Merge pull request open-mmlab#1 from open-mmlab/master

Master

.github/workflows/build.yml

@@ -31,21 +31,20 @@ jobs:
     runs-on: ubuntu-18.04
     strategy:
       matrix:
-        python-version: [3.7]
-        torch: [1.5.0, 1.6.0, 1.7.0, 1.8.0]
+        torch: [1.5.0, 1.8.0, 1.10.0]
         include:
           - torch: 1.5.0
-            torch_version: torch1.5
             torchvision: 0.6.0
-          - torch: 1.6.0
-            torch_version: torch1.6
-            torchvision: 0.7.0
-          - torch: 1.7.0
-            torch_version: torch1.7
-            torchvision: 0.8.1
+            torch_version: 1.5.0
+            python-version: '3.7'
           - torch: 1.8.0
-            torch_version: torch1.8
             torchvision: 0.9.0
+            torch_version: 1.8.0
+            python-version: '3.8'
+          - torch: 1.10.0
+            torchvision: 0.11.1
+            torch_version: 1.10.0
+            python-version: '3.9'
 
     steps:
       - uses: actions/checkout@v2
@@ -54,7 +53,7 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
-        run: pip install pip --upgrade
+        run: pip install pip numpy --upgrade
       - name: Install onnx
         run: pip install onnx
       - name: Install PyTorch
@@ -82,21 +81,15 @@ jobs:
       image: pytorch/pytorch:1.6.0-cuda10.1-cudnn7-devel
     strategy:
       matrix:
-        python-version: [3.7]
-        torch: [1.5.0, 1.6.0, 1.7.0, 1.8.0]
+        python-version: [3.8]
+        torch: [1.5.0, 1.8.0]
         include:
           - torch: 1.5.0
-            torch_version: torch1.5
             torchvision: 0.6.0
-          - torch: 1.6.0
-            torch_version: torch1.6
-            torchvision: 0.7.0
-          - torch: 1.7.0
-            torch_version: torch1.7
-            torchvision: 0.8.1
+            torch_version: 1.5.0
           - torch: 1.8.0
-            torch_version: torch1.8
             torchvision: 0.9.0
+            torch_version: 1.8.0
 
     steps:
       - uses: actions/checkout@v2
@@ -114,7 +107,7 @@ jobs:
           apt-get clean
           rm -rf /var/lib/apt/lists/*
       - name: Upgrade pip
-        run: python -m pip install pip --upgrade
+        run: python -m pip install pip numpy --upgrade
       - name: Install dependencies for compiling onnx when python=3.9
         run: python -m pip install protobuf && apt-get install -y libprotobuf-dev protobuf-compiler
         if: ${{matrix.python-version == '3.9'}}
@@ -153,7 +146,7 @@ jobs:
       image: pytorch/pytorch:1.9.0-cuda10.2-cudnn7-devel
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8, 3.9]
+        python-version: [3.7, 3.8, 3.9]
         torch: [1.9.0, 1.10.0]
         include:
           - torch: 1.9.0
@@ -179,7 +172,7 @@ jobs:
           apt-get clean
           rm -rf /var/lib/apt/lists/*
       - name: Upgrade pip
-        run: python -m pip install pip --upgrade
+        run: python -m pip install pip numpy --upgrade
       - name: Install dependencies for compiling onnx when python=3.9
         run: python -m pip install protobuf && apt-get update && apt-get -y install libprotobuf-dev protobuf-compiler cmake
         if: ${{matrix.python-version == '3.9'}}
@@ -226,7 +219,7 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
       - name: Upgrade pip
-        run: python -m pip install pip --upgrade --user
+        run: python -m pip install pip numpy --upgrade --user
       - name: Install PyTorch
         # As a complement to Linux CI, we test on PyTorch LTS version
         run: python -m pip install torch==1.8.2+${{ matrix.platform }} torchvision==0.9.2+${{ matrix.platform }} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
@@ -254,7 +247,7 @@ jobs:
         uses: codecov/codecov-action@v2
         with:
           file: ./coverage.xml
-          flags: unittests
+          flags: unittestsstat
           env_vars: OS,PYTHON
           name: codecov-umbrella
           fail_ci_if_error: false

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
     hooks:
       - id: flake8
   - repo: https://github.com/PyCQA/isort
-    rev: 5.10.1
+    rev: 5.11.5
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf

docs/en/faq.md

@@ -25,7 +25,11 @@ If the contents here do not cover your issue, please create an issue using the [
 
 | MMPose version |       MMCV version        |
 | :------------: | :-----------------------: |
-|     master     | mmcv-full>=1.3.8, \<1.7.0 |
+|     master     | mmcv-full>=1.3.8, \<1.8.0 |
+|     0.29.0     | mmcv-full>=1.3.8, \<1.7.0 |
+|     0.28.1     | mmcv-full>=1.3.8, \<1.7.0 |
+|     0.28.0     | mmcv-full>=1.3.8, \<1.6.0 |
+|     0.27.0     | mmcv-full>=1.3.8, \<1.6.0 |
 |     0.26.0     | mmcv-full>=1.3.8, \<1.6.0 |
 |     0.25.1     | mmcv-full>=1.3.8, \<1.6.0 |
 |     0.25.0     | mmcv-full>=1.3.8, \<1.5.0 |

docs/zh_cn/faq.md

@@ -25,7 +25,11 @@
 
 | MMPose 版本 |         MMCV 版本         |
 | :---------: | :-----------------------: |
-|   master    | mmcv-full>=1.3.8, \<1.7.0 |
+|   master    | mmcv-full>=1.3.8, \<1.8.0 |
+|   0.29.0    | mmcv-full>=1.3.8, \<1.7.0 |
+|   0.28.1    | mmcv-full>=1.3.8, \<1.7.0 |
+|   0.28.0    | mmcv-full>=1.3.8, \<1.6.0 |
+|   0.27.0    | mmcv-full>=1.3.8, \<1.6.0 |
 |   0.26.0    | mmcv-full>=1.3.8, \<1.6.0 |
 |   0.25.1    | mmcv-full>=1.3.8, \<1.6.0 |
 |   0.25.0    | mmcv-full>=1.3.8, \<1.5.0 |

mmpose/datasets/datasets/base/kpt_3d_mview_rgb_img_direct_dataset.py

@@ -250,7 +250,7 @@ def _load_files(self):
         assert osp.exists(self.gt_pose_db_file), f'gt_pose_db_file ' \
             f"{self.gt_pose_db_file} doesn't exist, please check again"
         gt = loadmat(self.gt_pose_db_file)
-        self.gt_pose_db = np.array(np.array(
-            gt['actor3D'].tolist()).tolist()).squeeze()
+
+        self.gt_pose_db = np.array(gt['actor3D'].tolist()).squeeze()
 
         self.num_persons = len(self.gt_pose_db)

mmpose/datasets/datasets/body3d/body3d_mview_direct_panoptic_dataset.py

@@ -211,7 +211,7 @@ def _get_db(self):
                             dtype=np.float32)
 
                         cnt = 0
-                        person_ids = -np.ones(self.max_persons, dtype=np.int)
+                        person_ids = -np.ones(self.max_persons, dtype=int)
                         for body in bodies:
                             if cnt >= self.max_persons:
                                 break

mmpose/datasets/pipelines/bottom_up_transform.py

@@ -169,8 +169,7 @@ def __init__(self, output_size, num_joints, sigma=-1, use_udp=False):
             assert len(output_size) == 2
             self.output_size = output_size
         else:
-            self.output_size = np.array([output_size, output_size],
-                                        dtype=np.int)
+            self.output_size = np.array([output_size, output_size], dtype=int)
         self.num_joints = num_joints
         if sigma < 0:
             sigma = self.output_size.prod()**0.5 / 64
@@ -243,8 +242,7 @@ def __init__(self, output_size, num_joints, radius=4):
             assert len(output_size) == 2
             self.output_size = output_size
         else:
-            self.output_size = np.array([output_size, output_size],
-                                        dtype=np.int)
+            self.output_size = np.array([output_size, output_size], dtype=int)
         self.num_joints = num_joints
         assert radius > 0, f'`radius` must be a positive value, ' \
                            f'but got {radius}'
@@ -320,8 +318,7 @@ def __init__(self, max_num_people, num_joints, output_size, tag_per_joint):
             assert len(output_size) == 2
             self.output_size = output_size
         else:
-            self.output_size = np.array([output_size, output_size],
-                                        dtype=np.int)
+            self.output_size = np.array([output_size, output_size], dtype=int)
         self.tag_per_joint = tag_per_joint
 
     def __call__(self, joints):
@@ -371,8 +368,7 @@ def __init__(self, output_size, limb_width, skeleton):
             assert len(output_size) == 2
             self.output_size = output_size
         else:
-            self.output_size = np.array([output_size, output_size],
-                                        dtype=np.int)
+            self.output_size = np.array([output_size, output_size], dtype=int)
         self.limb_width = limb_width
         self.skeleton = skeleton
 
@@ -527,7 +523,7 @@ def __call__(self, results):
                     assert len(_output_size) == 2
                 else:
                     _output_size = np.array([_output_size, _output_size],
-                                            dtype=np.int)
+                                            dtype=int)
                 mask[i] = mask[i][:, ::-1].copy()
                 joints[i] = joints[i][:, self.flip_index]
                 joints[i][:, :, 0] = _output_size[0] - joints[i][:, :, 0] - 1
@@ -1053,13 +1049,13 @@ def __call__(self, results):
         results['multi_heatmap'] = heatmap_with_center
         results['multi_mask'] = mask[None, :, :]
         # pad instance targets for batching
-        instance_coord = np.zeros((self.max_num_people, 2), dtype=np.int32)
+        instance_coord = np.zeros((self.max_num_people, 2), dtype=int)
         instance_heatmap = np.zeros(
             (self.max_num_people, num_keypoints, output_size, output_size),
             dtype=np.float32)
         instance_mask = np.zeros((self.max_num_people, num_keypoints, 1, 1),
                                  dtype=np.float32)
-        instance_valid = np.zeros((self.max_num_people), dtype=np.int32)
+        instance_valid = np.zeros((self.max_num_people), dtype=int)
         if len(inst_coords) > 0:
             idx_list = list(range(len(inst_coords)))
             random.shuffle(idx_list)
@@ -1156,7 +1152,7 @@ def __call__(self, results):
         if input_size.size > 1:
             assert len(input_size) == 2
         else:
-            input_size = np.array([input_size, input_size], dtype=np.int)
+            input_size = np.array([input_size, input_size], dtype=int)
         img = results['img']
 
         base_size, center, scale = _get_multi_scale_size(
@@ -1199,7 +1195,7 @@ def __call__(self, results):
         if input_size.size > 1:
             assert len(input_size) == 2
         else:
-            input_size = np.array([input_size, input_size], dtype=np.int)
+            input_size = np.array([input_size, input_size], dtype=int)
         test_scale_factor = results['ann_info']['test_scale_factor']
         aug_data = []
 

tests/test_models/test_bottom_up_forward.py

@@ -234,10 +234,10 @@ def _demo_cid_inputs(input_shape=(1, 3, 512, 512)):
     imgs = rng.rand(*input_shape)
     multi_heatmap = np.zeros([N, 18, H // 4, W // 4], dtype=np.float32)
     multi_mask = np.ones([N, 1, H // 4, W // 4], dtype=np.float32)
-    instance_coord = np.zeros([N, 30, 2], dtype=np.int64)
+    instance_coord = np.zeros([N, 30, 2], dtype=int)
     instance_heatmap = np.zeros([N, 30, 17, H // 4, W // 4], dtype=np.float32)
     instance_mask = np.ones([N, 30, 17, 1, 1], dtype=np.float32)
-    instance_valid = np.ones([N, 30], dtype=np.int64)
+    instance_valid = np.ones([N, 30], dtype=int)
 
     img_metas = [{
         'image_file':