add infer (#1640)

ppdet/modeling/bbox.py

@@ -97,10 +97,7 @@ class BBoxPostProcessYOLO(object):
             scores_list.append(paddle.transpose(scores, perm=[0, 2, 1]))
         yolo_boxes = paddle.concat(boxes_list, axis=1)
         yolo_scores = paddle.concat(scores_list, axis=2)
-        bbox = self.nms(bboxes=yolo_boxes, scores=yolo_scores)
-        # TODO: parse the lod of nmsed_bbox
-        # default batch size is 1
-        bbox_num = np.array([int(bbox.shape[0])], dtype=np.int32)
+        bbox, bbox_num = self.nms(bboxes=yolo_boxes, scores=yolo_scores)
         return bbox, bbox_num
 
 

ppdet/py_op/post_process.py

@@ -136,8 +136,6 @@ def get_det_res(bboxes, bbox_nums, image_id, num_id_to_cat_id_map):
     k = 0
     for i in range(len(bbox_nums)):
         image_id = int(image_id[i][0])
-        if bboxes.shape == (1, 1):
-            continue
         det_nums = bbox_nums[i]
         for j in range(det_nums):
             dt = bboxes[k]

ppdet/utils/check.py

@@ -47,7 +47,7 @@ def check_gpu(use_gpu):
         pass
 
 
-def check_version(version='1.7.0'):
+def check_version(version='2.0'):
     """
     Log error and exit when the installed version of paddlepaddle is
     not satisfied.

ppdet/utils/eval_utils.py

@@ -3,6 +3,11 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+import sys
+import json
+from ppdet.py_op.post_process import get_det_res, get_seg_res
+import logging
+logger = logging.getLogger(__name__)
 
 
 def json_eval_results(metric, json_directory=None, dataset=None):
@@ -28,49 +33,62 @@ def json_eval_results(metric, json_directory=None, dataset=None):
             logger.info("{} not exists!".format(v_json))
 
 
-def coco_eval_results(outs_res=None, include_mask=False, dataset=None):
-    print("start evaluate bbox using coco api")
-    import io
-    import six
-    import json
-    from pycocotools.coco import COCO
-    from pycocotools.cocoeval import COCOeval
-    from ppdet.py_op.post_process import get_det_res, get_seg_res
-    anno_file = os.path.join(dataset.dataset_dir, dataset.anno_path)
-    cocoGt = COCO(anno_file)
-    catid = {
-        i + dataset.with_background: v
-        for i, v in enumerate(cocoGt.getCatIds())
-    }
-
-    if outs_res is not None and len(outs_res) > 0:
-        det_res = []
-        for outs in outs_res:
-            det_res += get_det_res(outs['bbox'], outs['bbox_num'],
-                                   outs['im_id'], catid)
+def get_infer_results(outs_res, eval_type, catid):
+    """
+    Get result at the stage of inference.
+    The output format is dictionary containing bbox or mask result.
 
-        with io.open("bbox.json", 'w') as outfile:
-            encode_func = unicode if six.PY2 else str
-            outfile.write(encode_func(json.dumps(det_res)))
+    For example, bbox result is a list and each element contains
+    image_id, category_id, bbox and score. 
+    """
+    if outs_res is None or len(outs_res) == 0:
+        raise ValueError(
+            'The number of valid detection result if zero. Please use reasonable model and check input data.'
+        )
+    infer_res = {}
 
-        cocoDt = cocoGt.loadRes("bbox.json")
-        cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
-        cocoEval.evaluate()
-        cocoEval.accumulate()
-        cocoEval.summarize()
+    if 'bbox' in eval_type:
+        box_res = []
+        for outs in outs_res:
+            box_res += get_det_res(outs['bbox'], outs['bbox_num'],
+                                   outs['im_id'], catid)
+        infer_res['bbox'] = box_res
 
-    if outs_res is not None and len(outs_res) > 0 and include_mask:
+    if 'mask' in eval_type:
         seg_res = []
         for outs in outs_res:
             seg_res += get_seg_res(outs['mask'], outs['bbox_num'],
                                    outs['im_id'], catid)
+        infer_res['mask'] = seg_res
+
+    return infer_res
+
 
-        with io.open("mask.json", 'w') as outfile:
-            encode_func = unicode if six.PY2 else str
-            outfile.write(encode_func(json.dumps(seg_res)))
+def eval_results(res, metric, anno_file):
+    """
+    Evalute the inference result
+    """
+    eval_res = []
+    if metric == 'COCO':
+        from ppdet.utils.coco_eval import cocoapi_eval
+
+        if 'bbox' in res:
+            with open("bbox.json", 'w') as f:
+                json.dump(res['bbox'], f)
+                logger.info('The bbox result is saved to bbox.json.')
+
+            bbox_stats = cocoapi_eval('bbox.json', 'bbox', anno_file=anno_file)
+            eval_res.append(bbox_stats)
+            sys.stdout.flush()
+        if 'mask' in res:
+            with open("mask.json", 'w') as f:
+                json.dump(res['mask'], f)
+                logger.info('The mask result is saved to mask.json.')
+
+            seg_stats = cocoapi_eval('mask.json', 'mask', anno_file=anno_file)
+            eval_res.append(seg_stats)
+            sys.stdout.flush()
+    else:
+        raise NotImplemented("Only COCO metric is supported now.")
 
-        cocoSg = cocoGt.loadRes("mask.json")
-        cocoEval = COCOeval(cocoGt, cocoSg, 'segm')
-        cocoEval.evaluate()
-        cocoEval.accumulate()
-        cocoEval.summarize()
+    return eval_res

ppdet/utils/visualizer.py

@@ -26,18 +26,18 @@ __all__ = ['visualize_results']
 
 
 def visualize_results(image,
+                      bbox_res,
+                      mask_res,
                       im_id,
                       catid2name,
-                      threshold=0.5,
-                      bbox_results=None,
-                      mask_results=None):
+                      threshold=0.5):
     """
     Visualize bbox and mask results
     """
-    if mask_results:
-        image = draw_mask(image, im_id, mask_results, threshold)
-    if bbox_results:
-        image = draw_bbox(image, im_id, catid2name, bbox_results, threshold)
+    if bbox_res is not None:
+        image = draw_bbox(image, im_id, catid2name, bbox_res, threshold)
+    if mask_res is not None:
+        image = draw_mask(image, im_id, mask_res, threshold)
     return image
 
 

tools/eval.py

@@ -18,7 +18,7 @@ from paddle.distributed import ParallelEnv
 from ppdet.core.workspace import load_config, merge_config, create
 from ppdet.utils.check import check_gpu, check_version, check_config
 from ppdet.utils.cli import ArgsParser
-from ppdet.utils.eval_utils import coco_eval_results
+from ppdet.utils.eval_utils import get_infer_results, eval_results
 from ppdet.data.reader import create_reader
 from ppdet.utils.checkpoint import load_dygraph_ckpt, save_dygraph_ckpt
 import logging
@@ -79,11 +79,22 @@ def run(FLAGS, cfg):
     cost_time = time.time() - start_time
     logger.info('Total sample number: {}, averge FPS: {}'.format(
         sample_num, sample_num / cost_time))
+
+    eval_type = ['bbox']
+    if getattr(cfg, 'MaskHead', None):
+        eval_type.append('mask')
     # Metric
-    coco_eval_results(
-        outs_res,
-        include_mask=True if getattr(cfg, 'MaskHead', None) else False,
-        dataset=cfg['EvalReader']['dataset'])
+    # TODO: support other metric
+    dataset = cfg.EvalReader['dataset']
+    from ppdet.utils.coco_eval import get_category_info
+    anno_file = dataset.get_anno()
+    with_background = dataset.with_background
+    use_default_label = dataset.use_default_label
+    clsid2catid, catid2name = get_category_info(anno_file, with_background,
+                                                use_default_label)
+
+    infer_res = get_infer_results(outs_res, eval_type, clsid2catid)
+    eval_results(infer_res, cfg.metric, anno_file)
 
 
 def main():

tools/infer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import os, sys
+# add python path of PadleDetection to sys.path
+parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
+if parent_path not in sys.path:
+    sys.path.append(parent_path)
+
+# ignore numba warning
+import warnings
+warnings.filterwarnings('ignore')
+import glob
+import numpy as np
+from PIL import Image
+import paddle
+from paddle.distributed import ParallelEnv
+from ppdet.core.workspace import load_config, merge_config, create
+from ppdet.utils.check import check_gpu, check_version, check_config
+from ppdet.utils.visualizer import visualize_results
+from ppdet.utils.cli import ArgsParser
+from ppdet.data.reader import create_reader
+from ppdet.utils.checkpoint import load_dygraph_ckpt
+from ppdet.utils.eval_utils import get_infer_results
+import logging
+FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    parser = ArgsParser()
+    parser.add_argument(
+        "--infer_dir",
+        type=str,
+        default=None,
+        help="Directory for images to perform inference on.")
+    parser.add_argument(
+        "--infer_img",
+        type=str,
+        default=None,
+        help="Image path, has higher priority over --infer_dir")
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="output",
+        help="Directory for storing the output visualization files.")
+    parser.add_argument(
+        "--draw_threshold",
+        type=float,
+        default=0.5,
+        help="Threshold to reserve the result for visualization.")
+    parser.add_argument(
+        "--use_vdl",
+        type=bool,
+        default=False,
+        help="whether to record the data to VisualDL.")
+    parser.add_argument(
+        '--vdl_log_dir',
+        type=str,
+        default="vdl_log_dir/image",
+        help='VisualDL logging directory for image.')
+    args = parser.parse_args()
+    return args
+
+
+def get_save_image_name(output_dir, image_path):
+    """
+    Get save image name from source image path.
+    """
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    image_name = os.path.split(image_path)[-1]
+    name, ext = os.path.splitext(image_name)
+    return os.path.join(output_dir, "{}".format(name)) + ext
+
+
+def get_test_images(infer_dir, infer_img):
+    """
+    Get image path list in TEST mode
+    """
+    assert infer_img is not None or infer_dir is not None, \
+        "--infer_img or --infer_dir should be set"
+    assert infer_img is None or os.path.isfile(infer_img), \
+            "{} is not a file".format(infer_img)
+    assert infer_dir is None or os.path.isdir(infer_dir), \
+            "{} is not a directory".format(infer_dir)
+
+    # infer_img has a higher priority
+    if infer_img and os.path.isfile(infer_img):
+        return [infer_img]
+
+    images = set()
+    infer_dir = os.path.abspath(infer_dir)
+    assert os.path.isdir(infer_dir), \
+        "infer_dir {} is not a directory".format(infer_dir)
+    exts = ['jpg', 'jpeg', 'png', 'bmp']
+    exts += [ext.upper() for ext in exts]
+    for ext in exts:
+        images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
+    images = list(images)
+
+    assert len(images) > 0, "no image found in {}".format(infer_dir)
+    logger.info("Found {} inference images in total.".format(len(images)))
+
+    return images
+
+
+def run(FLAGS, cfg):
+
+    # Model
+    main_arch = cfg.architecture
+    model = create(cfg.architecture)
+
+    dataset = cfg.TestReader['dataset']
+    test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
+    dataset.set_images(test_images)
+
+    # TODO: support other metrics
+    imid2path = dataset.get_imid2path()
+
+    from ppdet.utils.coco_eval import get_category_info
+    anno_file = dataset.get_anno()
+    with_background = dataset.with_background
+    use_default_label = dataset.use_default_label
+    clsid2catid, catid2name = get_category_info(anno_file, with_background,
+                                                use_default_label)
+
+    # Init Model
+    model = load_dygraph_ckpt(model, ckpt=cfg.weights)
+
+    # Data Reader
+    test_reader = create_reader(cfg.TestReader)
+
+    # Run Infer 
+    for iter_id, data in enumerate(test_reader()):
+        # forward
+        model.eval()
+        outs = model(data, cfg.TestReader['inputs_def']['fields'], 'infer')
+
+        batch_res = get_infer_results([outs], outs.keys(), clsid2catid)
+        logger.info('Infer iter {}'.format(iter_id))
+        bbox_res = None
+        mask_res = None
+
+        im_ids = outs['im_id']
+        bbox_num = outs['bbox_num']
+        start = 0
+        for i, im_id in enumerate(im_ids):
+            im_id = im_ids[i]
+            image_path = imid2path[int(im_id)]
+            image = Image.open(image_path).convert('RGB')
+            end = start + bbox_num[i]
+
+            # use VisualDL to log original image
+            if FLAGS.use_vdl:
+                original_image_np = np.array(image)
+                vdl_writer.add_image(
+                    "original/frame_{}".format(vdl_image_frame),
+                    original_image_np, vdl_image_step)
+
+            if 'bbox' in batch_res:
+                bbox_res = batch_res['bbox'][start:end]
+            if 'mask' in batch_res:
+                mask_res = batch_res['mask'][start:end]
+
+            image = visualize_results(image, bbox_res, mask_res,
+                                      int(im_id), catid2name,
+                                      FLAGS.draw_threshold)
+
+            # use VisualDL to log image with bbox
+            if FLAGS.use_vdl:
+                infer_image_np = np.array(image)
+                vdl_writer.add_image("bbox/frame_{}".format(vdl_image_frame),
+                                     infer_image_np, vdl_image_step)
+                vdl_image_step += 1
+                if vdl_image_step % 10 == 0:
+                    vdl_image_step = 0
+                    vdl_image_frame += 1
+
+            # save image with detection
+            save_name = get_save_image_name(FLAGS.output_dir, image_path)
+            logger.info("Detection bbox results save in {}".format(save_name))
+            image.save(save_name, quality=95)
+            start = end
+
+
+def main():
+    FLAGS = parse_args()
+
+    cfg = load_config(FLAGS.config)
+    merge_config(FLAGS.opt)
+    check_config(cfg)
+    check_gpu(cfg.use_gpu)
+    check_version()
+
+    run(FLAGS, cfg)
+
+
+if __name__ == '__main__':
+    main()