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Fix E2E tests for detection #2520

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Sep 26, 2023
Merged

Fix E2E tests for detection #2520

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abf7516
Add yolo data pipeline for semi-sl
sungmanc Sep 26, 2023
dcdf1cf
Add skip marks to the YOLOX, Tiling ATSS
sungmanc Sep 26, 2023
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142 changes: 142 additions & 0 deletions src/otx/algorithms/detection/configs/base/data/semisl/semisl_is_yolo_data_pipeline.py
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"""Data Pipeline for Semi-Supervised Learning Detection Task."""

# Copyright (C) 2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

# pylint: disable=invalid-name

# This is from otx/recipes/stages/_base_/data/pipelines/ubt.py
# This could be needed sync with incr-learning's data pipeline
__img_scale_test = (640, 640)
__img_norm_cfg = dict(mean=[0.0, 0.0, 0.0], std=[1.0, 1.0, 1.0], to_rgb=False)

common_pipeline = [
dict(
type="Resize",
img_scale=__img_scale_test,
multiscale_mode="value",
keep_ratio=False,
),
dict(type="RandomFlip", flip_ratio=0.5),
dict(type="BranchImage", key_map=dict(img="img0")),
dict(type="NDArrayToPILImage", keys=["img"]),
dict(
type="RandomApply",
transform_cfgs=[
dict(
type="ColorJitter",
brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.1,
)
],
p=0.8,
),
dict(type="RandomGrayscale", p=0.2),
dict(
type="RandomApply",
transform_cfgs=[
dict(
type="RandomGaussianBlur",
sigma_min=0.1,
sigma_max=2.0,
)
],
p=0.5,
),
dict(type="PILImageToNDArray", keys=["img"]),
dict(type="Normalize", **__img_norm_cfg),
dict(type="Pad", size_divisor=32),
dict(type="NDArrayToTensor", keys=["img", "img0"]),
dict(
type="RandomErasing",
p=0.7,
scale=[0.05, 0.2],
ratio=[0.3, 3.3],
value="random",
),
dict(
type="RandomErasing",
p=0.5,
scale=[0.02, 0.2],
ratio=[0.10, 6.0],
value="random",
),
dict(
type="RandomErasing",
p=0.3,
scale=[0.02, 0.2],
ratio=[0.05, 8.0],
value="random",
),
]

train_pipeline = [
dict(type="LoadImageFromOTXDataset", enable_memcache=True),
dict(type="LoadAnnotationFromOTXDataset", with_bbox=True),
dict(type="MinIoURandomCrop", min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3),
dict(
type="Resize",
img_scale=__img_scale_test,
multiscale_mode="value",
keep_ratio=False,
),
dict(type="RandomFlip", flip_ratio=0.5),
dict(type="Normalize", **__img_norm_cfg),
dict(type="DefaultFormatBundle"),
dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]

unlabeled_pipeline = [
dict(type="LoadImageFromOTXDataset", enable_memcache=True),
*common_pipeline,
dict(
type="ToDataContainer",
fields=[
dict(key="img", stack=True),
dict(key="img0", stack=True),
],
),
dict(
type="Collect",
keys=[
"img",
"img0",
],
),
]

test_pipeline = [
dict(type="LoadImageFromOTXDataset"),
dict(
type="MultiScaleFlipAug",
img_scale=__img_scale_test,
flip=False,
transforms=[
dict(type="Resize", keep_ratio=False),
dict(type="Normalize", **__img_norm_cfg),
dict(type="Pad", size_divisor=32),
dict(type="ImageToTensor", keys=["img"]),
dict(type="Collect", keys=["img"]),
],
),
]
data = dict(
train=dict(
type="OTXDetDataset",
pipeline=train_pipeline,
),
val=dict(
type="OTXDetDataset",
pipeline=test_pipeline,
),
test=dict(
type="OTXDetDataset",
pipeline=test_pipeline,
),
unlabeled=dict(
type="OTXDetDataset",
pipeline=unlabeled_pipeline,
),
)
2 changes: 1 addition & 1 deletion src/otx/algorithms/detection/configs/detection/cspdarknet_yolox_l/semisl/data_pipeline.py
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Expand Up @@ -4,4 +4,4 @@
# SPDX-License-Identifier: Apache-2.0


_base_ = ["../../../base/data/semisl/base_semisl_det_data_pipeline.py"]
_base_ = ["../../../base/data/semisl/semisl_is_yolo_data_pipeline.py"]
2 changes: 1 addition & 1 deletion src/otx/algorithms/detection/configs/detection/cspdarknet_yolox_s/semisl/data_pipeline.py
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Expand Up @@ -4,4 +4,4 @@
# SPDX-License-Identifier: Apache-2.0


_base_ = ["../../../base/data/semisl/base_semisl_det_data_pipeline.py"]
_base_ = ["../../../base/data/semisl/semisl_is_yolo_data_pipeline.py"]
168 changes: 4 additions & 164 deletions src/otx/algorithms/detection/configs/detection/cspdarknet_yolox_tiny/semisl/data_pipeline.py
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@@ -1,167 +1,7 @@
"""Data Pipeline of YOLOX Tiny model for Semi-Supervised Learning Detection Task."""
"""Data Pipeline of YOLOX_Tiny model for Semi-Supervised Learning Detection Task."""

# Copyright (C) 2022 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions
# and limitations under the License.
# Copyright (C) 2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

# pylint: disable=invalid-name

# This is from src/otx/recipes/stages/_base_/data/pipelines/ubt.py
# This could be needed sync with incr-learning's data pipeline
_base_ = ["../../../base/data/semisl/base_semisl_det_data_pipeline.py"]

__img_scale = (992, 736)
__img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

common_pipeline = [
dict(
type="Resize",
img_scale=[
(992, 736),
(896, 736),
(1088, 736),
(992, 672),
(992, 800),
],
multiscale_mode="value",
keep_ratio=False,
),
dict(type="RandomFlip", flip_ratio=0.5),
dict(type="BranchImage", key_map=dict(img="img0")),
dict(type="NDArrayToPILImage", keys=["img"]),
dict(
type="RandomApply",
transform_cfgs=[
dict(
type="ColorJitter",
brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.1,
)
],
p=0.8,
),
dict(type="RandomGrayscale", p=0.2),
dict(
type="RandomApply",
transform_cfgs=[
dict(
type="RandomGaussianBlur",
sigma_min=0.1,
sigma_max=2.0,
)
],
p=0.5,
),
dict(type="PILImageToNDArray", keys=["img"]),
dict(type="Normalize", **__img_norm_cfg),
dict(type="Pad", size_divisor=32),
dict(type="NDArrayToTensor", keys=["img", "img0"]),
dict(
type="RandomErasing",
p=0.7,
scale=[0.05, 0.2],
ratio=[0.3, 3.3],
value="random",
),
dict(
type="RandomErasing",
p=0.5,
scale=[0.02, 0.2],
ratio=[0.10, 6.0],
value="random",
),
dict(
type="RandomErasing",
p=0.3,
scale=[0.02, 0.2],
ratio=[0.05, 8.0],
value="random",
),
]

train_pipeline = [
dict(type="LoadImageFromOTXDataset", enable_memcache=True),
dict(type="LoadAnnotationFromOTXDataset", with_bbox=True),
dict(type="MinIoURandomCrop", min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3),
dict(
type="Resize",
img_scale=[
(992, 736),
(896, 736),
(1088, 736),
(992, 672),
(992, 800),
],
multiscale_mode="value",
keep_ratio=False,
),
dict(type="RandomFlip", flip_ratio=0.5),
dict(type="Normalize", **__img_norm_cfg),
dict(type="DefaultFormatBundle"),
dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]

unlabeled_pipeline = [
dict(type="LoadImageFromOTXDataset", enable_memcache=True),
*common_pipeline,
dict(
type="ToDataContainer",
fields=[
dict(key="img", stack=True),
dict(key="img0", stack=True),
],
),
dict(
type="Collect",
keys=[
"img",
"img0",
],
),
]

test_pipeline = [
dict(type="LoadImageFromOTXDataset"),
dict(
type="MultiScaleFlipAug",
img_scale=__img_scale,
flip=False,
transforms=[
dict(type="Resize", keep_ratio=False),
dict(type="Normalize", **__img_norm_cfg),
dict(type="Pad", size_divisor=32),
dict(type="ImageToTensor", keys=["img"]),
dict(type="Collect", keys=["img"]),
],
),
]
data = dict(
train=dict(
type="OTXDetDataset",
pipeline=train_pipeline,
),
val=dict(
type="OTXDetDataset",
pipeline=test_pipeline,
),
test=dict(
type="OTXDetDataset",
pipeline=test_pipeline,
),
unlabeled=dict(
type="OTXDetDataset",
pipeline=unlabeled_pipeline,
),
)
_base_ = ["../../../base/data/semisl/semisl_is_yolo_data_pipeline.py"]
2 changes: 1 addition & 1 deletion src/otx/algorithms/detection/configs/detection/cspdarknet_yolox_x/semisl/data_pipeline.py
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Expand Up @@ -4,4 +4,4 @@
# SPDX-License-Identifier: Apache-2.0


_base_ = ["../../../base/data/semisl/base_semisl_det_data_pipeline.py"]
_base_ = ["../../../base/data/semisl/semisl_is_yolo_data_pipeline.py"]
2 changes: 2 additions & 0 deletions tests/e2e/cli/detection/test_detection.py
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Expand Up @@ -147,6 +147,8 @@ def test_otx_eval(self, template, tmp_dir_path):
@pytest.mark.parametrize("template", templates, ids=templates_ids)
@pytest.mark.parametrize("half_precision", [True, False])
def test_otx_eval_openvino(self, template, tmp_dir_path, half_precision):
if template.name == "YOLOX-L":
pytest.skip(reason="Issue#2518: YOLOX-L, Tiling-ATSS showed 0.0 after export")
tmp_dir_path = tmp_dir_path / "detection"
otx_eval_openvino_testing(template, tmp_dir_path, otx_dir, args, threshold=0.2, half_precision=half_precision)

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2 changes: 2 additions & 0 deletions tests/e2e/cli/detection/test_tiling_detection.py
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Expand Up @@ -128,6 +128,8 @@ def test_otx_eval(self, template, tmp_dir_path):
@pytest.mark.parametrize("template", templates, ids=templates_ids)
@pytest.mark.parametrize("half_precision", [True, False])
def test_otx_eval_openvino(self, template, tmp_dir_path, half_precision):
if template.name == "MobileNetV2-ATSS":
pytest.skip(reason="Issue#2518: YOLOX-L, Tiling-ATSS showed 0.0 after export")
tmp_dir_path = tmp_dir_path / "tiling_det"
otx_eval_openvino_testing(template, tmp_dir_path, otx_dir, args, threshold=0.2, half_precision=half_precision)

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