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from typing import Optional |
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from torch import nn |
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from detectron2.config import CfgNode |
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from .cse.embedder import Embedder |
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from .filter import DensePoseDataFilter |
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def build_densepose_predictor(cfg: CfgNode, input_channels: int): |
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""" |
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Create an instance of DensePose predictor based on configuration options. |
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Args: |
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cfg (CfgNode): configuration options |
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input_channels (int): input tensor size along the channel dimension |
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Return: |
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An instance of DensePose predictor |
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""" |
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from .predictors import DENSEPOSE_PREDICTOR_REGISTRY |
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predictor_name = cfg.MODEL.ROI_DENSEPOSE_HEAD.PREDICTOR_NAME |
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return DENSEPOSE_PREDICTOR_REGISTRY.get(predictor_name)(cfg, input_channels) |
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def build_densepose_data_filter(cfg: CfgNode): |
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""" |
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Build DensePose data filter which selects data for training |
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Args: |
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cfg (CfgNode): configuration options |
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Return: |
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Callable: list(Tensor), list(Instances) -> list(Tensor), list(Instances) |
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An instance of DensePose filter, which takes feature tensors and proposals |
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as an input and returns filtered features and proposals |
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""" |
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dp_filter = DensePoseDataFilter(cfg) |
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return dp_filter |
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def build_densepose_head(cfg: CfgNode, input_channels: int): |
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""" |
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Build DensePose head based on configurations options |
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Args: |
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cfg (CfgNode): configuration options |
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input_channels (int): input tensor size along the channel dimension |
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Return: |
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An instance of DensePose head |
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""" |
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from .roi_heads.registry import ROI_DENSEPOSE_HEAD_REGISTRY |
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head_name = cfg.MODEL.ROI_DENSEPOSE_HEAD.NAME |
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return ROI_DENSEPOSE_HEAD_REGISTRY.get(head_name)(cfg, input_channels) |
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def build_densepose_losses(cfg: CfgNode): |
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""" |
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Build DensePose loss based on configurations options |
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Args: |
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cfg (CfgNode): configuration options |
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Return: |
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An instance of DensePose loss |
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""" |
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from .losses import DENSEPOSE_LOSS_REGISTRY |
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loss_name = cfg.MODEL.ROI_DENSEPOSE_HEAD.LOSS_NAME |
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return DENSEPOSE_LOSS_REGISTRY.get(loss_name)(cfg) |
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def build_densepose_embedder(cfg: CfgNode) -> Optional[nn.Module]: |
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""" |
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Build embedder used to embed mesh vertices into an embedding space. |
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Embedder contains sub-embedders, one for each mesh ID. |
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Args: |
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cfg (cfgNode): configuration options |
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Return: |
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Embedding module |
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""" |
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if cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS: |
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return Embedder(cfg) |
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return None |
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