def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
super().__init__()
# fmt: off
self.in_features = cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
in_channels = [input_shape[f].channels for f in self.in_features]
aspp_channels = cfg.MODEL.SEM_SEG_HEAD.ASPP_CHANNELS
aspp_dilations = cfg.MODEL.SEM_SEG_HEAD.ASPP_DILATIONS
self.ignore_value = cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE
num_classes = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES
conv_dims = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
self.common_stride = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE # output stride
norm = cfg.MODEL.SEM_SEG_HEAD.NORM
self.loss_weight = cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT
self.loss_type = cfg.MODEL.SEM_SEG_HEAD.LOSS_TYPE
train_crop_size = cfg.INPUT.CROP.SIZE
aspp_dropout = cfg.MODEL.SEM_SEG_HEAD.ASPP_DROPOUT
# fmt: on
assert len(self.in_features) == 1
assert len(in_channels) == 1
# ASPP module
if cfg.INPUT.CROP.ENABLED:
assert cfg.INPUT.CROP.TYPE == "absolute"
train_crop_h, train_crop_w = train_crop_size
if train_crop_h % self.common_stride or train_crop_w % self.common_stride:
raise ValueError(
"Crop size need to be divisible by output stride.")
pool_h = train_crop_h // self.common_stride
pool_w = train_crop_w // self.common_stride
pool_kernel_size = (pool_h, pool_w)
else:
pool_kernel_size = None
self.aspp = ASPP(
in_channels[0],
aspp_channels,
aspp_dilations,
norm=norm,
activation=F.relu,
pool_kernel_size=pool_kernel_size,
dropout=aspp_dropout,
)
self.predictor = Conv2d(conv_dims,
num_classes,
kernel_size=1,
stride=1,
padding=0)
nn.init.normal_(self.predictor.weight, 0, 0.001)
nn.init.constant_(self.predictor.bias, 0)
if self.loss_type == "cross_entropy":
self.loss = nn.CrossEntropyLoss(reduction="mean",
ignore_index=self.ignore_value)
elif self.loss_type == "hard_pixel_mining":
self.loss = DeepLabCE(ignore_label=self.ignore_value,
top_k_percent_pixels=0.2)
else:
raise ValueError("Unexpected loss type: %s" % self.loss_type)
def test_aspp(self):
m = ASPP(3, 10, [2, 3, 4], norm="", activation=nn.PReLU())
self.assertIsNot(m.convs[0].activation.weight,
m.convs[1].activation.weight)
self.assertIsNot(m.convs[0].activation.weight,
m.project.activation.weight)
def __init__(
self,
input_shape: Dict[str, ShapeSpec],
*,
in_features: List[str],
project_channels: List[int],
aspp_dilations: List[int],
aspp_dropout: float,
decoder_channels: List[int],
common_stride: int,
norm: Union[str, Callable],
train_size: Optional[Tuple],
loss_weight: float = 1.0,
loss_type: str = "cross_entropy",
ignore_value: int = -1,
num_classes: Optional[int] = None,
):
"""
NOTE: this interface is experimental.
Args:
input_shape (ShapeSpec): shape of the input feature
in_features (list[str]): a list of input feature names, the last
name of "in_features" is used as the input to the decoder (i.e.
the ASPP module) and rest of "in_features" are low-level feature
the the intermediate levels of decoder. "in_features" should be
ordered from highest resolution to lowest resolution. For
example: ["res2", "res3", "res4", "res5"].
project_channels (list[int]): a list of low-level feature channels.
The length should be len(in_features) - 1.
aspp_dilations (list(int)): a list of 3 dilations in ASPP.
aspp_dropout (float): apply dropout on the output of ASPP.
decoder_channels (list[int]): a list of output channels of each
decoder stage. It should have the same length as "in_features"
(each element in "in_features" corresponds to one decoder stage).
common_stride (int): output stride of decoder.
norm (str or callable): normalization for all conv layers.
train_size (tuple): (height, width) of training images.
loss_weight (float): loss weight.
loss_type (str): type of loss function, 2 opptions:
(1) "cross_entropy" is the standard cross entropy loss.
(2) "hard_pixel_mining" is the loss in DeepLab that samples
top k% hardest pixels.
ignore_value (int): category to be ignored during training.
num_classes (int): number of classes, if set to None, the decoder
will not construct a predictor.
"""
super().__init__()
# fmt: off
self.in_features = in_features # starting from "res2" to "res5"
in_channels = [input_shape[f].channels for f in self.in_features]
aspp_channels = decoder_channels[-1]
self.ignore_value = ignore_value
self.common_stride = common_stride # output stride
self.loss_weight = loss_weight
self.loss_type = loss_type
self.decoder_only = num_classes is None
# fmt: on
assert (len(project_channels) == len(self.in_features) -
1), "Expected {} project_channels, got {}".format(
len(self.in_features) - 1, len(project_channels))
assert len(decoder_channels) == len(
self.in_features), "Expected {} decoder_channels, got {}".format(
len(self.in_features), len(decoder_channels))
self.decoder = nn.ModuleDict()
use_bias = norm == ""
for idx, in_channel in enumerate(in_channels):
decoder_stage = nn.ModuleDict()
if idx == len(self.in_features) - 1:
# ASPP module
if train_size is not None:
train_h, train_w = train_size
encoder_stride = input_shape[self.in_features[-1]].stride
if train_h % encoder_stride or train_w % encoder_stride:
raise ValueError(
"Crop size need to be divisible by encoder stride."
)
pool_h = train_h // encoder_stride
pool_w = train_w // encoder_stride
pool_kernel_size = (pool_h, pool_w)
else:
pool_kernel_size = None
project_conv = ASPP(
in_channel,
aspp_channels,
aspp_dilations,
norm=norm,
activation=F.relu,
pool_kernel_size=pool_kernel_size,
dropout=aspp_dropout,
)
fuse_conv = None
else:
project_conv = Conv2d(
in_channel,
project_channels[idx],
kernel_size=1,
bias=use_bias,
norm=get_norm(norm, project_channels[idx]),
activation=F.relu,
)
fuse_conv = nn.Sequential(
Conv2d(
project_channels[idx] + decoder_channels[idx + 1],
decoder_channels[idx],
kernel_size=3,
padding=1,
bias=use_bias,
norm=get_norm(norm, decoder_channels[idx]),
activation=F.relu,
),
Conv2d(
decoder_channels[idx],
decoder_channels[idx],
kernel_size=3,
padding=1,
bias=use_bias,
norm=get_norm(norm, decoder_channels[idx]),
activation=F.relu,
),
)
weight_init.c2_xavier_fill(project_conv)
weight_init.c2_xavier_fill(fuse_conv[0])
weight_init.c2_xavier_fill(fuse_conv[1])
decoder_stage["project_conv"] = project_conv
decoder_stage["fuse_conv"] = fuse_conv
self.decoder[self.in_features[idx]] = decoder_stage
if not self.decoder_only:
self.predictor = Conv2d(decoder_channels[0],
num_classes,
kernel_size=1,
stride=1,
padding=0)
nn.init.normal_(self.predictor.weight, 0, 0.001)
nn.init.constant_(self.predictor.bias, 0)
if self.loss_type == "cross_entropy":
self.loss = nn.CrossEntropyLoss(reduction="mean",
ignore_index=self.ignore_value)
elif self.loss_type == "hard_pixel_mining":
self.loss = DeepLabCE(ignore_label=self.ignore_value,
top_k_percent_pixels=0.2)
else:
raise ValueError("Unexpected loss type: %s" % self.loss_type)
def __init__(
self,
input_shape: Dict[str, ShapeSpec],
*,
project_channels: List[int],
aspp_dilations: List[int],
aspp_dropout: float,
decoder_channels: List[int],
common_stride: int,
norm: Union[str, Callable],
train_size: Optional[Tuple],
loss_weight: float = 1.0,
loss_type: str = "cross_entropy",
ignore_value: int = -1,
num_classes: Optional[int] = None,
use_depthwise_separable_conv: bool = False,
):
"""
NOTE: this interface is experimental.
Args:
input_shape: shape of the input features. They will be ordered by stride
and the last one (with largest stride) is used as the input to the
decoder (i.e. the ASPP module); the rest are low-level feature for
the intermediate levels of decoder.
project_channels (list[int]): a list of low-level feature channels.
The length should be len(in_features) - 1.
aspp_dilations (list(int)): a list of 3 dilations in ASPP.
aspp_dropout (float): apply dropout on the output of ASPP.
decoder_channels (list[int]): a list of output channels of each
decoder stage. It should have the same length as "in_features"
(each element in "in_features" corresponds to one decoder stage).
common_stride (int): output stride of decoder.
norm (str or callable): normalization for all conv layers.
train_size (tuple): (height, width) of training images.
loss_weight (float): loss weight.
loss_type (str): type of loss function, 2 opptions:
(1) "cross_entropy" is the standard cross entropy loss.
(2) "hard_pixel_mining" is the loss in DeepLab that samples
top k% hardest pixels.
ignore_value (int): category to be ignored during training.
num_classes (int): number of classes, if set to None, the decoder
will not construct a predictor.
use_depthwise_separable_conv (bool): use DepthwiseSeparableConv2d
in ASPP and decoder.
"""
super().__init__()
input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
# fmt: off
self.in_features = [k for k, v in input_shape
] # starting from "res2" to "res5"
in_channels = [x[1].channels for x in input_shape]
in_strides = [x[1].stride for x in input_shape]
aspp_channels = decoder_channels[-1]
self.ignore_value = ignore_value
self.common_stride = common_stride # output stride
self.loss_weight = loss_weight
self.loss_type = loss_type
self.decoder_only = num_classes is None
self.use_depthwise_separable_conv = use_depthwise_separable_conv
# fmt: on
assert (len(project_channels) == len(self.in_features) -
1), "Expected {} project_channels, got {}".format(
len(self.in_features) - 1, len(project_channels))
assert len(decoder_channels) == len(
self.in_features), "Expected {} decoder_channels, got {}".format(
len(self.in_features), len(decoder_channels))
self.decoder = nn.ModuleDict()
use_bias = norm == ""
for idx, in_channel in enumerate(in_channels):
decoder_stage = nn.ModuleDict()
if idx == len(self.in_features) - 1:
# ASPP module
if train_size is not None:
train_h, train_w = train_size
print('train size: ', train_h, train_w)
encoder_stride = in_strides[-1]
if train_h % encoder_stride or train_w % encoder_stride:
raise ValueError(
"Crop size need to be divisible by encoder stride."
)
pool_h = train_h // encoder_stride
pool_w = train_w // encoder_stride
pool_kernel_size = (pool_h, pool_w)
else:
pool_kernel_size = None
project_conv = ASPP(
in_channel,
aspp_channels,
aspp_dilations,
norm=norm,
activation=F.relu,
pool_kernel_size=pool_kernel_size,
dropout=aspp_dropout,
use_depthwise_separable_conv=use_depthwise_separable_conv,
)
fuse_conv = None
else:
project_conv = Conv2d(
in_channel,
project_channels[idx],
kernel_size=1,
bias=use_bias,
norm=get_norm(norm, project_channels[idx]),
activation=F.relu,
)
weight_init.c2_xavier_fill(project_conv)
if use_depthwise_separable_conv:
# We use a single 5x5 DepthwiseSeparableConv2d to replace
# 2 3x3 Conv2d since they have the same receptive field,
# proposed in :paper:`Panoptic-DeepLab`.
fuse_conv = DepthwiseSeparableConv2d(
project_channels[idx] + decoder_channels[idx + 1],
decoder_channels[idx],
kernel_size=5,
padding=2,
norm1=norm,
activation1=F.relu,
norm2=norm,
activation2=F.relu,
)
else:
fuse_conv = nn.Sequential(
Conv2d(
project_channels[idx] + decoder_channels[idx + 1],
decoder_channels[idx],
kernel_size=3,
padding=1,
bias=use_bias,
norm=get_norm(norm, decoder_channels[idx]),
activation=F.relu,
),
Conv2d(
decoder_channels[idx],
decoder_channels[idx],
kernel_size=3,
padding=1,
bias=use_bias,
norm=get_norm(norm, decoder_channels[idx]),
activation=F.relu,
),
)
weight_init.c2_xavier_fill(fuse_conv[0])
weight_init.c2_xavier_fill(fuse_conv[1])
decoder_stage["project_conv"] = project_conv
decoder_stage["fuse_conv"] = fuse_conv
self.decoder[self.in_features[idx]] = decoder_stage
if not self.decoder_only:
self.predictor = Conv2d(decoder_channels[0],
num_classes,
kernel_size=1,
stride=1,
padding=0)
nn.init.normal_(self.predictor.weight, 0, 0.001)
nn.init.constant_(self.predictor.bias, 0)
if self.loss_type == "cross_entropy":
self.loss = nn.CrossEntropyLoss(reduction="mean",
ignore_index=self.ignore_value)
elif self.loss_type == "hard_pixel_mining":
self.loss = DeepLabCE(ignore_label=self.ignore_value,
top_k_percent_pixels=0.2)
else:
raise ValueError("Unexpected loss type: %s" % self.loss_type)