Add more test

.github/workflows/build.yml

@@ -39,15 +39,18 @@ jobs:
     strategy:
       matrix:
         python-version: [3.6, 3.7]
-        torch: [1.3.0, 1.5.0]
+        torch: [1.3.0+cpu, 1.5.0+cpu]
         include:
-          - torch: 1.3.0
-            torchvision: 0.4.2
-          - torch: 1.5.0
-            torchvision: 0.6.0
-          - python-version: 3.8
-            torch: 1.5.0
-            torchvision: 0.6.0
+          - torch: 1.3.0+cpu
+            torchvision: 0.4.2+cpu
+          - torch: 1.5.0+cpu
+            torchvision: 0.6.0+cpu
+          - torch: 1.5.0+cpu
+            torchvision: 0.6.0+cpu
+            python-version: 3.8
+          - torch: 1.5.0+cu101
+            torchvision: 0.6.0+cu101
+            python-version: 3.7
 
     steps:
     - uses: actions/checkout@v2
@@ -56,6 +59,7 @@ jobs:
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install CUDA
+      if: ${{matrix.torch == '1.5.0+cu101'}}
       run: |
         export INSTALLER=cuda-repo-${UBUNTU_VERSION}_${CUDA}_amd64.deb
         wget http://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/${INSTALLER}
@@ -70,13 +74,13 @@ jobs:
         export PATH=${CUDA_HOME}/bin:${PATH}
         sudo apt-get install -y ninja-build
     - name: Install Pillow
+      if: ${{matrix.torchvision == '0.4.2+cpu'}}
       run: pip install Pillow==6.2.2
-      if: ${{matrix.torchvision == '0.4.2'}}
     - name: Install PyTorch
       run: pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html
     - name: Install mmseg dependencies
       run: |
-        pip install mmcv==1.0rc0+torch${{matrix.torch}}+cu101 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
+        pip install mmcv==1.0rc0+torch${{matrix.torch}} -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
         pip install -r requirements.txt
     - name: Build and install
       run: rm -rf .eggs && pip install -e .
@@ -87,7 +91,7 @@ jobs:
         coverage report -m --omit="mmseg/utils/*","mmseg/apis/*"
     # Only upload coverage report for python3.7 && pytorch1.5
     - name: Upload coverage to Codecov
-      if: ${{matrix.torch == '1.5.0' && matrix.python-version == '3.7'}}
+      if: ${{matrix.torch == '1.5.0+cu101' && matrix.python-version == '3.7'}}
       uses: codecov/codecov-action@v1.0.10
       with:
         file: ./coverage.xml

README.md

@@ -53,7 +53,7 @@ We wish that the toolbox and benchmark could serve the growing research
 community by providing a flexible as well as standardized toolkit to reimplement existing methods
 and develop their own new semantic segmentation methods.
 
-Many thanks to Ruobing Han ([@drcut](https://github.com/drcut)), Xiaoming Ma([@aishangmaxiaoming](https://github.com/aishangmaxiaoming)), Shiguang Wang ([@sunnyxiaohu](https://github.com/aishangmaxiaoming)) for deployment support.
+Many thanks to Ruobing Han ([@drcut](https://github.com/drcut)), Xiaoming Ma([@aishangmaxiaoming](https://github.com/aishangmaxiaoming)), Shiguang Wang ([@sunnyxiaohu](https://github.com/sunnyxiaohu)) for deployment support.
 
 ## Citation
 

docs/tutorials/new_dataset.md

@@ -33,10 +33,10 @@ xxx
 zzz
 ```
 Only
-`data/my_dataset/img_dir/train/xxx{img_suffix}.png`,
-`data/my_dataset/img_dir/train/zzz{img_suffix}.png`,
-`data/my_dataset/ann_dir/train/xxx{seg_map_suffix}.png`,
-`data/my_dataset/ann_dir/train/zzz{seg_map_suffix}.png` will be loaded.
+`data/my_dataset/img_dir/train/xxx{img_suffix}`,
+`data/my_dataset/img_dir/train/zzz{img_suffix}`,
+`data/my_dataset/ann_dir/train/xxx{seg_map_suffix}`,
+`data/my_dataset/ann_dir/train/zzz{seg_map_suffix}` will be loaded.
 
 ## Customize datasets by mixing dataset
 

docs/tutorials/training_tricks.md

@@ -4,7 +4,7 @@ MMSegmentation support following training tricks out of box.
 
 ## Different Learning Rate(LR) for Backbone and Heads
 
-In semantic segmentation, some methods make the LR of heads larger than backbone to achieve better performance.
+In semantic segmentation, some methods make the LR of heads larger than backbone to achieve better performance or faster convergence.
 
 In MMSegmentation, you may add following lines to config to make the LR of heads 10 times of backbone.
 ```python
@@ -13,6 +13,7 @@ optimizer_config=dict(
         custom_keys={
             'head': dict(lr_mult=10.)}))
 ```
+With this modification, the LR of any parameter group with `'head'` in name will be multiplied by 10.
 You may refer to [MMCV doc](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.DefaultOptimizerConstructor) for further details.
 
 ## Online Hard Example Mining (OHEM)

mmseg/datasets/custom.py

@@ -46,15 +46,15 @@ class CustomDataset(Dataset):
     Args:
         pipeline (list[dict]): Processing pipeline
         img_dir (str): Path to image directory
-        img_suffix (str): Suffix of images. Default: '.png'
+        img_suffix (str): Suffix of images. Default: '.jpg'
         ann_dir (str, optional): Path to annotation directory. Default: None
         seg_map_suffix (str): Suffix of segmentation maps. Default: '.png'
         split (str, optional): Split txt file. If split is specified, only
             file with suffix in the splits will be loaded. Otherwise, all
             images in img_dir/ann_dir will be loaded. Default: None
         data_root (str, optional): Data root for img_dir/ann_dir. Default:
             None.
-        test_mode (str): If test_mode=True, gt wouldn't be loaded.
+        test_mode (bool): If test_mode=True, gt wouldn't be loaded.
         ignore_index (int): The label index to be ignored. Default: 255
         reduce_zero_label (bool): Whether to mark label zero as ignored.
             Default: False
@@ -67,7 +67,7 @@ class CustomDataset(Dataset):
     def __init__(self,
                  pipeline,
                  img_dir,
-                 img_suffix='.png',
+                 img_suffix='.jpg',
                  ann_dir=None,
                  seg_map_suffix='.png',
                  split=None,

mmseg/models/decode_heads/enc_head.py

@@ -3,7 +3,7 @@
 import torch.nn.functional as F
 from mmcv.cnn import ConvModule, build_norm_layer
 
-from mmseg.ops import Encoding
+from mmseg.ops import Encoding, resize
 from ..builder import HEADS, build_loss
 from .decode_head import BaseDecodeHead
 
@@ -30,12 +30,16 @@ def __init__(self, in_channels, num_codes, conv_cfg, norm_cfg, act_cfg):
             act_cfg=act_cfg)
         # TODO: resolve this hack
         # change to 1d
-        encoding_norm_cfg = norm_cfg.copy()
-        if encoding_norm_cfg['type'] in ['BN', 'IN']:
-            encoding_norm_cfg['type'] += '1d'
+        if norm_cfg is not None:
+            encoding_norm_cfg = norm_cfg.copy()
+            if encoding_norm_cfg['type'] in ['BN', 'IN']:
+                encoding_norm_cfg['type'] += '1d'
+            else:
+                encoding_norm_cfg['type'] = encoding_norm_cfg['type'].replace(
+                    '2d', '1d')
         else:
-            encoding_norm_cfg['type'] = encoding_norm_cfg['type'].replace(
-                '2d', '1d')
+            # fallback to BN1d
+            encoding_norm_cfg = dict(type='BN1d')
         self.encoding = nn.Sequential(
             Encoding(channels=in_channels, num_codes=num_codes),
             build_norm_layer(encoding_norm_cfg, num_codes)[1],
@@ -128,7 +132,11 @@ def forward(self, inputs):
         feat = self.bottleneck(inputs[-1])
         if self.add_lateral:
             laterals = [
-                lateral_conv(inputs[i])
+                resize(
+                    lateral_conv(inputs[i]),
+                    size=feat.shape[2:],
+                    mode='bilinear',
+                    align_corners=self.align_corners)
                 for i, lateral_conv in enumerate(self.lateral_convs)
             ]
             feat = self.fusion(torch.cat([feat, *laterals], 1))

mmseg/models/segmentors/encoder_decoder.py

@@ -37,6 +37,8 @@ def __init__(self,
 
         self.init_weights(pretrained=pretrained)
 
+        assert self.with_decode_head
+
     def _init_decode_head(self, decode_head):
         """Initialize ``decode_head``"""
         self.decode_head = builder.build_head(decode_head)

tests/data/pseudo_dataset/splits/all.txt → tests/data/pseudo_dataset/splits/train.txt

@@ -2,4 +2,3 @@
 00001
 00002
 00003
-00004

tests/data/pseudo_dataset/splits/val.txt

@@ -0,0 +1 @@
+00004

tests/test_data/test_dataset.py

@@ -1,6 +1,9 @@
 import os.path as osp
 from unittest.mock import MagicMock, patch
 
+import numpy as np
+import pytest
+
 from mmseg.core.evaluation import get_classes, get_palette
 from mmseg.datasets import (ADE20KDataset, CityscapesDataset, ConcatDataset,
                             CustomDataset, PascalVOCDataset, RepeatDataset)
@@ -13,13 +16,19 @@ def test_classes():
     assert list(
         ADE20KDataset.CLASSES) == get_classes('ade') == get_classes('ade20k')
 
+    with pytest.raises(ValueError):
+        get_classes('unsupported')
+
 
 def test_palette():
     assert CityscapesDataset.PALETTE == get_palette('cityscapes')
     assert PascalVOCDataset.PALETTE == get_palette('voc') == get_palette(
         'pascal_voc')
     assert ADE20KDataset.PALETTE == get_palette('ade') == get_palette('ade20k')
 
+    with pytest.raises(ValueError):
+        get_palette('unsupported')
+
 
 @patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
 @patch('mmseg.datasets.CustomDataset.__getitem__',
@@ -82,7 +91,7 @@ def test_custom_dataset():
             ])
     ]
 
-    # train dataset
+    # with img_dir and ann_dir
     train_dataset = CustomDataset(
         train_pipeline,
         data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
@@ -92,6 +101,17 @@ def test_custom_dataset():
         seg_map_suffix='gt.png')
     assert len(train_dataset) == 5
 
+    # with img_dir, ann_dir, split
+    train_dataset = CustomDataset(
+        train_pipeline,
+        data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
+        img_dir='imgs/',
+        ann_dir='gts/',
+        img_suffix='img.jpg',
+        seg_map_suffix='gt.png',
+        split='splits/train.txt')
+    assert len(train_dataset) == 4
+
     # no data_root
     train_dataset = CustomDataset(
         train_pipeline,
@@ -101,10 +121,53 @@ def test_custom_dataset():
         seg_map_suffix='gt.png')
     assert len(train_dataset) == 5
 
-    # test dataset
+    # with data_root but img_dir/ann_dir are abs path
+    train_dataset = CustomDataset(
+        train_pipeline,
+        data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
+        img_dir=osp.abspath(
+            osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs')),
+        ann_dir=osp.abspath(
+            osp.join(osp.dirname(__file__), '../data/pseudo_dataset/gts')),
+        img_suffix='img.jpg',
+        seg_map_suffix='gt.png')
+    assert len(train_dataset) == 5
+
+    # test_mode=True
     test_dataset = CustomDataset(
         test_pipeline,
         img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'),
         img_suffix='img.jpg',
         test_mode=True)
     assert len(test_dataset) == 5
+
+    # training data get
+    train_data = train_dataset[0]
+    assert isinstance(train_data, dict)
+
+    # test data get
+    test_data = test_dataset[0]
+    assert isinstance(test_data, dict)
+
+    # get gt seg map
+    gt_seg_maps = train_dataset.get_gt_seg_maps()
+    assert len(gt_seg_maps) == 5
+
+    # evaluation
+    pseudo_results = []
+    for gt_seg_map in gt_seg_maps:
+        h, w = gt_seg_map.shape
+        pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
+    eval_results = train_dataset.evaluate(pseudo_results)
+    assert isinstance(eval_results, dict)
+    assert 'mIoU' in eval_results
+    assert 'mAcc' in eval_results
+    assert 'aAcc' in eval_results
+
+    # evaluation with CLASSES
+    train_dataset.CLASSES = tuple(['a'] * 7)
+    eval_results = train_dataset.evaluate(pseudo_results)
+    assert isinstance(eval_results, dict)
+    assert 'mIoU' in eval_results
+    assert 'mAcc' in eval_results
+    assert 'aAcc' in eval_results