Spaces:
Sleeping
Sleeping
File size: 3,693 Bytes
b213d84 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
# Copyright (c) Facebook, Inc. and its affiliates.
import torch
from torch import nn
from detectron2.config import CfgNode
from detectron2.layers import ConvTranspose2d, interpolate
from ...structures import DensePoseChartPredictorOutput
from ..utils import initialize_module_params
from .registry import DENSEPOSE_PREDICTOR_REGISTRY
@DENSEPOSE_PREDICTOR_REGISTRY.register()
class DensePoseChartPredictor(nn.Module):
"""
Predictor (last layers of a DensePose model) that takes DensePose head outputs as an input
and produces 4 tensors which represent DensePose results for predefined body parts
(patches / charts):
* coarse segmentation, a tensor of shape [N, K, Hout, Wout]
* fine segmentation, a tensor of shape [N, C, Hout, Wout]
* U coordinates, a tensor of shape [N, C, Hout, Wout]
* V coordinates, a tensor of shape [N, C, Hout, Wout]
where
- N is the number of instances
- K is the number of coarse segmentation channels (
2 = foreground / background,
15 = one of 14 body parts / background)
- C is the number of fine segmentation channels (
24 fine body parts / background)
- Hout and Wout are height and width of predictions
"""
def __init__(self, cfg: CfgNode, input_channels: int):
"""
Initialize predictor using configuration options
Args:
cfg (CfgNode): configuration options
input_channels (int): input tensor size along the channel dimension
"""
super().__init__()
dim_in = input_channels
n_segm_chan = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS
dim_out_patches = cfg.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES + 1
kernel_size = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL
# coarse segmentation
self.ann_index_lowres = ConvTranspose2d(
dim_in, n_segm_chan, kernel_size, stride=2, padding=int(kernel_size / 2 - 1)
)
# fine segmentation
self.index_uv_lowres = ConvTranspose2d(
dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1)
)
# U
self.u_lowres = ConvTranspose2d(
dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1)
)
# V
self.v_lowres = ConvTranspose2d(
dim_in, dim_out_patches, kernel_size, stride=2, padding=int(kernel_size / 2 - 1)
)
self.scale_factor = cfg.MODEL.ROI_DENSEPOSE_HEAD.UP_SCALE
initialize_module_params(self)
def interp2d(self, tensor_nchw: torch.Tensor):
"""
Bilinear interpolation method to be used for upscaling
Args:
tensor_nchw (tensor): tensor of shape (N, C, H, W)
Return:
tensor of shape (N, C, Hout, Wout), where Hout and Wout are computed
by applying the scale factor to H and W
"""
return interpolate(
tensor_nchw, scale_factor=self.scale_factor, mode="bilinear", align_corners=False
)
def forward(self, head_outputs: torch.Tensor):
"""
Perform forward step on DensePose head outputs
Args:
head_outputs (tensor): DensePose head outputs, tensor of shape [N, D, H, W]
Return:
An instance of DensePoseChartPredictorOutput
"""
return DensePoseChartPredictorOutput(
coarse_segm=self.interp2d(self.ann_index_lowres(head_outputs)),
fine_segm=self.interp2d(self.index_uv_lowres(head_outputs)),
u=self.interp2d(self.u_lowres(head_outputs)),
v=self.interp2d(self.v_lowres(head_outputs)),
)
|