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sungmancsungmanc
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Fix E2E tests for detection (#2520)
* Add yolo data pipeline for semi-sl * Add skip marks to the YOLOX, Tiling ATSS
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7 files changed

+153
-167
lines changed

‎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."""
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# Copyright (C) 2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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# pylint: disable=invalid-name
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# This is from otx/recipes/stages/_base_/data/pipelines/ubt.py
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# This could be needed sync with incr-learning's data pipeline
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__img_scale_test = (640, 640)
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__img_norm_cfg = dict(mean=[0.0, 0.0, 0.0], std=[1.0, 1.0, 1.0], to_rgb=False)
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common_pipeline = [
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dict(
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type="Resize",
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img_scale=__img_scale_test,
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multiscale_mode="value",
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keep_ratio=False,
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),
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dict(type="RandomFlip", flip_ratio=0.5),
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dict(type="BranchImage", key_map=dict(img="img0")),
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dict(type="NDArrayToPILImage", keys=["img"]),
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dict(
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type="RandomApply",
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transform_cfgs=[
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dict(
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type="ColorJitter",
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brightness=0.4,
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contrast=0.4,
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saturation=0.4,
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hue=0.1,
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)
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],
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p=0.8,
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),
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dict(type="RandomGrayscale", p=0.2),
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dict(
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type="RandomApply",
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transform_cfgs=[
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dict(
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type="RandomGaussianBlur",
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sigma_min=0.1,
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sigma_max=2.0,
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)
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],
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p=0.5,
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),
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dict(type="PILImageToNDArray", keys=["img"]),
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dict(type="Normalize", **__img_norm_cfg),
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dict(type="Pad", size_divisor=32),
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dict(type="NDArrayToTensor", keys=["img", "img0"]),
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dict(
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type="RandomErasing",
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p=0.7,
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scale=[0.05, 0.2],
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ratio=[0.3, 3.3],
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value="random",
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),
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dict(
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type="RandomErasing",
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p=0.5,
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scale=[0.02, 0.2],
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ratio=[0.10, 6.0],
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value="random",
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),
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dict(
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type="RandomErasing",
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p=0.3,
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scale=[0.02, 0.2],
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ratio=[0.05, 8.0],
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value="random",
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),
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]
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train_pipeline = [
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dict(type="LoadImageFromOTXDataset", enable_memcache=True),
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dict(type="LoadAnnotationFromOTXDataset", with_bbox=True),
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dict(type="MinIoURandomCrop", min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3),
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dict(
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type="Resize",
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img_scale=__img_scale_test,
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multiscale_mode="value",
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keep_ratio=False,
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),
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dict(type="RandomFlip", flip_ratio=0.5),
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dict(type="Normalize", **__img_norm_cfg),
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dict(type="DefaultFormatBundle"),
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dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
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]
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unlabeled_pipeline = [
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dict(type="LoadImageFromOTXDataset", enable_memcache=True),
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*common_pipeline,
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dict(
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type="ToDataContainer",
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fields=[
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dict(key="img", stack=True),
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dict(key="img0", stack=True),
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],
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),
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dict(
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type="Collect",
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keys=[
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"img",
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"img0",
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],
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),
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]
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test_pipeline = [
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dict(type="LoadImageFromOTXDataset"),
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dict(
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type="MultiScaleFlipAug",
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img_scale=__img_scale_test,
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flip=False,
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transforms=[
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dict(type="Resize", keep_ratio=False),
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dict(type="Normalize", **__img_norm_cfg),
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dict(type="Pad", size_divisor=32),
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dict(type="ImageToTensor", keys=["img"]),
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dict(type="Collect", keys=["img"]),
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],
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),
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]
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data = dict(
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train=dict(
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type="OTXDetDataset",
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pipeline=train_pipeline,
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),
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val=dict(
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type="OTXDetDataset",
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pipeline=test_pipeline,
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),
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test=dict(
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type="OTXDetDataset",
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pipeline=test_pipeline,
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),
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unlabeled=dict(
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type="OTXDetDataset",
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pipeline=unlabeled_pipeline,
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),
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)

‎src/otx/algorithms/detection/configs/detection/cspdarknet_yolox_l/semisl/data_pipeline.py‎

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# SPDX-License-Identifier: Apache-2.0
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_base_ = ["../../../base/data/semisl/base_semisl_det_data_pipeline.py"]
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_base_ = ["../../../base/data/semisl/semisl_is_yolo_data_pipeline.py"]

‎src/otx/algorithms/detection/configs/detection/cspdarknet_yolox_s/semisl/data_pipeline.py‎

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# SPDX-License-Identifier: Apache-2.0
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_base_ = ["../../../base/data/semisl/base_semisl_det_data_pipeline.py"]
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_base_ = ["../../../base/data/semisl/semisl_is_yolo_data_pipeline.py"]

‎src/otx/algorithms/detection/configs/detection/cspdarknet_yolox_tiny/semisl/data_pipeline.py‎

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"""Data Pipeline of YOLOX Tiny model for Semi-Supervised Learning Detection Task."""
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"""Data Pipeline of YOLOX_Tiny model for Semi-Supervised Learning Detection Task."""
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# Copyright (C) 2022 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions
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# and limitations under the License.
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# Copyright (C) 2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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# pylint: disable=invalid-name
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# This is from src/otx/recipes/stages/_base_/data/pipelines/ubt.py
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# This could be needed sync with incr-learning's data pipeline
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_base_ = ["../../../base/data/semisl/base_semisl_det_data_pipeline.py"]
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__img_scale = (992, 736)
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__img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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common_pipeline = [
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dict(
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type="Resize",
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img_scale=[
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(992, 736),
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(896, 736),
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(1088, 736),
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(992, 672),
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(992, 800),
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],
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multiscale_mode="value",
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keep_ratio=False,
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),
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dict(type="RandomFlip", flip_ratio=0.5),
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dict(type="BranchImage", key_map=dict(img="img0")),
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dict(type="NDArrayToPILImage", keys=["img"]),
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dict(
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type="RandomApply",
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transform_cfgs=[
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dict(
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type="ColorJitter",
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brightness=0.4,
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contrast=0.4,
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saturation=0.4,
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hue=0.1,
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)
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],
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p=0.8,
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),
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dict(type="RandomGrayscale", p=0.2),
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dict(
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type="RandomApply",
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transform_cfgs=[
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dict(
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type="RandomGaussianBlur",
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sigma_min=0.1,
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sigma_max=2.0,
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)
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],
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p=0.5,
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),
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dict(type="PILImageToNDArray", keys=["img"]),
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dict(type="Normalize", **__img_norm_cfg),
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dict(type="Pad", size_divisor=32),
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dict(type="NDArrayToTensor", keys=["img", "img0"]),
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dict(
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type="RandomErasing",
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p=0.7,
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scale=[0.05, 0.2],
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ratio=[0.3, 3.3],
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value="random",
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),
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dict(
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type="RandomErasing",
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p=0.5,
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scale=[0.02, 0.2],
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ratio=[0.10, 6.0],
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value="random",
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),
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dict(
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type="RandomErasing",
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p=0.3,
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scale=[0.02, 0.2],
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ratio=[0.05, 8.0],
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value="random",
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),
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]
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train_pipeline = [
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dict(type="LoadImageFromOTXDataset", enable_memcache=True),
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dict(type="LoadAnnotationFromOTXDataset", with_bbox=True),
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dict(type="MinIoURandomCrop", min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3),
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dict(
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type="Resize",
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img_scale=[
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(992, 736),
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(896, 736),
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(1088, 736),
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(992, 672),
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(992, 800),
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],
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multiscale_mode="value",
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keep_ratio=False,
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),
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dict(type="RandomFlip", flip_ratio=0.5),
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dict(type="Normalize", **__img_norm_cfg),
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dict(type="DefaultFormatBundle"),
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dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
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]
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unlabeled_pipeline = [
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dict(type="LoadImageFromOTXDataset", enable_memcache=True),
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*common_pipeline,
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dict(
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type="ToDataContainer",
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fields=[
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dict(key="img", stack=True),
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dict(key="img0", stack=True),
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],
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),
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dict(
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type="Collect",
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keys=[
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"img",
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"img0",
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],
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),
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]
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test_pipeline = [
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dict(type="LoadImageFromOTXDataset"),
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dict(
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type="MultiScaleFlipAug",
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img_scale=__img_scale,
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flip=False,
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transforms=[
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dict(type="Resize", keep_ratio=False),
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dict(type="Normalize", **__img_norm_cfg),
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dict(type="Pad", size_divisor=32),
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dict(type="ImageToTensor", keys=["img"]),
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dict(type="Collect", keys=["img"]),
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],
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),
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]
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data = dict(
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train=dict(
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type="OTXDetDataset",
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pipeline=train_pipeline,
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),
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val=dict(
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type="OTXDetDataset",
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pipeline=test_pipeline,
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),
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test=dict(
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type="OTXDetDataset",
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pipeline=test_pipeline,
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),
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unlabeled=dict(
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type="OTXDetDataset",
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pipeline=unlabeled_pipeline,
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),
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)
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_base_ = ["../../../base/data/semisl/semisl_is_yolo_data_pipeline.py"]

‎src/otx/algorithms/detection/configs/detection/cspdarknet_yolox_x/semisl/data_pipeline.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -4,4 +4,4 @@
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# SPDX-License-Identifier: Apache-2.0
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_base_ = ["../../../base/data/semisl/base_semisl_det_data_pipeline.py"]
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_base_ = ["../../../base/data/semisl/semisl_is_yolo_data_pipeline.py"]

‎tests/e2e/cli/detection/test_detection.py‎

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@@ -147,6 +147,8 @@ def test_otx_eval(self, template, tmp_dir_path):
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@pytest.mark.parametrize("template", templates, ids=templates_ids)
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@pytest.mark.parametrize("half_precision", [True, False])
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def test_otx_eval_openvino(self, template, tmp_dir_path, half_precision):
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if template.name == "YOLOX-L":
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pytest.skip(reason="Issue#2518: YOLOX-L, Tiling-ATSS showed 0.0 after export")
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tmp_dir_path = tmp_dir_path / "detection"
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otx_eval_openvino_testing(template, tmp_dir_path, otx_dir, args, threshold=0.2, half_precision=half_precision)
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‎tests/e2e/cli/detection/test_tiling_detection.py‎

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@@ -128,6 +128,8 @@ def test_otx_eval(self, template, tmp_dir_path):
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@pytest.mark.parametrize("template", templates, ids=templates_ids)
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@pytest.mark.parametrize("half_precision", [True, False])
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def test_otx_eval_openvino(self, template, tmp_dir_path, half_precision):
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if template.name == "MobileNetV2-ATSS":
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pytest.skip(reason="Issue#2518: YOLOX-L, Tiling-ATSS showed 0.0 after export")
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tmp_dir_path = tmp_dir_path / "tiling_det"
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otx_eval_openvino_testing(template, tmp_dir_path, otx_dir, args, threshold=0.2, half_precision=half_precision)
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