[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 * seperatepull/165/head
Jerry Jiarui XU
GitHub
parent
b9fd8e4bea
commit
f95079ba8a
|
|
@ -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()
|
||||
|
|
@ -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()
|
||||
|
|
@ -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()
|
||||
|
|
@ -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()
|
||||
|
|
@ -1,4 +1,3 @@
|
|||
from .dist_utils import allreduce_grads
|
||||
from .misc import add_prefix
|
||||
|
||||
__all__ = ['add_prefix', 'allreduce_grads']
|
||||
__all__ = ['add_prefix']
|
||||
|
|
|
|||
|
|
@ -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))
|
||||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
from mmseg.ops import DepthwiseSeparableConvModule
|
||||
from mmcv.cnn import DepthwiseSeparableConvModule
|
||||
|
||||
from ..builder import HEADS
|
||||
from .fcn_head import FCNHead
|
||||
|
||||
|
|
|
|||
|
|
@ -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']
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
Loading…
Reference in New Issue