# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from detectron2.detectron2.utils.registry import Registry
META_ARCH_REGISTRY = Registry("META_ARCH") # noqa F401 isort:skip
META_ARCH_REGISTRY.__doc__ = """
Registry for meta-architectures, i.e. the whole model.
The registered object will be called with `obj(cfg)`
and expected to return a `nn.Module` object.
"""
def build_model(cfg):
"""
Built the whole model, defined by `cfg.MODEL.META_ARCHITECTURE`.
"""
meta_arch = cfg.MODEL.META_ARCHITECTURE
return META_ARCH_REGISTRY.get(meta_arch)(cfg)
from torch.nn import functional as F
from detectron2.detectron2.layers import Conv2d, ShapeSpec
from detectron2.detectron2.structures import ImageList
from detectron2.detectron2.utils.registry import Registry
from ..backbone import build_backbone
from ..postprocessing import sem_seg_postprocess
from .build import META_ARCH_REGISTRY
__all__ = [
"SemanticSegmentor", "SEM_SEG_HEADS_REGISTRY", "SemSegFPNHead",
"build_sem_seg_head"
]
SEM_SEG_HEADS_REGISTRY = Registry("SEM_SEG_HEADS")
"""
Registry for semantic segmentation heads, which make semantic segmentation predictions
from feature maps.
"""
@META_ARCH_REGISTRY.register()
class SemanticSegmentor(nn.Module):
"""
Main class for semantic segmentation architectures.
"""
def __init__(self, cfg):
super().__init__()
self.device = torch.device(cfg.MODEL.DEVICE)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from detectron2.detectron2.utils.registry import Registry
PROPOSAL_GENERATOR_REGISTRY = Registry("PROPOSAL_GENERATOR")
PROPOSAL_GENERATOR_REGISTRY.__doc__ = """
Registry for proposal generator, which produces object proposals from feature maps.
The registered object will be called with `obj(cfg, input_shape)`.
The call should return a `nn.Module` object.
"""
from . import rpn, rrpn # noqa F401 isort:skip
def build_proposal_generator(cfg, input_shape):
"""
Build a proposal generator from `cfg.MODEL.PROPOSAL_GENERATOR.NAME`.
The name can be "PrecomputedProposals" to use no proposal generator.
"""
name = cfg.MODEL.PROPOSAL_GENERATOR.NAME
if name == "PrecomputedProposals":
return None
return PROPOSAL_GENERATOR_REGISTRY.get(name)(cfg, input_shape)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import copy
import math
from typing import List
import torch
from torch import nn
from detectron2.detectron2.layers import ShapeSpec
from detectron2.detectron2.structures import Boxes, RotatedBoxes
from detectron2.detectron2.utils.registry import Registry
ANCHOR_GENERATOR_REGISTRY = Registry("ANCHOR_GENERATOR")
"""
Registry for modules that creates object detection anchors for feature maps.
"""
class BufferList(nn.Module):
"""
Similar to nn.ParameterList, but for buffers
"""
def __init__(self, buffers=None):
super(BufferList, self).__init__()
if buffers is not None:
self.extend(buffers)
def extend(self, buffers):
offset = len(self)
for i, buffer in enumerate(buffers):
self.register_buffer(str(offset + i), buffer)
return self
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from detectron2.detectron2.layers import ShapeSpec
from detectron2.detectron2.utils.registry import Registry
from .backbone import Backbone
BACKBONE_REGISTRY = Registry("BACKBONE")
BACKBONE_REGISTRY.__doc__ = """
Registry for backbones, which extract feature maps from images
The registered object must be a callable that accepts two arguments:
1. A :class:`detectron2.config.CfgNode`
2. A :class:`detectron2.layers.ShapeSpec`, which contains the input shape specification.
It must returns an instance of :class:`Backbone`.
"""
def build_backbone(cfg, input_shape=None):
"""
Build a backbone from `cfg.MODEL.BACKBONE.NAME`.
Returns:
an instance of :class:`Backbone`
"""
if input_shape is None:
input_shape = ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))
backbone_name = cfg.MODEL.BACKBONE.NAME
backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg, input_shape)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.detectron2.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, get_norm
from detectron2.detectron2.utils.events import get_event_storage
from detectron2.detectron2.utils.registry import Registry
ROI_MASK_HEAD_REGISTRY = Registry("ROI_MASK_HEAD")
ROI_MASK_HEAD_REGISTRY.__doc__ = """
Registry for mask heads, which predicts instance masks given
per-region features.
The registered object will be called with `obj(cfg, input_shape)`.
"""
def mask_rcnn_loss(pred_mask_logits, instances):
"""
Compute the mask prediction loss defined in the Mask R-CNN paper.
Args:
pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask)
for class-specific or class-agnostic, where B is the total number of predicted masks
in all images, C is the number of foreground classes, and Hmask, Wmask are the height
and width of the mask predictions. The values are logits.
instances (list[Instances]): A list of N Instances, where N is the number of images
in the batch. These instances are in 1:1
correspondence with the pred_mask_logits. The ground-truth labels (class, box, mask,
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from typing import Dict, List
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.detectron2.layers import ShapeSpec
from detectron2.detectron2.utils.registry import Registry
from ..anchor_generator import build_anchor_generator
from ..box_regression import Box2BoxTransform
from ..matcher import Matcher
from .build import PROPOSAL_GENERATOR_REGISTRY
from .rpn_outputs import RPNOutputs, find_top_rpn_proposals
RPN_HEAD_REGISTRY = Registry("RPN_HEAD")
"""
Registry for RPN heads, which take feature maps and perform
objectness classification and bounding box regression for anchors.
"""
def build_rpn_head(cfg, input_shape):
"""
Build an RPN head defined by `cfg.MODEL.RPN.HEAD_NAME`.
"""
name = cfg.MODEL.RPN.HEAD_NAME
return RPN_HEAD_REGISTRY.get(name)(cfg, input_shape)
@RPN_HEAD_REGISTRY.register()
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.detectron2.utils.registry import Registry
ROI_BOX_HEAD_REGISTRY = Registry("ROI_BOX_HEAD")
ROI_BOX_HEAD_REGISTRY.__doc__ = """
Registry for box heads, which make box predictions from per-region features.
The registered object will be called with `obj(cfg, input_shape)`.
"""
@ROI_BOX_HEAD_REGISTRY.register()
class FastRCNNConvFCHead(nn.Module):
"""
A head with several 3x3 conv layers (each followed by norm & relu) and
several fc layers (each followed by relu).
"""
def __init__(self, cfg, input_shape: ShapeSpec):
"""
The following attributes are parsed from config:
num_conv, num_fc: the number of conv/fc layers
conv_dim/fc_dim: the dimension of the conv/fc layers
norm: normalization for the conv layers
"""
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.detectron2.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, interpolate
from detectron2.detectron2.structures import heatmaps_to_keypoints
from detectron2.detectron2.utils.events import get_event_storage
from detectron2.detectron2.utils.registry import Registry
_TOTAL_SKIPPED = 0
ROI_KEYPOINT_HEAD_REGISTRY = Registry("ROI_KEYPOINT_HEAD")
ROI_KEYPOINT_HEAD_REGISTRY.__doc__ = """
Registry for keypoint heads, which make keypoint predictions from per-region features.
The registered object will be called with `obj(cfg, input_shape)`.
"""
def build_keypoint_head(cfg, input_shape):
"""
Build a keypoint head from `cfg.MODEL.ROI_KEYPOINT_HEAD.NAME`.
"""
name = cfg.MODEL.ROI_KEYPOINT_HEAD.NAME
return ROI_KEYPOINT_HEAD_REGISTRY.get(name)(cfg, input_shape)
def keypoint_rcnn_loss(pred_keypoint_logits, instances, normalizer):
"""
Arguments:
from detectron2.detectron2.structures import Boxes, Instances, pairwise_iou
from detectron2.detectron2.utils.events import get_event_storage
from detectron2.detectron2.utils.registry import Registry
from ..backbone.resnet import BottleneckBlock, make_stage
from ..box_regression import Box2BoxTransform
from ..matcher import Matcher
from ..poolers import ROIPooler
from ..proposal_generator.proposal_utils import add_ground_truth_to_proposals
from ..sampling import subsample_labels
from .box_head import build_box_head
from .fast_rcnn import FastRCNNOutputLayers, FastRCNNOutputs
from .keypoint_head import build_keypoint_head, keypoint_rcnn_inference, keypoint_rcnn_loss
from .mask_head import build_mask_head, mask_rcnn_inference, mask_rcnn_loss
ROI_HEADS_REGISTRY = Registry("ROI_HEADS")
ROI_HEADS_REGISTRY.__doc__ = """
Registry for ROI heads in a generalized R-CNN model.
ROIHeads take feature maps and region proposals, and
perform per-region computation.
The registered object will be called with `obj(cfg, input_shape)`.
The call is expected to return an :class:`ROIHeads`.
"""
logger = logging.getLogger(__name__)
def build_roi_heads(cfg, input_shape):
"""
Build ROIHeads defined by `cfg.MODEL.ROI_HEADS.NAME`.