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[Fix] Fix unit test in dev1.x (#1790) 

* fix erros in ut

* use nms_ratated ops instead of ext

* fix ut

* fix superresolution

* update super_resolution
pull/1850/head
q.yao 2023-03-08 14:32:50 +08:00 committed by GitHub
parent 84a289fd88
commit 91a0a9af0b
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13 changed files with 158 additions and 183 deletions
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4
docs/en/01-how-to-build/rockchip.md
View File

@ -140,7 +140,7 @@ label: 65, score: 0.95
- MMDet models.
YOLOV3 & YOLOX: you may paste the following partition configuration into [detection_rknn_static-320x320.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/configs/mmdet/detection/detection_rknn_static-320x320.py):
YOLOV3 & YOLOX: you may paste the following partition configuration into [detection_rknn_static-320x320.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/configs/mmdet/detection/detection_rknn-int8_static-320x320.py):
```python
# yolov3, yolox for rknn-toolkit and rknn-toolkit2
@ -172,7 +172,7 @@ label: 65, score: 0.95
])
```
RetinaNet & SSD & FSAF with rknn-toolkit2, you may paste the following partition configuration into [detection_rknn_static-320x320.py](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmdet/detection/detection_rknn_static-320x320.py). Users with rknn-toolkit can directly use default config.
RetinaNet & SSD & FSAF with rknn-toolkit2, you may paste the following partition configuration into [detection_rknn_static-320x320.py](https://github.com/open-mmlab/mmdeploy/blob/dev-1.x/configs/mmdet/detection/detection_rknn-int8_static-320x320.py). Users with rknn-toolkit can directly use default config.
```python
# retinanet, ssd for rknn-toolkit2

4
docs/zh_cn/01-how-to-build/rockchip.md
View File

@ -105,7 +105,7 @@ python tools/deploy.py \
- YOLOV3 & YOLOX
将下面的模型拆分配置写入到 [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/configs/mmdet/detection/detection_rknn_static-320x320.py)
将下面的模型拆分配置写入到 [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/configs/mmdet/detection/detection_rknn-int8_static-320x320.py)
```python
# yolov3, yolox for rknn-toolkit and rknn-toolkit2
@ -154,7 +154,7 @@ partition_config = dict(
- RetinaNet & SSD & FSAF with rknn-toolkit2
将下面的模型拆分配置写入到 [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/configs/mmdet/detection/detection_rknn_static-320x320.py)。使用 rknn-toolkit 的用户则不用。
将下面的模型拆分配置写入到 [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/configs/mmdet/detection/detection_rknn-int8_static-320x320.py)。使用 rknn-toolkit 的用户则不用。
```python
# retinanet, ssd and fsaf for rknn-toolkit2

2
mmdeploy/codebase/mmdet/models/dense_heads/detr_head.py
View File

@ -46,7 +46,7 @@ def detrhead__predict_by_feat__default(self,
bbox_preds = all_bbox_preds_list[-1][-1]
img_shape = batch_img_metas[0]['img_shape']
max_per_img = self.test_cfg.get('max_per_img', self.num_query)
max_per_img = self.test_cfg.get('max_per_img', len(cls_scores[0]))
batch_size = cls_scores.size(0)
# `batch_index_offset` is used for the gather of concatenated tensor

95
mmdeploy/codebase/mmedit/deploy/super_resolution_model.py
View File

@ -3,7 +3,7 @@ from typing import List, Optional, Sequence, Union
import mmengine
import torch
from mmedit.structures import EditDataSample, PixelData
from mmedit.structures import EditDataSample
from mmengine import Config
from mmengine.model.base_model.data_preprocessor import BaseDataPreprocessor
from mmengine.registry import Registry
@ -64,6 +64,35 @@ class End2EndModel(BaseBackendModel):
deploy_cfg=self.deploy_cfg,
**kwargs)
def convert_to_datasample(
self, predictions: EditDataSample, data_samples: EditDataSample,
inputs: Optional[torch.Tensor]) -> List[EditDataSample]:
"""Add predictions and destructed inputs (if passed) to data samples.
Args:
predictions (EditDataSample): The predictions of the model.
data_samples (EditDataSample): The data samples loaded from
dataloader.
inputs (Optional[torch.Tensor]): The input of model. Defaults to
None.
Returns:
List[EditDataSample]: Modified data samples.
"""
if inputs is not None:
destructed_input = self.data_preprocessor.destruct(
inputs, data_samples, 'img')
data_samples.set_tensor_data({'input': destructed_input})
# split to list of data samples
data_samples = data_samples.split()
predictions = predictions.split()
for data_sample, pred in zip(data_samples, predictions):
data_sample.output = pred
return data_samples
def forward(self,
inputs: torch.Tensor,
data_samples: Optional[List[BaseDataElement]] = None,
@ -94,20 +123,15 @@ class End2EndModel(BaseBackendModel):
lq = lq.to(self.device)
batch_outputs = self.wrapper({self.input_name:
lq})[self.output_names[0]].to('cpu')
if hasattr(self.data_preprocessor, 'destructor'):
batch_outputs = self.data_preprocessor.destructor(
batch_outputs.to(self.data_preprocessor.outputs_std.device))
predictions = []
assert hasattr(self.data_preprocessor, 'destruct')
batch_outputs = self.data_preprocessor.destruct(
batch_outputs, data_samples)
for sr_pred, data_sample in zip(batch_outputs, data_samples):
pred = EditDataSample()
pred.set_data(dict(pred_img=PixelData(**dict(data=sr_pred))))
data_sample.set_data(dict(output=pred))
'''
data_sample.set_data(
dict(pred_img=PixelData(**dict(data=sr_pred))))
'''
predictions.append(data_sample)
# create a stacked data sample here
predictions = EditDataSample(pred_img=batch_outputs.cpu())
predictions = self.convert_to_datasample(predictions, data_samples,
inputs)
return predictions
@ -118,6 +142,35 @@ class SDKEnd2EndModel(End2EndModel):
def __init__(self, *args, **kwargs):
super(SDKEnd2EndModel, self).__init__(*args, **kwargs)
def convert_to_datasample(
self, predictions: EditDataSample, data_samples: EditDataSample,
inputs: Optional[torch.Tensor]) -> List[EditDataSample]:
"""Add predictions and destructed inputs (if passed) to data samples.
Args:
predictions (EditDataSample): The predictions of the model.
data_samples (EditDataSample): The data samples loaded from
dataloader.
inputs (Optional[torch.Tensor]): The input of model. Defaults to
None.
Returns:
List[EditDataSample]: Modified data samples.
"""
if inputs is not None:
destructed_input = self.data_preprocessor.destruct(
inputs, data_samples, 'img')
data_samples.set_tensor_data({'input': destructed_input})
# split to list of data samples
data_samples = data_samples.split()
predictions = predictions.split()
for data_sample, pred in zip(data_samples, predictions):
data_sample.output = pred
return data_samples
def forward(self,
inputs: torch.Tensor,
data_samples: Optional[List[BaseDataElement]] = None,
@ -151,14 +204,14 @@ class SDKEnd2EndModel(End2EndModel):
outputs.append(
torch.from_numpy(output).permute(2, 0, 1).contiguous())
outputs = torch.stack(outputs, 0) / 255.
if hasattr(self.data_preprocessor, 'destructor'):
outputs = self.data_preprocessor.destructor(
outputs.to(self.data_preprocessor.outputs_std.device))
assert hasattr(self.data_preprocessor, 'destruct')
outputs = self.data_preprocessor.destruct(outputs, data_samples)
for i, sr_pred in enumerate(outputs):
pred = EditDataSample()
pred.set_data(dict(pred_img=PixelData(**dict(data=sr_pred))))
data_samples[i].set_data(dict(output=pred))
# create a stacked data sample here
predictions = EditDataSample(pred_img=outputs.cpu())
predictions = self.convert_to_datasample(predictions, data_samples,
inputs)
return data_samples

10
mmdeploy/mmcv/ops/nms_rotated.py
View File

@ -26,8 +26,7 @@ class ONNXNMSRotatedOp(torch.autograd.Function):
Returns:
Tensor: Selected indices of boxes.
"""
from mmcv.utils import ext_loader
ext_module = ext_loader.load_ext('_ext', ['nms_rotated'])
from mmcv.ops import nms_rotated
batch_size, num_class, _ = scores.shape
indices = []
@ -42,11 +41,8 @@ class ONNXNMSRotatedOp(torch.autograd.Function):
continue
valid_inds = torch.nonzero(
valid_mask, as_tuple=False).squeeze(dim=1)
_, order = _scores.sort(0, descending=True)
dets_sorted = _boxes.index_select(0, order)
box_inds = ext_module.nms_rotated(_boxes, _scores, order,
dets_sorted, iou_threshold,
0)
_, box_inds = nms_rotated(
_boxes, _scores, iou_threshold=iou_threshold)
box_inds = valid_inds[box_inds]
batch_inds = torch.zeros_like(box_inds) + batch_id
cls_inds = torch.zeros_like(box_inds) + cls_id

3
tests/test_apis/test_torch2torchscript.py
View File

@ -42,12 +42,11 @@ def test_torch2torchscript(input_name, output_name):
import numpy as np
deploy_cfg = get_deploy_cfg(input_name, output_name)
torch2torchscript(
np.random.rand(8, 8, 3),
np.random.randint(0, 255, (8, 8, 3)),
'',
ts_file,
deploy_cfg,
model_cfg=get_model_cfg(),
device='cpu')
print(ts_file)
assert osp.exists(ts_file)

2
tests/test_codebase/test_mmaction/test_mmaction_model.py
View File

@ -29,7 +29,7 @@ def test_forward_of_base_recognizer(model_cfg_path, backend):
keep_initializers_as_inputs=False,
opset_version=11,
input_shape=None,
input_names=['input'],
input_names=['inputs'],
output_names=['output'])))
model_cfg = load_config(model_cfg_path)[0]

27
tests/test_codebase/test_mmcls/test_mmcls_models.py
View File

@ -29,7 +29,7 @@ def get_invertedresidual_model():
return model
def get_vit_model():
def get_vit_backbone():
from mmcls.models.classifiers.image import ImageClassifier
model = ImageClassifier(
backbone={
@ -60,7 +60,7 @@ def get_vit_model():
},
'topk': (1, 5)
},
)
).backbone
model.requires_grad_(False)
return model
@ -151,39 +151,30 @@ def test_shufflenetv2_backbone__forward(backend_type: Backend):
@pytest.mark.parametrize('backend_type', [Backend.NCNN])
def test_vision_transformer_backbone__forward(backend_type: Backend):
from mmcls.structures import ClsDataSample
from mmdeploy.core import patch_model
import_codebase(Codebase.MMCLS)
check_backend(backend_type, True)
model = get_vit_model()
model = get_vit_backbone()
model.eval()
deploy_cfg = Config(
dict(
backend_config=dict(type=backend_type.value),
onnx_config=dict(input_shape=None, output_names=['output']),
onnx_config=dict(input_shape=None, output_names=['out0', 'out1']),
codebase_config=dict(type='mmcls', task='Classification')))
imgs = torch.rand((1, 3, 384, 384))
data_sample = ClsDataSample(
metainfo=dict(
scale_factor=(1, 1),
ori_shape=imgs.shape[2:],
img_shape=imgs.shape[2:]))
model = patch_model(
model, {}, backend=backend_type.value, data_samples=[data_sample])
model_outputs = model.forward(imgs)
model_outputs = model.forward(imgs)[0]
wrapped_model = WrapModel(model, 'forward')
rewrite_inputs = {'batch_inputs': imgs}
rewrite_inputs = {'x': imgs}
rewrite_outputs, is_backend_output = get_rewrite_outputs(
wrapped_model=wrapped_model,
model_inputs=rewrite_inputs,
deploy_cfg=deploy_cfg)
if isinstance(rewrite_outputs, dict):
rewrite_outputs = rewrite_outputs['output']
rewrite_outputs = [
rewrite_outputs[out_name] for out_name in ['out0', 'out1']
]
for model_output, rewrite_output in zip(model_outputs, rewrite_outputs):
if isinstance(rewrite_output, torch.Tensor):
rewrite_output = rewrite_output.cpu().numpy()

59
tests/test_codebase/test_mmdet/test_mmdet_models.py
View File

@ -406,54 +406,15 @@ def get_detrhead_model():
dict(
type='DETRHead',
num_classes=4,
in_channels=1,
transformer=dict(
type='Transformer',
encoder=dict(
type='DetrTransformerEncoder',
num_layers=1,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=4,
num_heads=1)
],
ffn_cfgs=dict(
type='FFN',
embed_dims=4,
feedforward_channels=32,
num_fcs=2,
ffn_drop=0.,
act_cfg=dict(type='ReLU', inplace=True),
),
operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
decoder=dict(
type='DetrTransformerDecoder',
return_intermediate=True,
num_layers=1,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=dict(
type='MultiheadAttention',
embed_dims=4,
num_heads=1),
ffn_cfgs=dict(
type='FFN',
embed_dims=4,
feedforward_channels=32,
num_fcs=2,
ffn_drop=0.,
act_cfg=dict(type='ReLU', inplace=True),
),
feedforward_channels=32,
operation_order=('self_attn', 'norm', 'cross_attn',
'norm', 'ffn', 'norm')),
)),
positional_encoding=dict(
type='SinePositionalEncoding', num_feats=2, normalize=True),
test_cfg=dict(max_per_img=100)))
embed_dims=4,
loss_cls=dict(
type='CrossEntropyLoss',
bg_cls_weight=0.1,
use_sigmoid=False,
loss_weight=1.0,
class_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0)))
model.requires_grad_(False)
return model
@ -715,7 +676,7 @@ def test_forward_of_base_detector(model_cfg_path, backend):
model_cfg = Config(dict(model=mmengine.load(model_cfg_path)))
model_cfg.model = _replace_r50_with_r18(model_cfg.model)
from mmdet.apis import init_detector
model = init_detector(model_cfg, None, device='cpu')
model = init_detector(model_cfg, None, device='cpu', palette='coco')
img = torch.randn(1, 3, 64, 64)
from mmdet.structures import DetDataSample

65
tests/test_codebase/test_mmedit/data/model.py
View File

@ -74,7 +74,6 @@ train_pipeline = [
dict(
type='Flip', keys=['img', 'gt'], flip_ratio=0.5, direction='vertical'),
dict(type='RandomTransposeHW', keys=['img', 'gt'], transpose_ratio=0.5),
dict(type='ToTensor', keys=['img', 'gt']),
dict(type='PackEditInputs')
]
val_pipeline = [
@ -90,7 +89,6 @@ val_pipeline = [
color_type='color',
channel_order='rgb',
imdecode_backend='cv2'),
dict(type='ToTensor', keys=['img', 'gt']),
dict(type='PackEditInputs')
]
@ -100,6 +98,7 @@ data_root = 'data'
train_dataloader = dict(
num_workers=4,
batch_size=16,
persistent_workers=False,
sampler=dict(type='InfiniteSampler', shuffle=True),
dataset=dict(
@ -124,15 +123,17 @@ val_dataloader = dict(
data_prefix=dict(img='LRbicx4', gt='GTmod12'),
pipeline=val_pipeline))
val_evaluator = [
dict(type='MAE'),
dict(type='PSNR', crop_border=scale),
dict(type='SSIM', crop_border=scale),
]
val_evaluator = dict(
type='EditEvaluator',
metrics=[
dict(type='MAE'),
dict(type='PSNR', crop_border=scale),
dict(type='SSIM', crop_border=scale),
])
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=1000000, val_interval=5000)
val_cfg = dict(type='ValLoop')
val_cfg = dict(type='EditValLoop')
# optimizer
optim_wrapper = dict(
@ -175,7 +176,6 @@ test_pipeline = [
color_type='color',
channel_order='rgb',
imdecode_backend='cv2'),
dict(type='ToTensor', keys=['img', 'gt']),
dict(type='PackEditInputs')
]
@ -190,12 +190,14 @@ set5_dataloader = dict(
type='BasicImageDataset',
metainfo=dict(dataset_type='set5', task_name='sisr'),
data_root=set5_data_root,
data_prefix=dict(img='LRbicx4', gt='GTmod12'),
data_prefix=dict(img='imgs', gt='imgs'),
pipeline=test_pipeline))
set5_evaluator = [
dict(type='PSNR', crop_border=2, prefix='Set5'),
dict(type='SSIM', crop_border=2, prefix='Set5'),
]
set5_evaluator = dict(
type='EditEvaluator',
metrics=[
dict(type='PSNR', crop_border=4, prefix='Set5'),
dict(type='SSIM', crop_border=4, prefix='Set5'),
])
set14_data_root = 'data/Set14'
set14_dataloader = dict(
@ -207,12 +209,14 @@ set14_dataloader = dict(
type='BasicImageDataset',
metainfo=dict(dataset_type='set14', task_name='sisr'),
data_root=set5_data_root,
data_prefix=dict(img='LRbicx4', gt='GTmod12'),
data_prefix=dict(img='imgs', gt='imgs'),
pipeline=test_pipeline))
set14_evaluator = [
dict(type='PSNR', crop_border=2, prefix='Set14'),
dict(type='SSIM', crop_border=2, prefix='Set14'),
]
set14_evaluator = dict(
type='EditEvaluator',
metrics=[
dict(type='PSNR', crop_border=4, prefix='Set14'),
dict(type='SSIM', crop_border=4, prefix='Set14'),
])
ut_data_root = 'tests/test_codebase/test_mmedit/data'
ut_dataloader = dict(
@ -227,28 +231,7 @@ ut_dataloader = dict(
data_prefix=dict(img='imgs', gt='imgs'),
pipeline=test_pipeline))
# test config for DIV2K
div2k_data_root = 'data/DIV2K'
div2k_dataloader = dict(
num_workers=4,
persistent_workers=False,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='BasicImageDataset',
ann_file='meta_info_DIV2K100sub_GT.txt',
metainfo=dict(dataset_type='div2k', task_name='sisr'),
data_root=div2k_data_root,
data_prefix=dict(
img='DIV2K_train_LR_bicubic/X4_sub', gt='DIV2K_train_HR_sub'),
# filename_tmpl=dict(img='{}_x4', gt='{}'),
pipeline=test_pipeline))
div2k_evaluator = [
dict(type='PSNR', crop_border=2, prefix='DIV2K'),
dict(type='SSIM', crop_border=2, prefix='DIV2K'),
]
# test config
test_cfg = dict(type='MultiTestLoop')
test_cfg = dict(type='EditTestLoop')
test_dataloader = [ut_dataloader, ut_dataloader]
test_evaluator = [set5_evaluator, set14_evaluator]

23
tests/test_codebase/test_mmedit/test_super_resolution.py
View File

@ -55,7 +55,7 @@ def backend_model():
ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
wrapper = SwitchBackendWrapper(ORTWrapper)
wrapper.set(outputs={
'output': torch.rand(3, 50, 50),
'output': torch.rand(1, 3, 50, 50),
})
yield task_processor.build_backend_model([''])
@ -65,17 +65,14 @@ def backend_model():
def test_build_test_runner():
# Prepare dummy model
from mmedit.structures import EditDataSample, PixelData
from mmedit.structures import EditDataSample
data_sample = EditDataSample()
img_meta = dict(img_shape=(800, 1196, 3))
img = torch.rand((3, 800, 1196))
gt_img = PixelData(data=img, metainfo=img_meta)
data_sample.gt_img = gt_img
pred_img = PixelData(data=img, metainfo=img_meta)
data_sample.set_data(dict(output=pred_img))
# data_sample.output.pred_img = pred_img
img_meta = dict(ori_img_shape=(32, 32, 3))
img = torch.rand(3, 32, 32)
data_sample = EditDataSample(gt_img=img, metainfo=img_meta)
data_sample.set_data(
dict(output=EditDataSample(pred_img=img, metainfo=img_meta)))
data_sample.set_data(dict(input=img))
outputs = [data_sample]
model = DummyModel(outputs=outputs)
assert model is not None
@ -104,9 +101,7 @@ def test_create_input():
def test_visualize(backend_model):
data_preprocessor = task_processor.build_data_preprocessor()
input_dict, _ = task_processor.create_input(input_img, img_shape,
data_preprocessor)
input_dict, _ = task_processor.create_input(input_img, img_shape)
with torch.no_grad():
results = backend_model.test_step(input_dict)[0]

45
tests/test_codebase/test_mmedit/test_super_resolution_model.py
View File

@ -2,9 +2,7 @@
import pytest
import torch
from mmengine import Config
from mmengine.structures import BaseDataElement
import mmdeploy.backend.onnxruntime as ort_apis
from mmdeploy.codebase import import_codebase
from mmdeploy.utils import Backend, Codebase, load_config
from mmdeploy.utils.test import SwitchBackendWrapper, backend_checker
@ -19,33 +17,34 @@ except ImportError:
@backend_checker(Backend.ONNXRUNTIME)
class TestEnd2EndModel:
@classmethod
def setup_class(cls):
@pytest.fixture(scope='class')
def end2end_model(self):
# force add backend wrapper regardless of plugins
# make sure ONNXRuntimeEditor can use ORTWrapper inside itself
from mmdeploy.backend.onnxruntime import ORTWrapper
ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
# simplify backend inference
cls.wrapper = SwitchBackendWrapper(ORTWrapper)
cls.outputs = {
'outputs': torch.rand(3, 64, 64),
}
cls.wrapper.set(outputs=cls.outputs)
deploy_cfg = Config({'onnx_config': {'output_names': ['outputs']}})
model_cfg = 'tests/test_codebase/test_mmedit/data/model.py'
model_cfg = load_config(model_cfg)[0]
from mmdeploy.codebase.mmedit.deploy.super_resolution_model import \
End2EndModel
cls.end2end_model = End2EndModel(Backend.ONNXRUNTIME, [''], 'cpu',
model_cfg, deploy_cfg)
@classmethod
def teardown_class(cls):
cls.wrapper.recover()
# simplify backend inference
with SwitchBackendWrapper(ORTWrapper) as wrapper:
outputs = {
'outputs': torch.rand(3, 64, 64),
}
wrapper.set(outputs=outputs)
deploy_cfg = Config({'onnx_config': {'output_names': ['outputs']}})
model_cfg = 'tests/test_codebase/test_mmedit/data/model.py'
model_cfg = load_config(model_cfg)[0]
model = End2EndModel(
Backend.ONNXRUNTIME, [''],
'cpu',
model_cfg,
deploy_cfg,
data_preprocessor=model_cfg.model.data_preprocessor)
yield model
def test_forward(self):
def test_forward(self, end2end_model):
input_img = torch.rand(1, 3, 32, 32)
img_metas = [BaseDataElement(metainfo={'ori_img_shape': [3, 32, 32]})]
results = self.end2end_model.forward(input_img, img_metas)
from mmedit.structures import EditDataSample
img_metas = EditDataSample(metainfo={'ori_img_shape': [(32, 32, 3)]})
results = end2end_model.forward(input_img, img_metas)
assert results is not None

2
tests/test_codebase/test_mmrotate/data/model.py
View File

@ -34,7 +34,6 @@ val_dataloader = dict(
data_root=data_root,
ann_file=ann_file,
data_prefix=dict(img_path='trainval/images/'),
img_shape=(1024, 1024),
test_mode=True,
pipeline=[
dict(
@ -62,7 +61,6 @@ test_dataloader = dict(
data_root=data_root,
ann_file=ann_file,
data_prefix=dict(img_path='trainval/images/'),
img_shape=(1024, 1024),
test_mode=True,
pipeline=[
dict(