first_dilation = dilation // 2
else:
first_dilation = dilation
else:
first_dilation = multi_grid[0]
layers.append(
block(inplanes=inplanes,
planes=planes,
stride=stride,
dilation=first_dilation,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
inplanes = planes * block.expansion
for i in range(1, num_blocks):
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=1,
dilation=dilation if multi_grid is None else multi_grid[i],
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
super(ResLayer, self).__init__(*layers)
def __init__(self,
extra,
in_channels=3,
conv_cfg=None,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=False,
with_cp=False,
frozen_stages=-1,
zero_init_residual=False,
pretrained=None,
init_cfg=None):
super(HRNet, self).__init__(init_cfg)
self.pretrained = pretrained
self.zero_init_residual = zero_init_residual
assert not (init_cfg and pretrained), \
'init_cfg and pretrained cannot be setting at the same time'
if isinstance(pretrained, str):
warnings.warn('DeprecationWarning: pretrained is a deprecated, '
'please use "init_cfg" instead')
self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
elif pretrained is None:
if init_cfg is None:
self.init_cfg = [
dict(type='Kaiming', layer='Conv2d'),
dict(type='Constant',
val=1,
layer=['_BatchNorm', 'GroupNorm'])
]
else:
raise TypeError('pretrained must be a str or None')
self.extra = extra
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.norm_eval = norm_eval
self.with_cp = with_cp
self.frozen_stages = frozen_stages
# stem net
self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
self.conv1 = build_conv_layer(self.conv_cfg,
in_channels,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(self.conv_cfg,
64,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
# stage 1
self.stage1_cfg = self.extra['stage1']
num_channels = self.stage1_cfg['num_channels'][0]
block_type = self.stage1_cfg['block']
num_blocks = self.stage1_cfg['num_blocks'][0]
block = self.blocks_dict[block_type]
stage1_out_channels = num_channels * block.expansion
self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
# stage 2
self.stage2_cfg = self.extra['stage2']
num_channels = self.stage2_cfg['num_channels']
block_type = self.stage2_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition1 = self._make_transition_layer([stage1_out_channels],
num_channels)
self.stage2, pre_stage_channels = self._make_stage(
self.stage2_cfg, num_channels)
# stage 3
self.stage3_cfg = self.extra['stage3']
num_channels = self.stage3_cfg['num_channels']
block_type = self.stage3_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition2 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage3, pre_stage_channels = self._make_stage(
self.stage3_cfg, num_channels)
# stage 4
self.stage4_cfg = self.extra['stage4']
num_channels = self.stage4_cfg['num_channels']
block_type = self.stage4_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition3 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage4, pre_stage_channels = self._make_stage(
self.stage4_cfg, num_channels)
self._freeze_stages()
def __init__(self,
in_channels=512,
out_channels=17,
num_stages=1,
num_deconv_layers=3,
num_deconv_filters=(256, 256, 256),
num_deconv_kernels=(4, 4, 4),
extra=None,
loss_keypoint=None,
train_cfg=None,
test_cfg=None):
super().__init__()
self.in_channels = in_channels
self.num_stages = num_stages
self.loss = build_loss(loss_keypoint)
self.train_cfg = {} if train_cfg is None else train_cfg
self.test_cfg = {} if test_cfg is None else test_cfg
self.target_type = self.test_cfg.get('target_type', 'GaussianHeatMap')
if extra is not None and not isinstance(extra, dict):
raise TypeError('extra should be dict or None.')
# build multi-stage deconv layers
self.multi_deconv_layers = nn.ModuleList([])
for _ in range(self.num_stages):
if num_deconv_layers > 0:
deconv_layers = self._make_deconv_layer(
num_deconv_layers,
num_deconv_filters,
num_deconv_kernels,
)
elif num_deconv_layers == 0:
deconv_layers = nn.Identity()
else:
raise ValueError(
f'num_deconv_layers ({num_deconv_layers}) should >= 0.')
self.multi_deconv_layers.append(deconv_layers)
identity_final_layer = False
if extra is not None and 'final_conv_kernel' in extra:
assert extra['final_conv_kernel'] in [0, 1, 3]
if extra['final_conv_kernel'] == 3:
padding = 1
elif extra['final_conv_kernel'] == 1:
padding = 0
else:
# 0 for Identity mapping.
identity_final_layer = True
kernel_size = extra['final_conv_kernel']
else:
kernel_size = 1
padding = 0
# build multi-stage final layers
self.multi_final_layers = nn.ModuleList([])
for i in range(self.num_stages):
if identity_final_layer:
final_layer = nn.Identity()
else:
final_layer = build_conv_layer(
cfg=dict(type='Conv2d'),
in_channels=num_deconv_filters[-1]
if num_deconv_layers > 0 else in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=1,
padding=padding)
self.multi_final_layers.append(final_layer)
def __init__(self,
num_convs=4,
roi_feat_size=14,
in_channels=256,
conv_kernel_size=3,
conv_out_channels=256,
num_classes=80,
class_agnostic=False,
upsample_cfg=dict(type='deconv', scale_factor=2),
conv_cfg=None,
norm_cfg=None,
predictor_cfg=dict(type='Conv'),
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0),
init_cfg=None):
assert init_cfg is None, 'To prevent abnormal initialization ' \
'behavior, init_cfg is not allowed to be set'
super(FCNMaskHead, self).__init__(init_cfg)
self.upsample_cfg = upsample_cfg.copy()
if self.upsample_cfg['type'] not in [
None, 'deconv', 'nearest', 'bilinear', 'carafe'
]:
raise ValueError(
f'Invalid upsample method {self.upsample_cfg["type"]}, '
'accepted methods are "deconv", "nearest", "bilinear", '
'"carafe"')
self.num_convs = num_convs
# WARN: roi_feat_size is reserved and not used
self.roi_feat_size = _pair(roi_feat_size)
self.in_channels = in_channels
self.conv_kernel_size = conv_kernel_size
self.conv_out_channels = conv_out_channels
self.upsample_method = self.upsample_cfg.get('type')
self.scale_factor = self.upsample_cfg.pop('scale_factor', None)
self.num_classes = num_classes
self.class_agnostic = class_agnostic
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.predictor_cfg = predictor_cfg
self.fp16_enabled = False
self.loss_mask = build_loss(loss_mask)
self.convs = ModuleList()
for i in range(self.num_convs):
in_channels = (
self.in_channels if i == 0 else self.conv_out_channels)
padding = (self.conv_kernel_size - 1) // 2
self.convs.append(
ConvModule(
in_channels,
self.conv_out_channels,
self.conv_kernel_size,
padding=padding,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg))
upsample_in_channels = (
self.conv_out_channels if self.num_convs > 0 else in_channels)
upsample_cfg_ = self.upsample_cfg.copy()
if self.upsample_method is None:
self.upsample = None
elif self.upsample_method == 'deconv':
upsample_cfg_.update(
in_channels=upsample_in_channels,
out_channels=self.conv_out_channels,
kernel_size=self.scale_factor,
stride=self.scale_factor)
self.upsample = build_upsample_layer(upsample_cfg_)
elif self.upsample_method == 'carafe':
upsample_cfg_.update(
channels=upsample_in_channels, scale_factor=self.scale_factor)
self.upsample = build_upsample_layer(upsample_cfg_)
else:
# suppress warnings
align_corners = (None
if self.upsample_method == 'nearest' else False)
upsample_cfg_.update(
scale_factor=self.scale_factor,
mode=self.upsample_method,
align_corners=align_corners)
self.upsample = build_upsample_layer(upsample_cfg_)
out_channels = 1 if self.class_agnostic else self.num_classes
logits_in_channel = (
self.conv_out_channels
if self.upsample_method == 'deconv' else upsample_in_channels)
self.conv_logits = build_conv_layer(self.predictor_cfg,
logits_in_channel, out_channels, 1)
self.relu = nn.ReLU(inplace=True)
self.debug_imgs = None
def __init__(self,
extra,
in_channels=3,
conv_cfg=None,
norm_cfg=dict(type='BN'),
norm_eval=False,
with_cp=False,
zero_init_residual=False):
super(HRNet, self).__init__()
self.extra = extra
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.norm_eval = norm_eval
self.with_cp = with_cp
self.zero_init_residual = zero_init_residual
# stem net
self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
self.conv1 = build_conv_layer(self.conv_cfg,
in_channels,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(self.conv_cfg,
64,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
# stage 1
self.stage1_cfg = self.extra['stage1']
num_channels = self.stage1_cfg['num_channels'][0]
block_type = self.stage1_cfg['block']
num_blocks = self.stage1_cfg['num_blocks'][0]
block = self.blocks_dict[block_type]
stage1_out_channels = num_channels * get_expansion(block)
self.layer1 = self._make_layer(block, 64, stage1_out_channels,
num_blocks)
# stage 2
self.stage2_cfg = self.extra['stage2']
num_channels = self.stage2_cfg['num_channels']
block_type = self.stage2_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [
channel * get_expansion(block) for channel in num_channels
]
self.transition1 = self._make_transition_layer([stage1_out_channels],
num_channels)
self.stage2, pre_stage_channels = self._make_stage(
self.stage2_cfg, num_channels)
# stage 3
self.stage3_cfg = self.extra['stage3']
num_channels = self.stage3_cfg['num_channels']
block_type = self.stage3_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [
channel * get_expansion(block) for channel in num_channels
]
self.transition2 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage3, pre_stage_channels = self._make_stage(
self.stage3_cfg, num_channels)
# stage 4
self.stage4_cfg = self.extra['stage4']
num_channels = self.stage4_cfg['num_channels']
block_type = self.stage4_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [
channel * get_expansion(block) for channel in num_channels
]
self.transition3 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage4, pre_stage_channels = self._make_stage(
self.stage4_cfg, num_channels, multiscale_output=False)
def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg1=dict(type='BN'),
norm_cfg2=dict(type='BN'),
norm_cfg3=dict(type='BN'),
dcn=None,
plugins=None):
super(BasicBlock, self).__init__()
assert dcn is None, 'Not implemented yet.'
assert plugins is None or isinstance(plugins, list)
if plugins is not None:
allowed_position = ['after_conv1', 'after_bn1', 'after_conv2']
assert all(p['position'] in allowed_position for p in plugins)
self.norm1_name, norm1 = build_norm_layer(norm_cfg1, planes, postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg2, planes, postfix=2)
self.conv1 = build_conv_layer(conv_cfg,
inplanes,
planes,
3,
stride=stride,
padding=dilation,
dilation=dilation,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(conv_cfg,
planes,
planes,
3,
padding=1,
bias=False)
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
self.with_cp = with_cp
self.plugins = plugins
self.with_plugins = plugins is not None
if self.with_plugins:
# collect plugins for conv1/conv2/conv3
self.after_conv1_plugins = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_conv1'
]
self.after_bn1_plugins = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_bn1'
]
self.after_conv2_plugins = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_conv2'
]
if self.with_plugins:
self.after_conv1_plugin_names = self.make_block_plugins(
planes, self.after_conv1_plugins)
self.after_bn1_plugin_names = self.make_block_plugins(
planes, self.after_bn1_plugins)
self.after_conv2_plugin_names = self.make_block_plugins(
planes, self.after_conv2_plugins)
def __init__(self,
in_channels=4,
feat_channels=[],
with_distance=False,
with_cluster_center=False,
with_voxel_center=False,
voxel_size=(0.2, 0.2, 4),
point_cloud_range=(0, -40, -3, 70.4, 40, 1),
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
mode='max',
fusion_layer=None,
return_point_feats=False,
model_mode='voxel',
compress_dim=None):
super(Cylinder_VFE, self).__init__()
assert mode in ['avg', 'max']
assert len(feat_channels) > 0
if with_cluster_center:
in_channels += 3
if with_voxel_center:
in_channels += 3
if with_distance:
in_channels += 3
self.in_channels = in_channels
self._with_distance = with_distance
self._with_cluster_center = with_cluster_center
self._with_voxel_center = with_voxel_center
self.return_point_feats = return_point_feats
self.fp16_enabled = False
# Need pillar (voxel) size and x/y offset in order to calculate offset
self.vx = voxel_size[0]
self.vy = voxel_size[1]
self.vz = voxel_size[2]
self.x_offset = self.vx / 2 + point_cloud_range[0]
self.y_offset = self.vy / 2 + point_cloud_range[1]
self.z_offset = self.vz / 2 + point_cloud_range[2]
self.point_cloud_range = point_cloud_range
'''
feat_channels = [self.in_channels] + list(feat_channels)
vfe_layers = []
for i in range(len(feat_channels) - 1):
in_filters = feat_channels[i]
out_filters = feat_channels[i + 1]
if i > 0:
in_filters *= 2
norm_name, norm_layer = build_norm_layer(norm_cfg, out_filters)
vfe_layers.append(
nn.Sequential(
nn.Linear(in_filters, out_filters, bias=False), norm_layer,
nn.ReLU(inplace=True)))
self.vfe_layers = nn.ModuleList(vfe_layers)
'''
build_conv_layer(conv_cfg, in_channels, 64, 1)
if model_mode == 'voxel':
self.feat_model = nn.Sequential(
build_norm_layer(norm_cfg, in_channels)[1],
build_conv_layer(conv_cfg, in_channels, 64, 1, bias=False),
build_norm_layer(norm_cfg, 64)[1], nn.ReLU(),
build_conv_layer(conv_cfg, 64, 128, 1, bias=False),
build_norm_layer(norm_cfg, 128)[1], nn.ReLU(),
build_conv_layer(conv_cfg, 128, 256, 1, bias=False),
build_norm_layer(norm_cfg, 256)[1], nn.ReLU(),
build_conv_layer(conv_cfg, 256, 256, 1))
else:
self.feat_model = nn.Sequential(
build_conv_layer(conv_cfg, in_channels, 64, 1, bias=False),
build_norm_layer(norm_cfg, 64)[1],
nn.ReLU(),
)
self.compress_dim = compress_dim
if compress_dim is not None:
self.feat_compression = nn.Sequential(nn.Linear(256, compress_dim),
nn.ReLU())
def __init__(self,
block,
num_blocks,
in_channels,
out_channels,
expansion=None,
stride=1,
avg_down=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
**kwargs):
self.block = block
self.expansion = get_expansion(block, expansion)
downsample = None
if stride != 1 or in_channels != out_channels:
downsample = []
conv_stride = stride
if avg_down and stride != 1:
conv_stride = 1
downsample.append(
nn.AvgPool2d(
kernel_size=stride,
stride=stride,
ceil_mode=True,
count_include_pad=False))
downsample.extend([
build_conv_layer(
conv_cfg,
in_channels,
out_channels,
kernel_size=1,
stride=conv_stride,
bias=False),
build_norm_layer(norm_cfg, out_channels)[1]
])
downsample = nn.Sequential(*downsample)
layers = []
layers.append(
block(
in_channels=in_channels,
out_channels=out_channels,
expansion=self.expansion,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
in_channels = out_channels
for i in range(1, num_blocks):
layers.append(
block(
in_channels=in_channels,
out_channels=out_channels,
expansion=self.expansion,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
super(ResLayer, self).__init__(*layers)
def make_sparse_convmodule(in_channels,
out_channels,
kernel_size,
indice_key,
stride=1,
padding=0,
conv_type='SubMConv3d',
norm_cfg=None,
order=('conv', 'norm', 'act')):
"""Make sparse convolution module.
Args:
in_channels (int): the number of input channels
out_channels (int): the number of out channels
kernel_size (int|tuple(int)): kernel size of convolution
indice_key (str): the indice key used for sparse tensor
stride (int|tuple(int)): the stride of convolution
padding (int or list[int]): the padding number of input
conv_type (str): sparse conv type in spconv
norm_cfg (dict[str]): config of normalization layer
order (tuple[str]): The order of conv/norm/activation layers. It is a
sequence of "conv", "norm" and "act". Common examples are
("conv", "norm", "act") and ("act", "conv", "norm").
Returns:
spconv.SparseSequential: sparse convolution module.
"""
assert isinstance(order, tuple) and len(order) <= 3
assert set(order) | {'conv', 'norm', 'act'} == {'conv', 'norm', 'act'}
conv_cfg = dict(type=conv_type, indice_key=indice_key)
layers = list()
for layer in order:
if layer == 'conv':
if conv_type not in [
'SparseInverseConv3d', 'SparseInverseConv2d',
'SparseInverseConv1d'
]:
layers.append(
build_conv_layer(conv_cfg,
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
bias=False))
else:
layers.append(
build_conv_layer(conv_cfg,
in_channels,
out_channels,
kernel_size,
bias=False))
elif layer == 'norm':
layers.append(build_norm_layer(norm_cfg, out_channels)[1])
elif layer == 'act':
layers.append(nn.ReLU(inplace=True))
layers = spconv.SparseSequential(*layers)
return layers
def __init__(self,
in_channels,
out_channels,
groups=1,
width_per_group=4,
base_channels=64,
radix=2,
reduction_factor=4,
avg_down_stride=True,
**kwargs):
super().__init__(in_channels, out_channels, **kwargs)
self.groups = groups
self.width_per_group = width_per_group
# For ResNet bottleneck, middle channels are determined by expansion
# and out_channels, but for ResNeXt bottleneck, it is determined by
# groups and width_per_group and the stage it is located in.
if groups != 1:
assert self.mid_channels % base_channels == 0
self.mid_channels = (
groups * width_per_group * self.mid_channels // base_channels)
self.avg_down_stride = avg_down_stride and self.conv2_stride > 1
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, self.mid_channels, postfix=1)
self.norm3_name, norm3 = build_norm_layer(
self.norm_cfg, self.out_channels, postfix=3)
self.conv1 = build_conv_layer(
self.conv_cfg,
self.in_channels,
self.mid_channels,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = SplitAttentionConv2d(
self.mid_channels,
self.mid_channels,
kernel_size=3,
stride=1 if self.avg_down_stride else self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
radix=radix,
reduction_factor=reduction_factor,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg)
delattr(self, self.norm2_name)
if self.avg_down_stride:
self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1)
self.conv3 = build_conv_layer(
self.conv_cfg,
self.mid_channels,
self.out_channels,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
def __init__(self,
in_channels,
out_channels,
expansion=4,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN')):
super(Bottleneck, self).__init__()
assert style in ['pytorch', 'caffe']
self.in_channels = in_channels
self.out_channels = out_channels
self.expansion = expansion
assert out_channels % expansion == 0
self.mid_channels = out_channels // expansion
self.stride = stride
self.dilation = dilation
self.style = style
self.with_cp = with_cp
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
if self.style == 'pytorch':
self.conv1_stride = 1
self.conv2_stride = stride
else:
self.conv1_stride = stride
self.conv2_stride = 1
self.norm1_name, norm1 = build_norm_layer(norm_cfg,
self.mid_channels,
postfix=1)
self.norm2_name, norm2 = build_norm_layer(norm_cfg,
self.mid_channels,
postfix=2)
self.norm3_name, norm3 = build_norm_layer(norm_cfg,
out_channels,
postfix=3)
self.conv1 = build_conv_layer(conv_cfg,
in_channels,
self.mid_channels,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
# self.conv2 = build_conv_layer(
# conv_cfg,
# self.mid_channels,
# self.mid_channels,
# kernel_size=3,
# stride=self.conv2_stride,
# padding=dilation,
# dilation=dilation,
# bias=False)
if self.conv2_stride > 1:
self.conv2 = nn.Sequential(
CARAFE_Downsample(self.mid_channels, scale_factor=2),
build_conv_layer(conv_cfg,
self.mid_channels,
self.mid_channels,
kernel_size=3,
stride=1,
padding=1,
dilation=1,
bias=False))
else:
self.conv2 = nn.Sequential(
build_conv_layer(conv_cfg,
self.mid_channels,
self.mid_channels,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
bias=False))
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(conv_cfg,
self.mid_channels,
out_channels,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
def __init__(self,
inplanes,
planes,
groups=1,
base_width=4,
base_channels=64,
**kwargs):
"""Bottleneck block for ResNeXt.
If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
if groups == 1:
width = self.planes
else:
width = math.floor(self.planes *
(base_width / base_channels)) * groups
self.norm1_name, norm1 = build_norm_layer(
self.norm_cfg, width, postfix=1)
self.norm2_name, norm2 = build_norm_layer(
self.norm_cfg, width, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
self.norm_cfg, self.planes * self.expansion, postfix=3)
self.conv1 = build_conv_layer(
self.conv_cfg,
self.inplanes,
width,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
fallback_on_stride = False
self.with_modulated_dcn = False
if self.with_dcn:
fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(
self.conv_cfg,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
bias=False)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
self.conv2 = build_conv_layer(
self.dcn,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
groups=groups,
bias=False)
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
self.conv_cfg,
width,
self.planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
def __init__(self,
extra,
in_channels=3,
conv_cfg=None,
norm_cfg=dict(type='BN'),
norm_eval=True,
with_cp=False,
zero_init_residual=False):
super(HRNet_upsamp, self).__init__()
self.extra = extra
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.norm_eval = norm_eval
self.with_cp = with_cp
self.zero_init_residual = zero_init_residual
# stem net
self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
self.conv1 = build_conv_layer(self.conv_cfg,
in_channels,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(self.conv_cfg,
64,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
# start upsamp diff
#self.do_upsample = nn.Upsample(scale_factor=2,mode='bilinear')
# end upsamp diff
# stage 1
self.stage1_cfg = self.extra['stage1']
num_channels = self.stage1_cfg['num_channels'][0]
block_type = self.stage1_cfg['block']
num_blocks = self.stage1_cfg['num_blocks'][0]
block = self.blocks_dict[block_type]
stage1_out_channels = num_channels * block.expansion
self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
# stage 2
self.stage2_cfg = self.extra['stage2']
num_channels = self.stage2_cfg['num_channels']
block_type = self.stage2_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition1 = self._make_transition_layer([stage1_out_channels],
num_channels)
self.stage2, pre_stage_channels = self._make_stage(
self.stage2_cfg, num_channels)
# stage 3
self.stage3_cfg = self.extra['stage3']
num_channels = self.stage3_cfg['num_channels']
block_type = self.stage3_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition2 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage3, pre_stage_channels = self._make_stage(
self.stage3_cfg, num_channels)
# stage 4
# start upsamp diff
deconv_cfg = {}
deconv_cfg["NUM_CHANNELS"] = [32, 64, 128, 256]
deconv_cfg["NUM_DECONVS"] = 1
deconv_cfg["KERNEL_SIZE"] = [3, 3, 3, 3]
deconv_cfg["NUM_BASIC_BLOCKS"] = 1
self.num_deconvs = len(pre_stage_channels) #deconv_cfg["NUM_DECONVS"]
self.deconv_layers = self._make_deconv_layers(deconv_cfg,
pre_stage_channels)
#self.upsample = nn.Upsample(scale_factor=2,mode='bilinear')
# end upsamp diff
self.stage4_cfg = self.extra['stage4']
num_channels = self.stage4_cfg['num_channels']
block_type = self.stage4_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [channel * block.expansion for channel in num_channels]
self.transition3 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage4, pre_stage_channels = self._make_stage(
self.stage4_cfg, num_channels)
# start upsamp diff
#deconv_cfg["NUM_BASIC_BLOCKS"] = 1
self.num_deconvs = len(pre_stage_channels) #deconv_cfg["NUM_DECONVS"]
self.deconv_layers_2 = self._make_deconv_layers(
deconv_cfg, pre_stage_channels)
def __init__(self,
block,
num_blocks,
in_channels,
out_channels,
expansion=None,
stride=1,
avg_down=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
downsample_first=True,
**kwargs):
# Protect mutable default arguments
norm_cfg = copy.deepcopy(norm_cfg)
self.block = block
self.expansion = get_expansion(block, expansion)
downsample = None
if stride != 1 or in_channels != out_channels:
downsample = []
conv_stride = stride
if avg_down and stride != 1:
conv_stride = 1
downsample.append(
nn.AvgPool2d(
kernel_size=stride,
stride=stride,
ceil_mode=True,
count_include_pad=False))
downsample.extend([
build_conv_layer(
conv_cfg,
in_channels,
out_channels,
kernel_size=1,
stride=conv_stride,
bias=False),
build_norm_layer(norm_cfg, out_channels)[1]
])
downsample = nn.Sequential(*downsample)
layers = []
if downsample_first:
layers.append(
block(
in_channels=in_channels,
out_channels=out_channels,
expansion=self.expansion,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
in_channels = out_channels
for _ in range(1, num_blocks):
layers.append(
block(
in_channels=in_channels,
out_channels=out_channels,
expansion=self.expansion,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
else: # downsample_first=False is for HourglassModule
for i in range(0, num_blocks - 1):
layers.append(
block(
in_channels=in_channels,
out_channels=in_channels,
expansion=self.expansion,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
layers.append(
block(
in_channels=in_channels,
out_channels=out_channels,
expansion=self.expansion,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
super().__init__(*layers)
def _make_fuse_layers(self):
"""Make fuse layer."""
if self.num_branches == 1:
return None
num_branches = self.num_branches
in_channels = self.in_channels
fuse_layers = []
num_out_branches = num_branches if self.multiscale_output else 1
for i in range(num_out_branches):
fuse_layer = []
for j in range(num_branches):
if j > i:
fuse_layer.append(
nn.Sequential(
build_conv_layer(self.conv_cfg,
in_channels[j],
in_channels[i],
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg, in_channels[i])[1],
nn.Upsample(scale_factor=2**(j - i),
mode='nearest')))
elif j == i:
fuse_layer.append(None)
else:
conv_downsamples = []
for k in range(i - j):
if k == i - j - 1:
conv_downsamples.append(
nn.Sequential(
build_conv_layer(self.conv_cfg,
in_channels[j],
in_channels[j],
kernel_size=3,
stride=2,
padding=1,
groups=in_channels[j],
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[j])[1],
build_conv_layer(self.conv_cfg,
in_channels[j],
in_channels[i],
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[i])[1]))
else:
conv_downsamples.append(
nn.Sequential(
build_conv_layer(self.conv_cfg,
in_channels[j],
in_channels[j],
kernel_size=3,
stride=2,
padding=1,
groups=in_channels[j],
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[j])[1],
build_conv_layer(self.conv_cfg,
in_channels[j],
in_channels[j],
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[j])[1],
nn.ReLU(inplace=True)))
fuse_layer.append(nn.Sequential(*conv_downsamples))
fuse_layers.append(nn.ModuleList(fuse_layer))
return nn.ModuleList(fuse_layers)
def __init__(self,
block,
inplanes,
planes,
num_blocks,
stride=1,
avg_down=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
downsample_first=True,
**kwargs):
self.block = block
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = []
conv_stride = stride
if avg_down and stride != 1:
conv_stride = 1
downsample.append(
nn.AvgPool2d(kernel_size=stride,
stride=stride,
ceil_mode=True,
count_include_pad=False))
if inplanes == 64:
downsample.extend([
build_conv_layer(conv_cfg,
512,
planes * block.expansion,
kernel_size=1,
stride=conv_stride,
bias=False),
build_norm_layer(norm_cfg, planes * block.expansion)[1]
])
else:
downsample.extend([
build_conv_layer(conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
stride=conv_stride,
bias=False),
build_norm_layer(norm_cfg, planes * block.expansion)[1]
])
downsample = nn.Sequential(*downsample)
layers = []
if downsample_first:
if inplanes == 64:
layers.append(
block(inplanes=inplanes,
planes=planes,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
else:
layers.append(
block(inplanes=inplanes,
planes=planes,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
inplanes = planes * block.expansion
for _ in range(1, num_blocks):
layers.append(
block(inplanes=inplanes,
planes=planes,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
else: # downsample_first=False is for HourglassModule
for _ in range(num_blocks - 1):
layers.append(
block(inplanes=inplanes,
planes=inplanes,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
layers.append(
block(inplanes=inplanes,
planes=planes,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
super(ResLayerM, self).__init__(*layers)
def _make_transition_layer(self, num_channels_pre_layer,
num_channels_cur_layer):
"""Make transition 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(
build_conv_layer(self.conv_cfg,
num_channels_pre_layer[i],
num_channels_pre_layer[i],
kernel_size=3,
stride=1,
padding=1,
groups=num_channels_pre_layer[i],
bias=False),
build_norm_layer(self.norm_cfg,
num_channels_pre_layer[i])[1],
build_conv_layer(self.conv_cfg,
num_channels_pre_layer[i],
num_channels_cur_layer[i],
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg,
num_channels_cur_layer[i])[1],
nn.ReLU()))
else:
transition_layers.append(None)
else:
conv_downsamples = []
for j in range(i + 1 - num_branches_pre):
in_channels = num_channels_pre_layer[-1]
out_channels = num_channels_cur_layer[i] \
if j == i - num_branches_pre else in_channels
conv_downsamples.append(
nn.Sequential(
build_conv_layer(self.conv_cfg,
in_channels,
in_channels,
kernel_size=3,
stride=2,
padding=1,
groups=in_channels,
bias=False),
build_norm_layer(self.norm_cfg, in_channels)[1],
build_conv_layer(self.conv_cfg,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg, out_channels)[1],
nn.ReLU()))
transition_layers.append(nn.Sequential(*conv_downsamples))
return nn.ModuleList(transition_layers)
def __init__(self,
inplanes,
planes,
scales=4,
base_width=26,
base_channels=64,
stage_type='normal',
**kwargs):
"""Bottle2neck block for Res2Net.
If style is "pytorch", the stride-two layer is the 3x3 conv layer,
if it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(Bottle2neck, self).__init__(inplanes, planes, **kwargs)
assert scales > 1, 'Res2Net degenerates to ResNet when scales = 1.'
width = int(math.floor(self.planes * (base_width / base_channels)))
self.norm1_name, norm1 = build_norm_layer(self.norm_cfg,
width * scales,
postfix=1)
self.norm3_name, norm3 = build_norm_layer(self.norm_cfg,
self.planes * self.expansion,
postfix=3)
self.conv1 = build_conv_layer(self.conv_cfg,
self.inplanes,
width * scales,
kernel_size=1,
stride=self.conv1_stride,
bias=False)
self.add_module(self.norm1_name, norm1)
if stage_type == 'stage' and self.conv2_stride != 1:
self.pool = nn.AvgPool2d(kernel_size=3,
stride=self.conv2_stride,
padding=1)
convs = []
bns = []
fallback_on_stride = False
if self.with_dcn:
fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
if not self.with_dcn or fallback_on_stride:
for i in range(scales - 1):
convs.append(
build_conv_layer(self.conv_cfg,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
bias=False))
bns.append(
build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
for i in range(scales - 1):
convs.append(
build_conv_layer(self.dcn,
width,
width,
kernel_size=3,
stride=self.conv2_stride,
padding=self.dilation,
dilation=self.dilation,
bias=False))
bns.append(
build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv3 = build_conv_layer(self.conv_cfg,
width * scales,
self.planes * self.expansion,
kernel_size=1,
bias=False)
self.add_module(self.norm3_name, norm3)
self.stage_type = stage_type
self.scales = scales
self.width = width
delattr(self, 'conv2')
delattr(self, self.norm2_name)
def make_res_layer(block,
inplanes,
planes,
blocks,
stride=1,
dilation=1,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None,
gcb=None,
gen_attention=None,
gen_attention_blocks=[]):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
build_conv_layer(
conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False),
build_norm_layer(norm_cfg, planes * block.expansion)[1],
)
layers = []
if dilation in (1, 2):
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=stride,
dilation=1,
downsample=downsample,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
dcn=dcn,
gcb=gcb,
gen_attention=gen_attention if
(0 in gen_attention_blocks) else None))
elif dilation == 4:
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=stride,
dilation=2,
downsample=downsample,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
dcn=dcn,
gcb=gcb,
gen_attention=gen_attention if
(0 in gen_attention_blocks) else None))
else:
raise RuntimeError('=> unknown dilation size: {}'.format(dilation))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=1,
dilation=dilation,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
dcn=dcn,
gcb=gcb,
gen_attention=gen_attention if
(i in gen_attention_blocks) else None))
return nn.Sequential(*layers)
