[to #42322933]update easycv pipelines

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

    * update easycv pipelines

modelscope/pipelines/cv/easycv_pipelines/base.py

@@ -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)

modelscope/pipelines/cv/easycv_pipelines/face_2d_keypoints_pipeline.py

@@ -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

modelscope/pipelines/cv/hand_2d_keypoints_pipeline.py

@@ -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

requirements/cv.txt

@@ -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

tests/pipelines/easycv_pipelines/test_segmentation_pipeline.py

@@ -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)

tests/pipelines/test_face_2d_keypoints.py

@@ -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]

tests/run_config.yaml

@@ -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.