From f3f443ff719bf4f17b0e17182b8261bb68b8b1dc Mon Sep 17 00:00:00 2001
From: Jerry Jiarui XU <xvjiarui0826@gmail.com>
Date: Fri, 25 Sep 2020 19:56:10 +0800
Subject: [PATCH] [Enhance] Migrate to MMCV DepthwiseSeparableConv (#158)
* Add D16-MG124 models
* Use MMCV DepthSepConv
* add OHEM
* add warmup
* fixed test
* fixed test
* change to bs 16
* revert config
* add models
* seperate
---
.dev/clean_models.py | 125 -----------------
.dev/generate_table.py | 152 ---------------------
.dev/modelzoo_json2md.py | 58 --------
.dev/upload_modelzoo.py | 44 ++++++
mmseg/core/utils/__init__.py | 3 +-
mmseg/core/utils/dist_utils.py | 49 -------
mmseg/models/backbones/fast_scnn.py | 5 +-
mmseg/models/decode_heads/sep_aspp_head.py | 4 +-
mmseg/models/decode_heads/sep_fcn_head.py | 3 +-
mmseg/ops/__init__.py | 3 +-
mmseg/ops/separable_conv_module.py | 88 ------------
setup.cfg | 2 +-
tests/test_models/test_heads.py | 4 +-
tests/test_ops/test_sep_conv_module.py | 71 ----------
14 files changed, 55 insertions(+), 556 deletions(-)
delete mode 100644 .dev/clean_models.py
delete mode 100644 .dev/generate_table.py
delete mode 100644 .dev/modelzoo_json2md.py
create mode 100644 .dev/upload_modelzoo.py
delete mode 100644 mmseg/core/utils/dist_utils.py
delete mode 100644 mmseg/ops/separable_conv_module.py
delete mode 100644 tests/test_ops/test_sep_conv_module.py
diff --git a/.dev/clean_models.py b/.dev/clean_models.py
deleted file mode 100644
index c9ac2acb..00000000
--- a/.dev/clean_models.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import argparse
-import glob
-import json
-import os
-import os.path as osp
-
-import mmcv
-
-# build schedule look-up table to automatically find the final model
-SCHEDULES_LUT = {
- '20ki': 20000,
- '40ki': 40000,
- '60ki': 60000,
- '80ki': 80000,
- '160ki': 160000
-}
-RESULTS_LUT = ['mIoU', 'mAcc', 'aAcc']
-
-
-def get_final_iter(config):
- iter_num = SCHEDULES_LUT[config.split('_')[-2]]
- return iter_num
-
-
-def get_final_results(log_json_path, iter_num):
- result_dict = dict()
- with open(log_json_path, 'r') as f:
- for line in f.readlines():
- log_line = json.loads(line)
- if 'mode' not in log_line.keys():
- continue
-
- if log_line['mode'] == 'train' and log_line['iter'] == iter_num:
- result_dict['memory'] = log_line['memory']
-
- if log_line['iter'] == iter_num:
- result_dict.update({
- key: log_line[key]
- for key in RESULTS_LUT if key in log_line
- })
- return result_dict
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description='Gather benchmarked models')
- parser.add_argument(
- 'root',
- type=str,
- help='root path of benchmarked models to be gathered')
- parser.add_argument(
- 'config',
- type=str,
- help='root path of benchmarked configs to be gathered')
-
- args = parser.parse_args()
- return args
-
-
-def main():
- args = parse_args()
- models_root = args.root
- config_name = args.config
-
- # find all models in the root directory to be gathered
- raw_configs = list(mmcv.scandir(config_name, '.py', recursive=True))
-
- # filter configs that is not trained in the experiments dir
- used_configs = []
- for raw_config in raw_configs:
- work_dir = osp.splitext(osp.basename(raw_config))[0]
- if osp.exists(osp.join(models_root, work_dir)):
- used_configs.append(work_dir)
- print(f'Find {len(used_configs)} models to be gathered')
-
- # find final_ckpt and log file for trained each config
- # and parse the best performance
- model_infos = []
- for used_config in used_configs:
- exp_dir = osp.join(models_root, used_config)
- # check whether the exps is finished
- final_iter = get_final_iter(used_config)
- final_model = 'iter_{}.pth'.format(final_iter)
- model_path = osp.join(exp_dir, final_model)
-
- # skip if the model is still training
- if not osp.exists(model_path):
- print(f'{used_config} not finished yet')
- continue
-
- # get logs
- log_json_path = glob.glob(osp.join(exp_dir, '*.log.json'))[0]
- log_txt_path = glob.glob(osp.join(exp_dir, '*.log'))[0]
- model_performance = get_final_results(log_json_path, final_iter)
-
- if model_performance is None:
- print(f'{used_config} does not have performance')
- continue
-
- model_time = osp.split(log_txt_path)[-1].split('.')[0]
- model_infos.append(
- dict(
- config=used_config,
- results=model_performance,
- iters=final_iter,
- model_time=model_time,
- log_json_path=osp.split(log_json_path)[-1]))
-
- # publish model for each checkpoint
- for model in model_infos:
-
- model_name = osp.split(model['config'])[-1].split('.')[0]
-
- model_name += '_' + model['model_time']
- for checkpoints in mmcv.scandir(
- osp.join(models_root, model['config']), suffix='.pth'):
- if checkpoints.endswith(f"iter_{model['iters']}.pth"
- ) or checkpoints.endswith('latest.pth'):
- continue
- print('removing {}'.format(
- osp.join(models_root, model['config'], checkpoints)))
- os.remove(osp.join(models_root, model['config'], checkpoints))
-
-
-if __name__ == '__main__':
- main()
diff --git a/.dev/generate_table.py b/.dev/generate_table.py
deleted file mode 100644
index 25142cae..00000000
--- a/.dev/generate_table.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import argparse
-import csv
-import glob
-import json
-import os.path as osp
-from collections import OrderedDict
-
-import mmcv
-
-# build schedule look-up table to automatically find the final model
-RESULTS_LUT = ['mIoU', 'mAcc', 'aAcc']
-
-
-def get_final_iter(config):
- iter_num = config.split('_')[-2]
- assert iter_num.endswith('ki')
- return int(iter_num[:-2]) * 1000
-
-
-def get_final_results(log_json_path, iter_num):
- result_dict = dict()
- with open(log_json_path, 'r') as f:
- for line in f.readlines():
- log_line = json.loads(line)
- if 'mode' not in log_line.keys():
- continue
-
- if log_line['mode'] == 'train' and log_line[
- 'iter'] == iter_num - 50:
- result_dict['memory'] = log_line['memory']
-
- if log_line['iter'] == iter_num:
- result_dict.update({
- key: log_line[key] * 100
- for key in RESULTS_LUT if key in log_line
- })
- return result_dict
-
-
-def get_total_time(log_json_path, iter_num):
-
- def convert(seconds):
- hour = seconds // 3600
- seconds %= 3600
- minutes = seconds // 60
- seconds %= 60
-
- return f'{hour:d}:{minutes:2d}:{seconds:2d}'
-
- time_dict = dict()
- with open(log_json_path, 'r') as f:
- last_iter = 0
- total_sec = 0
- for line in f.readlines():
- log_line = json.loads(line)
- if 'mode' not in log_line.keys():
- continue
-
- if log_line['mode'] == 'train':
- cur_iter = log_line['iter']
- total_sec += (cur_iter - last_iter) * log_line['time']
- last_iter = cur_iter
- time_dict['time'] = convert(int(total_sec))
-
- return time_dict
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description='Gather benchmarked models')
- parser.add_argument(
- 'root',
- type=str,
- help='root path of benchmarked models to be gathered')
- parser.add_argument(
- 'config',
- type=str,
- help='root path of benchmarked configs to be gathered')
- parser.add_argument(
- 'out', type=str, help='output path of gathered models to be stored')
-
- args = parser.parse_args()
- return args
-
-
-def main():
- args = parse_args()
- models_root = args.root
- models_out = args.out
- config_name = args.config
- mmcv.mkdir_or_exist(models_out)
-
- # find all models in the root directory to be gathered
- raw_configs = list(mmcv.scandir(config_name, '.py', recursive=True))
-
- # filter configs that is not trained in the experiments dir
- exp_dirs = []
- for raw_config in raw_configs:
- work_dir = osp.splitext(osp.basename(raw_config))[0]
- if osp.exists(osp.join(models_root, work_dir)):
- exp_dirs.append(work_dir)
- print(f'Find {len(exp_dirs)} models to be gathered')
-
- # find final_ckpt and log file for trained each config
- # and parse the best performance
- model_infos = []
- for work_dir in exp_dirs:
- exp_dir = osp.join(models_root, work_dir)
- # check whether the exps is finished
- final_iter = get_final_iter(work_dir)
- final_model = 'iter_{}.pth'.format(final_iter)
- model_path = osp.join(exp_dir, final_model)
-
- # skip if the model is still training
- if not osp.exists(model_path):
- print(f'{model_path} not finished yet')
- continue
-
- # get logs
- log_json_path = glob.glob(osp.join(exp_dir, '*.log.json'))[0]
- model_performance = get_final_results(log_json_path, final_iter)
-
- if model_performance is None:
- continue
-
- head = work_dir.split('_')[0]
- backbone = work_dir.split('_')[1]
- crop_size = work_dir.split('_')[-3]
- dataset = work_dir.split('_')[-1]
- model_info = OrderedDict(
- head=head,
- backbone=backbone,
- crop_size=crop_size,
- dataset=dataset,
- iters=f'{final_iter//1000}ki')
- model_info.update(model_performance)
- model_time = get_total_time(log_json_path, final_iter)
- model_info.update(model_time)
- model_info['config'] = work_dir
- model_infos.append(model_info)
-
- with open(
- osp.join(models_out, 'models_table.csv'), 'w',
- newline='') as csvfile:
- writer = csv.writer(
- csvfile, delimiter='\t', quotechar='|', quoting=csv.QUOTE_MINIMAL)
- writer.writerow(model_infos[0].keys())
- for model_info in model_infos:
- writer.writerow(model_info.values())
-
-
-if __name__ == '__main__':
- main()
diff --git a/.dev/modelzoo_json2md.py b/.dev/modelzoo_json2md.py
deleted file mode 100644
index 7cb44bff..00000000
--- a/.dev/modelzoo_json2md.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import argparse
-import os
-import os.path as osp
-
-import mmcv
-from pytablewriter import Align, MarkdownTableWriter
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description='Gather benchmarked models')
- parser.add_argument('table_cache', type=str, help='table_cache input')
- parser.add_argument('out', type=str, help='output path md')
-
- args = parser.parse_args()
- return args
-
-
-def main():
- args = parse_args()
- table_cache = mmcv.load(args.table_cache)
- output_dir = args.out
-
- writer = MarkdownTableWriter()
- writer.headers = [
- 'Method', 'Backbone', 'Crop Size', 'Lr schd', 'Mem (GB)',
- 'Inf time (fps)', 'mIoU', 'mIoU(ms+flip)', 'download'
- ]
- writer.margin = 1
- writer.align_list = [Align.CENTER] * len(writer.headers)
- dataset_maps = {
- 'cityscapes': 'Cityscapes',
- 'ade20k': 'ADE20K',
- 'voc12aug': 'Pascal VOC 2012 + Aug'
- }
- for directory in table_cache:
- for dataset in table_cache[directory]:
- table = table_cache[directory][dataset][0]
- writer.table_name = dataset_maps[dataset]
- writer.value_matrix = table
- for i in range(len(table)):
- if table[i][-4] != '-':
- table[i][-4] = f'{table[i][-4]:.2f}'
- mmcv.mkdir_or_exist(osp.join(output_dir, directory))
- writer.dump(
- osp.join(output_dir, directory, f'README_{dataset}.md'))
- with open(osp.join(output_dir, directory, 'README.md'), 'w') as dst_f:
- for dataset in dataset_maps:
- dataset_md_file = osp.join(output_dir, directory,
- f'README_{dataset}.md')
- with open(dataset_md_file) as src_f:
- for line in src_f:
- dst_f.write(line)
- dst_f.write('\n')
- os.remove(dataset_md_file)
-
-
-if __name__ == '__main__':
- main()
diff --git a/.dev/upload_modelzoo.py b/.dev/upload_modelzoo.py
new file mode 100644
index 00000000..bd78bc41
--- /dev/null
+++ b/.dev/upload_modelzoo.py
@@ -0,0 +1,44 @@
+import argparse
+import os
+import os.path as osp
+
+import oss2
+
+ACCESS_KEY_ID = os.getenv('OSS_ACCESS_KEY_ID', None)
+ACCESS_KEY_SECRET = os.getenv('OSS_ACCESS_KEY_SECRET', None)
+BUCKET_NAME = 'openmmlab'
+ENDPOINT = 'https://oss-accelerate.aliyuncs.com'
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(description='Upload models to OSS')
+ parser.add_argument('model_zoo', type=str, help='model_zoo input')
+ parser.add_argument(
+ '--dst-folder',
+ type=str,
+ default='mmsegmentation/v0.5',
+ help='destination folder')
+ args = parser.parse_args()
+ return args
+
+
+def main():
+ args = parse_args()
+ model_zoo = args.model_zoo
+ dst_folder = args.dst_folder
+ bucket = oss2.Bucket(
+ oss2.Auth(ACCESS_KEY_ID, ACCESS_KEY_SECRET), ENDPOINT, BUCKET_NAME)
+
+ for root, dirs, files in os.walk(model_zoo):
+ for file in files:
+ file_path = osp.relpath(osp.join(root, file), model_zoo)
+ print(f'Uploading {file_path}')
+
+ oss2.resumable_upload(bucket, osp.join(dst_folder, file_path),
+ osp.join(model_zoo, file_path))
+ bucket.put_object_acl(
+ osp.join(dst_folder, file_path), oss2.OBJECT_ACL_PUBLIC_READ)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/mmseg/core/utils/__init__.py b/mmseg/core/utils/__init__.py
index 79d62f02..f2678b32 100644
--- a/mmseg/core/utils/__init__.py
+++ b/mmseg/core/utils/__init__.py
@@ -1,4 +1,3 @@
-from .dist_utils import allreduce_grads
from .misc import add_prefix
-__all__ = ['add_prefix', 'allreduce_grads']
+__all__ = ['add_prefix']
diff --git a/mmseg/core/utils/dist_utils.py b/mmseg/core/utils/dist_utils.py
deleted file mode 100644
index 25219a79..00000000
--- a/mmseg/core/utils/dist_utils.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from collections import OrderedDict
-
-import torch.distributed as dist
-from torch._utils import (_flatten_dense_tensors, _take_tensors,
- _unflatten_dense_tensors)
-
-
-def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
- if bucket_size_mb > 0:
- bucket_size_bytes = bucket_size_mb * 1024 * 1024
- buckets = _take_tensors(tensors, bucket_size_bytes)
- else:
- buckets = OrderedDict()
- for tensor in tensors:
- tp = tensor.type()
- if tp not in buckets:
- buckets[tp] = []
- buckets[tp].append(tensor)
- buckets = buckets.values()
-
- for bucket in buckets:
- flat_tensors = _flatten_dense_tensors(bucket)
- dist.all_reduce(flat_tensors)
- flat_tensors.div_(world_size)
- for tensor, synced in zip(
- bucket, _unflatten_dense_tensors(flat_tensors, bucket)):
- tensor.copy_(synced)
-
-
-def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
- """Allreduce gradients.
-
- Args:
- params (list[torch.Parameters]): List of parameters of a model
- coalesce (bool, optional): Whether allreduce parameters as a whole.
- Defaults to True.
- bucket_size_mb (int, optional): Size of bucket, the unit is MB.
- Defaults to -1.
- """
- grads = [
- param.grad.data for param in params
- if param.requires_grad and param.grad is not None
- ]
- world_size = dist.get_world_size()
- if coalesce:
- _allreduce_coalesced(grads, world_size, bucket_size_mb)
- else:
- for tensor in grads:
- dist.all_reduce(tensor.div_(world_size))
diff --git a/mmseg/models/backbones/fast_scnn.py b/mmseg/models/backbones/fast_scnn.py
index dcb24214..4aaec221 100644
--- a/mmseg/models/backbones/fast_scnn.py
+++ b/mmseg/models/backbones/fast_scnn.py
@@ -1,10 +1,11 @@
import torch
import torch.nn as nn
-from mmcv.cnn import ConvModule, constant_init, kaiming_init
+from mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule, constant_init,
+ kaiming_init)
from torch.nn.modules.batchnorm import _BatchNorm
from mmseg.models.decode_heads.psp_head import PPM
-from mmseg.ops import DepthwiseSeparableConvModule, resize
+from mmseg.ops import resize
from mmseg.utils import InvertedResidual
from ..builder import BACKBONES
diff --git a/mmseg/models/decode_heads/sep_aspp_head.py b/mmseg/models/decode_heads/sep_aspp_head.py
index 71881890..50bd52bc 100644
--- a/mmseg/models/decode_heads/sep_aspp_head.py
+++ b/mmseg/models/decode_heads/sep_aspp_head.py
@@ -1,8 +1,8 @@
import torch
import torch.nn as nn
-from mmcv.cnn import ConvModule
+from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
-from mmseg.ops import DepthwiseSeparableConvModule, resize
+from mmseg.ops import resize
from ..builder import HEADS
from .aspp_head import ASPPHead, ASPPModule
diff --git a/mmseg/models/decode_heads/sep_fcn_head.py b/mmseg/models/decode_heads/sep_fcn_head.py
index 18779512..a636f702 100644
--- a/mmseg/models/decode_heads/sep_fcn_head.py
+++ b/mmseg/models/decode_heads/sep_fcn_head.py
@@ -1,4 +1,5 @@
-from mmseg.ops import DepthwiseSeparableConvModule
+from mmcv.cnn import DepthwiseSeparableConvModule
+
from ..builder import HEADS
from .fcn_head import FCNHead
diff --git a/mmseg/ops/__init__.py b/mmseg/ops/__init__.py
index 7a0b930c..bec51c75 100644
--- a/mmseg/ops/__init__.py
+++ b/mmseg/ops/__init__.py
@@ -1,5 +1,4 @@
from .encoding import Encoding
-from .separable_conv_module import DepthwiseSeparableConvModule
from .wrappers import Upsample, resize
-__all__ = ['Upsample', 'resize', 'DepthwiseSeparableConvModule', 'Encoding']
+__all__ = ['Upsample', 'resize', 'Encoding']
diff --git a/mmseg/ops/separable_conv_module.py b/mmseg/ops/separable_conv_module.py
deleted file mode 100644
index 4e5922cc..00000000
--- a/mmseg/ops/separable_conv_module.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule
-
-
-class DepthwiseSeparableConvModule(nn.Module):
- """Depthwise separable convolution module.
-
- See https://arxiv.org/pdf/1704.04861.pdf for details.
-
- This module can replace a ConvModule with the conv block replaced by two
- conv block: depthwise conv block and pointwise conv block. The depthwise
- conv block contains depthwise-conv/norm/activation layers. The pointwise
- conv block contains pointwise-conv/norm/activation layers. It should be
- noted that there will be norm/activation layer in the depthwise conv block
- if `norm_cfg` and `act_cfg` are specified.
-
- Args:
- in_channels (int): Same as nn.Conv2d.
- out_channels (int): Same as nn.Conv2d.
- kernel_size (int or tuple[int]): Same as nn.Conv2d.
- stride (int or tuple[int]): Same as nn.Conv2d. Default: 1.
- padding (int or tuple[int]): Same as nn.Conv2d. Default: 0.
- dilation (int or tuple[int]): Same as nn.Conv2d. Default: 1.
- norm_cfg (dict): Default norm config for both depthwise ConvModule and
- pointwise ConvModule. Default: None.
- act_cfg (dict): Default activation config for both depthwise ConvModule
- and pointwise ConvModule. Default: dict(type='ReLU').
- dw_norm_cfg (dict): Norm config of depthwise ConvModule. If it is
- 'default', it will be the same as `norm_cfg`. Default: 'default'.
- dw_act_cfg (dict): Activation config of depthwise ConvModule. If it is
- 'default', it will be the same as `act_cfg`. Default: 'default'.
- pw_norm_cfg (dict): Norm config of pointwise ConvModule. If it is
- 'default', it will be the same as `norm_cfg`. Default: 'default'.
- pw_act_cfg (dict): Activation config of pointwise ConvModule. If it is
- 'default', it will be the same as `act_cfg`. Default: 'default'.
- kwargs (optional): Other shared arguments for depthwise and pointwise
- ConvModule. See ConvModule for ref.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- norm_cfg=None,
- act_cfg=dict(type='ReLU'),
- dw_norm_cfg='default',
- dw_act_cfg='default',
- pw_norm_cfg='default',
- pw_act_cfg='default',
- **kwargs):
- super(DepthwiseSeparableConvModule, self).__init__()
- assert 'groups' not in kwargs, 'groups should not be specified'
-
- # if norm/activation config of depthwise/pointwise ConvModule is not
- # specified, use default config.
- dw_norm_cfg = dw_norm_cfg if dw_norm_cfg != 'default' else norm_cfg
- dw_act_cfg = dw_act_cfg if dw_act_cfg != 'default' else act_cfg
- pw_norm_cfg = pw_norm_cfg if pw_norm_cfg != 'default' else norm_cfg
- pw_act_cfg = pw_act_cfg if pw_act_cfg != 'default' else act_cfg
-
- # depthwise convolution
- self.depthwise_conv = ConvModule(
- in_channels,
- in_channels,
- kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=in_channels,
- norm_cfg=dw_norm_cfg,
- act_cfg=dw_act_cfg,
- **kwargs)
-
- self.pointwise_conv = ConvModule(
- in_channels,
- out_channels,
- 1,
- norm_cfg=pw_norm_cfg,
- act_cfg=pw_act_cfg,
- **kwargs)
-
- def forward(self, x):
- x = self.depthwise_conv(x)
- x = self.pointwise_conv(x)
- return x
diff --git a/setup.cfg b/setup.cfg
index 21aad54e..cb533f4b 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -8,6 +8,6 @@ line_length = 79
multi_line_output = 0
known_standard_library = setuptools
known_first_party = mmseg
-known_third_party = PIL,cityscapesscripts,detail,matplotlib,mmcv,numpy,onnxruntime,pytablewriter,pytest,scipy,torch
+known_third_party = PIL,cityscapesscripts,detail,matplotlib,mmcv,numpy,onnxruntime,oss2,pytest,scipy,torch
no_lines_before = STDLIB,LOCALFOLDER
default_section = THIRDPARTY
diff --git a/tests/test_models/test_heads.py b/tests/test_models/test_heads.py
index 02460cbc..8e60a915 100644
--- a/tests/test_models/test_heads.py
+++ b/tests/test_models/test_heads.py
@@ -2,7 +2,7 @@ from unittest.mock import patch
import pytest
import torch
-from mmcv.cnn import ConvModule
+from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmcv.utils import ConfigDict
from mmcv.utils.parrots_wrapper import SyncBatchNorm
@@ -557,7 +557,6 @@ def test_sep_fcn_head():
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)
@@ -573,7 +572,6 @@ def test_sep_fcn_head():
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)
diff --git a/tests/test_ops/test_sep_conv_module.py b/tests/test_ops/test_sep_conv_module.py
deleted file mode 100644
index 4eb65011..00000000
--- a/tests/test_ops/test_sep_conv_module.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import pytest
-import torch
-import torch.nn as nn
-
-from mmseg.ops import DepthwiseSeparableConvModule
-
-
-def test_depthwise_separable_conv():
- with pytest.raises(AssertionError):
- # conv_cfg must be a dict or None
- DepthwiseSeparableConvModule(4, 8, 2, groups=2)
-
- # test default config
- conv = DepthwiseSeparableConvModule(3, 8, 2)
- assert conv.depthwise_conv.conv.groups == 3
- assert conv.pointwise_conv.conv.kernel_size == (1, 1)
- assert not conv.depthwise_conv.with_norm
- assert not conv.pointwise_conv.with_norm
- assert conv.depthwise_conv.activate.__class__.__name__ == 'ReLU'
- assert conv.pointwise_conv.activate.__class__.__name__ == 'ReLU'
- x = torch.rand(1, 3, 256, 256)
- output = conv(x)
- assert output.shape == (1, 8, 255, 255)
-
- # test
- conv = DepthwiseSeparableConvModule(3, 8, 2, dw_norm_cfg=dict(type='BN'))
- assert conv.depthwise_conv.norm_name == 'bn'
- assert not conv.pointwise_conv.with_norm
- x = torch.rand(1, 3, 256, 256)
- output = conv(x)
- assert output.shape == (1, 8, 255, 255)
-
- conv = DepthwiseSeparableConvModule(3, 8, 2, pw_norm_cfg=dict(type='BN'))
- assert not conv.depthwise_conv.with_norm
- assert conv.pointwise_conv.norm_name == 'bn'
- x = torch.rand(1, 3, 256, 256)
- output = conv(x)
- assert output.shape == (1, 8, 255, 255)
-
- # add test for ['norm', 'conv', 'act']
- conv = DepthwiseSeparableConvModule(3, 8, 2, order=('norm', 'conv', 'act'))
- x = torch.rand(1, 3, 256, 256)
- output = conv(x)
- assert output.shape == (1, 8, 255, 255)
-
- conv = DepthwiseSeparableConvModule(
- 3, 8, 3, padding=1, with_spectral_norm=True)
- assert hasattr(conv.depthwise_conv.conv, 'weight_orig')
- assert hasattr(conv.pointwise_conv.conv, 'weight_orig')
- output = conv(x)
- assert output.shape == (1, 8, 256, 256)
-
- conv = DepthwiseSeparableConvModule(
- 3, 8, 3, padding=1, padding_mode='reflect')
- assert isinstance(conv.depthwise_conv.padding_layer, nn.ReflectionPad2d)
- output = conv(x)
- assert output.shape == (1, 8, 256, 256)
-
- conv = DepthwiseSeparableConvModule(
- 3, 8, 3, padding=1, dw_act_cfg=dict(type='LeakyReLU'))
- assert conv.depthwise_conv.activate.__class__.__name__ == 'LeakyReLU'
- assert conv.pointwise_conv.activate.__class__.__name__ == 'ReLU'
- output = conv(x)
- assert output.shape == (1, 8, 256, 256)
-
- conv = DepthwiseSeparableConvModule(
- 3, 8, 3, padding=1, pw_act_cfg=dict(type='LeakyReLU'))
- assert conv.depthwise_conv.activate.__class__.__name__ == 'ReLU'
- assert conv.pointwise_conv.activate.__class__.__name__ == 'LeakyReLU'
- output = conv(x)
- assert output.shape == (1, 8, 256, 256)
--
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