# Copyright (c) OpenMMLab. All rights reserved.
# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py # noqa
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import point_sample
from mmseg.models.builder import HEADS
from mmseg.ops import resize
from ..losses import accuracy
from .cascade_decode_head import BaseCascadeDecodeHead
def calculate_uncertainty(seg_logits):
"""Estimate uncertainty based on seg logits.
For each location of the prediction ``seg_logits`` we estimate
uncertainty as the difference between top first and top second
predicted logits.
Args:
seg_logits (Tensor): Semantic segmentation logits,
shape (batch_size, num_classes, height, width).
Returns:
scores (Tensor): T uncertainty scores with the most uncertain
locations having the highest uncertainty score, shape (
batch_size, 1, height, width)
"""
top2_scores = torch.topk(seg_logits, k=2, dim=1)[0]
return (top2_scores[:, 1] - top2_scores[:, 0]).unsqueeze(1)
@HEADS.register_module()
class PointHead(BaseCascadeDecodeHead):
"""A mask point head use in PointRend.
This head is implemented of `PointRend: Image Segmentation as
Rendering <https://arxiv.org/abs/1912.08193>`_.
``PointHead`` use shared multi-layer perceptron (equivalent to
nn.Conv1d) to predict the logit of input points. The fine-grained feature
and coarse feature will be concatenate together for predication.
Args:
num_fcs (int): Number of fc layers in the head. Default: 3.
in_channels (int): Number of input channels. Default: 256.
fc_channels (int): Number of fc channels. Default: 256.
num_classes (int): Number of classes for logits. Default: 80.
class_agnostic (bool): Whether use class agnostic classification.
If so, the output channels of logits will be 1. Default: False.
coarse_pred_each_layer (bool): Whether concatenate coarse feature with
the output of each fc layer. Default: True.
conv_cfg (dict|None): Dictionary to construct and config conv layer.
Default: dict(type='Conv1d'))
norm_cfg (dict|None): Dictionary to construct and config norm layer.
Default: None.
loss_point (dict): Dictionary to construct and config loss layer of
point head. Default: dict(type='CrossEntropyLoss', use_mask=True,
loss_weight=1.0).
"""
def __init__(self,
num_fcs=3,
coarse_pred_each_layer=True,
conv_cfg=dict(type='Conv1d'),
norm_cfg=None,
act_cfg=dict(type='ReLU', inplace=False),
**kwargs):
super(PointHead, self).__init__(
input_transform='multiple_select',
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
init_cfg=dict(
type='Normal', std=0.01, override=dict(name='fc_seg')),
**kwargs)
self.num_fcs = num_fcs
self.coarse_pred_each_layer = coarse_pred_each_layer
fc_in_channels = sum(self.in_channels) + self.num_classes
fc_channels = self.channels
self.fcs = nn.ModuleList()
for k in range(num_fcs):
fc = ConvModule(
fc_in_channels,
fc_channels,
kernel_size=1,
stride=1,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.fcs.append(fc)
fc_in_channels = fc_channels
fc_in_channels += self.num_classes if self.coarse_pred_each_layer \
else 0
self.fc_seg = nn.Conv1d(
fc_in_channels,
self.num_classes,
kernel_size=1,
stride=1,
padding=0)
if self.dropout_ratio > 0:
self.dropout = nn.Dropout(self.dropout_ratio)
delattr(self, 'conv_seg')
def cls_seg(self, feat):
"""Classify each pixel with fc."""
if self.dropout is not None:
feat = self.dropout(feat)
output = self.fc_seg(feat)
return output
def forward(self, fine_grained_point_feats, coarse_point_feats):
x = torch.cat([fine_grained_point_feats, coarse_point_feats], dim=1)
for fc in self.fcs:
x = fc(x)
if self.coarse_pred_each_layer:
x = torch.cat((x, coarse_point_feats), dim=1)
return self.cls_seg(x)
def _get_fine_grained_point_feats(self, x, points):
"""Sample from fine grained features.
Args:
x (list[Tensor]): Feature pyramid from by neck or backbone.
points (Tensor): Point coordinates, shape (batch_size,
num_points, 2).
Returns:
fine_grained_feats (Tensor): Sampled fine grained feature,
shape (batch_size, sum(channels of x), num_points).
"""
fine_grained_feats_list = [
point_sample(_, points, align_corners=self.align_corners)
for _ in x
]
if len(fine_grained_feats_list) > 1:
fine_grained_feats = torch.cat(fine_grained_feats_list, dim=1)
else:
fine_grained_feats = fine_grained_feats_list[0]
return fine_grained_feats
def _get_coarse_point_feats(self, prev_output, points):
"""Sample from fine grained features.
Args:
prev_output (list[Tensor]): Prediction of previous decode head.
points (Tensor): Point coordinates, shape (batch_size,
num_points, 2).
Returns:
coarse_feats (Tensor): Sampled coarse feature, shape (batch_size,
num_classes, num_points).
"""
coarse_feats = point_sample(
prev_output, points, align_corners=self.align_corners)
return coarse_feats
def forward_train(self, inputs, prev_output, img_metas, gt_semantic_seg,
train_cfg):
"""Forward function for training.
Args:
inputs (list[Tensor]): List of multi-level img features.
prev_output (Tensor): The output of previous decode head.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmseg/datasets/pipelines/formatting.py:Collect`.
gt_semantic_seg (Tensor): Semantic segmentation masks
used if the architecture supports semantic segmentation task.
train_cfg (dict): The training config.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
x = self._transform_inputs(inputs)
with torch.no_grad():
points = self.get_points_train(
prev_output, calculate_uncertainty, cfg=train_cfg)
fine_grained_point_feats = self._get_fine_grained_point_feats(
x, points)
coarse_point_feats = self._get_coarse_point_feats(prev_output, points)
point_logits = self.forward(fine_grained_point_feats,
coarse_point_feats)
point_label = point_sample(
gt_semantic_seg.float(),
points,
mode='nearest',
align_corners=self.align_corners)
point_label = point_label.squeeze(1).long()
losses = self.losses(point_logits, point_label)
return losses
def forward_test(self, inputs, prev_output, img_metas, test_cfg):
"""Forward function for testing.
Args:
inputs (list[Tensor]): List of multi-level img features.
prev_output (Tensor): The output of previous decode head.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmseg/datasets/pipelines/formatting.py:Collect`.
test_cfg (dict): The testing config.
Returns:
Tensor: Output segmentation map.
"""
x = self._transform_inputs(inputs)
refined_seg_logits = prev_output.clone()
for _ in range(test_cfg.subdivision_steps):
refined_seg_logits = resize(
refined_seg_logits,
scale_factor=test_cfg.scale_factor,
mode='bilinear',
align_corners=self.align_corners)
batch_size, channels, height, width = refined_seg_logits.shape
point_indices, points = self.get_points_test(
refined_seg_logits, calculate_uncertainty, cfg=test_cfg)
fine_grained_point_feats = self._get_fine_grained_point_feats(
x, points)
coarse_point_feats = self._get_coarse_point_feats(
prev_output, points)
point_logits = self.forward(fine_grained_point_feats,
coarse_point_feats)
point_indices = point_indices.unsqueeze(1).expand(-1, channels, -1)
refined_seg_logits = refined_seg_logits.reshape(
batch_size, channels, height * width)
refined_seg_logits = refined_seg_logits.scatter_(
2, point_indices, point_logits)
refined_seg_logits = refined_seg_logits.view(
batch_size, channels, height, width)
return refined_seg_logits
def losses(self, point_logits, point_label):
"""Compute segmentation loss."""
loss = dict()
if not isinstance(self.loss_decode, nn.ModuleList):
losses_decode = [self.loss_decode]
else:
losses_decode = self.loss_decode
for loss_module in losses_decode:
loss['point' + loss_module.loss_name] = loss_module(
point_logits, point_label, ignore_index=self.ignore_index)
loss['acc_point'] = accuracy(point_logits, point_label)
return loss
def get_points_train(self, seg_logits, uncertainty_func, cfg):
"""Sample points for training.
Sample points in [0, 1] x [0, 1] coordinate space based on their
uncertainty. The uncertainties are calculated for each point using
'uncertainty_func' function that takes point's logit prediction as
input.
Args:
seg_logits (Tensor): Semantic segmentation logits, shape (
batch_size, num_classes, height, width).
uncertainty_func (func): uncertainty calculation function.
cfg (dict): Training config of point head.
Returns:
point_coords (Tensor): A tensor of shape (batch_size, num_points,
2) that contains the coordinates of ``num_points`` sampled
points.
"""
num_points = cfg.num_points
oversample_ratio = cfg.oversample_ratio
importance_sample_ratio = cfg.importance_sample_ratio
assert oversample_ratio >= 1
assert 0 <= importance_sample_ratio <= 1
batch_size = seg_logits.shape[0]
num_sampled = int(num_points * oversample_ratio)
point_coords = torch.rand(
batch_size, num_sampled, 2, device=seg_logits.device)
point_logits = point_sample(seg_logits, point_coords)
# It is crucial to calculate uncertainty based on the sampled
# prediction value for the points. Calculating uncertainties of the
# coarse predictions first and sampling them for points leads to
# incorrect results. To illustrate this: assume uncertainty func(
# logits)=-abs(logits), a sampled point between two coarse
# predictions with -1 and 1 logits has 0 logits, and therefore 0
# uncertainty value. However, if we calculate uncertainties for the
# coarse predictions first, both will have -1 uncertainty,
# and sampled point will get -1 uncertainty.
point_uncertainties = uncertainty_func(point_logits)
num_uncertain_points = int(importance_sample_ratio * num_points)
num_random_points = num_points - num_uncertain_points
idx = torch.topk(
point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
shift = num_sampled * torch.arange(
batch_size, dtype=torch.long, device=seg_logits.device)
idx += shift[:, None]
point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(
batch_size, num_uncertain_points, 2)
if num_random_points > 0:
rand_point_coords = torch.rand(
batch_size, num_random_points, 2, device=seg_logits.device)
point_coords = torch.cat((point_coords, rand_point_coords), dim=1)
return point_coords
def get_points_test(self, seg_logits, uncertainty_func, cfg):
"""Sample points for testing.
Find ``num_points`` most uncertain points from ``uncertainty_map``.
Args:
seg_logits (Tensor): A tensor of shape (batch_size, num_classes,
height, width) for class-specific or class-agnostic prediction.
uncertainty_func (func): uncertainty calculation function.
cfg (dict): Testing config of point head.
Returns:
point_indices (Tensor): A tensor of shape (batch_size, num_points)
that contains indices from [0, height x width) of the most
uncertain points.
point_coords (Tensor): A tensor of shape (batch_size, num_points,
2) that contains [0, 1] x [0, 1] normalized coordinates of the
most uncertain points from the ``height x width`` grid .
"""
num_points = cfg.subdivision_num_points
uncertainty_map = uncertainty_func(seg_logits)
batch_size, _, height, width = uncertainty_map.shape
h_step = 1.0 / height
w_step = 1.0 / width
uncertainty_map = uncertainty_map.view(batch_size, height * width)
num_points = min(height * width, num_points)
point_indices = uncertainty_map.topk(num_points, dim=1)[1]
point_coords = torch.zeros(
batch_size,
num_points,
2,
dtype=torch.float,
device=seg_logits.device)
point_coords[:, :, 0] = w_step / 2.0 + (point_indices %
width).float() * w_step
point_coords[:, :, 1] = h_step / 2.0 + (point_indices //
width).float() * h_step
return point_indices, point_coords