mmdeploy/tests/test_codebase/test_mmpose/data/model.py

68 lines
1.9 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
# model settings
codec = dict(
type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)
test_cfg = dict(
flip_test=False,
flip_mode='heatmap',
shift_heatmap=True,
)
model = dict(
type='TopdownPoseEstimator',
data_preprocessor=dict(
type='PoseDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(type='ResNet', depth=18),
head=dict(
type='HeatmapHead',
in_channels=512,
out_channels=17,
deconv_out_channels=None,
loss=dict(type='KeypointMSELoss', use_target_weight=True),
decoder=codec),
test_cfg=test_cfg)
# dataset settings
dataset_type = 'CocoDataset'
data_mode = 'topdown'
data_root = 'tests/test_codebase/test_mmpose/data/'
file_client_args = dict(backend='disk')
test_pipeline = [
dict(type='LoadImage', file_client_args=file_client_args),
dict(type='GetBBoxCenterScale'),
dict(type='TopdownAffine', input_size=codec['input_size']),
dict(type='PackPoseInputs')
]
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/person_keypoints_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
lazy_init=True,
serialize_data=False,
pipeline=test_pipeline,
))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/person_keypoints_val2017.json')
test_evaluator = val_evaluator
# default_runtime
default_scope = 'mmpose'
default_hooks = dict()
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='PoseLocalVisualizer', vis_backends=vis_backends, name='visualizer')