diff --git a/easycv/core/evaluation/custom_cocotools/cocoeval.py b/easycv/core/evaluation/custom_cocotools/cocoeval.py index 4606f5c4..7400e13b 100644 --- a/easycv/core/evaluation/custom_cocotools/cocoeval.py +++ b/easycv/core/evaluation/custom_cocotools/cocoeval.py @@ -467,8 +467,8 @@ def accumulate(self, p=None): fps = np.logical_and( np.logical_not(dtm), np.logical_not(dtIg)) - tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) - fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float32) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float32) for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): tp = np.array(tp) fp = np.array(fp) diff --git a/easycv/datasets/face/pipelines/face_keypoint_transform.py b/easycv/datasets/face/pipelines/face_keypoint_transform.py index bda83859..0add55e4 100644 --- a/easycv/datasets/face/pipelines/face_keypoint_transform.py +++ b/easycv/datasets/face/pipelines/face_keypoint_transform.py @@ -252,7 +252,7 @@ def aug_clr_noise_blur(self, img): skin_factor_list = [0.6, 0.8, 1.0, 1.2, 1.4] skin_factor = np.random.choice(skin_factor_list) img_ycrcb_raw[:, :, 0:1] = np.clip( - img_ycrcb_raw[:, :, 0:1].astype(np.float) * skin_factor, 0, + img_ycrcb_raw[:, :, 0:1].astype(np.float32) * skin_factor, 0, 255).astype(np.uint8) img = cv2.cvtColor(img_ycrcb_raw, cv2.COLOR_YCR_CB2BGR) diff --git a/easycv/models/utils/pos_embed.py b/easycv/models/utils/pos_embed.py index e8616486..853a3ed2 100644 --- a/easycv/models/utils/pos_embed.py +++ b/easycv/models/utils/pos_embed.py @@ -47,7 +47,7 @@ def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): out: (M, D) """ assert embed_dim % 2 == 0 - omega = np.arange(embed_dim // 2, dtype=np.float) + omega = np.arange(embed_dim // 2, dtype=np.float32) omega /= embed_dim / 2. omega = 1. / 10000**omega # (D/2,) diff --git a/easycv/thirdparty/mot/bytetrack/byte_tracker.py b/easycv/thirdparty/mot/bytetrack/byte_tracker.py index c7576448..dab52b7b 100644 --- a/easycv/thirdparty/mot/bytetrack/byte_tracker.py +++ b/easycv/thirdparty/mot/bytetrack/byte_tracker.py @@ -38,7 +38,7 @@ class STrack(BaseTrack): def __init__(self, tlwh, score): # wait activate - self._tlwh = np.asarray(tlwh, dtype=np.float) + self._tlwh = np.asarray(tlwh, dtype=np.float32) self.kalman_filter = None self.mean, self.covariance = None, None self.is_activated = False diff --git a/easycv/thirdparty/mot/bytetrack/matching.py b/easycv/thirdparty/mot/bytetrack/matching.py index 301740ac..bda5265e 100644 --- a/easycv/thirdparty/mot/bytetrack/matching.py +++ b/easycv/thirdparty/mot/bytetrack/matching.py @@ -86,15 +86,15 @@ def ious(atlbrs, btlbrs): :rtype ious np.ndarray """ - ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float) + ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32) if ious.size == 0: return ious from cython_bbox import bbox_overlaps as bbox_ious ious = bbox_ious( - np.ascontiguousarray(atlbrs, dtype=np.float), - np.ascontiguousarray(btlbrs, dtype=np.float)) + np.ascontiguousarray(atlbrs, dtype=np.float32), + np.ascontiguousarray(btlbrs, dtype=np.float32)) return ious @@ -151,15 +151,15 @@ def embedding_distance(tracks, detections, metric='cosine'): :return: cost_matrix np.ndarray """ - cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float) + cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32) if cost_matrix.size == 0: return cost_matrix det_features = np.asarray([track.curr_feat for track in detections], - dtype=np.float) + dtype=np.float32) #for i, track in enumerate(tracks): #cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric)) track_features = np.asarray([track.smooth_feat for track in tracks], - dtype=np.float) + dtype=np.float32) cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features return cost_matrix diff --git a/easycv/version.py b/easycv/version.py index b9f3853d..6db1a976 100644 --- a/easycv/version.py +++ b/easycv/version.py @@ -2,5 +2,5 @@ # GENERATED VERSION FILE # TIME: Thu Nov 5 14:17:50 2020 -__version__ = '0.11.3' -short_version = '0.11.3' +__version__ = '0.11.4' +short_version = '0.11.4' diff --git a/tests/test_core/evaluation/test_coco_tools.py b/tests/test_core/evaluation/test_coco_tools.py index ef30b33e..a15d7ae7 100644 --- a/tests/test_core/evaluation/test_coco_tools.py +++ b/tests/test_core/evaluation/test_coco_tools.py @@ -89,8 +89,8 @@ def testCocoWrappers(self): def testExportGroundtruthToCOCO(self): image_ids = ['first', 'second'] groundtruth_boxes = [ - np.array([[100, 100, 200, 200]], np.float), - np.array([[50, 50, 100, 100]], np.float) + np.array([[100, 100, 200, 200]], np.float32), + np.array([[50, 50, 100, 100]], np.float32) ] groundtruth_classes = [ np.array([1], np.int32), @@ -126,12 +126,12 @@ def testExportGroundtruthToCOCO(self): def testExportDetectionsToCOCO(self): image_ids = ['first', 'second'] detections_boxes = [ - np.array([[100, 100, 200, 200]], np.float), - np.array([[50, 50, 100, 100]], np.float) + np.array([[100, 100, 200, 200]], np.float32), + np.array([[50, 50, 100, 100]], np.float32) ] detections_scores = [ - np.array([.8], np.float), - np.array([.7], np.float) + np.array([.8], np.float32), + np.array([.7], np.float32) ] detections_classes = [np.array([1], np.int32), np.array([1], np.int32)] categories = [{ @@ -152,7 +152,17 @@ def testExportDetectionsToCOCO(self): detections_classes, categories, output_path=output_path) - self.assertListEqual(result, self._detections_list) + + self.assertEqual(len(result), len(detections_boxes)) + self.assertEqual(len(detections_boxes), len(detections_boxes)) + + score_list = [] + for i in range(len(detections_boxes)): + score = self._detections_list[i].pop('score') + score_list.append(score) + self.assertAlmostEqual(result[i].pop('score'), score) + self.assertDictEqual(result[i], self._detections_list[i]) + with io.open(output_path, 'r') as f: written_result = f.read() # The json output should have floats written to 4 digits of precision. @@ -160,7 +170,10 @@ def testExportDetectionsToCOCO(self): re.MULTILINE) self.assertTrue(matcher.findall(written_result)) written_result = json.loads(written_result) - self.assertAlmostEqual(result, written_result) + for i in range(len(result)): + self.assertAlmostEqual(written_result[i].pop('score'), + score_list[i]) + self.assertDictEqual(result[i], written_result[i]) def testExportSegmentsToCOCO(self): image_ids = ['first', 'second'] @@ -176,7 +189,10 @@ def testExportSegmentsToCOCO(self): for i, detection_mask in enumerate(detection_masks): detection_masks[i] = detection_mask[:, :, :, None] - detection_scores = [np.array([.8], np.float), np.array([.7], np.float)] + detection_scores = [ + np.array([.8], np.float32), + np.array([.7], np.float32) + ] detection_classes = [np.array([1], np.int32), np.array([1], np.int32)] categories = [{ @@ -202,7 +218,12 @@ def testExportSegmentsToCOCO(self): written_result = json.loads(written_result) mask_load = mask.decode([written_result[0]['segmentation']]) self.assertTrue(np.allclose(mask_load, detection_masks[0])) - self.assertAlmostEqual(result, written_result) + self.assertEqual(len(result), len(detection_masks)) + self.assertEqual(len(written_result), len(detection_masks)) + for i in range(len(detection_masks)): + self.assertAlmostEqual(result[i].pop('score'), + written_result[i].pop('score')) + self.assertDictEqual(result[i], written_result[i]) def testExportKeypointsToCOCO(self): image_ids = ['first', 'second'] @@ -216,8 +237,8 @@ def testExportKeypointsToCOCO(self): ] detection_scores = [ - np.array([.8, 0.2], np.float), - np.array([.7, 0.3], np.float) + np.array([.8, 0.2], np.float32), + np.array([.7, 0.3], np.float32) ] detection_classes = [ np.array([1, 1], np.int32), @@ -248,7 +269,12 @@ def testExportKeypointsToCOCO(self): with io.open(output_path, 'r') as f: written_result = f.read() written_result = json.loads(written_result) - self.assertAlmostEqual(result, written_result) + self.assertEqual(len(result), 4) + self.assertEqual(len(written_result), 4) + for i in range(4): + self.assertAlmostEqual(result[i].pop('score'), + written_result[i].pop('score')) + self.assertDictEqual(result[i], written_result[i]) def testSingleImageDetectionBoxesExport(self): boxes = np.array([[0, 0, 1, 1], [0, 0, .5, .5], [.5, .5, 1, 1]], diff --git a/tests/test_models/detection/yolox/test_yolox.py b/tests/test_models/detection/yolox/test_yolox.py index 98325c73..7826b714 100644 --- a/tests/test_models/detection/yolox/test_yolox.py +++ b/tests/test_models/detection/yolox/test_yolox.py @@ -40,9 +40,9 @@ def test_yolox(self): } output = model(imgs, mode='train', **kwargs) self.assertEqual(output['img_h'].cpu().numpy(), - np.array(640, dtype=np.float)) + np.array(640, dtype=np.float32)) self.assertEqual(output['img_w'].cpu().numpy(), - np.array(640, dtype=np.float)) + np.array(640, dtype=np.float32)) self.assertEqual(output['total_loss'].shape, torch.Size([])) self.assertEqual(output['iou_l'].shape, torch.Size([])) self.assertEqual(output['conf_l'].shape, torch.Size([])) diff --git a/tests/test_models/detection/yolox_edge/test_yolox_edge.py b/tests/test_models/detection/yolox_edge/test_yolox_edge.py index 2eeed017..cc868646 100644 --- a/tests/test_models/detection/yolox_edge/test_yolox_edge.py +++ b/tests/test_models/detection/yolox_edge/test_yolox_edge.py @@ -45,9 +45,9 @@ def test_yolox_edge(self): } output = model(imgs, mode='train', **kwargs) self.assertEqual(output['img_h'].cpu().numpy(), - np.array(640, dtype=np.float)) + np.array(640, dtype=np.float32)) self.assertEqual(output['img_w'].cpu().numpy(), - np.array(640, dtype=np.float)) + np.array(640, dtype=np.float32)) self.assertEqual(output['total_loss'].shape, torch.Size([])) self.assertEqual(output['iou_l'].shape, torch.Size([])) self.assertEqual(output['conf_l'].shape, torch.Size([])) diff --git a/tests/test_toolkit/modelscope/pipelines/test_panoptic_segmentation_pipeline.py b/tests/test_toolkit/modelscope/pipelines/test_panoptic_segmentation_pipeline.py index 4de349b1..8766e642 100644 --- a/tests/test_toolkit/modelscope/pipelines/test_panoptic_segmentation_pipeline.py +++ b/tests/test_toolkit/modelscope/pipelines/test_panoptic_segmentation_pipeline.py @@ -8,13 +8,11 @@ from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks from modelscope.utils.cv.image_utils import panoptic_seg_masks_to_image -from modelscope.utils.demo_utils import DemoCompatibilityCheck from modelscope.utils.test_utils import test_level from tests.ut_config import BASE_LOCAL_PATH -class EasyCVPanopticSegmentationPipelineTest(unittest.TestCase, - DemoCompatibilityCheck): +class EasyCVPanopticSegmentationPipelineTest(unittest.TestCase): img_path = os.path.join( BASE_LOCAL_PATH, 'data/test_images/image_semantic_segmentation.jpg') @@ -32,10 +30,6 @@ def test_r50(self): cv2.imwrite(tmp_save_path, draw_img) print('print ' + self.model_id + ' success') - @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') - def test_demo_compatibility(self): - self.compatibility_check() - if __name__ == '__main__': unittest.main() diff --git a/tests/test_toolkit/modelscope/pipelines/test_segmentation_pipeline.py b/tests/test_toolkit/modelscope/pipelines/test_segmentation_pipeline.py index 5e2ac3ca..dade7ef9 100644 --- a/tests/test_toolkit/modelscope/pipelines/test_segmentation_pipeline.py +++ b/tests/test_toolkit/modelscope/pipelines/test_segmentation_pipeline.py @@ -9,14 +9,12 @@ from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks from modelscope.utils.cv.image_utils import semantic_seg_masks_to_image -from modelscope.utils.demo_utils import DemoCompatibilityCheck from modelscope.utils.test_utils import test_level from PIL import Image from tests.ut_config import BASE_LOCAL_PATH -class EasyCVSegmentationPipelineTest(unittest.TestCase, - DemoCompatibilityCheck): +class EasyCVSegmentationPipelineTest(unittest.TestCase): img_path = os.path.join(BASE_LOCAL_PATH, 'data/test_images/image_segmentation.jpg') @@ -82,10 +80,6 @@ def test_segformer_b5(self): model_id = 'damo/cv_segformer-b5_image_semantic-segmentation_coco-stuff164k' self._internal_test_(model_id) - @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') - def test_demo_compatibility(self): - self.compatibility_check() - if __name__ == '__main__': unittest.main()