[Feature] Support PointRend (#109)

* [Feature] Support PointRend

* add previous test

* update modelzoo

README.md

@@ -73,6 +73,7 @@ Supported methods:
 - [x] [OCRNet](configs/ocrnet)
 - [x] [Fast-SCNN](configs/fastscnn)
 - [x] [Semantic FPN](configs/sem_fpn)
+- [x] [PointRend](configs/point_rend)
 - [x] [EMANet](configs/emanet)
 - [x] [DNLNet](configs/dnlnet)
 - [x] [Mixed Precision (FP16) Training](configs/fp16/README.md)

configs/_base_/models/pointrend_r50.py

@@ -0,0 +1,56 @@
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='CascadeEncoderDecoder',
+    num_stages=2,
+    pretrained='open-mmlab://resnet50_v1c',
+    backbone=dict(
+        type='ResNetV1c',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 1, 1),
+        strides=(1, 2, 2, 2),
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=4),
+    decode_head=[
+        dict(
+            type='FPNHead',
+            in_channels=[256, 256, 256, 256],
+            in_index=[0, 1, 2, 3],
+            feature_strides=[4, 8, 16, 32],
+            channels=128,
+            dropout_ratio=-1,
+            num_classes=19,
+            norm_cfg=norm_cfg,
+            align_corners=False,
+            loss_decode=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+        dict(
+            type='PointHead',
+            in_channels=[256],
+            in_index=[0],
+            channels=256,
+            num_fcs=3,
+            coarse_pred_each_layer=True,
+            dropout_ratio=-1,
+            num_classes=19,
+            align_corners=False,
+            loss_decode=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
+    ])
+# model training and testing settings
+train_cfg = dict(
+    num_points=2048, oversample_ratio=3, importance_sample_ratio=0.75)
+test_cfg = dict(
+    mode='whole',
+    subdivision_steps=2,
+    subdivision_num_points=8196,
+    scale_factor=2)

configs/point_rend/README.md

@@ -0,0 +1,27 @@
+# PointRend: Image Segmentation as Rendering
+
+## Introduction
+```
+@misc{alex2019pointrend,
+    title={PointRend: Image Segmentation as Rendering},
+    author={Alexander Kirillov and Yuxin Wu and Kaiming He and Ross Girshick},
+    year={2019},
+    eprint={1912.08193},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+## Results and models
+
+### Cityscapes
+|  Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) |                                                                                                                                                                                              download                                                                                                                                                                                              |
+|-----------|----------|-----------|--------:|---------:|----------------|------:|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| PointRend | R-50     | 512x1024  |   80000 |      3.1 | 8.48           | 76.47 | 78.13         | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes-20200715_214714.log.json)     |
+| PointRend | R-101    | 512x1024  |   80000 |      4.2 | 7.00           | 78.30 | 79.97         | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes-20200715_214824.log.json) |
+
+### ADE20K
+|  Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) |                                                                                                                                                                                      download                                                                                                                                                                                      |
+|-----------|----------|-----------|--------:|---------:|----------------|------:|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| PointRend | R-50     | 512x512   |  160000 |      5.1 | 17.31          | 37.64 | 39.17         | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k-20200807_232644.log.json)     |
+| PointRend | R-101    | 512x512   |  160000 |      6.1 | 15.50          | 40.02 | 41.60         | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k-20200808_030852.log.json) |

configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './pointrend_r50_512x1024_80k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/point_rend/pointrend_r101_512x512_160k_ade20k.py

@@ -0,0 +1,2 @@
+_base_ = './pointrend_r50_512x512_160k_ade20k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py

@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/pointrend_r50.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+lr_config = dict(warmup='linear', warmup_iters=200)

configs/point_rend/pointrend_r50_512x512_160k_ade20k.py

@@ -0,0 +1,32 @@
+_base_ = [
+    '../_base_/models/pointrend_r50.py', '../_base_/datasets/ade20k.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+]
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(decode_head=[
+    dict(
+        type='FPNHead',
+        in_channels=[256, 256, 256, 256],
+        in_index=[0, 1, 2, 3],
+        feature_strides=[4, 8, 16, 32],
+        channels=128,
+        dropout_ratio=-1,
+        num_classes=150,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    dict(
+        type='PointHead',
+        in_channels=[256],
+        in_index=[0],
+        channels=256,
+        num_fcs=3,
+        coarse_pred_each_layer=True,
+        dropout_ratio=-1,
+        num_classes=150,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
+])
+lr_config = dict(warmup='linear', warmup_iters=200)

docs/model_zoo.md

@@ -89,6 +89,22 @@ Please refer to [Fast-SCNN](https://github.com/open-mmlab/mmsegmentation/blob/ma
 
 Please refer to [ResNeSt](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest) for details.
 
+### Semantic FPN
+
+Please refer to [Semantic FPN](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/semfpn) for details.
+
+### PointRend
+
+Please refer to [PointRend](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/point_rend) for details.
+
+### EMANet
+
+Please refer to [EMANet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet) for details.
+
+### DNLNet
+
+Please refer to [DNLNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet) for details.
+
 ### Mixed Precision (FP16) Training
 
 Please refer [Mixed Precision (FP16) Training](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fp16/README.md) for details.

mmseg/models/decode_heads/__init__.py

@@ -10,6 +10,7 @@ from .fpn_head import FPNHead
 from .gc_head import GCHead
 from .nl_head import NLHead
 from .ocr_head import OCRHead
+from .point_head import PointHead
 from .psa_head import PSAHead
 from .psp_head import PSPHead
 from .sep_aspp_head import DepthwiseSeparableASPPHead
@@ -19,5 +20,6 @@ from .uper_head import UPerHead
 __all__ = [
     'FCNHead', 'PSPHead', 'ASPPHead', 'PSAHead', 'NLHead', 'GCHead', 'CCHead',
     'UPerHead', 'DepthwiseSeparableASPPHead', 'ANNHead', 'DAHead', 'OCRHead',
-    'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead'
+    'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead',
+    'PointHead'
 ]

mmseg/models/decode_heads/point_head.py

@@ -0,0 +1,349 @@
+# 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, normal_init
+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.
+
+    ``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,
+            **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 init_weights(self):
+        """Initialize weights of classification layer."""
+        normal_init(self.fc_seg, std=0.001)
+
+    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()
+        loss['loss_point'] = self.loss_decode(
+            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

tests/test_models/test_forward.py

@@ -157,6 +157,11 @@ def test_sem_fpn_forward():
     _test_encoder_decoder_forward('sem_fpn/fpn_r50_512x1024_80k_cityscapes.py')
 
 
+def test_point_rend_forward():
+    _test_encoder_decoder_forward(
+        'point_rend/pointrend_r50_512x1024_80k_cityscapes.py')
+
+
 def test_mobilenet_v2_forward():
     _test_encoder_decoder_forward(
         'mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py')

tests/test_models/test_heads.py

@@ -3,13 +3,15 @@ from unittest.mock import patch
 import pytest
 import torch
 from mmcv.cnn import ConvModule
+from mmcv.utils import ConfigDict
 from mmcv.utils.parrots_wrapper import SyncBatchNorm
 
 from mmseg.models.decode_heads import (ANNHead, ASPPHead, CCHead, DAHead,
-                                       DepthwiseSeparableASPPHead, DNLHead,
+                                       DepthwiseSeparableASPPHead,
+                                       DepthwiseSeparableFCNHead, DNLHead,
                                        EMAHead, EncHead, FCNHead, GCHead,
-                                       NLHead, OCRHead, PSAHead, PSPHead,
-                                       UPerHead)
+                                       NLHead, OCRHead, PointHead, PSAHead,
+                                       PSPHead, UPerHead)
 from mmseg.models.decode_heads.decode_head import BaseDecodeHead
 
 
@@ -542,6 +544,40 @@ def test_dw_aspp_head():
     assert outputs.shape == (1, head.num_classes, 45, 45)
 
 
+def test_sep_fcn_head():
+    # test sep_fcn_head with concat_input=False
+    head = DepthwiseSeparableFCNHead(
+        in_channels=128,
+        channels=128,
+        concat_input=False,
+        num_classes=19,
+        in_index=-1,
+        norm_cfg=dict(type='BN', requires_grad=True, momentum=0.01))
+    x = [torch.rand(2, 128, 32, 32)]
+    output = head(x)
+    assert output.shape == (2, head.num_classes, 32, 32)
+    assert not head.concat_input
+    from mmseg.ops.separable_conv_module import DepthwiseSeparableConvModule
+    assert isinstance(head.convs[0], DepthwiseSeparableConvModule)
+    assert isinstance(head.convs[1], DepthwiseSeparableConvModule)
+    assert head.conv_seg.kernel_size == (1, 1)
+
+    head = DepthwiseSeparableFCNHead(
+        in_channels=64,
+        channels=64,
+        concat_input=True,
+        num_classes=19,
+        in_index=-1,
+        norm_cfg=dict(type='BN', requires_grad=True, momentum=0.01))
+    x = [torch.rand(3, 64, 32, 32)]
+    output = head(x)
+    assert output.shape == (3, head.num_classes, 32, 32)
+    assert head.concat_input
+    from mmseg.ops.separable_conv_module import DepthwiseSeparableConvModule
+    assert isinstance(head.convs[0], DepthwiseSeparableConvModule)
+    assert isinstance(head.convs[1], DepthwiseSeparableConvModule)
+
+
 def test_dnl_head():
     # DNL with 'embedded_gaussian' mode
     head = DNLHead(in_channels=32, channels=16, num_classes=19)
@@ -598,3 +634,20 @@ def test_emanet_head():
         head, inputs = to_cuda(head, inputs)
     outputs = head(inputs)
     assert outputs.shape == (1, head.num_classes, 45, 45)
+
+
+def test_point_head():
+
+    inputs = [torch.randn(1, 32, 45, 45)]
+    point_head = PointHead(
+        in_channels=[32], in_index=[0], channels=16, num_classes=19)
+    assert len(point_head.fcs) == 3
+    fcn_head = FCNHead(in_channels=32, channels=16, num_classes=19)
+    if torch.cuda.is_available():
+        head, inputs = to_cuda(point_head, inputs)
+        head, inputs = to_cuda(fcn_head, inputs)
+    prev_output = fcn_head(inputs)
+    test_cfg = ConfigDict(
+        subdivision_steps=2, subdivision_num_points=8196, scale_factor=2)
+    output = point_head.forward_test(inputs, prev_output, None, test_cfg)
+    assert output.shape == (1, point_head.num_classes, 180, 180)