Refine rfsearch and fix a typo

docs/en/api/cnn.rst

@@ -40,6 +40,7 @@ Module
    NonLocal3d
    Scale
    Swish
+   Conv2dRFSearchOp
 
 Build Function
 ----------------

docs/zh_cn/api/cnn.rst

@@ -40,6 +40,7 @@ Module
    NonLocal3d
    Scale
    Swish
+   Conv2dRFSearchOp
 
 Build Function
 ----------------

mmcv/cnn/rfsearch/operator.py

@@ -4,14 +4,12 @@ import copy
 import numpy as np
 import torch
 import torch.nn as nn
-from mmengine.logging import MMLogger
+from mmengine.logging import print_log
 from mmengine.model import BaseModule
 from torch import Tensor
 
 from .utils import expand_rates, get_single_padding
 
-logger = MMLogger.get_current_instance()
-
 
 class BaseConvRFSearchOp(BaseModule):
     """Based class of ConvRFSearchOp.
@@ -84,7 +82,7 @@ class Conv2dRFSearchOp(BaseConvRFSearchOp):
 
         self.branch_weights = nn.Parameter(torch.Tensor(self.num_branches))
         if self.verbose:
-            logger.info(f'Expand as {self.dilation_rates}')
+            print_log(f'Expand as {self.dilation_rates}', 'current')
         nn.init.constant_(self.branch_weights, global_config['init_alphas'])
 
     def forward(self, input: Tensor) -> Tensor:
@@ -118,13 +116,14 @@ class Conv2dRFSearchOp(BaseConvRFSearchOp):
             output += outputs[i]
         return output
 
-    def estimate_rates(self):
+    def estimate_rates(self) -> None:
         """Estimate new dilation rate based on trained branch_weights."""
         norm_w = self.normlize(self.branch_weights[:len(self.dilation_rates)])
         if self.verbose:
-            logger.info('Estimate dilation {} with weight {}.'.format(
+            print_log(
+                'Estimate dilation {} with weight {}.'.format(
                     self.dilation_rates,
-                norm_w.detach().cpu().numpy().tolist()))
+                    norm_w.detach().cpu().numpy().tolist()), 'current')
 
         sum0, sum1, w_sum = 0, 0, 0
         for i in range(len(self.dilation_rates)):
@@ -143,9 +142,9 @@ class Conv2dRFSearchOp(BaseConvRFSearchOp):
         self.op_layer.padding = self.get_padding(self.op_layer.dilation)
         self.dilation_rates = [tuple(estimated)]
         if self.verbose:
-            logger.info(f'Estimate as {tuple(estimated)}')
+            print_log(f'Estimate as {tuple(estimated)}', 'current')
 
-    def expand_rates(self):
+    def expand_rates(self) -> None:
         """Expand dilation rate."""
         dilation = self.op_layer.dilation
         dilation_rates = expand_rates(dilation, self.global_config)
@@ -158,11 +157,11 @@ class Conv2dRFSearchOp(BaseConvRFSearchOp):
 
         self.dilation_rates = copy.deepcopy(dilation_rates)
         if self.verbose:
-            logger.info(f'Expand as {self.dilation_rates}')
+            print_log(f'Expand as {self.dilation_rates}', 'current')
         nn.init.constant_(self.branch_weights,
                           self.global_config['init_alphas'])
 
-    def get_padding(self, dilation):
+    def get_padding(self, dilation) -> tuple:
         padding = (get_single_padding(self.op_layer.kernel_size[0],
                                       self.op_layer.stride[0], dilation[0]),
                    get_single_padding(self.op_layer.kernel_size[1],

mmcv/cnn/rfsearch/search.py

@@ -3,15 +3,14 @@ import os
 from typing import Dict, Optional
 
 import mmengine
+import torch  # noqa
 import torch.nn as nn
 from mmengine.hooks import Hook
-from mmengine.logging import MMLogger
+from mmengine.logging import print_log
 from mmengine.registry import HOOKS
 
-from mmcv.cnn.rfsearch.utils import get_single_padding, write_to_json
-from .operator import BaseConvRFSearchOp
-
-logger = MMLogger.get_current_instance()
+from .operator import BaseConvRFSearchOp, Conv2dRFSearchOp  # noqa
+from .utils import get_single_padding, write_to_json
 
 
 @HOOKS.register_module()
@@ -82,7 +81,7 @@ class RFSearchHook(Hook):
                 search/fixed_single_branch/fixed_multi_branch
         """
         if self.verbose:
-            logger.info('RFSearch init begin.')
+            print_log('RFSearch init begin.', 'current')
         if self.mode == 'search':
             if self.config['structure']:
                 self.set_model(model, search_op='Conv2d')
@@ -95,19 +94,19 @@ class RFSearchHook(Hook):
         else:
             raise NotImplementedError
         if self.verbose:
-            logger.info('RFSearch init end.')
+            print_log('RFSearch init end.', 'current')
 
     def after_train_epoch(self, runner):
         """Performs a dilation searching step after one training epoch."""
         if self.by_epoch and self.mode == 'search':
             self.step(runner.model, runner.work_dir)
 
-    def after_train_iter(self, runner):
+    def after_train_iter(self, runner, batch_idx, data_batch, outputs):
         """Performs a dilation searching step after one training iteration."""
         if not self.by_epoch and self.mode == 'search':
             self.step(runner.model, runner.work_dir)
 
-    def step(self, model: nn.Module, work_dir: str):
+    def step(self, model: nn.Module, work_dir: str) -> None:
         """Performs a dilation searching step.
 
         Args:
@@ -132,7 +131,7 @@ class RFSearchHook(Hook):
                 ),
             )
 
-    def estimate_and_expand(self, model: nn.Module):
+    def estimate_and_expand(self, model: nn.Module) -> None:
         """estimate and search for RFConvOp.
 
         Args:
@@ -146,7 +145,7 @@ class RFSearchHook(Hook):
     def wrap_model(self,
                    model: nn.Module,
                    search_op: str = 'Conv2d',
-                   prefix: str = ''):
+                   prefix: str = '') -> None:
         """wrap model to support searchable conv op.
 
         Args:
@@ -176,8 +175,9 @@ class RFSearchHook(Hook):
                         module, self.config['search'], self.verbose)
                     moduleWrap = moduleWrap.to(module.weight.device)
                     if self.verbose:
-                        logger.info('Wrap model %s to %s.' %
-                                    (str(module), str(moduleWrap)))
+                        print_log(
+                            'Wrap model %s to %s.' %
+                            (str(module), str(moduleWrap)), 'current')
                     setattr(model, name, moduleWrap)
             elif not isinstance(module, BaseConvRFSearchOp):
                 self.wrap_model(module, search_op, fullname)
@@ -186,7 +186,7 @@ class RFSearchHook(Hook):
                   model: nn.Module,
                   search_op: str = 'Conv2d',
                   init_rates: Optional[int] = None,
-                  prefix: str = ''):
+                  prefix: str = '') -> None:
         """set model based on config.
 
         Args:
@@ -231,8 +231,9 @@ class RFSearchHook(Hook):
                             self.config['structure'][fullname][1]))
                     setattr(model, name, module)
                     if self.verbose:
-                        logger.info(
+                        print_log(
                             'Set module %s dilation as: [%d %d]' %
-                            (fullname, module.dilation[0], module.dilation[1]))
+                            (fullname, module.dilation[0], module.dilation[1]),
+                            'current')
             elif not isinstance(module, BaseConvRFSearchOp):
                 self.set_model(module, search_op, init_rates, fullname)

mmcv/image/geometric.py

@@ -440,7 +440,7 @@ def imcrop(
         img (ndarray): Image to be cropped.
         bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes.
         scale (float, optional): Scale ratio of bboxes, the default value
-            1.0 means no padding.
+            1.0 means no scaling.
         pad_fill (Number | list[Number]): Value to be filled for padding.
             Default: None, which means no padding.
 

tests/test_cnn/test_rfsearch/test_search.py

 # Copyright (c) OpenMMLab. All rights reserved.
-"""Tests the rfsearch with runners.
 
-CommandLine:
-    pytest tests/test_runner/test_hooks.py
-    xdoctest tests/test_hooks.py zero
-"""
-
-import torch
 import torch.nn as nn
-from torch.utils.data import DataLoader
 
 from mmcv.cnn.rfsearch import Conv2dRFSearchOp, RFSearchHook
-from tests.test_runner.test_hooks import _build_demo_runner
 
 
 def test_rfsearchhook():
@@ -114,20 +105,6 @@ def test_rfsearchhook():
     assert model.conv2.dilation_rates == [(1, 1), (3, 3)]
     assert model.conv3.dilation_rates == [(1, 1), (1, 2)]
 
-    # 1. test step() with mode of search
-    loader = DataLoader(torch.ones((1, 1, 1, 1)))
-    runner = _build_demo_runner()
-    runner.model = model
-    runner.register_hook(rfsearchhook_search)
-    runner.run([loader], [('train', 1)])
-
-    test_skip_layer()
-    assert not isinstance(model.conv1, Conv2dRFSearchOp)
-    assert isinstance(model.conv2, Conv2dRFSearchOp)
-    assert isinstance(model.conv3, Conv2dRFSearchOp)
-    assert model.conv2.dilation_rates == [(1, 1), (3, 3)]
-    assert model.conv3.dilation_rates == [(1, 1), (1, 3)]
-
     # 2. test init_model() with mode of fixed_single_branch
     model = Model()
     rfsearchhook_fixed_single_branch.init_model(model)
@@ -139,19 +116,6 @@ def test_rfsearchhook():
     assert model.conv2.dilation == (2, 2)
     assert model.conv3.dilation == (1, 1)
 
-    # 2. test step() with mode of fixed_single_branch
-    runner = _build_demo_runner()
-    runner.model = model
-    runner.register_hook(rfsearchhook_fixed_single_branch)
-    runner.run([loader], [('train', 1)])
-
-    assert not isinstance(model.conv1, Conv2dRFSearchOp)
-    assert not isinstance(model.conv2, Conv2dRFSearchOp)
-    assert not isinstance(model.conv3, Conv2dRFSearchOp)
-    assert model.conv1.dilation == (1, 1)
-    assert model.conv2.dilation == (2, 2)
-    assert model.conv3.dilation == (1, 1)
-
     # 3. test init_model() with mode of fixed_multi_branch
     model = Model()
     rfsearchhook_fixed_multi_branch.init_model(model)
@@ -162,16 +126,3 @@ def test_rfsearchhook():
     assert isinstance(model.conv3, Conv2dRFSearchOp)
     assert model.conv2.dilation_rates == [(1, 1), (3, 3)]
     assert model.conv3.dilation_rates == [(1, 1), (1, 2)]
-
-    # 3. test step() with mode of fixed_single_branch
-    runner = _build_demo_runner()
-    runner.model = model
-    runner.register_hook(rfsearchhook_fixed_multi_branch)
-    runner.run([loader], [('train', 1)])
-
-    test_skip_layer()
-    assert not isinstance(model.conv1, Conv2dRFSearchOp)
-    assert isinstance(model.conv2, Conv2dRFSearchOp)
-    assert isinstance(model.conv3, Conv2dRFSearchOp)
-    assert model.conv2.dilation_rates == [(1, 1), (3, 3)]
-    assert model.conv3.dilation_rates == [(1, 1), (1, 2)]