# 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 mydl.layers import ShapeSpec from mydl.structures import Boxes, RotatedBoxes from mydl.utils.registry import Registry ANCHOR_GENERATOR_REGISTRY = Registry("ANCHOR_GENERATOR") """ Registry for modules that creates object detection anchors for feature maps. The registered object will be called with `obj(cfg, input_shape)`. """ 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):
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from mydl.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): """ Build the whole model architecture, defined by ``cfg.MODEL.META_ARCHITECTURE``. Note that it does not load any weights from ``cfg``. """ meta_arch = cfg.MODEL.META_ARCHITECTURE return META_ARCH_REGISTRY.get(meta_arch)(cfg)
out_channels=out_channels, num_groups=num_groups, stride_in_1x1=stride_in_1x1, stride=stride, dilation=dilation, norm_func=group_norm, dcn_config=dcn_config) class StemWithGN(BaseStem): def __init__(self, cfg): super(StemWithGN, self).__init__(cfg, norm_func=group_norm) _TRANSFORMATION_MODULES = Registry({ "BottleneckWithFixedBatchNorm": BottleneckWithFixedBatchNorm, "BottleneckWithGN": BottleneckWithGN, }) _STEM_MODULES = Registry({ "StemWithFixedBatchNorm": StemWithFixedBatchNorm, "StemWithGN": StemWithGN, }) _STAGE_SPECS = Registry({ "R-50-C4": ResNet50StagesTo4, "R-50-C5": ResNet50StagesTo5, "R-101-C4": ResNet101StagesTo4, "R-101-C5": ResNet101StagesTo5, "R-50-FPN": ResNet50FPNStagesTo5, "R-50-FPN-RETINANET": ResNet50FPNStagesTo5, "R-101-FPN": ResNet101FPNStagesTo5,
# 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 mydl.layers import ShapeSpec, cat from mydl.structures import BitMasks from mydl.utils.events import get_event_storage from mydl.utils.registry import Registry from .point_features import point_sample POINT_HEAD_REGISTRY = Registry("POINT_HEAD") POINT_HEAD_REGISTRY.__doc__ = """ Registry for point heads, which makes prediction for a given set of per-point features. The registered object will be called with `obj(cfg, input_shape)`. """ def roi_mask_point_loss(mask_logits, instances, points_coord): """ Compute the point-based loss for instance segmentation mask predictions. Args: mask_logits (Tensor): A tensor of shape (R, C, P) or (R, 1, P) for class-specific or class-agnostic, where R is the total number of predicted masks in all images, C is the number of foreground classes, and P is the number of points sampled for each mask. The values are logits. instances (list[Instances]): A list of N Instances, where N is the number of images
# 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 mydl.layers import Conv2d, ConvTranspose2d, interpolate from mydl.structures.boxes import matched_boxlist_iou from mydl.utils.registry import Registry from .structures import DensePoseOutput ROI_DENSEPOSE_HEAD_REGISTRY = Registry("ROI_DENSEPOSE_HEAD") def initialize_module_params(module): for name, param in module.named_parameters(): if "bias" in name: nn.init.constant_(param, 0) elif "weight" in name: nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu") @ROI_DENSEPOSE_HEAD_REGISTRY.register() class DensePoseDeepLabHead(nn.Module): def __init__(self, cfg, input_channels): super(DensePoseDeepLabHead, self).__init__() # fmt: off hidden_dim = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL norm = cfg.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NORM
# 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 mydl.layers import ShapeSpec from mydl.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. The registered object will be called with `obj(cfg, input_shape)`. The call should return a `nn.Module` object. """ 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)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from typing import List import fvcore.nn.weight_init as weight_init import torch from torch import nn from torch.nn import functional as F from mydl.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, get_norm from mydl.structures import Instances from mydl.utils.events import get_event_storage from mydl.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, vis_period=0): """ 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
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from typing import List import torch from torch import nn from torch.nn import functional as F from mydl.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, interpolate from mydl.structures import Instances, heatmaps_to_keypoints from mydl.utils.events import get_event_storage from mydl.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): """
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from mydl.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 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 mydl.layers import Conv2d, Linear, ShapeSpec, get_norm from mydl.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
import torch from torch import nn from torch.nn import functional as F from mydl.layers import Conv2d, ShapeSpec from mydl.structures import ImageList from mydl.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__()
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from mydl.utils.registry import Registry BACKBONES = Registry() RPN_HEADS = Registry() ROI_BOX_FEATURE_EXTRACTORS = Registry() ROI_BOX_PREDICTOR = Registry() ROI_KEYPOINT_FEATURE_EXTRACTORS = Registry() ROI_KEYPOINT_PREDICTOR = Registry() ROI_MASK_FEATURE_EXTRACTORS = Registry() ROI_MASK_PREDICTOR = Registry()
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from mydl.layers import ShapeSpec from mydl.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:`mydl.config.CfgNode` 2. A :class:`mydl.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)
from mydl.structures import Boxes, ImageList, Instances, pairwise_iou from mydl.utils.events import get_event_storage from mydl.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 from .mask_head import build_mask_head 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`.
new_key = old_key.replace("conv2.{}".format(param), "conv2.conv.{}".format(param)) logger.info("pattern: {}, old_key: {}, new_key: {}".format( pattern, old_key, new_key)) state_dict[new_key] = state_dict[old_key] del state_dict[old_key] return state_dict _C2_STAGE_NAMES = { "R-50": ["1.2", "2.3", "3.5", "4.2"], "R-101": ["1.2", "2.3", "3.22", "4.2"], "R-152": ["1.2", "2.7", "3.35", "4.2"], } C2_FORMAT_LOADER = Registry() @C2_FORMAT_LOADER.register("R-50-C4") @C2_FORMAT_LOADER.register("R-50-C5") @C2_FORMAT_LOADER.register("R-101-C4") @C2_FORMAT_LOADER.register("R-101-C5") @C2_FORMAT_LOADER.register("R-50-FPN") @C2_FORMAT_LOADER.register("R-50-FPN-RETINANET") @C2_FORMAT_LOADER.register("R-101-FPN") @C2_FORMAT_LOADER.register("R-101-FPN-RETINANET") @C2_FORMAT_LOADER.register("R-152-FPN") def load_resnet_c2_format(cfg, f): state_dict = _load_c2_pickled_weights(f) conv_body = cfg.MODEL.BACKBONE.CONV_BODY arch = conv_body.replace("-C4", "").replace("-C5", "").replace("-FPN", "")