[Fix] Fix some warnings in unittest (#1522)

* [Fix] fix some warnings in unittest

* [Impl] standardize some warnings

* [Fix] fix warning type in test_deprecation

* [Fix] fix warning type

* [Fix] continue fixing

* [Fix] fix some details

* [Fix] fix docstring

* [Fix] del useless statement

* [Fix] keep compatibility for torch < 1.5.0

mmcv/cnn/bricks/generalized_attention.py

@@ -131,7 +131,7 @@ class GeneralizedAttention(nn.Module):
 
             max_len_kv = int((max_len - 1.0) / self.kv_stride + 1)
             local_constraint_map = np.ones(
-                (max_len, max_len, max_len_kv, max_len_kv), dtype=np.int)
+                (max_len, max_len, max_len_kv, max_len_kv), dtype=int)
             for iy in range(max_len):
                 for ix in range(max_len):
                     local_constraint_map[

mmcv/cnn/bricks/transformer.py

@@ -436,10 +436,11 @@ class MultiheadAttention(BaseModule):
                  **kwargs):
         super(MultiheadAttention, self).__init__(init_cfg)
         if 'dropout' in kwargs:
-            warnings.warn('The arguments `dropout` in MultiheadAttention '
+            warnings.warn(
+                'The arguments `dropout` in MultiheadAttention '
                 'has been deprecated, now you can separately '
                 'set `attn_drop`(float), proj_drop(float), '
-                          'and `dropout_layer`(dict) ')
+                'and `dropout_layer`(dict) ', DeprecationWarning)
             attn_drop = kwargs['dropout']
             dropout_layer['drop_prob'] = kwargs.pop('dropout')
 
@@ -689,7 +690,7 @@ class BaseTransformerLayer(BaseModule):
                     f'The arguments `{ori_name}` in BaseTransformerLayer '
                     f'has been deprecated, now you should set `{new_name}` '
                     f'and other FFN related arguments '
-                    f'to a dict named `ffn_cfgs`. ')
+                    f'to a dict named `ffn_cfgs`. ', DeprecationWarning)
                 ffn_cfgs[new_name] = kwargs[ori_name]
 
         super(BaseTransformerLayer, self).__init__(init_cfg)

mmcv/cnn/utils/flops_counter.py

@@ -24,6 +24,7 @@
 # SOFTWARE.
 
 import sys
+import warnings
 from functools import partial
 
 import numpy as np
@@ -502,8 +503,7 @@ def batch_counter_hook(module, input, output):
         input = input[0]
         batch_size = len(input)
     else:
-        pass
+        warnings.warn('No positional inputs found for a module, '
-        print('Warning! No positional inputs found for a module, '
                       'assuming batch size is 1.')
     module.__batch_counter__ += batch_size
 
@@ -530,7 +530,7 @@ def remove_batch_counter_hook_function(module):
 def add_flops_counter_variable_or_reset(module):
     if is_supported_instance(module):
         if hasattr(module, '__flops__') or hasattr(module, '__params__'):
-            print('Warning: variables __flops__ or __params__ are already '
+            warnings.warn('variables __flops__ or __params__ are already '
                           'defined for the module' + type(module).__name__ +
                           ' ptflops can affect your code!')
         module.__flops__ = 0

mmcv/fileio/file_client.py

@@ -64,7 +64,8 @@ class CephBackend(BaseStorageBackend):
             raise ImportError('Please install ceph to enable CephBackend.')
 
         warnings.warn(
-            'CephBackend will be deprecated, please use PetrelBackend instead')
+            'CephBackend will be deprecated, please use PetrelBackend instead',
+            DeprecationWarning)
         self._client = ceph.S3Client()
         assert isinstance(path_mapping, dict) or path_mapping is None
         self.path_mapping = path_mapping

mmcv/ops/deprecated_wrappers.py

@@ -12,7 +12,8 @@ class Conv2d_deprecated(Conv2d):
         super().__init__(*args, **kwargs)
         warnings.warn(
             'Importing Conv2d wrapper from "mmcv.ops" will be deprecated in'
-            ' the future. Please import them from "mmcv.cnn" instead')
+            ' the future. Please import them from "mmcv.cnn" instead',
+            DeprecationWarning)
 
 
 class ConvTranspose2d_deprecated(ConvTranspose2d):
@@ -22,7 +23,7 @@ class ConvTranspose2d_deprecated(ConvTranspose2d):
         warnings.warn(
             'Importing ConvTranspose2d wrapper from "mmcv.ops" will be '
             'deprecated in the future. Please import them from "mmcv.cnn" '
-            'instead')
+            'instead', DeprecationWarning)
 
 
 class MaxPool2d_deprecated(MaxPool2d):
@@ -31,7 +32,8 @@ class MaxPool2d_deprecated(MaxPool2d):
         super().__init__(*args, **kwargs)
         warnings.warn(
             'Importing MaxPool2d wrapper from "mmcv.ops" will be deprecated in'
-            ' the future. Please import them from "mmcv.cnn" instead')
+            ' the future. Please import them from "mmcv.cnn" instead',
+            DeprecationWarning)
 
 
 class Linear_deprecated(Linear):
@@ -40,4 +42,5 @@ class Linear_deprecated(Linear):
         super().__init__(*args, **kwargs)
         warnings.warn(
             'Importing Linear wrapper from "mmcv.ops" will be deprecated in'
-            ' the future. Please import them from "mmcv.cnn" instead')
+            ' the future. Please import them from "mmcv.cnn" instead',
+            DeprecationWarning)

mmcv/ops/fused_bias_leakyrelu.py

@@ -188,7 +188,7 @@ class FusedBiasLeakyReLUFunction(Function):
 
 
 class FusedBiasLeakyReLU(nn.Module):
-    """Fused bias leaky ReLU.
+    r"""Fused bias leaky ReLU.
 
     This function is introduced in the StyleGAN2:
     `Analyzing and Improving the Image Quality of StyleGAN
@@ -197,8 +197,8 @@ class FusedBiasLeakyReLU(nn.Module):
     The bias term comes from the convolution operation. In addition, to keep
     the variance of the feature map or gradients unchanged, they also adopt a
     scale similarly with Kaiming initialization. However, since the
-    :math:`1+{alpha}^2` : is too small, we can just ignore it. Therefore, the
+    :math:`1+{alpha}^2` is too small, we can just ignore it. Therefore, the
-    final scale is just :math:`\sqrt{2}`:. Of course, you may change it with # noqa: W605, E501
+    final scale is just :math:`\sqrt{2}`. Of course, you may change it with
     your own scale.
 
     TODO: Implement the CPU version.
@@ -224,7 +224,7 @@ class FusedBiasLeakyReLU(nn.Module):
 
 
 def fused_bias_leakyrelu(input, bias, negative_slope=0.2, scale=2**0.5):
-    """Fused bias leaky ReLU function.
+    r"""Fused bias leaky ReLU function.
 
     This function is introduced in the StyleGAN2:
     `Analyzing and Improving the Image Quality of StyleGAN
@@ -233,8 +233,8 @@ def fused_bias_leakyrelu(input, bias, negative_slope=0.2, scale=2**0.5):
     The bias term comes from the convolution operation. In addition, to keep
     the variance of the feature map or gradients unchanged, they also adopt a
     scale similarly with Kaiming initialization. However, since the
-    :math:`1+{alpha}^2` : is too small, we can just ignore it. Therefore, the
+    :math:`1+{alpha}^2` is too small, we can just ignore it. Therefore, the
-    final scale is just :math:`\sqrt{2}`:. Of course, you may change it with # noqa: W605, E501
+    final scale is just :math:`\sqrt{2}`. Of course, you may change it with
     your own scale.
 
     Args:

mmcv/ops/masked_conv.py

@@ -61,7 +61,6 @@ class MaskedConv2dFunction(Function):
                 kernel_w=kernel_w,
                 pad_h=pad_h,
                 pad_w=pad_w)
-
             masked_output = torch.addmm(1, bias[:, None], 1,
                                         weight.view(out_channel, -1), data_col)
             ext_module.masked_col2im_forward(

mmcv/ops/nms.py

@@ -389,7 +389,7 @@ def nms_match(dets, iou_threshold):
     if isinstance(dets, torch.Tensor):
         return [dets.new_tensor(m, dtype=torch.long) for m in matched]
     else:
-        return [np.array(m, dtype=np.int) for m in matched]
+        return [np.array(m, dtype=int) for m in matched]
 
 
 def nms_rotated(dets, scores, iou_threshold, labels=None):

mmcv/ops/scatter_points.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
+import torch.nn.functional as F
 from torch import nn
 from torch.autograd import Function
 
@@ -119,8 +120,7 @@ class DynamicScatter(nn.Module):
                 inds = torch.where(coors[:, 0] == i)
                 voxel, voxel_coor = self.forward_single(
                     points[inds], coors[inds][:, 1:])
-                coor_pad = nn.functional.pad(
+                coor_pad = F.pad(voxel_coor, (1, 0), mode='constant', value=i)
-                    voxel_coor, (1, 0), mode='constant', value=i)
                 voxel_coors.append(coor_pad)
                 voxels.append(voxel)
             features = torch.cat(voxels, dim=0)

mmcv/runner/base_runner.py

@@ -61,8 +61,10 @@ class BaseRunner(metaclass=ABCMeta):
             if not callable(batch_processor):
                 raise TypeError('batch_processor must be callable, '
                                 f'but got {type(batch_processor)}')
-            warnings.warn('batch_processor is deprecated, please implement '
+            warnings.warn(
-                          'train_step() and val_step() in the model instead.')
+                'batch_processor is deprecated, please implement '
+                'train_step() and val_step() in the model instead.',
+                DeprecationWarning)
             # raise an error is `batch_processor` is not None and
             # `model.train_step()` exists.
             if is_module_wrapper(model):

mmcv/runner/checkpoint.py

@@ -358,7 +358,8 @@ def load_from_ceph(filename, map_location=None, backend='petrel'):
 
     if backend == 'ceph':
         warnings.warn(
-            'CephBackend will be deprecated, please use PetrelBackend instead')
+            'CephBackend will be deprecated, please use PetrelBackend instead',
+            DeprecationWarning)
 
     # CephClient and PetrelBackend have the same prefix 's3://' and the latter
     # will be chosen as default. If PetrelBackend can not be instantiated
@@ -389,8 +390,9 @@ def load_from_torchvision(filename, map_location=None):
     """
     model_urls = get_torchvision_models()
     if filename.startswith('modelzoo://'):
-        warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
+        warnings.warn(
-                      'use "torchvision://" instead')
+            'The URL scheme of "modelzoo://" is deprecated, please '
+            'use "torchvision://" instead', DeprecationWarning)
         model_name = filename[11:]
     else:
         model_name = filename[14:]
@@ -422,8 +424,10 @@ def load_from_openmmlab(filename, map_location=None):
 
     deprecated_urls = get_deprecated_model_names()
     if model_name in deprecated_urls:
-        warnings.warn(f'{prefix_str}{model_name} is deprecated in favor '
+        warnings.warn(
-                      f'of {prefix_str}{deprecated_urls[model_name]}')
+            f'{prefix_str}{model_name} is deprecated in favor '
+            f'of {prefix_str}{deprecated_urls[model_name]}',
+            DeprecationWarning)
         model_name = deprecated_urls[model_name]
     model_url = model_urls[model_name]
     # check if is url

mmcv/runner/epoch_based_runner.py

@@ -183,5 +183,6 @@ class Runner(EpochBasedRunner):
 
     def __init__(self, *args, **kwargs):
         warnings.warn(
-            'Runner was deprecated, please use EpochBasedRunner instead')
+            'Runner was deprecated, please use EpochBasedRunner instead',
+            DeprecationWarning)
         super().__init__(*args, **kwargs)

mmcv/runner/fp16_utils.py

@@ -227,7 +227,8 @@ def force_fp32(apply_to=None, out_fp16=False):
 def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
     warnings.warning(
         '"mmcv.runner.fp16_utils.allreduce_grads" is deprecated, and will be '
-        'removed in v2.8. Please switch to "mmcv.runner.allreduce_grads')
+        'removed in v2.8. Please switch to "mmcv.runner.allreduce_grads',
+        DeprecationWarning)
     _allreduce_grads(params, coalesce=coalesce, bucket_size_mb=bucket_size_mb)
 
 

mmcv/tensorrt/tensorrt_utils.py

@@ -231,5 +231,6 @@ class TRTWraper(TRTWrapper):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        warnings.warn('TRTWraper will be deprecated in'
+        warnings.warn(
-                      ' future. Please use TRTWrapper instead')
+            'TRTWraper will be deprecated in'
+            ' future. Please use TRTWrapper instead', DeprecationWarning)

mmcv/utils/config.py

@@ -229,7 +229,7 @@ class Config:
             if 'reference' in deprecation_info:
                 warning_msg += ' More information can be found at ' \
                     f'{deprecation_info["reference"]}'
-            warnings.warn(warning_msg)
+            warnings.warn(warning_msg, DeprecationWarning)
 
         cfg_text = filename + '\n'
         with open(filename, 'r', encoding='utf-8') as f:

mmcv/utils/hub.py

@@ -28,10 +28,11 @@ if TORCH_VERSION != 'parrots' and digit_version(TORCH_VERSION) < digit_version(
         return False
 
     def _legacy_zip_load(filename, model_dir, map_location):
-        warnings.warn('Falling back to the old format < 1.6. This support will'
+        warnings.warn(
+            'Falling back to the old format < 1.6. This support will'
             ' be deprecated in favor of default zipfile format '
             'introduced in 1.6. Please redo torch.save() to save it '
-                      'in the new zipfile format.')
+            'in the new zipfile format.', DeprecationWarning)
         # Note: extractall() defaults to overwrite file if exists. No need to
         #       clean up beforehand. We deliberately don't handle tarfile here
         #       since our legacy serialization format was in tar.
@@ -84,8 +85,9 @@ if TORCH_VERSION != 'parrots' and digit_version(TORCH_VERSION) < digit_version(
         """
         # Issue warning to move data if old env is set
         if os.getenv('TORCH_MODEL_ZOO'):
-            warnings.warn('TORCH_MODEL_ZOO is deprecated, please use env '
+            warnings.warn(
-                          'TORCH_HOME instead')
+                'TORCH_MODEL_ZOO is deprecated, please use env '
+                'TORCH_HOME instead', DeprecationWarning)
 
         if model_dir is None:
             torch_home = _get_torch_home()

mmcv/utils/misc.py

@@ -315,7 +315,7 @@ def deprecated_api_warning(name_dict, cls_name=None):
                         warnings.warn(
                             f'"{src_arg_name}" is deprecated in '
                             f'`{func_name}`, please use "{dst_arg_name}" '
-                            'instead')
+                            'instead', DeprecationWarning)
                         arg_names[arg_names.index(src_arg_name)] = dst_arg_name
             if kwargs:
                 for src_arg_name, dst_arg_name in name_dict.items():
@@ -333,7 +333,7 @@ def deprecated_api_warning(name_dict, cls_name=None):
                         warnings.warn(
                             f'"{src_arg_name}" is deprecated in '
                             f'`{func_name}`, please use "{dst_arg_name}" '
-                            'instead')
+                            'instead', DeprecationWarning)
                         kwargs[dst_arg_name] = kwargs.pop(src_arg_name)
 
             # apply converted arguments to the decorated method

mmcv/utils/registry.py

@@ -251,7 +251,8 @@ class Registry:
         warnings.warn(
             'The old API of register_module(module, force=False) '
             'is deprecated and will be removed, please use the new API '
-            'register_module(name=None, force=False, module=None) instead.')
+            'register_module(name=None, force=False, module=None) instead.',
+            DeprecationWarning)
         if cls is None:
             return partial(self.deprecated_register_module, force=force)
         self._register_module(cls, force=force)

tests/test_cnn/test_swish.py

@@ -1,5 +1,5 @@
 import torch
-from torch.nn.functional import sigmoid
+import torch.nn.functional as F
 
 from mmcv.cnn.bricks import Swish
 
@@ -7,7 +7,7 @@ from mmcv.cnn.bricks import Swish
 def test_swish():
     act = Swish()
     input = torch.randn(1, 3, 64, 64)
-    expected_output = input * sigmoid(input)
+    expected_output = input * F.sigmoid(input)
     output = act(input)
     # test output shape
     assert output.shape == expected_output.shape

tests/test_image/test_photometric.py

@@ -333,7 +333,7 @@ class TestPhotometric:
 
         input_img = np.array(
             [[[0, 128, 255], [255, 128, 0]], [[0, 128, 255], [255, 128, 0]]],
-            dtype=np.float)
+            dtype=float)
         img = mmcv.lut_transform(input_img, lut_table)
         baseline = cv2.LUT(np.array(input_img, dtype=np.uint8), lut_table)
         assert np.allclose(img, baseline)

tests/test_ops/test_bilinear_grid_sample.py

@@ -1,6 +1,5 @@
 import numpy as np
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
 
 
@@ -15,7 +14,8 @@ class TestBilinearGridSample(object):
 
         input = torch.rand(1, 1, 20, 20, dtype=dtype)
         grid = torch.Tensor([[[1, 0, 0], [0, 1, 0]]])
-        grid = nn.functional.affine_grid(grid, (1, 1, 15, 15)).type_as(input)
+        grid = F.affine_grid(
+            grid, (1, 1, 15, 15), align_corners=align_corners).type_as(input)
         grid *= multiplier
 
         out = bilinear_grid_sample(input, grid, align_corners=align_corners)

tests/test_ops/test_onnx.py

@@ -8,6 +8,7 @@ import onnxruntime as rt
 import pytest
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from packaging import version
 
 onnx_file = 'tmp.onnx'
@@ -87,10 +88,11 @@ def test_grid_sample(mode, padding_mode, align_corners):
 
     input = torch.rand(1, 1, 10, 10)
     grid = torch.Tensor([[[1, 0, 0], [0, 1, 0]]])
-    grid = nn.functional.affine_grid(grid, (1, 1, 15, 15)).type_as(input)
+    grid = F.affine_grid(
+        grid, (1, 1, 15, 15), align_corners=align_corners).type_as(input)
 
     def func(input, grid):
-        return nn.functional.grid_sample(
+        return F.grid_sample(
             input,
             grid,
             mode=mode,
@@ -110,7 +112,8 @@ def test_bilinear_grid_sample(align_corners):
 
     input = torch.rand(1, 1, 10, 10)
     grid = torch.Tensor([[[1, 0, 0], [0, 1, 0]]])
-    grid = nn.functional.affine_grid(grid, (1, 1, 15, 15)).type_as(input)
+    grid = F.affine_grid(
+        grid, (1, 1, 15, 15), align_corners=align_corners).type_as(input)
 
     def func(input, grid):
         return bilinear_grid_sample(input, grid, align_corners=align_corners)
@@ -462,7 +465,7 @@ def test_interpolate():
     register_extra_symbolics(opset_version)
 
     def func(feat, scale_factor=2):
-        out = nn.functional.interpolate(feat, scale_factor=scale_factor)
+        out = F.interpolate(feat, scale_factor=scale_factor)
         return out
 
     net = WrapFunction(func)

tests/test_ops/test_tensorrt.py

@@ -7,6 +7,7 @@ import onnx
 import pytest
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 
 try:
     from mmcv.tensorrt import (TRTWrapper, is_tensorrt_plugin_loaded, onnx2trt,
@@ -487,11 +488,10 @@ def test_grid_sample(mode, padding_mode, align_corners):
 
     input = torch.rand(1, 1, 10, 10).cuda()
     grid = torch.Tensor([[[1, 0, 0], [0, 1, 0]]])
-    grid = nn.functional.affine_grid(grid,
+    grid = F.affine_grid(grid, (1, 1, 15, 15)).type_as(input).cuda()
-                                     (1, 1, 15, 15)).type_as(input).cuda()
 
     def func(input, grid):
-        return nn.functional.grid_sample(
+        return F.grid_sample(
             input,
             grid,
             mode=mode,

tests/test_ops/test_voxelization.py

@@ -39,7 +39,7 @@ def test_voxelization(device_type):
     device = torch.device(device_type)
 
     # test hard_voxelization on cpu/gpu
-    points = torch.tensor(points).contiguous().to(device)
+    points = points.contiguous().to(device)
     coors, voxels, num_points_per_voxel = hard_voxelization.forward(points)
     coors = coors.cpu().detach().numpy()
     voxels = voxels.cpu().detach().numpy()

tests/test_runner/test_runner.py

@@ -57,7 +57,7 @@ def test_build_runner():
 @pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
 def test_epoch_based_runner(runner_class):
 
-    with pytest.warns(UserWarning):
+    with pytest.warns(DeprecationWarning):
         # batch_processor is deprecated
         model = OldStyleModel()
 

tests/test_utils/test_config.py

@@ -533,6 +533,6 @@ def test_deprecation():
     ]
 
     for cfg_file in deprecated_cfg_files:
-        with pytest.warns(UserWarning):
+        with pytest.warns(DeprecationWarning):
             cfg = Config.fromfile(cfg_file)
         assert cfg.item1 == 'expected'

tests/test_utils/test_registry.py

@@ -96,15 +96,15 @@ def test_registry():
             pass
 
     # begin: test old APIs
-    with pytest.warns(UserWarning):
+    with pytest.warns(DeprecationWarning):
         CATS.register_module(SphynxCat)
         assert CATS.get('SphynxCat').__name__ == 'SphynxCat'
 
-    with pytest.warns(UserWarning):
+    with pytest.warns(DeprecationWarning):
         CATS.register_module(SphynxCat, force=True)
         assert CATS.get('SphynxCat').__name__ == 'SphynxCat'
 
-    with pytest.warns(UserWarning):
+    with pytest.warns(DeprecationWarning):
 
         @CATS.register_module
         class NewCat:
@@ -112,11 +112,11 @@ def test_registry():
 
         assert CATS.get('NewCat').__name__ == 'NewCat'
 
-    with pytest.warns(UserWarning):
+    with pytest.warns(DeprecationWarning):
         CATS.deprecated_register_module(SphynxCat, force=True)
         assert CATS.get('SphynxCat').__name__ == 'SphynxCat'
 
-    with pytest.warns(UserWarning):
+    with pytest.warns(DeprecationWarning):
 
         @CATS.deprecated_register_module
         class CuteCat:
@@ -124,7 +124,7 @@ def test_registry():
 
         assert CATS.get('CuteCat').__name__ == 'CuteCat'
 
-    with pytest.warns(UserWarning):
+    with pytest.warns(DeprecationWarning):
 
         @CATS.deprecated_register_module(force=True)
         class NewCat2:

tests/test_visualization.py

@@ -10,7 +10,7 @@ def test_color():
     assert mmcv.color_val('green') == (0, 255, 0)
     assert mmcv.color_val((1, 2, 3)) == (1, 2, 3)
     assert mmcv.color_val(100) == (100, 100, 100)
-    assert mmcv.color_val(np.zeros(3, dtype=np.int)) == (0, 0, 0)
+    assert mmcv.color_val(np.zeros(3, dtype=int)) == (0, 0, 0)
     with pytest.raises(TypeError):
         mmcv.color_val([255, 255, 255])
     with pytest.raises(TypeError):