mmdeploy/tests/test_apis/test_torch2onnx.py
q.yao 66a099faf9
[Feature] Apis unit test (#7)
* add apis test

* split torch2onnx impl, prepare for codebase test

* add is_available to backend

* lint
2021-07-05 12:51:43 +08:00

240 lines
7.5 KiB
Python

import os.path as osp
import shutil
import mmcv
import numpy as np
import pytest
import torch.multiprocessing as mp
from mmdeploy.apis import torch2onnx
backend = 'default'
ret_value = mp.Value('d', 0, lock=False)
work_dir = './tmp/'
save_file = 'tmp.onnx'
@pytest.fixture(autouse=True)
def clear_workdir_after_test():
# clear work_dir before test
if osp.exists(work_dir):
shutil.rmtree(work_dir)
yield
# clear work_dir after test
if osp.exists(work_dir):
shutil.rmtree(work_dir)
def test_torch2onnx_mmcls():
codebase = 'mmcls'
# skip if codebase is not installed
pytest.importorskip(codebase, reason='Can not import {}.'.format(codebase))
# deploy config
deploy_cfg = mmcv.Config(
dict(
codebase=codebase,
backend=backend,
pytorch2onnx=dict(
export_params=True,
keep_initializers_as_inputs=False,
opset_version=11,
save_file=save_file,
input_names=['input'],
output_names=['output'],
dynamic_axes={'input': {
0: 'batch'
}})))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True)
dataset_type = 'ImageNet'
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', size=(256, -1)),
dict(type='CenterCrop', crop_size=224),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
]
# model config
model_cfg = mmcv.Config(
dict(
model=dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=512,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
)),
dataset_type=dataset_type,
img_norm_cfg=img_norm_cfg,
test_pipeline=test_pipeline,
data=dict(
samples_per_gpu=32,
workers_per_gpu=2,
test=dict(
type=dataset_type,
data_prefix='data/imagenet/val',
ann_file='data/imagenet/meta/val.txt',
pipeline=test_pipeline))))
# dummy input
img = np.random.randint(0, 256, (224, 224, 3), dtype=np.uint8)
# export
torch2onnx(
img,
work_dir=work_dir,
save_file=save_file,
deploy_cfg=deploy_cfg,
model_cfg=model_cfg,
device='cpu',
ret_value=ret_value)
assert ret_value.value == 0
assert osp.exists(work_dir)
assert osp.exists(osp.join(work_dir, save_file))
def test_torch2onnx_mmdet():
codebase = 'mmdet'
# skip if codebase is not installed
pytest.importorskip(codebase, reason='Can not import {}.'.format(codebase))
deploy_cfg = mmcv.Config(
dict(
codebase=codebase,
backend=backend,
pytorch2onnx=dict(
export_params=True,
keep_initializers_as_inputs=False,
opset_version=11,
save_file=save_file,
input_names=['input'],
output_names=['dets', 'labels'],
dynamic_axes={'input': {
0: 'batch',
2: 'height',
3: 'width'
}})))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True)
dataset_type = 'CocoDataset'
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
model_cfg = mmcv.Config(
dict(
model=dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5),
bbox_head=dict(
type='RetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
# model training and testing settings
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)),
dataset_type=dataset_type,
img_norm_cfg=img_norm_cfg,
test_pipeline=test_pipeline,
data=dict(
samples_per_gpu=2,
workers_per_gpu=2,
test=dict(type=dataset_type, pipeline=test_pipeline))))
img = np.random.randint(0, 256, (640, 960, 3), dtype=np.uint8)
torch2onnx(
img,
work_dir=work_dir,
save_file=save_file,
deploy_cfg=deploy_cfg,
model_cfg=model_cfg,
device='cpu',
ret_value=ret_value)
assert ret_value.value == 0
assert osp.exists(work_dir)
assert osp.exists(osp.join(work_dir, save_file))