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[to #42322933]update easycv pipelines

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Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10208603

    * update easycv pipelines
This commit is contained in:
jiangnana.jnn
2022-09-22 15:15:29 +08:00
committed by Yingda Chen
parent 2d3f2e6008
commit e1c9f8a31f
7 changed files with 53 additions and 23 deletions
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11
modelscope/pipelines/cv/easycv_pipelines/base.py
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@@ -10,6 +10,7 @@ from modelscope.hub.snapshot_download import snapshot_download
from modelscope.pipelines.util import is_official_hub_path
from modelscope.utils.config import Config
from modelscope.utils.constant import DEFAULT_MODEL_REVISION, ModelFile
from modelscope.utils.device import create_device
class EasyCVPipeline(object):
@@ -53,16 +54,19 @@ class EasyCVPipeline(object):
), f'Not find "{ModelFile.CONFIGURATION}" in model directory!'
self.cfg = Config.from_file(self.config_file)
self.predict_op = self._build_predict_op()
if 'device' in kwargs:
kwargs['device'] = create_device(kwargs['device'])
self.predict_op = self._build_predict_op(**kwargs)
def _build_predict_op(self):
def _build_predict_op(self, **kwargs):
"""Build EasyCV predictor."""
from easycv.predictors.builder import build_predictor
easycv_config = self._to_easycv_config()
pipeline_op = build_predictor(self.cfg.pipeline.predictor_config, {
'model_path': self.model_path,
'config_file': easycv_config
'config_file': easycv_config,
**kwargs
})
return pipeline_op
@@ -91,5 +95,4 @@ class EasyCVPipeline(object):
return easycv_config
def __call__(self, inputs) -> Any:
# TODO: support image url
return self.predict_op(inputs)

12
modelscope/pipelines/cv/easycv_pipelines/face_2d_keypoints_pipeline.py
View File

@@ -4,7 +4,6 @@ from typing import Any
from modelscope.metainfo import Pipelines
from modelscope.outputs import OutputKeys
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import LoadImage
from modelscope.utils.constant import ModelFile, Tasks
from .base import EasyCVPipeline
@@ -34,8 +33,11 @@ class Face2DKeypointsPipeline(EasyCVPipeline):
return self.predict_op.show_result(img, points, scale, save_path)
def __call__(self, inputs) -> Any:
output = self.predict_op(inputs)[0][0]
points = output['point']
poses = output['pose']
outputs = self.predict_op(inputs)
return {OutputKeys.KEYPOINTS: points, OutputKeys.POSES: poses}
results = [{
OutputKeys.KEYPOINTS: output['point'],
OutputKeys.POSES: output['pose']
} for output in outputs]
return results

5
modelscope/pipelines/cv/hand_2d_keypoints_pipeline.py
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@@ -28,7 +28,7 @@ class Hand2DKeypointsPipeline(EasyCVPipeline):
*args,
**kwargs)
def _build_predict_op(self):
def _build_predict_op(self, **kwargs):
"""Build EasyCV predictor."""
from easycv.predictors.builder import build_predictor
detection_predictor_type = self.cfg['DETECTION']['type']
@@ -46,6 +46,7 @@ class Hand2DKeypointsPipeline(EasyCVPipeline):
easycv_config = self._to_easycv_config()
pipeline_op = build_predictor(self.cfg.pipeline.predictor_config, {
'model_path': self.model_path,
'config_file': easycv_config
'config_file': easycv_config,
**kwargs
})
return pipeline_op

2
requirements/cv.txt
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@@ -14,7 +14,7 @@ mmcls>=0.21.0
mmdet>=2.25.0
networkx>=2.5
onnxruntime>=1.10
pai-easycv>=0.6.0
pai-easycv>=0.6.3.4
pandas
psutil
regex

43
tests/pipelines/easycv_pipelines/test_segmentation_pipeline.py
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@@ -1,10 +1,11 @@
# Copyright (c) Alibaba, Inc. and its affiliates.
import unittest
from distutils.version import LooseVersion
import easycv
import numpy as np
from PIL import Image
from modelscope.metainfo import Pipelines
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.test_utils import test_level
@@ -24,38 +25,60 @@ class EasyCVSegmentationPipelineTest(unittest.TestCase):
results = outputs[0]
self.assertListEqual(
list(img.shape)[:2], list(results['seg_pred'][0].shape))
self.assertListEqual(results['seg_pred'][0][1, 4:10].tolist(),
list(img.shape)[:2], list(results['seg_pred'].shape))
self.assertListEqual(results['seg_pred'][1, 4:10].tolist(),
[161 for i in range(6)])
self.assertListEqual(results['seg_pred'][0][-1, -10:].tolist(),
self.assertListEqual(results['seg_pred'][-1, -10:].tolist(),
[133 for i in range(10)])
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
def _internal_test_batch(self, model_id, num_samples=2, batch_size=2):
# TODO: support in the future
img = np.asarray(Image.open(self.img_path))
num_samples = num_samples
batch_size = batch_size
semantic_seg = pipeline(
task=Tasks.image_segmentation,
model=model_id,
batch_size=batch_size)
outputs = semantic_seg([self.img_path] * num_samples)
self.assertEqual(semantic_seg.predict_op.batch_size, batch_size)
self.assertEqual(len(outputs), num_samples)
for output in outputs:
self.assertListEqual(
list(img.shape)[:2], list(output['seg_pred'].shape))
self.assertListEqual(output['seg_pred'][1, 4:10].tolist(),
[161 for i in range(6)])
self.assertListEqual(output['seg_pred'][-1, -10:].tolist(),
[133 for i in range(10)])
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b0(self):
model_id = 'damo/cv_segformer-b0_image_semantic-segmentation_coco-stuff164k'
self._internal_test__(model_id)
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b1(self):
model_id = 'damo/cv_segformer-b1_image_semantic-segmentation_coco-stuff164k'
self._internal_test__(model_id)
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b2(self):
model_id = 'damo/cv_segformer-b2_image_semantic-segmentation_coco-stuff164k'
self._internal_test__(model_id)
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b3(self):
model_id = 'damo/cv_segformer-b3_image_semantic-segmentation_coco-stuff164k'
self._internal_test__(model_id)
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b4(self):
model_id = 'damo/cv_segformer-b4_image_semantic-segmentation_coco-stuff164k'
self._internal_test__(model_id)
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b5(self):
model_id = 'damo/cv_segformer-b5_image_semantic-segmentation_coco-stuff164k'
self._internal_test__(model_id)

2
tests/pipelines/test_face_2d_keypoints.py
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@@ -18,7 +18,7 @@ class EasyCVFace2DKeypointsPipelineTest(unittest.TestCase):
face_2d_keypoints_align = pipeline(
task=Tasks.face_2d_keypoints, model=model_id)
output = face_2d_keypoints_align(img_path)
output = face_2d_keypoints_align(img_path)[0]
output_keypoints = output[OutputKeys.KEYPOINTS]
output_pose = output[OutputKeys.POSES]

1
tests/run_config.yaml
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@@ -9,6 +9,7 @@ isolated: # test cases that may require excessive anmount of GPU memory, which
- test_image_super_resolution.py
- test_easycv_trainer.py
- test_segformer.py
- test_segmentation_pipeline.py
envs:
default: # default env, case not in other env will in default, pytorch.