[0m", buffer))
- buffer = re.sub(r"___(\d+)___(.*?):", r"[\1m\2[0m:", buffer)
- print(buffer)
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/coco_eval.py b/PaddleCV/PaddleDetection/ppdet/utils/coco_eval.py
deleted file mode 100644
index cb5df475fb16f10fffa18a02920687780a3fe298..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/coco_eval.py
+++ /dev/null
@@ -1,560 +0,0 @@
-# Copyright (c) 2019 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
-from __future__ import unicode_literals
-
-import os
-import sys
-import json
-import cv2
-import numpy as np
-import matplotlib
-matplotlib.use('Agg')
-
-import logging
-logger = logging.getLogger(__name__)
-
-__all__ = [
- 'bbox_eval',
- 'mask_eval',
- 'bbox2out',
- 'mask2out',
- 'get_category_info',
- 'proposal_eval',
- 'cocoapi_eval',
-]
-
-
-def clip_bbox(bbox):
- xmin = max(min(bbox[0], 1.), 0.)
- ymin = max(min(bbox[1], 1.), 0.)
- xmax = max(min(bbox[2], 1.), 0.)
- ymax = max(min(bbox[3], 1.), 0.)
- return xmin, ymin, xmax, ymax
-
-
-def proposal_eval(results, anno_file, outfile, max_dets=(100, 300, 1000)):
- assert 'proposal' in results[0]
- assert outfile.endswith('.json')
-
- xywh_results = proposal2out(results)
- assert len(
- xywh_results) > 0, "The number of valid proposal detected is zero.\n \
- Please use reasonable model and check input data."
-
- with open(outfile, 'w') as f:
- json.dump(xywh_results, f)
-
- cocoapi_eval(outfile, 'proposal', anno_file=anno_file, max_dets=max_dets)
- # flush coco evaluation result
- sys.stdout.flush()
-
-
-def bbox_eval(results,
- anno_file,
- outfile,
- with_background=True,
- is_bbox_normalized=False):
- assert 'bbox' in results[0]
- assert outfile.endswith('.json')
- from pycocotools.coco import COCO
-
- coco_gt = COCO(anno_file)
- cat_ids = coco_gt.getCatIds()
-
- # when with_background = True, mapping category to classid, like:
- # background:0, first_class:1, second_class:2, ...
- clsid2catid = dict(
- {i + int(with_background): catid
- for i, catid in enumerate(cat_ids)})
-
- xywh_results = bbox2out(
- results, clsid2catid, is_bbox_normalized=is_bbox_normalized)
-
- if len(xywh_results) == 0:
- logger.warning("The number of valid bbox detected is zero.\n \
- Please use reasonable model and check input data.\n \
- stop eval!")
- return [0.0]
- with open(outfile, 'w') as f:
- json.dump(xywh_results, f)
-
- map_stats = cocoapi_eval(outfile, 'bbox', coco_gt=coco_gt)
- # flush coco evaluation result
- sys.stdout.flush()
- return map_stats
-
-
-def mask_eval(results, anno_file, outfile, resolution, thresh_binarize=0.5):
- assert 'mask' in results[0]
- assert outfile.endswith('.json')
- from pycocotools.coco import COCO
-
- coco_gt = COCO(anno_file)
- clsid2catid = {i + 1: v for i, v in enumerate(coco_gt.getCatIds())}
-
- segm_results = mask2out(results, clsid2catid, resolution, thresh_binarize)
- if len(segm_results) == 0:
- logger.warning("The number of valid mask detected is zero.\n \
- Please use reasonable model and check input data.")
- return
-
- with open(outfile, 'w') as f:
- json.dump(segm_results, f)
-
- cocoapi_eval(outfile, 'segm', coco_gt=coco_gt)
-
-
-def cocoapi_eval(jsonfile,
- style,
- coco_gt=None,
- anno_file=None,
- max_dets=(100, 300, 1000)):
- """
- Args:
- jsonfile: Evaluation json file, eg: bbox.json, mask.json.
- style: COCOeval style, can be `bbox` , `segm` and `proposal`.
- coco_gt: Whether to load COCOAPI through anno_file,
- eg: coco_gt = COCO(anno_file)
- anno_file: COCO annotations file.
- max_dets: COCO evaluation maxDets.
- """
- assert coco_gt != None or anno_file != None
- from pycocotools.coco import COCO
- from pycocotools.cocoeval import COCOeval
-
- if coco_gt == None:
- coco_gt = COCO(anno_file)
- logger.info("Start evaluate...")
- coco_dt = coco_gt.loadRes(jsonfile)
- if style == 'proposal':
- coco_eval = COCOeval(coco_gt, coco_dt, 'bbox')
- coco_eval.params.useCats = 0
- coco_eval.params.maxDets = list(max_dets)
- else:
- coco_eval = COCOeval(coco_gt, coco_dt, style)
- coco_eval.evaluate()
- coco_eval.accumulate()
- coco_eval.summarize()
- return coco_eval.stats
-
-
-def proposal2out(results, is_bbox_normalized=False):
- xywh_res = []
- for t in results:
- bboxes = t['proposal'][0]
- lengths = t['proposal'][1][0]
- im_ids = np.array(t['im_id'][0])
- if bboxes.shape == (1, 1) or bboxes is None:
- continue
-
- k = 0
- for i in range(len(lengths)):
- num = lengths[i]
- im_id = int(im_ids[i][0])
- for j in range(num):
- dt = bboxes[k]
- xmin, ymin, xmax, ymax = dt.tolist()
-
- if is_bbox_normalized:
- xmin, ymin, xmax, ymax = \
- clip_bbox([xmin, ymin, xmax, ymax])
- w = xmax - xmin
- h = ymax - ymin
- else:
- w = xmax - xmin + 1
- h = ymax - ymin + 1
-
- bbox = [xmin, ymin, w, h]
- coco_res = {
- 'image_id': im_id,
- 'category_id': 1,
- 'bbox': bbox,
- 'score': 1.0
- }
- xywh_res.append(coco_res)
- k += 1
- return xywh_res
-
-
-def bbox2out(results, clsid2catid, is_bbox_normalized=False):
- """
- Args:
- results: request a dict, should include: `bbox`, `im_id`,
- if is_bbox_normalized=True, also need `im_shape`.
- clsid2catid: class id to category id map of COCO2017 dataset.
- is_bbox_normalized: whether or not bbox is normalized.
- """
- xywh_res = []
- for t in results:
- bboxes = t['bbox'][0]
- lengths = t['bbox'][1][0]
- im_ids = np.array(t['im_id'][0])
- if bboxes.shape == (1, 1) or bboxes is None:
- continue
-
- k = 0
- for i in range(len(lengths)):
- num = lengths[i]
- im_id = int(im_ids[i][0])
- for j in range(num):
- dt = bboxes[k]
- clsid, score, xmin, ymin, xmax, ymax = dt.tolist()
- catid = (clsid2catid[int(clsid)])
-
- if is_bbox_normalized:
- xmin, ymin, xmax, ymax = \
- clip_bbox([xmin, ymin, xmax, ymax])
- w = xmax - xmin
- h = ymax - ymin
- im_height, im_width = t['im_shape'][0][i].tolist()
- xmin *= im_width
- ymin *= im_height
- w *= im_width
- h *= im_height
- else:
- w = xmax - xmin + 1
- h = ymax - ymin + 1
-
- bbox = [xmin, ymin, w, h]
- coco_res = {
- 'image_id': im_id,
- 'category_id': catid,
- 'bbox': bbox,
- 'score': score
- }
- xywh_res.append(coco_res)
- k += 1
- return xywh_res
-
-
-def mask2out(results, clsid2catid, resolution, thresh_binarize=0.5):
- import pycocotools.mask as mask_util
- scale = (resolution + 2.0) / resolution
-
- segm_res = []
-
- # for each batch
- for t in results:
- bboxes = t['bbox'][0]
-
- lengths = t['bbox'][1][0]
- im_ids = np.array(t['im_id'][0])
- if bboxes.shape == (1, 1) or bboxes is None:
- continue
- if len(bboxes.tolist()) == 0:
- continue
-
- masks = t['mask'][0]
-
- s = 0
- # for each sample
- for i in range(len(lengths)):
- num = lengths[i]
- im_id = int(im_ids[i][0])
- im_shape = t['im_shape'][0][i]
-
- bbox = bboxes[s:s + num][:, 2:]
- clsid_scores = bboxes[s:s + num][:, 0:2]
- mask = masks[s:s + num]
- s += num
-
- im_h = int(im_shape[0])
- im_w = int(im_shape[1])
-
- expand_bbox = expand_boxes(bbox, scale)
- expand_bbox = expand_bbox.astype(np.int32)
-
- padded_mask = np.zeros(
- (resolution + 2, resolution + 2), dtype=np.float32)
-
- for j in range(num):
- xmin, ymin, xmax, ymax = expand_bbox[j].tolist()
- clsid, score = clsid_scores[j].tolist()
- clsid = int(clsid)
- padded_mask[1:-1, 1:-1] = mask[j, clsid, :, :]
-
- catid = clsid2catid[clsid]
-
- w = xmax - xmin + 1
- h = ymax - ymin + 1
- w = np.maximum(w, 1)
- h = np.maximum(h, 1)
-
- resized_mask = cv2.resize(padded_mask, (w, h))
- resized_mask = np.array(
- resized_mask > thresh_binarize, dtype=np.uint8)
- im_mask = np.zeros((im_h, im_w), dtype=np.uint8)
-
- x0 = min(max(xmin, 0), im_w)
- x1 = min(max(xmax + 1, 0), im_w)
- y0 = min(max(ymin, 0), im_h)
- y1 = min(max(ymax + 1, 0), im_h)
-
- im_mask[y0:y1, x0:x1] = resized_mask[(y0 - ymin):(y1 - ymin), (
- x0 - xmin):(x1 - xmin)]
- segm = mask_util.encode(
- np.array(
- im_mask[:, :, np.newaxis], order='F'))[0]
- catid = clsid2catid[clsid]
- segm['counts'] = segm['counts'].decode('utf8')
- coco_res = {
- 'image_id': im_id,
- 'category_id': catid,
- 'segmentation': segm,
- 'score': score
- }
- segm_res.append(coco_res)
- return segm_res
-
-
-def expand_boxes(boxes, scale):
- """
- Expand an array of boxes by a given scale.
- """
- w_half = (boxes[:, 2] - boxes[:, 0]) * .5
- h_half = (boxes[:, 3] - boxes[:, 1]) * .5
- x_c = (boxes[:, 2] + boxes[:, 0]) * .5
- y_c = (boxes[:, 3] + boxes[:, 1]) * .5
-
- w_half *= scale
- h_half *= scale
-
- boxes_exp = np.zeros(boxes.shape)
- boxes_exp[:, 0] = x_c - w_half
- boxes_exp[:, 2] = x_c + w_half
- boxes_exp[:, 1] = y_c - h_half
- boxes_exp[:, 3] = y_c + h_half
-
- return boxes_exp
-
-
-def get_category_info(anno_file=None,
- with_background=True,
- use_default_label=False):
- if use_default_label or anno_file is None \
- or not os.path.exists(anno_file):
- logger.info("Not found annotation file {}, load "
- "coco17 categories.".format(anno_file))
- return coco17_category_info(with_background)
- else:
- logger.info("Load categories from {}".format(anno_file))
- return get_category_info_from_anno(anno_file, with_background)
-
-
-def get_category_info_from_anno(anno_file, with_background=True):
- """
- Get class id to category id map and category id
- to category name map from annotation file.
-
- Args:
- anno_file (str): annotation file path
- with_background (bool, default True):
- whether load background as class 0.
- """
- from pycocotools.coco import COCO
- coco = COCO(anno_file)
- cats = coco.loadCats(coco.getCatIds())
- clsid2catid = {
- i + int(with_background): cat['id']
- for i, cat in enumerate(cats)
- }
- catid2name = {cat['id']: cat['name'] for cat in cats}
-
- return clsid2catid, catid2name
-
-
-def coco17_category_info(with_background=True):
- """
- Get class id to category id map and category id
- to category name map of COCO2017 dataset
-
- Args:
- with_background (bool, default True):
- whether load background as class 0.
- """
- clsid2catid = {
- 1: 1,
- 2: 2,
- 3: 3,
- 4: 4,
- 5: 5,
- 6: 6,
- 7: 7,
- 8: 8,
- 9: 9,
- 10: 10,
- 11: 11,
- 12: 13,
- 13: 14,
- 14: 15,
- 15: 16,
- 16: 17,
- 17: 18,
- 18: 19,
- 19: 20,
- 20: 21,
- 21: 22,
- 22: 23,
- 23: 24,
- 24: 25,
- 25: 27,
- 26: 28,
- 27: 31,
- 28: 32,
- 29: 33,
- 30: 34,
- 31: 35,
- 32: 36,
- 33: 37,
- 34: 38,
- 35: 39,
- 36: 40,
- 37: 41,
- 38: 42,
- 39: 43,
- 40: 44,
- 41: 46,
- 42: 47,
- 43: 48,
- 44: 49,
- 45: 50,
- 46: 51,
- 47: 52,
- 48: 53,
- 49: 54,
- 50: 55,
- 51: 56,
- 52: 57,
- 53: 58,
- 54: 59,
- 55: 60,
- 56: 61,
- 57: 62,
- 58: 63,
- 59: 64,
- 60: 65,
- 61: 67,
- 62: 70,
- 63: 72,
- 64: 73,
- 65: 74,
- 66: 75,
- 67: 76,
- 68: 77,
- 69: 78,
- 70: 79,
- 71: 80,
- 72: 81,
- 73: 82,
- 74: 84,
- 75: 85,
- 76: 86,
- 77: 87,
- 78: 88,
- 79: 89,
- 80: 90
- }
-
- catid2name = {
- 0: 'background',
- 1: 'person',
- 2: 'bicycle',
- 3: 'car',
- 4: 'motorcycle',
- 5: 'airplane',
- 6: 'bus',
- 7: 'train',
- 8: 'truck',
- 9: 'boat',
- 10: 'traffic light',
- 11: 'fire hydrant',
- 13: 'stop sign',
- 14: 'parking meter',
- 15: 'bench',
- 16: 'bird',
- 17: 'cat',
- 18: 'dog',
- 19: 'horse',
- 20: 'sheep',
- 21: 'cow',
- 22: 'elephant',
- 23: 'bear',
- 24: 'zebra',
- 25: 'giraffe',
- 27: 'backpack',
- 28: 'umbrella',
- 31: 'handbag',
- 32: 'tie',
- 33: 'suitcase',
- 34: 'frisbee',
- 35: 'skis',
- 36: 'snowboard',
- 37: 'sports ball',
- 38: 'kite',
- 39: 'baseball bat',
- 40: 'baseball glove',
- 41: 'skateboard',
- 42: 'surfboard',
- 43: 'tennis racket',
- 44: 'bottle',
- 46: 'wine glass',
- 47: 'cup',
- 48: 'fork',
- 49: 'knife',
- 50: 'spoon',
- 51: 'bowl',
- 52: 'banana',
- 53: 'apple',
- 54: 'sandwich',
- 55: 'orange',
- 56: 'broccoli',
- 57: 'carrot',
- 58: 'hot dog',
- 59: 'pizza',
- 60: 'donut',
- 61: 'cake',
- 62: 'chair',
- 63: 'couch',
- 64: 'potted plant',
- 65: 'bed',
- 67: 'dining table',
- 70: 'toilet',
- 72: 'tv',
- 73: 'laptop',
- 74: 'mouse',
- 75: 'remote',
- 76: 'keyboard',
- 77: 'cell phone',
- 78: 'microwave',
- 79: 'oven',
- 80: 'toaster',
- 81: 'sink',
- 82: 'refrigerator',
- 84: 'book',
- 85: 'clock',
- 86: 'vase',
- 87: 'scissors',
- 88: 'teddy bear',
- 89: 'hair drier',
- 90: 'toothbrush'
- }
-
- if not with_background:
- clsid2catid = {k - 1: v for k, v in clsid2catid.items()}
-
- return clsid2catid, catid2name
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/colormap.py b/PaddleCV/PaddleDetection/ppdet/utils/colormap.py
deleted file mode 100644
index 566185ef90390e2f45747bee48e3b008f7dfc0e9..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/colormap.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# Copyright (c) 2019 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
-from __future__ import unicode_literals
-
-import numpy as np
-
-
-def colormap(rgb=False):
- """
- Get colormap
- """
- color_list = np.array([
- 0.000, 0.447, 0.741, 0.850, 0.325, 0.098, 0.929, 0.694, 0.125, 0.494,
- 0.184, 0.556, 0.466, 0.674, 0.188, 0.301, 0.745, 0.933, 0.635, 0.078,
- 0.184, 0.300, 0.300, 0.300, 0.600, 0.600, 0.600, 1.000, 0.000, 0.000,
- 1.000, 0.500, 0.000, 0.749, 0.749, 0.000, 0.000, 1.000, 0.000, 0.000,
- 0.000, 1.000, 0.667, 0.000, 1.000, 0.333, 0.333, 0.000, 0.333, 0.667,
- 0.000, 0.333, 1.000, 0.000, 0.667, 0.333, 0.000, 0.667, 0.667, 0.000,
- 0.667, 1.000, 0.000, 1.000, 0.333, 0.000, 1.000, 0.667, 0.000, 1.000,
- 1.000, 0.000, 0.000, 0.333, 0.500, 0.000, 0.667, 0.500, 0.000, 1.000,
- 0.500, 0.333, 0.000, 0.500, 0.333, 0.333, 0.500, 0.333, 0.667, 0.500,
- 0.333, 1.000, 0.500, 0.667, 0.000, 0.500, 0.667, 0.333, 0.500, 0.667,
- 0.667, 0.500, 0.667, 1.000, 0.500, 1.000, 0.000, 0.500, 1.000, 0.333,
- 0.500, 1.000, 0.667, 0.500, 1.000, 1.000, 0.500, 0.000, 0.333, 1.000,
- 0.000, 0.667, 1.000, 0.000, 1.000, 1.000, 0.333, 0.000, 1.000, 0.333,
- 0.333, 1.000, 0.333, 0.667, 1.000, 0.333, 1.000, 1.000, 0.667, 0.000,
- 1.000, 0.667, 0.333, 1.000, 0.667, 0.667, 1.000, 0.667, 1.000, 1.000,
- 1.000, 0.000, 1.000, 1.000, 0.333, 1.000, 1.000, 0.667, 1.000, 0.167,
- 0.000, 0.000, 0.333, 0.000, 0.000, 0.500, 0.000, 0.000, 0.667, 0.000,
- 0.000, 0.833, 0.000, 0.000, 1.000, 0.000, 0.000, 0.000, 0.167, 0.000,
- 0.000, 0.333, 0.000, 0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000,
- 0.833, 0.000, 0.000, 1.000, 0.000, 0.000, 0.000, 0.167, 0.000, 0.000,
- 0.333, 0.000, 0.000, 0.500, 0.000, 0.000, 0.667, 0.000, 0.000, 0.833,
- 0.000, 0.000, 1.000, 0.000, 0.000, 0.000, 0.143, 0.143, 0.143, 0.286,
- 0.286, 0.286, 0.429, 0.429, 0.429, 0.571, 0.571, 0.571, 0.714, 0.714,
- 0.714, 0.857, 0.857, 0.857, 1.000, 1.000, 1.000
- ]).astype(np.float32)
- color_list = color_list.reshape((-1, 3)) * 255
- if not rgb:
- color_list = color_list[:, ::-1]
- return color_list
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/dist_utils.py b/PaddleCV/PaddleDetection/ppdet/utils/dist_utils.py
deleted file mode 100644
index 32eead4a797ba70cb6980e0368ff9873102680c2..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/dist_utils.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) 2019 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
-
-import os
-
-import paddle.fluid as fluid
-
-
-def nccl2_prepare(trainer_id, startup_prog, main_prog):
- config = fluid.DistributeTranspilerConfig()
- config.mode = "nccl2"
- t = fluid.DistributeTranspiler(config=config)
- t.transpile(
- trainer_id,
- trainers=os.environ.get('PADDLE_TRAINER_ENDPOINTS'),
- current_endpoint=os.environ.get('PADDLE_CURRENT_ENDPOINT'),
- startup_program=startup_prog,
- program=main_prog)
-
-
-def prepare_for_multi_process(exe, build_strategy, startup_prog, main_prog):
- trainer_id = int(os.environ.get('PADDLE_TRAINER_ID', 0))
- num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
- if num_trainers < 2:
- return
- build_strategy.num_trainers = num_trainers
- build_strategy.trainer_id = trainer_id
- nccl2_prepare(trainer_id, startup_prog, main_prog)
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/download.py b/PaddleCV/PaddleDetection/ppdet/utils/download.py
deleted file mode 100644
index 772e19044c320bc0fb7ba5e75216951915074cf5..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/download.py
+++ /dev/null
@@ -1,361 +0,0 @@
-# Copyright (c) 2019 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
-import os.path as osp
-import shutil
-import requests
-import tqdm
-import hashlib
-import tarfile
-import zipfile
-
-from .voc_utils import create_list
-
-import logging
-logger = logging.getLogger(__name__)
-
-__all__ = ['get_weights_path', 'get_dataset_path']
-
-WEIGHTS_HOME = osp.expanduser("~/.cache/paddle/weights")
-DATASET_HOME = osp.expanduser("~/.cache/paddle/dataset")
-
-# dict of {dataset_name: (download_info, sub_dirs)}
-# download info: (url, md5sum)
-DATASETS = {
- 'coco': ([
- (
- 'http://images.cocodataset.org/zips/train2017.zip',
- 'cced6f7f71b7629ddf16f17bbcfab6b2', ),
- (
- 'http://images.cocodataset.org/zips/val2017.zip',
- '442b8da7639aecaf257c1dceb8ba8c80', ),
- (
- 'http://images.cocodataset.org/annotations/annotations_trainval2017.zip',
- 'f4bbac642086de4f52a3fdda2de5fa2c', ),
- ], ["annotations", "train2017", "val2017"]),
- 'voc': ([
- (
- 'http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar',
- '6cd6e144f989b92b3379bac3b3de84fd', ),
- (
- 'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar',
- 'c52e279531787c972589f7e41ab4ae64', ),
- (
- 'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar',
- 'b6e924de25625d8de591ea690078ad9f', ),
- ], ["VOCdevkit/VOC2012", "VOCdevkit/VOC2007"]),
- 'wider_face': ([
- (
- 'https://dataset.bj.bcebos.com/wider_face/WIDER_train.zip',
- '3fedf70df600953d25982bcd13d91ba2', ),
- (
- 'https://dataset.bj.bcebos.com/wider_face/WIDER_val.zip',
- 'dfa7d7e790efa35df3788964cf0bbaea', ),
- (
- 'https://dataset.bj.bcebos.com/wider_face/wider_face_split.zip',
- 'a4a898d6193db4b9ef3260a68bad0dc7', ),
- ], ["WIDER_train", "WIDER_val", "wider_face_split"]),
- 'fruit': ([(
- 'https://dataset.bj.bcebos.com/PaddleDetection_demo/fruit-detection.tar',
- 'ee4a1bf2e321b75b0850cc6e063f79d7', ), ], ["fruit-detection"]),
- 'objects365': (),
-}
-
-DOWNLOAD_RETRY_LIMIT = 3
-
-
-def get_weights_path(url):
- """Get weights path from WEIGHT_HOME, if not exists,
- download it from url.
- """
- path, _ = get_path(url, WEIGHTS_HOME)
- return path
-
-
-def get_dataset_path(path, annotation, image_dir):
- """
- If path exists, return path.
- Otherwise, get dataset path from DATASET_HOME, if not exists,
- download it.
- """
- if _dataset_exists(path, annotation, image_dir):
- return path
-
- logger.info("Dataset {} is not valid for reason above, try searching {} or "
- "downloading dataset...".format(
- osp.realpath(path), DATASET_HOME))
-
- for name, dataset in DATASETS.items():
- if os.path.split(path.strip().lower())[-1] == name:
- logger.info("Parse dataset_dir {} as dataset "
- "{}".format(path, name))
- if name == 'objects365':
- raise NotImplementedError(
- "Dataset {} is not valid for download automatically."
- "Please apply and download the dataset from."
- "https://www.objects365.org/download.html")
- data_dir = osp.join(DATASET_HOME, name)
-
- # For voc, only check dir VOCdevkit/VOC2012, VOCdevkit/VOC2007
- if name == 'voc':
- exists = True
- for sub_dir in dataset[1]:
- check_dir = osp.join(data_dir, sub_dir)
- if osp.exists(check_dir):
- logger.info("Found {}".format(check_dir))
- else:
- exists = False
- if exists:
- return data_dir
-
- # voc exist is checked above, voc is not exist here
- check_exist = name != 'voc'
- for url, md5sum in dataset[0]:
- get_path(url, data_dir, md5sum, check_exist)
-
- # voc should create list after download
- if name == 'voc':
- create_voc_list(data_dir)
- return data_dir
-
- # not match any dataset in DATASETS
- raise ValueError("Dataset {} is not valid and cannot parse dataset type "
- "'{}' for automaticly downloading, which only supports "
- "'voc', 'coco', 'wider_face' and 'fruit' currently".format(
- path, osp.split(path)[-1]))
-
-
-def create_voc_list(data_dir, devkit_subdir='VOCdevkit'):
- logger.info("Create voc file list...")
- devkit_dir = osp.join(data_dir, devkit_subdir)
- years = ['2007', '2012']
-
- # NOTE: since using auto download VOC
- # dataset, VOC default label list should be used,
- # do not generate label_list.txt here. For default
- # label, see ../data/source/voc_loader.py
- create_list(devkit_dir, years, data_dir)
- logger.info("Create voc file list finished")
-
-
-def map_path(url, root_dir):
- # parse path after download to decompress under root_dir
- fname = osp.split(url)[-1]
- zip_formats = ['.zip', '.tar', '.gz']
- fpath = fname
- for zip_format in zip_formats:
- fpath = fpath.replace(zip_format, '')
- return osp.join(root_dir, fpath)
-
-
-def get_path(url, root_dir, md5sum=None, check_exist=True):
- """ Download from given url to root_dir.
- if file or directory specified by url is exists under
- root_dir, return the path directly, otherwise download
- from url and decompress it, return the path.
-
- url (str): download url
- root_dir (str): root dir for downloading, it should be
- WEIGHTS_HOME or DATASET_HOME
- md5sum (str): md5 sum of download package
- """
- # parse path after download to decompress under root_dir
- fullpath = map_path(url, root_dir)
-
- # For same zip file, decompressed directory name different
- # from zip file name, rename by following map
- decompress_name_map = {
- "VOCtrainval_11-May-2012": "VOCdevkit/VOC2012",
- "VOCtrainval_06-Nov-2007": "VOCdevkit/VOC2007",
- "VOCtest_06-Nov-2007": "VOCdevkit/VOC2007",
- "annotations_trainval": "annotations"
- }
- for k, v in decompress_name_map.items():
- if fullpath.find(k) >= 0:
- fullpath = osp.join(osp.split(fullpath)[0], v)
-
- exist_flag = False
- if osp.exists(fullpath) and check_exist:
- exist_flag = True
- logger.info("Found {}".format(fullpath))
- else:
- exist_flag = False
- fullname = _download(url, root_dir, md5sum)
- _decompress(fullname)
-
- return fullpath, exist_flag
-
-
-def download_dataset(path, dataset=None):
- if dataset not in DATASETS.keys():
- logger.error("Unknown dataset {}, it should be "
- "{}".format(dataset, DATASETS.keys()))
- return
- dataset_info = DATASETS[dataset][0]
- for info in dataset_info:
- get_path(info[0], path, info[1], False)
- logger.info("Download dataset {} finished.".format(dataset))
-
-
-def _dataset_exists(path, annotation, image_dir):
- """
- Check if user define dataset exists
- """
- if not osp.exists(path):
- logger.info("Config dataset_dir {} is not exits, "
- "dataset config is not valid".format(path))
- return False
-
- if annotation:
- annotation_path = osp.join(path, annotation)
- if not osp.isfile(annotation_path):
- logger.info("Config annotation {} is not a "
- "file, dataset config is not "
- "valid".format(annotation_path))
- return False
- if image_dir:
- image_path = osp.join(path, image_dir)
- if not osp.isdir(image_path):
- logger.info("Config image_dir {} is not a "
- "directory, dataset config is not "
- "valid".format(image_path))
- return False
- return True
-
-
-def _download(url, path, md5sum=None):
- """
- Download from url, save to path.
-
- url (str): download url
- path (str): download to given path
- """
- if not osp.exists(path):
- os.makedirs(path)
-
- fname = osp.split(url)[-1]
- fullname = osp.join(path, fname)
- retry_cnt = 0
-
- while not (osp.exists(fullname) and _md5check(fullname, md5sum)):
- if retry_cnt < DOWNLOAD_RETRY_LIMIT:
- retry_cnt += 1
- else:
- raise RuntimeError("Download from {} failed. "
- "Retry limit reached".format(url))
-
- logger.info("Downloading {} from {}".format(fname, url))
-
- req = requests.get(url, stream=True)
- if req.status_code != 200:
- raise RuntimeError("Downloading from {} failed with code "
- "{}!".format(url, req.status_code))
-
- # For protecting download interupted, download to
- # tmp_fullname firstly, move tmp_fullname to fullname
- # after download finished
- tmp_fullname = fullname + "_tmp"
- total_size = req.headers.get('content-length')
- with open(tmp_fullname, 'wb') as f:
- if total_size:
- for chunk in tqdm.tqdm(
- req.iter_content(chunk_size=1024),
- total=(int(total_size) + 1023) // 1024,
- unit='KB'):
- f.write(chunk)
- else:
- for chunk in req.iter_content(chunk_size=1024):
- if chunk:
- f.write(chunk)
- shutil.move(tmp_fullname, fullname)
-
- return fullname
-
-
-def _md5check(fullname, md5sum=None):
- if md5sum is None:
- return True
-
- logger.info("File {} md5 checking...".format(fullname))
- md5 = hashlib.md5()
- with open(fullname, 'rb') as f:
- for chunk in iter(lambda: f.read(4096), b""):
- md5.update(chunk)
- calc_md5sum = md5.hexdigest()
-
- if calc_md5sum != md5sum:
- logger.info("File {} md5 check failed, {}(calc) != "
- "{}(base)".format(fullname, calc_md5sum, md5sum))
- return False
- return True
-
-
-def _decompress(fname):
- """
- Decompress for zip and tar file
- """
- logger.info("Decompressing {}...".format(fname))
-
- # For protecting decompressing interupted,
- # decompress to fpath_tmp directory firstly, if decompress
- # successed, move decompress files to fpath and delete
- # fpath_tmp and remove download compress file.
- fpath = osp.split(fname)[0]
- fpath_tmp = osp.join(fpath, 'tmp')
- if osp.isdir(fpath_tmp):
- shutil.rmtree(fpath_tmp)
- os.makedirs(fpath_tmp)
-
- if fname.find('tar') >= 0:
- with tarfile.open(fname) as tf:
- tf.extractall(path=fpath_tmp)
- elif fname.find('zip') >= 0:
- with zipfile.ZipFile(fname) as zf:
- zf.extractall(path=fpath_tmp)
- else:
- raise TypeError("Unsupport compress file type {}".format(fname))
-
- for f in os.listdir(fpath_tmp):
- src_dir = osp.join(fpath_tmp, f)
- dst_dir = osp.join(fpath, f)
- _move_and_merge_tree(src_dir, dst_dir)
-
- shutil.rmtree(fpath_tmp)
- os.remove(fname)
-
-
-def _move_and_merge_tree(src, dst):
- """
- Move src directory to dst, if dst is already exists,
- merge src to dst
- """
- if not osp.exists(dst):
- shutil.move(src, dst)
- else:
- for fp in os.listdir(src):
- src_fp = osp.join(src, fp)
- dst_fp = osp.join(dst, fp)
- if osp.isdir(src_fp):
- if osp.isdir(dst_fp):
- _move_and_merge_tree(src_fp, dst_fp)
- else:
- shutil.move(src_fp, dst_fp)
- elif osp.isfile(src_fp) and \
- not osp.isfile(dst_fp):
- shutil.move(src_fp, dst_fp)
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/eval_utils.py b/PaddleCV/PaddleDetection/ppdet/utils/eval_utils.py
deleted file mode 100644
index dbded30197b46e12282d50991406a6585c0cb572..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/eval_utils.py
+++ /dev/null
@@ -1,247 +0,0 @@
-# Copyright (c) 2019 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 logging
-import numpy as np
-import os
-import time
-
-import paddle.fluid as fluid
-
-from ppdet.utils.voc_eval import bbox_eval as voc_bbox_eval
-from ppdet.utils.post_process import mstest_box_post_process, mstest_mask_post_process, box_flip
-
-__all__ = ['parse_fetches', 'eval_run', 'eval_results', 'json_eval_results']
-
-logger = logging.getLogger(__name__)
-
-
-def parse_fetches(fetches, prog=None, extra_keys=None):
- """
- Parse fetch variable infos from model fetches,
- values for fetch_list and keys for stat
- """
- keys, values = [], []
- cls = []
- for k, v in fetches.items():
- if hasattr(v, 'name'):
- keys.append(k)
- v.persistable = True
- values.append(v.name)
- else:
- cls.append(v)
-
- if prog is not None and extra_keys is not None:
- for k in extra_keys:
- try:
- v = fluid.framework._get_var(k, prog)
- keys.append(k)
- values.append(v.name)
- except Exception:
- pass
-
- return keys, values, cls
-
-
-def length2lod(length_lod):
- offset_lod = [0]
- for i in length_lod:
- offset_lod.append(offset_lod[-1] + i)
- return [offset_lod]
-
-
-def get_sub_feed(input, place):
- new_dict = {}
- res_feed = {}
- key_name = ['bbox', 'im_info', 'im_id', 'im_shape', 'bbox_flip']
- for k in key_name:
- if k in input.keys():
- new_dict[k] = input[k]
- for k in input.keys():
- if 'image' in k:
- new_dict[k] = input[k]
- for k, v in new_dict.items():
- data_t = fluid.LoDTensor()
- data_t.set(v[0], place)
- if 'bbox' in k:
- lod = length2lod(v[1][0])
- data_t.set_lod(lod)
- res_feed[k] = data_t
- return res_feed
-
-
-def clean_res(result, keep_name_list):
- clean_result = {}
- for k in result.keys():
- if k in keep_name_list:
- clean_result[k] = result[k]
- result.clear()
- return clean_result
-
-
-def eval_run(exe,
- compile_program,
- pyreader,
- keys,
- values,
- cls,
- cfg=None,
- sub_prog=None,
- sub_keys=None,
- sub_values=None):
- """
- Run evaluation program, return program outputs.
- """
- iter_id = 0
- results = []
- if len(cls) != 0:
- values = []
- for i in range(len(cls)):
- _, accum_map = cls[i].get_map_var()
- cls[i].reset(exe)
- values.append(accum_map)
-
- images_num = 0
- start_time = time.time()
- has_bbox = 'bbox' in keys
-
- try:
- pyreader.start()
- while True:
- outs = exe.run(compile_program,
- fetch_list=values,
- return_numpy=False)
- res = {
- k: (np.array(v), v.recursive_sequence_lengths())
- for k, v in zip(keys, outs)
- }
- multi_scale_test = getattr(cfg, 'MultiScaleTEST', None)
- mask_multi_scale_test = multi_scale_test and 'Mask' in cfg.architecture
-
- if multi_scale_test:
- post_res = mstest_box_post_process(res, cfg)
- res.update(post_res)
- if mask_multi_scale_test:
- place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
- sub_feed = get_sub_feed(res, place)
- sub_prog_outs = exe.run(sub_prog,
- feed=sub_feed,
- fetch_list=sub_values,
- return_numpy=False)
- sub_prog_res = {
- k: (np.array(v), v.recursive_sequence_lengths())
- for k, v in zip(sub_keys, sub_prog_outs)
- }
- post_res = mstest_mask_post_process(sub_prog_res, cfg)
- res.update(post_res)
- if multi_scale_test:
- res = clean_res(
- res, ['im_info', 'bbox', 'im_id', 'im_shape', 'mask'])
- results.append(res)
- if iter_id % 100 == 0:
- logger.info('Test iter {}'.format(iter_id))
- iter_id += 1
- images_num += len(res['bbox'][1][0]) if has_bbox else 1
- except (StopIteration, fluid.core.EOFException):
- pyreader.reset()
- logger.info('Test finish iter {}'.format(iter_id))
-
- end_time = time.time()
- fps = images_num / (end_time - start_time)
- if has_bbox:
- logger.info('Total number of images: {}, inference time: {} fps.'.
- format(images_num, fps))
- else:
- logger.info('Total iteration: {}, inference time: {} batch/s.'.format(
- images_num, fps))
-
- return results
-
-
-def eval_results(results,
- feed,
- metric,
- num_classes,
- resolution=None,
- is_bbox_normalized=False,
- output_directory=None,
- map_type='11point'):
- """Evaluation for evaluation program results"""
- box_ap_stats = []
- if metric == 'COCO':
- from ppdet.utils.coco_eval import proposal_eval, bbox_eval, mask_eval
- anno_file = getattr(feed.dataset, 'annotation', None)
- with_background = getattr(feed, 'with_background', True)
- if 'proposal' in results[0]:
- output = 'proposal.json'
- if output_directory:
- output = os.path.join(output_directory, 'proposal.json')
- proposal_eval(results, anno_file, output)
- if 'bbox' in results[0]:
- output = 'bbox.json'
- if output_directory:
- output = os.path.join(output_directory, 'bbox.json')
-
- box_ap_stats = bbox_eval(
- results,
- anno_file,
- output,
- with_background,
- is_bbox_normalized=is_bbox_normalized)
-
- if 'mask' in results[0]:
- output = 'mask.json'
- if output_directory:
- output = os.path.join(output_directory, 'mask.json')
- mask_eval(results, anno_file, output, resolution)
- else:
- if 'accum_map' in results[-1]:
- res = np.mean(results[-1]['accum_map'][0])
- logger.info('mAP: {:.2f}'.format(res * 100.))
- box_ap_stats.append(res * 100.)
- elif 'bbox' in results[0]:
- box_ap = voc_bbox_eval(
- results,
- num_classes,
- is_bbox_normalized=is_bbox_normalized,
- map_type=map_type)
- box_ap_stats.append(box_ap)
- return box_ap_stats
-
-
-def json_eval_results(feed, metric, json_directory=None):
- """
- cocoapi eval with already exists proposal.json, bbox.json or mask.json
- """
- assert metric == 'COCO'
- from ppdet.utils.coco_eval import cocoapi_eval
- anno_file = getattr(feed.dataset, 'annotation', None)
- json_file_list = ['proposal.json', 'bbox.json', 'mask.json']
- if json_directory:
- assert os.path.exists(
- json_directory), "The json directory:{} does not exist".format(
- json_directory)
- for k, v in enumerate(json_file_list):
- json_file_list[k] = os.path.join(str(json_directory), v)
-
- coco_eval_style = ['proposal', 'bbox', 'segm']
- for i, v_json in enumerate(json_file_list):
- if os.path.exists(v_json):
- cocoapi_eval(v_json, coco_eval_style[i], anno_file=anno_file)
- else:
- logger.info("{} not exists!".format(v_json))
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/map_utils.py b/PaddleCV/PaddleDetection/ppdet/utils/map_utils.py
deleted file mode 100644
index fe0530596bb54920a81235ee6716e2d43918899a..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/map_utils.py
+++ /dev/null
@@ -1,214 +0,0 @@
-# Copyright (c) 2019 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
-from __future__ import unicode_literals
-
-import sys
-import numpy as np
-import logging
-logger = logging.getLogger(__name__)
-
-__all__ = ['bbox_area', 'jaccard_overlap', 'DetectionMAP']
-
-
-def bbox_area(bbox, is_bbox_normalized):
- """
- Calculate area of a bounding box
- """
- norm = 1. - float(is_bbox_normalized)
- width = bbox[2] - bbox[0] + norm
- height = bbox[3] - bbox[1] + norm
- return width * height
-
-
-def jaccard_overlap(pred, gt, is_bbox_normalized=False):
- """
- Calculate jaccard overlap ratio between two bounding box
- """
- if pred[0] >= gt[2] or pred[2] <= gt[0] or \
- pred[1] >= gt[3] or pred[3] <= gt[1]:
- return 0.
- inter_xmin = max(pred[0], gt[0])
- inter_ymin = max(pred[1], gt[1])
- inter_xmax = min(pred[2], gt[2])
- inter_ymax = min(pred[3], gt[3])
- inter_size = bbox_area([inter_xmin, inter_ymin, inter_xmax, inter_ymax],
- is_bbox_normalized)
- pred_size = bbox_area(pred, is_bbox_normalized)
- gt_size = bbox_area(gt, is_bbox_normalized)
- overlap = float(inter_size) / (pred_size + gt_size - inter_size)
- return overlap
-
-
-class DetectionMAP(object):
- """
- Calculate detection mean average precision.
- Currently support two types: 11point and integral
-
- Args:
- class_num (int): the class number.
- overlap_thresh (float): The threshold of overlap
- ratio between prediction bounding box and
- ground truth bounding box for deciding
- true/false positive. Default 0.5.
- map_type (str): calculation method of mean average
- precision, currently support '11point' and
- 'integral'. Default '11point'.
- is_bbox_normalized (bool): whther bounding boxes
- is normalized to range[0, 1]. Default False.
- evaluate_difficult (bool): whether to evaluate
- difficult bounding boxes. Default False.
- """
-
- def __init__(self,
- class_num,
- overlap_thresh=0.5,
- map_type='11point',
- is_bbox_normalized=False,
- evaluate_difficult=False):
- self.class_num = class_num
- self.overlap_thresh = overlap_thresh
- assert map_type in ['11point', 'integral'], \
- "map_type currently only support '11point' "\
- "and 'integral'"
- self.map_type = map_type
- self.is_bbox_normalized = is_bbox_normalized
- self.evaluate_difficult = evaluate_difficult
- self.reset()
-
- def update(self, bbox, gt_box, gt_label, difficult=None):
- """
- Update metric statics from given prediction and ground
- truth infomations.
- """
- if difficult is None:
- difficult = np.zeros_like(gt_label)
-
- # record class gt count
- for gtl, diff in zip(gt_label, difficult):
- if self.evaluate_difficult or int(diff) == 0:
- self.class_gt_counts[int(np.array(gtl))] += 1
-
- # record class score positive
- visited = [False] * len(gt_label)
- for b in bbox:
- label, score, xmin, ymin, xmax, ymax = b.tolist()
- pred = [xmin, ymin, xmax, ymax]
- max_idx = -1
- max_overlap = -1.0
- for i, gl in enumerate(gt_label):
- if int(gl) == int(label):
- overlap = jaccard_overlap(pred, gt_box[i],
- self.is_bbox_normalized)
- if overlap > max_overlap:
- max_overlap = overlap
- max_idx = i
-
- if max_overlap > self.overlap_thresh:
- if self.evaluate_difficult or \
- int(np.array(difficult[max_idx])) == 0:
- if not visited[max_idx]:
- self.class_score_poss[int(label)].append([score, 1.0])
- visited[max_idx] = True
- else:
- self.class_score_poss[int(label)].append([score, 0.0])
- else:
- self.class_score_poss[int(label)].append([score, 0.0])
-
- def reset(self):
- """
- Reset metric statics
- """
- self.class_score_poss = [[] for _ in range(self.class_num)]
- self.class_gt_counts = [0] * self.class_num
- self.mAP = None
-
- def accumulate(self):
- """
- Accumulate metric results and calculate mAP
- """
- mAP = 0.
- valid_cnt = 0
- for score_pos, count in zip(self.class_score_poss,
- self.class_gt_counts):
- if count == 0 or len(score_pos) == 0:
- continue
-
- accum_tp_list, accum_fp_list = \
- self._get_tp_fp_accum(score_pos)
- precision = []
- recall = []
- for ac_tp, ac_fp in zip(accum_tp_list, accum_fp_list):
- precision.append(float(ac_tp) / (ac_tp + ac_fp))
- recall.append(float(ac_tp) / count)
-
- if self.map_type == '11point':
- max_precisions = [0.] * 11
- start_idx = len(precision) - 1
- for j in range(10, -1, -1):
- for i in range(start_idx, -1, -1):
- if recall[i] < float(j) / 10.:
- start_idx = i
- if j > 0:
- max_precisions[j - 1] = max_precisions[j]
- break
- else:
- if max_precisions[j] < precision[i]:
- max_precisions[j] = precision[i]
- mAP += sum(max_precisions) / 11.
- valid_cnt += 1
- elif self.map_type == 'integral':
- import math
- ap = 0.
- prev_recall = 0.
- for i in range(len(precision)):
- recall_gap = math.fabs(recall[i] - prev_recall)
- if recall_gap > 1e-6:
- ap += precision[i] * recall_gap
- prev_recall = recall[i]
- mAP += ap
- valid_cnt += 1
- else:
- logger.error("Unspported mAP type {}".format(self.map_type))
- sys.exit(1)
-
- self.mAP = mAP / float(valid_cnt) if valid_cnt > 0 else mAP
-
- def get_map(self):
- """
- Get mAP result
- """
- if self.mAP is None:
- logger.error("mAP is not calculated.")
- return self.mAP
-
- def _get_tp_fp_accum(self, score_pos_list):
- """
- Calculate accumulating true/false positive results from
- [score, pos] records
- """
- sorted_list = sorted(score_pos_list, key=lambda s: s[0], reverse=True)
- accum_tp = 0
- accum_fp = 0
- accum_tp_list = []
- accum_fp_list = []
- for (score, pos) in sorted_list:
- accum_tp += int(pos)
- accum_tp_list.append(accum_tp)
- accum_fp += 1 - int(pos)
- accum_fp_list.append(accum_fp)
- return accum_tp_list, accum_fp_list
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/post_process.py b/PaddleCV/PaddleDetection/ppdet/utils/post_process.py
deleted file mode 100644
index cc80bc186626c4e983328ffd53f53d217230a244..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/post_process.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# Copyright (c) 2019 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 logging
-import numpy as np
-
-import paddle.fluid as fluid
-
-__all__ = ['nms']
-
-logger = logging.getLogger(__name__)
-
-
-def box_flip(boxes, im_shape):
- im_width = im_shape[0][1]
- flipped_boxes = boxes.copy()
-
- flipped_boxes[:, 0::4] = im_width - boxes[:, 2::4] - 1
- flipped_boxes[:, 2::4] = im_width - boxes[:, 0::4] - 1
- return flipped_boxes
-
-
-def nms(dets, thresh):
- """Apply classic DPM-style greedy NMS."""
- if dets.shape[0] == 0:
- return []
- scores = dets[:, 0]
- x1 = dets[:, 1]
- y1 = dets[:, 2]
- x2 = dets[:, 3]
- y2 = dets[:, 4]
-
- areas = (x2 - x1 + 1) * (y2 - y1 + 1)
- order = scores.argsort()[::-1]
-
- ndets = dets.shape[0]
- suppressed = np.zeros((ndets), dtype=np.int)
-
- # nominal indices
- # _i, _j
- # sorted indices
- # i, j
- # temp variables for box i's (the box currently under consideration)
- # ix1, iy1, ix2, iy2, iarea
-
- # variables for computing overlap with box j (lower scoring box)
- # xx1, yy1, xx2, yy2
- # w, h
- # inter, ovr
-
- for _i in range(ndets):
- i = order[_i]
- if suppressed[i] == 1:
- continue
- ix1 = x1[i]
- iy1 = y1[i]
- ix2 = x2[i]
- iy2 = y2[i]
- iarea = areas[i]
- for _j in range(_i + 1, ndets):
- j = order[_j]
- if suppressed[j] == 1:
- continue
- xx1 = max(ix1, x1[j])
- yy1 = max(iy1, y1[j])
- xx2 = min(ix2, x2[j])
- yy2 = min(iy2, y2[j])
- w = max(0.0, xx2 - xx1 + 1)
- h = max(0.0, yy2 - yy1 + 1)
- inter = w * h
- ovr = inter / (iarea + areas[j] - inter)
- if ovr >= thresh:
- suppressed[j] = 1
-
- return np.where(suppressed == 0)[0]
-
-
-def bbox_area(box):
- w = box[2] - box[0] + 1
- h = box[3] - box[1] + 1
- return w * h
-
-
-def bbox_overlaps(x, y):
- N = x.shape[0]
- K = y.shape[0]
- overlaps = np.zeros((N, K), dtype=np.float32)
- for k in range(K):
- y_area = bbox_area(y[k])
- for n in range(N):
- iw = min(x[n, 2], y[k, 2]) - max(x[n, 0], y[k, 0]) + 1
- if iw > 0:
- ih = min(x[n, 3], y[k, 3]) - max(x[n, 1], y[k, 1]) + 1
- if ih > 0:
- x_area = bbox_area(x[n])
- ua = x_area + y_area - iw * ih
- overlaps[n, k] = iw * ih / ua
- return overlaps
-
-
-def box_voting(nms_dets, dets, vote_thresh):
- top_dets = nms_dets.copy()
- top_boxes = nms_dets[:, 1:]
- all_boxes = dets[:, 1:]
- all_scores = dets[:, 0]
- top_to_all_overlaps = bbox_overlaps(top_boxes, all_boxes)
- for k in range(nms_dets.shape[0]):
- inds_to_vote = np.where(top_to_all_overlaps[k] >= vote_thresh)[0]
- boxes_to_vote = all_boxes[inds_to_vote, :]
- ws = all_scores[inds_to_vote]
- top_dets[k, 1:] = np.average(boxes_to_vote, axis=0, weights=ws)
-
- return top_dets
-
-
-def get_nms_result(boxes, scores, cfg):
- cls_boxes = [[] for _ in range(cfg.num_classes)]
- for j in range(1, cfg.num_classes):
- inds = np.where(scores[:, j] > cfg.MultiScaleTEST['score_thresh'])[0]
- scores_j = scores[inds, j]
- boxes_j = boxes[inds, j * 4:(j + 1) * 4]
- dets_j = np.hstack((scores_j[:, np.newaxis], boxes_j)).astype(
- np.float32, copy=False)
- keep = nms(dets_j, cfg.MultiScaleTEST['nms_thresh'])
- nms_dets = dets_j[keep, :]
- if cfg.MultiScaleTEST['enable_voting']:
- nms_dets = box_voting(nms_dets, dets_j,
- cfg.MultiScaleTEST['vote_thresh'])
- #add labels
- label = np.array([j for _ in range(len(keep))])
- nms_dets = np.hstack((label[:, np.newaxis], nms_dets)).astype(
- np.float32, copy=False)
- cls_boxes[j] = nms_dets
- # Limit to max_per_image detections **over all classes**
- image_scores = np.hstack(
- [cls_boxes[j][:, 1] for j in range(1, cfg.num_classes)])
- if len(image_scores) > cfg.MultiScaleTEST['detections_per_im']:
- image_thresh = np.sort(image_scores)[-cfg.MultiScaleTEST[
- 'detections_per_im']]
- for j in range(1, cfg.num_classes):
- keep = np.where(cls_boxes[j][:, 1] >= image_thresh)[0]
- cls_boxes[j] = cls_boxes[j][keep, :]
-
- im_results = np.vstack([cls_boxes[j] for j in range(1, cfg.num_classes)])
- return im_results
-
-
-def mstest_box_post_process(result, cfg):
- """
- Multi-scale Test
- Only available for batch_size=1 now.
- """
- post_bbox = {}
- use_flip = False
- ms_boxes = []
- ms_scores = []
- im_shape = result['im_shape'][0]
- for k in result.keys():
- if 'bbox' in k:
- boxes = result[k][0]
- boxes = np.reshape(boxes, (-1, 4 * cfg.num_classes))
- scores = result['score' + k[4:]][0]
- if 'flip' in k:
- boxes = box_flip(boxes, im_shape)
- use_flip = True
- ms_boxes.append(boxes)
- ms_scores.append(scores)
-
- ms_boxes = np.concatenate(ms_boxes)
- ms_scores = np.concatenate(ms_scores)
- bbox_pred = get_nms_result(ms_boxes, ms_scores, cfg)
- post_bbox.update({'bbox': (bbox_pred, [[len(bbox_pred)]])})
- if use_flip:
- bbox = bbox_pred[:, 2:]
- bbox_flip = np.append(
- bbox_pred[:, :2], box_flip(bbox, im_shape), axis=1)
- post_bbox.update({'bbox_flip': (bbox_flip, [[len(bbox_flip)]])})
- return post_bbox
-
-
-def mstest_mask_post_process(result, cfg):
- mask_list = []
- im_shape = result['im_shape'][0]
- M = cfg.FPNRoIAlign['mask_resolution']
- for k in result.keys():
- if 'mask' in k:
- masks = result[k][0]
- if len(masks.shape) != 4:
- masks = np.zeros((0, M, M))
- mask_list.append(masks)
- continue
- if 'flip' in k:
- masks = masks[:, :, :, ::-1]
- mask_list.append(masks)
-
- mask_pred = np.mean(mask_list, axis=0)
- return {'mask': (mask_pred, [[len(mask_pred)]])}
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/stats.py b/PaddleCV/PaddleDetection/ppdet/utils/stats.py
deleted file mode 100644
index 4d7e36babf8d53170162cfd5581f591e376ec8cd..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/stats.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright (c) 2019 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.
-
-import collections
-import numpy as np
-import datetime
-
-__all__ = ['TrainingStats', 'Time']
-
-
-class SmoothedValue(object):
- """Track a series of values and provide access to smoothed values over a
- window or the global series average.
- """
-
- def __init__(self, window_size):
- self.deque = collections.deque(maxlen=window_size)
-
- def add_value(self, value):
- self.deque.append(value)
-
- def get_median_value(self):
- return np.median(self.deque)
-
-
-def Time():
- return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
-
-
-class TrainingStats(object):
- def __init__(self, window_size, stats_keys):
- self.smoothed_losses_and_metrics = {
- key: SmoothedValue(window_size)
- for key in stats_keys
- }
-
- def update(self, stats):
- for k, v in self.smoothed_losses_and_metrics.items():
- v.add_value(stats[k])
-
- def get(self, extras=None):
- stats = collections.OrderedDict()
- if extras:
- for k, v in extras.items():
- stats[k] = v
- for k, v in self.smoothed_losses_and_metrics.items():
- stats[k] = format(v.get_median_value(), '.6f')
-
- return stats
-
- def log(self, extras=None):
- d = self.get(extras)
- strs = ', '.join(str(dict({x: y})).strip('{}') for x, y in d.items())
- return strs
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/visualizer.py b/PaddleCV/PaddleDetection/ppdet/utils/visualizer.py
deleted file mode 100644
index ff35dc2d577fcaff042583d63c709a498dadb1da..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/visualizer.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# Copyright (c) 2019 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
-from __future__ import unicode_literals
-
-import numpy as np
-import pycocotools.mask as mask_util
-from PIL import Image, ImageDraw
-
-from .colormap import colormap
-
-__all__ = ['visualize_results']
-
-
-def visualize_results(image,
- im_id,
- catid2name,
- threshold=0.5,
- bbox_results=None,
- mask_results=None):
- """
- 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)
- return image
-
-
-def draw_mask(image, im_id, segms, threshold, alpha=0.7):
- """
- Draw mask on image
- """
- mask_color_id = 0
- w_ratio = .4
- color_list = colormap(rgb=True)
- img_array = np.array(image).astype('float32')
- for dt in np.array(segms):
- if im_id != dt['image_id']:
- continue
- segm, score = dt['segmentation'], dt['score']
- if score < threshold:
- continue
- mask = mask_util.decode(segm) * 255
- color_mask = color_list[mask_color_id % len(color_list), 0:3]
- mask_color_id += 1
- for c in range(3):
- color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
- idx = np.nonzero(mask)
- img_array[idx[0], idx[1], :] *= 1.0 - alpha
- img_array[idx[0], idx[1], :] += alpha * color_mask
- return Image.fromarray(img_array.astype('uint8'))
-
-
-def draw_bbox(image, im_id, catid2name, bboxes, threshold):
- """
- Draw bbox on image
- """
- draw = ImageDraw.Draw(image)
-
- catid2color = {}
- color_list = colormap(rgb=True)[:40]
- for dt in np.array(bboxes):
- if im_id != dt['image_id']:
- continue
- catid, bbox, score = dt['category_id'], dt['bbox'], dt['score']
- if score < threshold:
- continue
-
- xmin, ymin, w, h = bbox
- xmax = xmin + w
- ymax = ymin + h
-
- if catid not in catid2color:
- idx = np.random.randint(len(color_list))
- catid2color[catid] = color_list[idx]
- color = tuple(catid2color[catid])
-
- # draw bbox
- draw.line(
- [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin),
- (xmin, ymin)],
- width=2,
- fill=color)
-
- # draw label
- text = "{} {:.2f}".format(catid2name[catid], score)
- tw, th = draw.textsize(text)
- draw.rectangle([(xmin + 1, ymin - th),
- (xmin + tw + 1, ymin)],
- fill=color)
- draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255))
-
- return image
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/voc_eval.py b/PaddleCV/PaddleDetection/ppdet/utils/voc_eval.py
deleted file mode 100644
index 67e66e4c683aaf7d5af2fd352817daf1ad3576d4..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/voc_eval.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# Copyright (c) 2019 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
-from __future__ import unicode_literals
-
-import os
-import sys
-import numpy as np
-
-from ..data.source.voc_loader import pascalvoc_label
-from .map_utils import DetectionMAP
-from .coco_eval import bbox2out
-
-import logging
-logger = logging.getLogger(__name__)
-
-__all__ = [
- 'bbox_eval', 'bbox2out', 'get_category_info'
-]
-
-
-def bbox_eval(results,
- class_num,
- overlap_thresh=0.5,
- map_type='11point',
- is_bbox_normalized=False,
- evaluate_difficult=False):
- """
- Bounding box evaluation for VOC dataset
-
- Args:
- results (list): prediction bounding box results.
- class_num (int): evaluation class number.
- overlap_thresh (float): the postive threshold of
- bbox overlap
- map_type (string): method for mAP calcualtion,
- can only be '11point' or 'integral'
- is_bbox_normalized (bool): whether bbox is normalized
- to range [0, 1].
- evaluate_difficult (bool): whether to evaluate
- difficult gt bbox.
- """
- assert 'bbox' in results[0]
- logger.info("Start evaluate...")
-
- detection_map = DetectionMAP(class_num=class_num,
- overlap_thresh=overlap_thresh,
- map_type=map_type,
- is_bbox_normalized=is_bbox_normalized,
- evaluate_difficult=evaluate_difficult)
-
- for t in results:
- bboxes = t['bbox'][0]
- bbox_lengths = t['bbox'][1][0]
-
- if bboxes.shape == (1, 1) or bboxes is None:
- continue
-
- gt_boxes = t['gt_box'][0]
- gt_labels = t['gt_label'][0]
- difficults = t['is_difficult'][0] if not evaluate_difficult \
- else None
-
- if len(t['gt_box'][1]) == 0:
- # gt_box, gt_label, difficult read as zero padded Tensor
- bbox_idx = 0
- for i in range(len(gt_boxes)):
- gt_box = gt_boxes[i]
- gt_label = gt_labels[i]
- difficult = None if difficults is None \
- else difficults[i]
- bbox_num = bbox_lengths[i]
- bbox = bboxes[bbox_idx: bbox_idx + bbox_num]
- gt_box, gt_label, difficult = prune_zero_padding(
- gt_box, gt_label, difficult)
- detection_map.update(bbox, gt_box, gt_label, difficult)
- bbox_idx += bbox_num
- else:
- # gt_box, gt_label, difficult read as LoDTensor
- gt_box_lengths = t['gt_box'][1][0]
- bbox_idx = 0
- gt_box_idx = 0
- for i in range(len(bbox_lengths)):
- bbox_num = bbox_lengths[i]
- gt_box_num = gt_box_lengths[i]
- bbox = bboxes[bbox_idx: bbox_idx + bbox_num]
- gt_box = gt_boxes[gt_box_idx: gt_box_idx + gt_box_num]
- gt_label = gt_labels[gt_box_idx: gt_box_idx + gt_box_num]
- difficult = None if difficults is None else \
- difficults[gt_box_idx: gt_box_idx + gt_box_num]
- detection_map.update(bbox, gt_box, gt_label, difficult)
- bbox_idx += bbox_num
- gt_box_idx += gt_box_num
-
- logger.info("Accumulating evaluatation results...")
- detection_map.accumulate()
- map_stat = 100. * detection_map.get_map()
- logger.info("mAP({:.2f}, {}) = {:.2f}".format(overlap_thresh,
- map_type, map_stat))
- return map_stat
-
-
-def prune_zero_padding(gt_box, gt_label, difficult=None):
- valid_cnt = 0
- for i in range(len(gt_box)):
- if gt_box[i, 0] == 0 and gt_box[i, 1] == 0 and \
- gt_box[i, 2] == 0 and gt_box[i, 3] == 0:
- break
- valid_cnt += 1
- return (gt_box[:valid_cnt], gt_label[:valid_cnt],
- difficult[:valid_cnt] if difficult is not None else None)
-
-
-def get_category_info(anno_file=None,
- with_background=True,
- use_default_label=False):
- if use_default_label or anno_file is None \
- or not os.path.exists(anno_file):
- logger.info("Not found annotation file {}, load "
- "voc2012 categories.".format(anno_file))
- return vocall_category_info(with_background)
- else:
- logger.info("Load categories from {}".format(anno_file))
- return get_category_info_from_anno(anno_file, with_background)
-
-
-def get_category_info_from_anno(anno_file, with_background=True):
- """
- Get class id to category id map and category id
- to category name map from annotation file.
-
- Args:
- anno_file (str): annotation file path
- with_background (bool, default True):
- whether load background as class 0.
- """
- cats = []
- with open(anno_file) as f:
- for line in f.readlines():
- cats.append(line.strip())
-
- if cats[0] != 'background' and with_background:
- cats.insert(0, 'background')
- if cats[0] == 'background' and not with_background:
- cats = cats[1:]
-
- clsid2catid = {i: i for i in range(len(cats))}
- catid2name = {i: name for i, name in enumerate(cats)}
-
- return clsid2catid, catid2name
-
-
-def vocall_category_info(with_background=True):
- """
- Get class id to category id map and category id
- to category name map of mixup voc dataset
-
- Args:
- with_background (bool, default True):
- whether load background as class 0.
- """
- label_map = pascalvoc_label(with_background)
- label_map = sorted(label_map.items(), key=lambda x: x[1])
- cats = [l[0] for l in label_map]
-
- if with_background:
- cats.insert(0, 'background')
-
- clsid2catid = {i: i for i in range(len(cats))}
- catid2name = {i: name for i, name in enumerate(cats)}
-
- return clsid2catid, catid2name
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/voc_utils.py b/PaddleCV/PaddleDetection/ppdet/utils/voc_utils.py
deleted file mode 100644
index 88252e2351164cf3402e94a2c0e6f49277d7736b..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/voc_utils.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright (c) 2019 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
-import os.path as osp
-import re
-import random
-import shutil
-
-__all__ = ['create_list']
-
-
-def create_list(devkit_dir, years, output_dir):
- """
- create following list:
- 1. trainval.txt
- 2. test.txt
- """
- trainval_list = []
- test_list = []
- for year in years:
- trainval, test = _walk_voc_dir(devkit_dir, year, output_dir)
- trainval_list.extend(trainval)
- test_list.extend(test)
-
- random.shuffle(trainval_list)
- with open(osp.join(output_dir, 'trainval.txt'), 'w') as ftrainval:
- for item in trainval_list:
- ftrainval.write(item[0] + ' ' + item[1] + '\n')
-
- with open(osp.join(output_dir, 'test.txt'), 'w') as fval:
- ct = 0
- for item in test_list:
- ct += 1
- fval.write(item[0] + ' ' + item[1] + '\n')
-
-
-def _get_voc_dir(devkit_dir, year, type):
- return osp.join(devkit_dir, 'VOC' + year, type)
-
-
-def _walk_voc_dir(devkit_dir, year, output_dir):
- filelist_dir = _get_voc_dir(devkit_dir, year, 'ImageSets/Main')
- annotation_dir = _get_voc_dir(devkit_dir, year, 'Annotations')
- img_dir = _get_voc_dir(devkit_dir, year, 'JPEGImages')
- trainval_list = []
- test_list = []
- added = set()
-
- for _, _, files in os.walk(filelist_dir):
- for fname in files:
- img_ann_list = []
- if re.match('[a-z]+_trainval\.txt', fname):
- img_ann_list = trainval_list
- elif re.match('[a-z]+_test\.txt', fname):
- img_ann_list = test_list
- else:
- continue
- fpath = osp.join(filelist_dir, fname)
- for line in open(fpath):
- name_prefix = line.strip().split()[0]
- if name_prefix in added:
- continue
- added.add(name_prefix)
- ann_path = osp.join(osp.relpath(annotation_dir, output_dir),
- name_prefix + '.xml')
- img_path = osp.join(osp.relpath(img_dir, output_dir),
- name_prefix + '.jpg')
- img_ann_list.append((img_path, ann_path))
-
- return trainval_list, test_list
diff --git a/PaddleCV/PaddleDetection/ppdet/utils/widerface_eval_utils.py b/PaddleCV/PaddleDetection/ppdet/utils/widerface_eval_utils.py
deleted file mode 100644
index a19cd08350e7be9b3c7db8aa45e95f86b7088bf5..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/ppdet/utils/widerface_eval_utils.py
+++ /dev/null
@@ -1,227 +0,0 @@
-# Copyright (c) 2019 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
-import numpy as np
-
-from ppdet.data.source.widerface_loader import widerface_label
-from ppdet.utils.coco_eval import bbox2out
-
-import logging
-logger = logging.getLogger(__name__)
-
-__all__ = [
- 'get_shrink', 'bbox_vote', 'save_widerface_bboxes', 'save_fddb_bboxes',
- 'to_chw_bgr', 'bbox2out', 'get_category_info'
-]
-
-
-def to_chw_bgr(image):
- """
- Transpose image from HWC to CHW and from RBG to BGR.
- Args:
- image (np.array): an image with HWC and RBG layout.
- """
- # HWC to CHW
- if len(image.shape) == 3:
- image = np.swapaxes(image, 1, 2)
- image = np.swapaxes(image, 1, 0)
- # RBG to BGR
- image = image[[2, 1, 0], :, :]
- return image
-
-
-def bbox_vote(det):
- order = det[:, 4].ravel().argsort()[::-1]
- det = det[order, :]
- if det.shape[0] == 0:
- dets = np.array([[10, 10, 20, 20, 0.002]])
- det = np.empty(shape=[0, 5])
- while det.shape[0] > 0:
- # IOU
- area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1)
- xx1 = np.maximum(det[0, 0], det[:, 0])
- yy1 = np.maximum(det[0, 1], det[:, 1])
- xx2 = np.minimum(det[0, 2], det[:, 2])
- yy2 = np.minimum(det[0, 3], det[:, 3])
- w = np.maximum(0.0, xx2 - xx1 + 1)
- h = np.maximum(0.0, yy2 - yy1 + 1)
- inter = w * h
- o = inter / (area[0] + area[:] - inter)
-
- # nms
- merge_index = np.where(o >= 0.3)[0]
- det_accu = det[merge_index, :]
- det = np.delete(det, merge_index, 0)
- if merge_index.shape[0] <= 1:
- if det.shape[0] == 0:
- try:
- dets = np.row_stack((dets, det_accu))
- except:
- dets = det_accu
- continue
- det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4))
- max_score = np.max(det_accu[:, 4])
- det_accu_sum = np.zeros((1, 5))
- det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4],
- axis=0) / np.sum(det_accu[:, -1:])
- det_accu_sum[:, 4] = max_score
- try:
- dets = np.row_stack((dets, det_accu_sum))
- except:
- dets = det_accu_sum
- dets = dets[0:750, :]
- # Only keep 0.3 or more
- keep_index = np.where(dets[:, 4] >= 0.01)[0]
- dets = dets[keep_index, :]
- return dets
-
-
-def get_shrink(height, width):
- """
- Args:
- height (int): image height.
- width (int): image width.
- """
- # avoid out of memory
- max_shrink_v1 = (0x7fffffff / 577.0 / (height * width))**0.5
- max_shrink_v2 = ((678 * 1024 * 2.0 * 2.0) / (height * width))**0.5
-
- def get_round(x, loc):
- str_x = str(x)
- if '.' in str_x:
- str_before, str_after = str_x.split('.')
- len_after = len(str_after)
- if len_after >= 3:
- str_final = str_before + '.' + str_after[0:loc]
- return float(str_final)
- else:
- return x
-
- max_shrink = get_round(min(max_shrink_v1, max_shrink_v2), 2) - 0.3
- if max_shrink >= 1.5 and max_shrink < 2:
- max_shrink = max_shrink - 0.1
- elif max_shrink >= 2 and max_shrink < 3:
- max_shrink = max_shrink - 0.2
- elif max_shrink >= 3 and max_shrink < 4:
- max_shrink = max_shrink - 0.3
- elif max_shrink >= 4 and max_shrink < 5:
- max_shrink = max_shrink - 0.4
- elif max_shrink >= 5:
- max_shrink = max_shrink - 0.5
-
- shrink = max_shrink if max_shrink < 1 else 1
- return shrink, max_shrink
-
-
-def save_widerface_bboxes(image_path, bboxes_scores, output_dir):
- image_name = image_path.split('/')[-1]
- image_class = image_path.split('/')[-2]
- odir = os.path.join(output_dir, image_class)
- if not os.path.exists(odir):
- os.makedirs(odir)
-
- ofname = os.path.join(odir, '%s.txt' % (image_name[:-4]))
- f = open(ofname, 'w')
- f.write('{:s}\n'.format(image_class + '/' + image_name))
- f.write('{:d}\n'.format(bboxes_scores.shape[0]))
- for box_score in bboxes_scores:
- xmin, ymin, xmax, ymax, score = box_score
- f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.format(xmin, ymin, (
- xmax - xmin + 1), (ymax - ymin + 1), score))
- f.close()
- logger.info("The predicted result is saved as {}".format(ofname))
-
-
-def save_fddb_bboxes(bboxes_scores,
- output_dir,
- output_fname='pred_fddb_res.txt'):
- if not os.path.exists(output_dir):
- os.makedirs(output_dir)
- predict_file = os.path.join(output_dir, output_fname)
- f = open(predict_file, 'w')
- for image_path, dets in bboxes_scores.iteritems():
- f.write('{:s}\n'.format(image_path))
- f.write('{:d}\n'.format(dets.shape[0]))
- for box_score in dets:
- xmin, ymin, xmax, ymax, score = box_score
- width, height = xmax - xmin, ymax - ymin
- f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'
- .format(xmin, ymin, width, height, score))
- logger.info("The predicted result is saved as {}".format(predict_file))
- return predict_file
-
-
-def get_category_info(anno_file=None,
- with_background=True,
- use_default_label=False):
- if use_default_label or anno_file is None \
- or not os.path.exists(anno_file):
- logger.info("Not found annotation file {}, load "
- "wider-face categories.".format(anno_file))
- return widerfaceall_category_info(with_background)
- else:
- logger.info("Load categories from {}".format(anno_file))
- return get_category_info_from_anno(anno_file, with_background)
-
-
-def get_category_info_from_anno(anno_file, with_background=True):
- """
- Get class id to category id map and category id
- to category name map from annotation file.
- Args:
- anno_file (str): annotation file path
- with_background (bool, default True):
- whether load background as class 0.
- """
- cats = []
- with open(anno_file) as f:
- for line in f.readlines():
- cats.append(line.strip())
-
- if cats[0] != 'background' and with_background:
- cats.insert(0, 'background')
- if cats[0] == 'background' and not with_background:
- cats = cats[1:]
-
- clsid2catid = {i: i for i in range(len(cats))}
- catid2name = {i: name for i, name in enumerate(cats)}
-
- return clsid2catid, catid2name
-
-
-def widerfaceall_category_info(with_background=True):
- """
- Get class id to category id map and category id
- to category name map of mixup wider_face dataset
-
- Args:
- with_background (bool, default True):
- whether load background as class 0.
- """
- label_map = widerface_label(with_background)
- label_map = sorted(label_map.items(), key=lambda x: x[1])
- cats = [l[0] for l in label_map]
-
- if with_background:
- cats.insert(0, 'background')
-
- clsid2catid = {i: i for i in range(len(cats))}
- catid2name = {i: name for i, name in enumerate(cats)}
-
- return clsid2catid, catid2name
diff --git a/PaddleCV/PaddleDetection/requirements.txt b/PaddleCV/PaddleDetection/requirements.txt
deleted file mode 100644
index 798b006ad7baa3b4f24b4a7db15a63d6d2e533f4..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-tqdm
-docstring_parser @ http://github.com/willthefrog/docstring_parser/tarball/master
-typeguard ; python_version >= '3.4'
-tb-paddle
-tb-nightly
diff --git a/PaddleCV/PaddleDetection/slim/distillation/README.md b/PaddleCV/PaddleDetection/slim/distillation/README.md
deleted file mode 100755
index e46e6a2c92ac502f48d7d929a81b61228ed10d7a..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/distillation/README.md
+++ /dev/null
@@ -1,141 +0,0 @@
->运行该示例前请安装Paddle1.6或更高版本
-
-# 检测模型蒸馏示例
-
-## 概述
-
-该示例使用PaddleSlim提供的[蒸馏策略](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#3-蒸馏)对检测库中的模型进行蒸馏训练。
-在阅读该示例前，建议您先了解以下内容：
-
-- [检测库的常规训练方法](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection)
-- [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md)
-
-
-## 配置文件说明
-
-关于配置文件如何编写您可以参考：
-
-- [PaddleSlim配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#122-%E9%85%8D%E7%BD%AE%E6%96%87%E4%BB%B6%E7%9A%84%E4%BD%BF%E7%94%A8)
-- [蒸馏策略配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#23-蒸馏)
-
-这里以ResNet34-YoloV3蒸馏MobileNetV1-YoloV3模型为例，首先，为了对`student model`和`teacher model`有个总体的认识，从而进一步确认蒸馏的对象，我们通过以下命令分别观察两个网络变量（Variable）的名称和形状：
-
-```python
-# 观察student model的Variable
-for v in fluid.default_main_program().list_vars():
- if "py_reader" not in v.name and "double_buffer" not in v.name and "generated_var" not in v.name:
- print(v.name, v.shape)
-# 观察teacher model的Variable
-for v in teacher_program.list_vars():
- print(v.name, v.shape)
-```
-
-经过对比可以发现，`student model`和`teacher model`的部分中间结果分别为：
-
-```bash
-# student model
-conv2d_15.tmp_0
-# teacher model
-teacher_teacher_conv2d_1.tmp_0
-```
-
-
-所以，我们用`l2_distiller`对这两个特征图做蒸馏。在配置文件中进行如下配置：
-
-```yaml
-distillers:
- l2_distiller:
- class: 'L2Distiller'
- teacher_feature_map: 'teacher_teacher_conv2d_1.tmp_0'
- student_feature_map: 'conv2d_15.tmp_0'
- distillation_loss_weight: 1
-strategies:
- distillation_strategy:
- class: 'DistillationStrategy'
- distillers: ['l2_distiller']
- start_epoch: 0
- end_epoch: 270
-```
-
-我们也可以根据上述操作为蒸馏策略选择其他loss，PaddleSlim支持的有`FSP_loss`, `L2_loss`和`softmax_with_cross_entropy_loss` 。
-
-## 训练
-
-根据[PaddleDetection/tools/train.py](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/tools/train.py)编写压缩脚本compress.py。
-在该脚本中定义了Compressor对象，用于执行压缩任务。
-
-
-
-
-您可以通过运行脚本`run.sh`运行该示例。
-
-
-### 保存断点（checkpoint）
-
-如果在配置文件中设置了`checkpoint_path`, 则在蒸馏任务执行过程中会自动保存断点，当任务异常中断时，
-重启任务会自动从`checkpoint_path`路径下按数字顺序加载最新的checkpoint文件。如果不想让重启的任务从断点恢复，
-需要修改配置文件中的`checkpoint_path`，或者将`checkpoint_path`路径下文件清空。
-
->注意：配置文件中的信息不会保存在断点中，重启前对配置文件的修改将会生效。
-
-
-## 评估
-
-如果在配置文件中设置了`checkpoint_path`，则每个epoch会保存一个压缩后的用于评估的模型，
-该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下，包含`__model__`和`__params__`两个文件。
-其中，`__model__`用于保存模型结构信息，`__params__`用于保存参数（parameters）信息。
-
-如果不需要保存评估模型，可以在定义Compressor对象时，将`save_eval_model`选项设置为False（默认为True）。
-
-运行命令为：
-```
-python ../eval.py \
- --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \
- --model_name __model__ \
- --params_name __params__ \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- -d "../../dataset/voc"
-```
-
-## 预测
-
-如果在配置文件中设置了`checkpoint_path`，并且在定义Compressor对象时指定了`prune_infer_model`选项，则每个epoch都会
-保存一个`inference model`。该模型是通过删除eval_program中多余的operators而得到的。
-
-该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下，包含`__model__.infer`和`__params__`两个文件。
-其中，`__model__.infer`用于保存模型结构信息，`__params__`用于保存参数（parameters）信息。
-
-更多关于`prune_infer_model`选项的介绍，请参考：[Compressor介绍](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#121-%E5%A6%82%E4%BD%95%E6%94%B9%E5%86%99%E6%99%AE%E9%80%9A%E8%AE%AD%E7%BB%83%E8%84%9A%E6%9C%AC)
-
-### python预测
-
-在脚本slim/infer.py中展示了如何使用fluid python API加载使用预测模型进行预测。
-
-运行命令为：
-```
-python ../infer.py \
- --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \
- --model_name __model__.infer \
- --params_name __params__ \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- --infer_dir ../../demo
-```
-
-### PaddleLite
-
-该示例中产出的预测（inference）模型可以直接用PaddleLite进行加载使用。
-关于PaddleLite如何使用，请参考：[PaddleLite使用文档](https://github.com/PaddlePaddle/Paddle-Lite/wiki#%E4%BD%BF%E7%94%A8)
-
-## 示例结果
-
->当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。
-
-### MobileNetV1-YOLO-V3
-
-| FLOPS |Box AP|
-|---|---|
-|baseline|76.2 |
-|蒸馏后|76.27 |
-
-
-## FAQ
diff --git a/PaddleCV/PaddleDetection/slim/distillation/compress.py b/PaddleCV/PaddleDetection/slim/distillation/compress.py
deleted file mode 100644
index 6dc4fff9742ec258b30cad4408b5e6f428273495..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/distillation/compress.py
+++ /dev/null
@@ -1,324 +0,0 @@
-# Copyright (c) 2019 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
-import time
-import multiprocessing
-import numpy as np
-from collections import deque, OrderedDict
-from paddle.fluid.contrib.slim.core import Compressor
-from paddle.fluid.framework import IrGraph
-
-
-def set_paddle_flags(**kwargs):
- for key, value in kwargs.items():
- if os.environ.get(key, None) is None:
- os.environ[key] = str(value)
-
-
-# NOTE(paddle-dev): All of these flags should be set before
-# `import paddle`. Otherwise, it would not take any effect.
-set_paddle_flags(
- FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
-)
-
-from paddle import fluid
-
-import sys
-sys.path.append("../../")
-from ppdet.core.workspace import load_config, merge_config, create
-from ppdet.data.data_feed import create_reader
-from ppdet.utils.eval_utils import parse_fetches, eval_results
-from ppdet.utils.stats import TrainingStats
-from ppdet.utils.cli import ArgsParser
-from ppdet.utils.check import check_gpu
-import ppdet.utils.checkpoint as checkpoint
-from ppdet.modeling.model_input import create_feed
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-
-def eval_run(exe, compile_program, reader, keys, values, cls, test_feed):
- """
- Run evaluation program, return program outputs.
- """
- iter_id = 0
- results = []
- if len(cls) != 0:
- values = []
- for i in range(len(cls)):
- _, accum_map = cls[i].get_map_var()
- cls[i].reset(exe)
- values.append(accum_map)
-
- images_num = 0
- start_time = time.time()
- has_bbox = 'bbox' in keys
- for data in reader():
- data = test_feed.feed(data)
- feed_data = {'image': data['image'], 'im_size': data['im_size']}
- outs = exe.run(compile_program,
- feed=feed_data,
- fetch_list=[values[0]],
- return_numpy=False)
- outs.append(data['gt_box'])
- outs.append(data['gt_label'])
- outs.append(data['is_difficult'])
- res = {
- k: (np.array(v), v.recursive_sequence_lengths())
- for k, v in zip(keys, outs)
- }
- results.append(res)
- if iter_id % 100 == 0:
- logger.info('Test iter {}'.format(iter_id))
- iter_id += 1
- images_num += len(res['bbox'][1][0]) if has_bbox else 1
- logger.info('Test finish iter {}'.format(iter_id))
-
- end_time = time.time()
- fps = images_num / (end_time - start_time)
- if has_bbox:
- logger.info('Total number of images: {}, inference time: {} fps.'.
- format(images_num, fps))
- else:
- logger.info('Total iteration: {}, inference time: {} batch/s.'.format(
- images_num, fps))
-
- return results
-
-
-def main():
- cfg = load_config(FLAGS.config)
- if 'architecture' in cfg:
- main_arch = cfg.architecture
- else:
- raise ValueError("'architecture' not specified in config file.")
-
- merge_config(FLAGS.opt)
- if 'log_iter' not in cfg:
- cfg.log_iter = 20
-
- # check if set use_gpu=True in paddlepaddle cpu version
- check_gpu(cfg.use_gpu)
-
- if cfg.use_gpu:
- devices_num = fluid.core.get_cuda_device_count()
- else:
- devices_num = int(
- os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-
- if 'train_feed' not in cfg:
- train_feed = create(main_arch + 'TrainFeed')
- else:
- train_feed = create(cfg.train_feed)
-
- if 'eval_feed' not in cfg:
- eval_feed = create(main_arch + 'EvalFeed')
- else:
- eval_feed = create(cfg.eval_feed)
-
- place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
- exe = fluid.Executor(place)
-
- lr_builder = create('LearningRate')
- optim_builder = create('OptimizerBuilder')
-
- # build program
- model = create(main_arch)
- _, train_feed_vars = create_feed(train_feed, False)
- train_fetches = model.train(train_feed_vars)
- loss = train_fetches['loss']
- lr = lr_builder()
- opt = optim_builder(lr)
- opt.minimize(loss)
- #for v in fluid.default_main_program().list_vars():
- # if "py_reader" not in v.name and "double_buffer" not in v.name and "generated_var" not in v.name:
- # print(v.name, v.shape)
-
- cfg.max_iters = 258
- train_reader = create_reader(train_feed, cfg.max_iters, FLAGS.dataset_dir)
-
- exe.run(fluid.default_startup_program())
-
- # parse train fetches
- train_keys, train_values, _ = parse_fetches(train_fetches)
- train_keys.append('lr')
- train_values.append(lr.name)
-
- train_fetch_list = []
- for k, v in zip(train_keys, train_values):
- train_fetch_list.append((k, v))
- print("train_fetch_list: {}".format(train_fetch_list))
-
- eval_prog = fluid.Program()
- startup_prog = fluid.Program()
- with fluid.program_guard(eval_prog, startup_prog):
- with fluid.unique_name.guard():
- model = create(main_arch)
- _, test_feed_vars = create_feed(eval_feed, False)
- fetches = model.eval(test_feed_vars)
- eval_prog = eval_prog.clone(True)
-
- eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
- test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
-
- # parse eval fetches
- extra_keys = []
- if cfg.metric == 'COCO':
- extra_keys = ['im_info', 'im_id', 'im_shape']
- if cfg.metric == 'VOC':
- extra_keys = ['gt_box', 'gt_label', 'is_difficult']
- eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
- extra_keys)
-
- eval_fetch_list = []
- for k, v in zip(eval_keys, eval_values):
- eval_fetch_list.append((k, v))
- print("eval_fetch_list: {}".format(eval_fetch_list))
-
- exe.run(startup_prog)
- checkpoint.load_params(exe,
- fluid.default_main_program(), cfg.pretrain_weights)
-
- best_box_ap_list = []
-
- def eval_func(program, scope):
- results = eval_run(exe, program, eval_reader, eval_keys, eval_values,
- eval_cls, test_data_feed)
-
- resolution = None
- is_bbox_normalized = False
- if 'mask' in results[0]:
- resolution = model.mask_head.resolution
- box_ap_stats = eval_results(results, eval_feed, cfg.metric,
- cfg.num_classes, resolution,
- is_bbox_normalized, FLAGS.output_eval)
- if len(best_box_ap_list) == 0:
- best_box_ap_list.append(box_ap_stats[0])
- elif box_ap_stats[0] > best_box_ap_list[0]:
- best_box_ap_list[0] = box_ap_stats[0]
- logger.info("Best test box ap: {}".format(best_box_ap_list[0]))
- return best_box_ap_list[0]
-
- test_feed = [('image', test_feed_vars['image'].name),
- ('im_size', test_feed_vars['im_size'].name)]
-
- teacher_cfg = load_config(FLAGS.teacher_config)
- teacher_arch = teacher_cfg.architecture
- teacher_programs = []
- teacher_program = fluid.Program()
- teacher_startup_program = fluid.Program()
- with fluid.program_guard(teacher_program, teacher_startup_program):
- with fluid.unique_name.guard('teacher_'):
- teacher_feed_vars = OrderedDict()
- for name, var in train_feed_vars.items():
- teacher_feed_vars[name] = teacher_program.global_block(
- )._clone_variable(
- var, force_persistable=False)
- model = create(teacher_arch)
- train_fetches = model.train(teacher_feed_vars)
- #print("="*50+"teacher_model_params"+"="*50)
- #for v in teacher_program.list_vars():
- # print(v.name, v.shape)
- #return
-
- exe.run(teacher_startup_program)
- assert FLAGS.teacher_pretrained and os.path.exists(
- FLAGS.teacher_pretrained
- ), "teacher_pretrained should be set when teacher_model is not None."
-
- def if_exist(var):
- return os.path.exists(os.path.join(FLAGS.teacher_pretrained, var.name))
-
- fluid.io.load_vars(
- exe,
- FLAGS.teacher_pretrained,
- main_program=teacher_program,
- predicate=if_exist)
-
- teacher_programs.append(teacher_program.clone(for_test=True))
-
- com = Compressor(
- place,
- fluid.global_scope(),
- fluid.default_main_program(),
- train_reader=train_reader,
- train_feed_list=[(key, value.name)
- for key, value in train_feed_vars.items()],
- train_fetch_list=train_fetch_list,
- eval_program=eval_prog,
- eval_reader=eval_reader,
- eval_feed_list=test_feed,
- eval_func={'map': eval_func},
- eval_fetch_list=eval_fetch_list[0:1],
- save_eval_model=True,
- prune_infer_model=[["image", "im_size"], ["multiclass_nms_0.tmp_0"]],
- teacher_programs=teacher_programs,
- train_optimizer=None,
- distiller_optimizer=opt,
- log_period=20)
- com.config(FLAGS.slim_file)
- com.run()
-
-
-if __name__ == '__main__':
- parser = ArgsParser()
- parser.add_argument(
- "-t",
- "--teacher_config",
- default=None,
- type=str,
- help="Config file of teacher architecture.")
- parser.add_argument(
- "-s",
- "--slim_file",
- default=None,
- type=str,
- help="Config file of PaddleSlim.")
- parser.add_argument(
- "-r",
- "--resume_checkpoint",
- default=None,
- type=str,
- help="Checkpoint path for resuming training.")
- parser.add_argument(
- "--eval",
- action='store_true',
- default=False,
- help="Whether to perform evaluation in train")
- parser.add_argument(
- "--teacher_pretrained",
- default=None,
- type=str,
- help="Whether to use pretrained model.")
- parser.add_argument(
- "--output_eval",
- default=None,
- type=str,
- help="Evaluation directory, default is current directory.")
- parser.add_argument(
- "-d",
- "--dataset_dir",
- default=None,
- type=str,
- help="Dataset path, same as DataFeed.dataset.dataset_dir")
- FLAGS = parser.parse_args()
- main()
diff --git a/PaddleCV/PaddleDetection/slim/distillation/run.sh b/PaddleCV/PaddleDetection/slim/distillation/run.sh
deleted file mode 100644
index a5497bdce2464c72e14fa2168b87db60685e83e8..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/distillation/run.sh
+++ /dev/null
@@ -1,47 +0,0 @@
-#!/usr/bin/env bash
-
-# download pretrain model
-root_url="https://paddlemodels.bj.bcebos.com/object_detection"
-yolov3_r34_voc="yolov3_r34_voc.tar"
-pretrain_dir='./pretrain'
-
-if [ ! -d ${pretrain_dir} ]; then
- mkdir ${pretrain_dir}
-fi
-
-cd ${pretrain_dir}
-
-if [ ! -f ${yolov3_r34_voc} ]; then
- wget ${root_url}/${yolov3_r34_voc}
- tar xf ${yolov3_r34_voc}
-fi
-cd -
-
-# enable GC strategy
-export FLAGS_fast_eager_deletion_mode=1
-export FLAGS_eager_delete_tensor_gb=0.0
-
-# for distillation
-#-----------------
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-
-
-# Fixing name conflicts in distillation
-cd ${pretrain_dir}/yolov3_r34_voc
-for files in $(ls teacher_*)
- do mv $files ${files#*_}
-done
-for files in $(ls *)
- do mv $files "teacher_"$files
-done
-cd -
-
-python -u compress.py \
--c ../../configs/yolov3_mobilenet_v1_voc.yml \
--t yolov3_resnet34.yml \
--s yolov3_mobilenet_v1_yolov3_resnet34_distillation.yml \
--o YoloTrainFeed.batch_size=64 \
--d ../../dataset/voc \
---teacher_pretrained ./pretrain/yolov3_r34_voc \
-> yolov3_distallation.log 2>&1 &
-tailf yolov3_distallation.log
diff --git a/PaddleCV/PaddleDetection/slim/distillation/yolov3_mobilenet_v1_yolov3_resnet34_distillation.yml b/PaddleCV/PaddleDetection/slim/distillation/yolov3_mobilenet_v1_yolov3_resnet34_distillation.yml
deleted file mode 100644
index 6a2a5a2575b71563c557c528a4dc94f00dce73ca..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/distillation/yolov3_mobilenet_v1_yolov3_resnet34_distillation.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-version: 1.0
-distillers:
- l2_distiller:
- class: 'L2Distiller'
- teacher_feature_map: 'teacher_teacher_conv2d_1.tmp_0'
- student_feature_map: 'conv2d_15.tmp_0'
- distillation_loss_weight: 1
-strategies:
- distillation_strategy:
- class: 'DistillationStrategy'
- distillers: ['l2_distiller']
- start_epoch: 0
- end_epoch: 270
-compressor:
- epoch: 271
- checkpoint_path: './checkpoints/'
- strategies:
- - distillation_strategy
diff --git a/PaddleCV/PaddleDetection/slim/distillation/yolov3_resnet34.yml b/PaddleCV/PaddleDetection/slim/distillation/yolov3_resnet34.yml
deleted file mode 100644
index c04bdde9f6e35a1ce53231e3862a37364ff7dbb8..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/distillation/yolov3_resnet34.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-architecture: YOLOv3
-log_smooth_window: 20
-metric: VOC
-map_type: 11point
-num_classes: 20
-weight_prefix_name: teacher_
-
-YOLOv3:
- backbone: ResNet
- yolo_head: YOLOv3Head
-
-ResNet:
- norm_type: sync_bn
- freeze_at: 0
- freeze_norm: false
- norm_decay: 0.
- depth: 34
- feature_maps: [3, 4, 5]
-
-YOLOv3Head:
- anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
- anchors: [[10, 13], [16, 30], [33, 23],
- [30, 61], [62, 45], [59, 119],
- [116, 90], [156, 198], [373, 326]]
- norm_decay: 0.
- ignore_thresh: 0.7
- label_smooth: false
- nms:
- background_label: -1
- keep_top_k: 100
- nms_threshold: 0.45
- nms_top_k: 1000
- normalized: false
- score_threshold: 0.01
diff --git a/PaddleCV/PaddleDetection/slim/eval.py b/PaddleCV/PaddleDetection/slim/eval.py
deleted file mode 100644
index 579f58d2932e215362efa9e8864c16d1a451b26a..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/eval.py
+++ /dev/null
@@ -1,194 +0,0 @@
-# Copyright (c) 2019 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
-import time
-import multiprocessing
-import numpy as np
-import datetime
-from collections import deque
-import sys
-sys.path.append("../../")
-from paddle.fluid.contrib.slim import Compressor
-from paddle.fluid.framework import IrGraph
-from paddle.fluid import core
-from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
-from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
-from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
-from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
-
-
-def set_paddle_flags(**kwargs):
- for key, value in kwargs.items():
- if os.environ.get(key, None) is None:
- os.environ[key] = str(value)
-
-
-# NOTE(paddle-dev): All of these flags should be set before
-# `import paddle`. Otherwise, it would not take any effect.
-set_paddle_flags(
- FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
-)
-
-from paddle import fluid
-
-from ppdet.core.workspace import load_config, merge_config, create
-from ppdet.data.data_feed import create_reader
-
-from ppdet.utils.eval_utils import parse_fetches, eval_results
-from ppdet.utils.stats import TrainingStats
-from ppdet.utils.cli import ArgsParser
-from ppdet.utils.check import check_gpu
-import ppdet.utils.checkpoint as checkpoint
-from ppdet.modeling.model_input import create_feed
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-
-def eval_run(exe, compile_program, reader, keys, values, cls, test_feed):
- """
- Run evaluation program, return program outputs.
- """
- iter_id = 0
- results = []
-
- images_num = 0
- start_time = time.time()
- has_bbox = 'bbox' in keys
- for data in reader():
- data = test_feed.feed(data)
- feed_data = {'image': data['image'], 'im_size': data['im_size']}
- outs = exe.run(compile_program,
- feed=feed_data,
- fetch_list=values[0],
- return_numpy=False)
- outs.append(data['gt_box'])
- outs.append(data['gt_label'])
- outs.append(data['is_difficult'])
- res = {
- k: (np.array(v), v.recursive_sequence_lengths())
- for k, v in zip(keys, outs)
- }
- results.append(res)
- if iter_id % 100 == 0:
- logger.info('Test iter {}'.format(iter_id))
- iter_id += 1
- images_num += len(res['bbox'][1][0]) if has_bbox else 1
- logger.info('Test finish iter {}'.format(iter_id))
-
- end_time = time.time()
- fps = images_num / (end_time - start_time)
- if has_bbox:
- logger.info('Total number of images: {}, inference time: {} fps.'.
- format(images_num, fps))
- else:
- logger.info('Total iteration: {}, inference time: {} batch/s.'.format(
- images_num, fps))
-
- return results
-
-
-def main():
- cfg = load_config(FLAGS.config)
- if 'architecture' in cfg:
- main_arch = cfg.architecture
- else:
- raise ValueError("'architecture' not specified in config file.")
-
- merge_config(FLAGS.opt)
- if 'log_iter' not in cfg:
- cfg.log_iter = 20
-
- # check if set use_gpu=True in paddlepaddle cpu version
- check_gpu(cfg.use_gpu)
-
- if cfg.use_gpu:
- devices_num = fluid.core.get_cuda_device_count()
- else:
- devices_num = int(
- os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-
- if 'eval_feed' not in cfg:
- eval_feed = create(main_arch + 'EvalFeed')
- else:
- eval_feed = create(cfg.eval_feed)
-
- place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
- exe = fluid.Executor(place)
-
- _, test_feed_vars = create_feed(eval_feed, False)
-
- eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
- #eval_pyreader.decorate_sample_list_generator(eval_reader, place)
- test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
-
- assert os.path.exists(FLAGS.model_path)
- infer_prog, feed_names, fetch_targets = fluid.io.load_inference_model(
- dirname=FLAGS.model_path,
- executor=exe,
- model_filename=FLAGS.model_name,
- params_filename=FLAGS.params_name)
-
- eval_keys = ['bbox', 'gt_box', 'gt_label', 'is_difficult']
- eval_values = [
- 'multiclass_nms_0.tmp_0', 'gt_box', 'gt_label', 'is_difficult'
- ]
- eval_cls = []
- eval_values[0] = fetch_targets[0]
-
- results = eval_run(exe, infer_prog, eval_reader, eval_keys, eval_values,
- eval_cls, test_data_feed)
-
- resolution = None
- if 'mask' in results[0]:
- resolution = model.mask_head.resolution
- eval_results(results, eval_feed, cfg.metric, cfg.num_classes, resolution,
- False, FLAGS.output_eval)
-
-
-if __name__ == '__main__':
- parser = ArgsParser()
- parser.add_argument(
- "-m", "--model_path", default=None, type=str, help="path of checkpoint")
- parser.add_argument(
- "--output_eval",
- default=None,
- type=str,
- help="Evaluation directory, default is current directory.")
- parser.add_argument(
- "-d",
- "--dataset_dir",
- default=None,
- type=str,
- help="Dataset path, same as DataFeed.dataset.dataset_dir")
- parser.add_argument(
- "--model_name",
- default='model',
- type=str,
- help="model file name to load_inference_model")
- parser.add_argument(
- "--params_name",
- default='params',
- type=str,
- help="params file name to load_inference_model")
-
- FLAGS = parser.parse_args()
- main()
diff --git a/PaddleCV/PaddleDetection/slim/infer.py b/PaddleCV/PaddleDetection/slim/infer.py
deleted file mode 100644
index a5c00de983a15c40db60deb9097c3d8f92a557aa..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/infer.py
+++ /dev/null
@@ -1,300 +0,0 @@
-# Copyright (c) 2019 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
-import sys
-import glob
-import time
-
-import numpy as np
-from PIL import Image
-sys.path.append("../../")
-
-
-def set_paddle_flags(**kwargs):
- for key, value in kwargs.items():
- if os.environ.get(key, None) is None:
- os.environ[key] = str(value)
-
-
-# NOTE(paddle-dev): All of these flags should be set before
-# `import paddle`. Otherwise, it would not take any effect.
-set_paddle_flags(
- FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
-)
-
-from paddle import fluid
-from ppdet.utils.cli import print_total_cfg
-from ppdet.core.workspace import load_config, merge_config, create
-from ppdet.modeling.model_input import create_feed
-from ppdet.data.data_feed import create_reader
-
-from ppdet.utils.eval_utils import parse_fetches
-from ppdet.utils.cli import ArgsParser
-from ppdet.utils.check import check_gpu
-from ppdet.utils.visualizer import visualize_results
-import ppdet.utils.checkpoint as checkpoint
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-
-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)
- images = []
-
- # infer_img has a higher priority
- if infer_img and os.path.isfile(infer_img):
- images.append(infer_img)
- return images
-
- 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.extend(glob.glob('{}/*.{}'.format(infer_dir, ext)))
-
- assert len(images) > 0, "no image found in {}".format(infer_dir)
- logger.info("Found {} inference images in total.".format(len(images)))
-
- return images
-
-
-def main():
- cfg = load_config(FLAGS.config)
-
- if 'architecture' in cfg:
- main_arch = cfg.architecture
- else:
- raise ValueError("'architecture' not specified in config file.")
-
- merge_config(FLAGS.opt)
-
- # check if set use_gpu=True in paddlepaddle cpu version
- check_gpu(cfg.use_gpu)
- # print_total_cfg(cfg)
-
- if 'test_feed' not in cfg:
- test_feed = create(main_arch + 'TestFeed')
- else:
- test_feed = create(cfg.test_feed)
-
- test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
- test_feed.dataset.add_images(test_images)
-
- place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
- exe = fluid.Executor(place)
-
- infer_prog, feed_var_names, fetch_list = fluid.io.load_inference_model(
- dirname=FLAGS.model_path,
- model_filename=FLAGS.model_name,
- params_filename=FLAGS.params_name,
- executor=exe)
-
- reader = create_reader(test_feed)
- feeder = fluid.DataFeeder(
- place=place, feed_list=feed_var_names, program=infer_prog)
-
- # parse infer fetches
- assert cfg.metric in ['COCO', 'VOC'], \
- "unknown metric type {}".format(cfg.metric)
- extra_keys = []
- if cfg['metric'] == 'COCO':
- extra_keys = ['im_info', 'im_id', 'im_shape']
- if cfg['metric'] == 'VOC':
- extra_keys = ['im_id', 'im_shape']
- keys, values, _ = parse_fetches({
- 'bbox': fetch_list
- }, infer_prog, extra_keys)
-
- # parse dataset category
- if cfg.metric == 'COCO':
- from ppdet.utils.coco_eval import bbox2out, mask2out, get_category_info
- if cfg.metric == "VOC":
- from ppdet.utils.voc_eval import bbox2out, get_category_info
-
- anno_file = getattr(test_feed.dataset, 'annotation', None)
- with_background = getattr(test_feed, 'with_background', True)
- use_default_label = getattr(test_feed, 'use_default_label', False)
- clsid2catid, catid2name = get_category_info(anno_file, with_background,
- use_default_label)
-
- # whether output bbox is normalized in model output layer
- is_bbox_normalized = False
-
- # use tb-paddle to log image
- if FLAGS.use_tb:
- from tb_paddle import SummaryWriter
- tb_writer = SummaryWriter(FLAGS.tb_log_dir)
- tb_image_step = 0
- tb_image_frame = 0 # each frame can display ten pictures at most.
-
- imid2path = reader.imid2path
- keys = ['bbox']
- infer_time = True
- compile_prog = fluid.compiler.CompiledProgram(infer_prog)
-
- for iter_id, data in enumerate(reader()):
- feed_data = [[d[0], d[1]] for d in data]
- # for infer time
- if infer_time:
- warmup_times = 10
- repeats_time = 100
- feed_data_dict = feeder.feed(feed_data)
- for i in range(warmup_times):
- exe.run(compile_prog,
- feed=feed_data_dict,
- fetch_list=fetch_list,
- return_numpy=False)
- start_time = time.time()
- for i in range(repeats_time):
- exe.run(compile_prog,
- feed=feed_data_dict,
- fetch_list=fetch_list,
- return_numpy=False)
-
- print("infer time: {} ms/sample".format((time.time() - start_time) *
- 1000 / repeats_time))
- infer_time = False
-
- outs = exe.run(compile_prog,
- feed=feeder.feed(feed_data),
- fetch_list=fetch_list,
- return_numpy=False)
- res = {
- k: (np.array(v), v.recursive_sequence_lengths())
- for k, v in zip(keys, outs)
- }
- res['im_id'] = [[d[2] for d in data]]
- logger.info('Infer iter {}'.format(iter_id))
-
- bbox_results = None
- mask_results = None
- if 'bbox' in res:
- bbox_results = bbox2out([res], clsid2catid, is_bbox_normalized)
- if 'mask' in res:
- mask_results = mask2out([res], clsid2catid,
- model.mask_head.resolution)
-
- # visualize result
- im_ids = res['im_id'][0]
- for im_id in im_ids:
- image_path = imid2path[int(im_id)]
- image = Image.open(image_path).convert('RGB')
-
- # use tb-paddle to log original image
- if FLAGS.use_tb:
- original_image_np = np.array(image)
- tb_writer.add_image(
- "original/frame_{}".format(tb_image_frame),
- original_image_np,
- tb_image_step,
- dataformats='HWC')
-
- image = visualize_results(image,
- int(im_id), catid2name,
- FLAGS.draw_threshold, bbox_results,
- mask_results)
-
- # use tb-paddle to log image with bbox
- if FLAGS.use_tb:
- infer_image_np = np.array(image)
- tb_writer.add_image(
- "bbox/frame_{}".format(tb_image_frame),
- infer_image_np,
- tb_image_step,
- dataformats='HWC')
- tb_image_step += 1
- if tb_image_step % 10 == 0:
- tb_image_step = 0
- tb_image_frame += 1
-
- 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)
-
-
-if __name__ == '__main__':
- 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_tb",
- type=bool,
- default=False,
- help="whether to record the data to Tensorboard.")
- parser.add_argument(
- '--tb_log_dir',
- type=str,
- default="tb_log_dir/image",
- help='Tensorboard logging directory for image.')
- parser.add_argument(
- '--model_path', type=str, default=None, help="inference model path")
- parser.add_argument(
- '--model_name',
- type=str,
- default='__model__.infer',
- help="model filename for inference model")
- parser.add_argument(
- '--params_name',
- type=str,
- default='__params__',
- help="params filename for inference model")
- FLAGS = parser.parse_args()
- main()
diff --git a/PaddleCV/PaddleDetection/slim/prune/README.md b/PaddleCV/PaddleDetection/slim/prune/README.md
deleted file mode 100644
index b06fdd2bdd6a3cd75eb00ab7952dfd546b2bfaad..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/prune/README.md
+++ /dev/null
@@ -1,221 +0,0 @@
->运行该示例前请安装Paddle1.6或更高版本
-
-# 检测模型卷积通道剪裁示例
-
-## 概述
-
-该示例使用PaddleSlim提供的[卷积通道剪裁压缩策略](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)对检测库中的模型进行压缩。
-在阅读该示例前，建议您先了解以下内容：
-
-- 检测库的常规训练方法
-- [检测模型数据准备](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/INSTALL_cn.md#%E6%95%B0%E6%8D%AE%E9%9B%86)
-- [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md)
-
-
-## 配置文件说明
-
-关于配置文件如何编写您可以参考：
-
-- [PaddleSlim配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#122-%E9%85%8D%E7%BD%AE%E6%96%87%E4%BB%B6%E7%9A%84%E4%BD%BF%E7%94%A8)
-- [裁剪策略配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#22-%E6%A8%A1%E5%9E%8B%E9%80%9A%E9%81%93%E5%89%AA%E8%A3%81)
-
-其中，配置文件中的`pruned_params`需要根据当前模型的网络结构特点设置，它用来指定要裁剪的parameters.
-
-这里以MobileNetV1-YoloV3模型为例，其卷积可以三种：主干网络中的普通卷积，主干网络中的`depthwise convolution`和`yolo block`里的普通卷积。PaddleSlim暂时无法对`depthwise convolution`直接进行剪裁， 因为`depthwise convolution`的`channel`的变化会同时影响到前后的卷积层。我们这里只对主干网络中的普通卷积和`yolo block`里的普通卷积做裁剪。
-
-通过以下方式可视化模型结构：
-
-```
-from paddle.fluid.framework import IrGraph
-from paddle.fluid import core
-
-graph = IrGraph(core.Graph(train_prog.desc), for_test=True)
-marked_nodes = set()
-for op in graph.all_op_nodes():
- print(op.name())
- if op.name().find('conv') > -1:
- marked_nodes.add(op)
-graph.draw('.', 'forward', marked_nodes)
-```
-
-该示例中MobileNetV1-YoloV3模型结构的可视化结果：MobileNetV1-YoloV3.pdf
-
-同时通过以下命令观察目标卷积层的参数（parameters）的名称和shape:
-
-```
-for param in fluid.default_main_program().global_block().all_parameters():
- if 'weights' in param.name:
- print(param.name, param.shape)
-```
-
-
-从可视化结果，我们可以排除后续会做concat的卷积层，最终得到如下要裁剪的参数名称：
-
-```
-conv2_1_sep_weights
-conv2_2_sep_weights
-conv3_1_sep_weights
-conv4_1_sep_weights
-conv5_1_sep_weights
-conv5_2_sep_weights
-conv5_3_sep_weights
-conv5_4_sep_weights
-conv5_5_sep_weights
-conv5_6_sep_weights
-yolo_block.0.0.0.conv.weights
-yolo_block.0.0.1.conv.weights
-yolo_block.0.1.0.conv.weights
-yolo_block.0.1.1.conv.weights
-yolo_block.1.0.0.conv.weights
-yolo_block.1.0.1.conv.weights
-yolo_block.1.1.0.conv.weights
-yolo_block.1.1.1.conv.weights
-yolo_block.1.2.conv.weights
-yolo_block.2.0.0.conv.weights
-yolo_block.2.0.1.conv.weights
-yolo_block.2.1.1.conv.weights
-yolo_block.2.2.conv.weights
-yolo_block.2.tip.conv.weights
-```
-
-```
-(conv2_1_sep_weights)|(conv2_2_sep_weights)|(conv3_1_sep_weights)|(conv4_1_sep_weights)|(conv5_1_sep_weights)|(conv5_2_sep_weights)|(conv5_3_sep_weights)|(conv5_4_sep_weights)|(conv5_5_sep_weights)|(conv5_6_sep_weights)|(yolo_block.0.0.0.conv.weights)|(yolo_block.0.0.1.conv.weights)|(yolo_block.0.1.0.conv.weights)|(yolo_block.0.1.1.conv.weights)|(yolo_block.1.0.0.conv.weights)|(yolo_block.1.0.1.conv.weights)|(yolo_block.1.1.0.conv.weights)|(yolo_block.1.1.1.conv.weights)|(yolo_block.1.2.conv.weights)|(yolo_block.2.0.0.conv.weights)|(yolo_block.2.0.1.conv.weights)|(yolo_block.2.1.1.conv.weights)|(yolo_block.2.2.conv.weights)|(yolo_block.2.tip.conv.weights)
-```
-
-综上，我们将MobileNetV2配置文件中的`pruned_params`设置为以下正则表达式：
-
-```
-(conv2_1_sep_weights)|(conv2_2_sep_weights)|(conv3_1_sep_weights)|(conv4_1_sep_weights)|(conv5_1_sep_weights)|(conv5_2_sep_weights)|(conv5_3_sep_weights)|(conv5_4_sep_weights)|(conv5_5_sep_weights)|(conv5_6_sep_weights)|(yolo_block.0.0.0.conv.weights)|(yolo_block.0.0.1.conv.weights)|(yolo_block.0.1.0.conv.weights)|(yolo_block.0.1.1.conv.weights)|(yolo_block.1.0.0.conv.weights)|(yolo_block.1.0.1.conv.weights)|(yolo_block.1.1.0.conv.weights)|(yolo_block.1.1.1.conv.weights)|(yolo_block.1.2.conv.weights)|(yolo_block.2.0.0.conv.weights)|(yolo_block.2.0.1.conv.weights)|(yolo_block.2.1.1.conv.weights)|(yolo_block.2.2.conv.weights)|(yolo_block.2.tip.conv.weights)
-```
-
-我们可以用上述操作观察其它检测模型的参数名称规律，然后设置合适的正则表达式来剪裁合适的参数。
-
-## 训练
-
-根据PaddleDetection/tools/train.py编写压缩脚本compress.py。
-在该脚本中定义了Compressor对象，用于执行压缩任务。
-
-### 执行示例
-
-step1: 设置gpu卡
-```
-export CUDA_VISIBLE_DEVICES=0
-```
-step2: 开始训练
-
-使用PaddleDetection提供的配置文件在用8卡进行训练：
-
-```
-python compress.py \
- -s yolov3_mobilenet_v1_slim.yaml \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- -o max_iters=258 \
- YoloTrainFeed.batch_size=64 \
- -d "../../dataset/voc"
-```
-
->通过命令行覆盖设置max_iters选项，因为PaddleDetection中训练是以`batch`为单位迭代的，并没有涉及`epoch`的概念，但是PaddleSlim需要知道当前训练进行到第几个`epoch`, 所以需要将`max_iters`设置为一个`epoch`内的`batch`的数量。
-
-如果要调整训练卡数，需要调整配置文件`yolov3_mobilenet_v1_voc.yml`中的以下参数：
-
-- **max_iters:** 一个`epoch`中batch的数量，需要设置为`total_num / batch_size`, 其中`total_num`为训练样本总数量，`batch_size`为多卡上总的batch size.
-- **YoloTrainFeed.batch_size:** 当使用DataLoader时，表示单张卡上的batch size; 当使用普通reader时，则表示多卡上的总的`batch_size`。`batch_size`受限于显存大小。
-- **LeaningRate.base_lr:** 根据多卡的总`batch_size`调整`base_lr`，两者大小正相关，可以简单的按比例进行调整。
-- **LearningRate.schedulers.PiecewiseDecay.milestones：** 请根据batch size的变化对其调整。
-- **LearningRate.schedulers.PiecewiseDecay.LinearWarmup.steps：** 请根据batch size的变化对其进行调整。
-
-
-以下为4卡训练示例，通过命令行覆盖`yolov3_mobilenet_v1_voc.yml`中的参数：
-
-```
-python compress.py \
- -s yolov3_mobilenet_v1_slim.yaml \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- -o max_iters=258 \
- YoloTrainFeed.batch_size=64 \
- -d "../../dataset/voc"
-```
-
-以下为2卡训练示例，受显存所制，单卡`batch_size`不变，总`batch_size`减小，`base_lr`减小，一个epoch内batch数量增加，同时需要调整学习率相关参数，如下：
-```
-python compress.py \
- -s yolov3_mobilenet_v1_slim.yaml \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- -o max_iters=516 \
- LeaningRate.base_lr=0.005 \
- YoloTrainFeed.batch_size=32 \
- LearningRate.schedulers='[!PiecewiseDecay {gamma: 0.1, milestones: [110000, 124000]}, !LinearWarmup {start_factor: 0., steps: 2000}]' \
- -d "../../dataset/voc"
-```
-
-通过`python compress.py --help`查看可配置参数。
-通过`python ../../tools/configure.py ${option_name} help`查看如何通过命令行覆盖配置文件`yolov3_mobilenet_v1_voc.yml`中的参数。
-
-### 保存断点（checkpoint）
-
-如果在配置文件中设置了`checkpoint_path`, 则在压缩任务执行过程中会自动保存断点，当任务异常中断时，
-重启任务会自动从`checkpoint_path`路径下按数字顺序加载最新的checkpoint文件。如果不想让重启的任务从断点恢复，
-需要修改配置文件中的`checkpoint_path`，或者将`checkpoint_path`路径下文件清空。
-
->注意：配置文件中的信息不会保存在断点中，重启前对配置文件的修改将会生效。
-
-
-## 评估
-
-如果在配置文件中设置了`checkpoint_path`，则每个epoch会保存一个压缩后的用于评估的模型，
-该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下，包含`__model__`和`__params__`两个文件。
-其中，`__model__`用于保存模型结构信息，`__params__`用于保存参数（parameters）信息。
-
-如果不需要保存评估模型，可以在定义Compressor对象时，将`save_eval_model`选项设置为False（默认为True）。
-
-运行命令为：
-```
-python ../eval.py \
- --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \
- --model_name __model__ \
- --params_name __params__ \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- -d "../../dataset/voc"
-```
-
-## 预测
-
-如果在配置文件中设置了`checkpoint_path`，并且在定义Compressor对象时指定了`prune_infer_model`选项，则每个epoch都会
-保存一个`inference model`。该模型是通过删除eval_program中多余的operators而得到的。
-
-该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下，包含`__model__.infer`和`__params__`两个文件。
-其中，`__model__.infer`用于保存模型结构信息，`__params__`用于保存参数（parameters）信息。
-
-更多关于`prune_infer_model`选项的介绍，请参考：[Compressor介绍](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#121-%E5%A6%82%E4%BD%95%E6%94%B9%E5%86%99%E6%99%AE%E9%80%9A%E8%AE%AD%E7%BB%83%E8%84%9A%E6%9C%AC)
-
-### python预测
-
-在脚本PaddleDetection/tools/infer.py中展示了如何使用fluid python API加载使用预测模型进行预测。
-
-运行命令为：
-```
-python ../infer.py \
- --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \
- --model_name __model__.infer \
- --params_name __params__ \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- --infer_dir ../../demo
-```
-
-### PaddleLite
-
-该示例中产出的预测（inference）模型可以直接用PaddleLite进行加载使用。
-关于PaddleLite如何使用，请参考：[PaddleLite使用文档](https://github.com/PaddlePaddle/Paddle-Lite/wiki#%E4%BD%BF%E7%94%A8)
-
-## 示例结果
-
-> 当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。
-
-### MobileNetV1-YOLO-V3
-
-| FLOPS |Box AP| model_size |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)|
-|---|---|---|---|---|
-|baseline|76.2 |93M |- |-|
-|-50%|69.48 |51M |- |-|
-
-## FAQ
diff --git a/PaddleCV/PaddleDetection/slim/prune/compress.py b/PaddleCV/PaddleDetection/slim/prune/compress.py
deleted file mode 100644
index 66f9a0cb50514bfbdf2964024064caf6702cfbc4..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/prune/compress.py
+++ /dev/null
@@ -1,255 +0,0 @@
-# Copyright (c) 2019 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
-import time
-import multiprocessing
-import numpy as np
-import sys
-sys.path.append("../../")
-from paddle.fluid.contrib.slim import Compressor
-
-
-def set_paddle_flags(**kwargs):
- for key, value in kwargs.items():
- if os.environ.get(key, None) is None:
- os.environ[key] = str(value)
-
-
-# NOTE(paddle-dev): All of these flags should be set before
-# `import paddle`. Otherwise, it would not take any effect.
-set_paddle_flags(
- FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
-)
-
-from paddle import fluid
-from ppdet.core.workspace import load_config, merge_config, create
-from ppdet.data.data_feed import create_reader
-from ppdet.utils.eval_utils import parse_fetches, eval_results
-from ppdet.utils.cli import ArgsParser
-from ppdet.utils.check import check_gpu
-import ppdet.utils.checkpoint as checkpoint
-from ppdet.modeling.model_input import create_feed
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-
-def eval_run(exe, compile_program, reader, keys, values, cls, test_feed):
- """
- Run evaluation program, return program outputs.
- """
- iter_id = 0
- results = []
- if len(cls) != 0:
- values = []
- for i in range(len(cls)):
- _, accum_map = cls[i].get_map_var()
- cls[i].reset(exe)
- values.append(accum_map)
-
- images_num = 0
- start_time = time.time()
- has_bbox = 'bbox' in keys
- for data in reader():
- data = test_feed.feed(data)
- feed_data = {'image': data['image'], 'im_size': data['im_size']}
- outs = exe.run(compile_program,
- feed=feed_data,
- fetch_list=[values[0]],
- return_numpy=False)
- outs.append(data['gt_box'])
- outs.append(data['gt_label'])
- outs.append(data['is_difficult'])
- res = {
- k: (np.array(v), v.recursive_sequence_lengths())
- for k, v in zip(keys, outs)
- }
- results.append(res)
- if iter_id % 100 == 0:
- logger.info('Test iter {}'.format(iter_id))
- iter_id += 1
- images_num += len(res['bbox'][1][0]) if has_bbox else 1
- logger.info('Test finish iter {}'.format(iter_id))
-
- end_time = time.time()
- fps = images_num / (end_time - start_time)
- if has_bbox:
- logger.info('Total number of images: {}, inference time: {} fps.'.
- format(images_num, fps))
- else:
- logger.info('Total iteration: {}, inference time: {} batch/s.'.format(
- images_num, fps))
-
- return results
-
-
-def main():
- cfg = load_config(FLAGS.config)
- if 'architecture' in cfg:
- main_arch = cfg.architecture
- else:
- raise ValueError("'architecture' not specified in config file.")
-
- merge_config(FLAGS.opt)
- if 'log_iter' not in cfg:
- cfg.log_iter = 20
-
- # check if set use_gpu=True in paddlepaddle cpu version
- check_gpu(cfg.use_gpu)
-
- if cfg.use_gpu:
- devices_num = fluid.core.get_cuda_device_count()
- else:
- devices_num = int(
- os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-
- if 'train_feed' not in cfg:
- train_feed = create(main_arch + 'TrainFeed')
- else:
- train_feed = create(cfg.train_feed)
-
- if 'eval_feed' not in cfg:
- eval_feed = create(main_arch + 'EvalFeed')
- else:
- eval_feed = create(cfg.eval_feed)
-
- place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
- exe = fluid.Executor(place)
-
- lr_builder = create('LearningRate')
- optim_builder = create('OptimizerBuilder')
-
- # build program
- startup_prog = fluid.Program()
- train_prog = fluid.Program()
- with fluid.program_guard(train_prog, startup_prog):
- with fluid.unique_name.guard():
- model = create(main_arch)
- _, feed_vars = create_feed(train_feed, False)
- train_fetches = model.train(feed_vars)
- loss = train_fetches['loss']
- lr = lr_builder()
- optimizer = optim_builder(lr)
- optimizer.minimize(loss)
-
- train_reader = create_reader(train_feed, cfg.max_iters, FLAGS.dataset_dir)
-
- # parse train fetches
- train_keys, train_values, _ = parse_fetches(train_fetches)
- train_keys.append("lr")
- train_values.append(lr.name)
-
- train_fetch_list = []
- for k, v in zip(train_keys, train_values):
- train_fetch_list.append((k, v))
-
- eval_prog = fluid.Program()
- with fluid.program_guard(eval_prog, startup_prog):
- with fluid.unique_name.guard():
- model = create(main_arch)
- _, test_feed_vars = create_feed(eval_feed, False)
- fetches = model.eval(test_feed_vars)
-
- eval_prog = eval_prog.clone(True)
-
- eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
- test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
-
- # parse eval fetches
- extra_keys = []
- if cfg.metric == 'COCO':
- extra_keys = ['im_info', 'im_id', 'im_shape']
- if cfg.metric == 'VOC':
- extra_keys = ['gt_box', 'gt_label', 'is_difficult']
- eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
- extra_keys)
- eval_fetch_list = []
- for k, v in zip(eval_keys, eval_values):
- eval_fetch_list.append((k, v))
-
- exe.run(startup_prog)
- checkpoint.load_params(exe, train_prog, cfg.pretrain_weights)
-
- best_box_ap_list = []
-
- def eval_func(program, scope):
-
- #place = fluid.CPUPlace()
- #exe = fluid.Executor(place)
- results = eval_run(exe, program, eval_reader, eval_keys, eval_values,
- eval_cls, test_data_feed)
-
- resolution = None
- if 'mask' in results[0]:
- resolution = model.mask_head.resolution
- box_ap_stats = eval_results(results, eval_feed, cfg.metric,
- cfg.num_classes, resolution, False,
- FLAGS.output_eval)
- if len(best_box_ap_list) == 0:
- best_box_ap_list.append(box_ap_stats[0])
- elif box_ap_stats[0] > best_box_ap_list[0]:
- best_box_ap_list[0] = box_ap_stats[0]
- logger.info("Best test box ap: {}".format(best_box_ap_list[0]))
- return best_box_ap_list[0]
-
- test_feed = [('image', test_feed_vars['image'].name),
- ('im_size', test_feed_vars['im_size'].name)]
-
- com = Compressor(
- place,
- fluid.global_scope(),
- train_prog,
- train_reader=train_reader,
- train_feed_list=[(key, value.name) for key, value in feed_vars.items()],
- train_fetch_list=train_fetch_list,
- eval_program=eval_prog,
- eval_reader=eval_reader,
- eval_feed_list=test_feed,
- eval_func={'map': eval_func},
- eval_fetch_list=[eval_fetch_list[0]],
- save_eval_model=True,
- prune_infer_model=[["image", "im_size"], ["multiclass_nms_0.tmp_0"]],
- train_optimizer=None)
- com.config(FLAGS.slim_file)
- com.run()
-
-
-if __name__ == '__main__':
- parser = ArgsParser()
- parser.add_argument(
- "-s",
- "--slim_file",
- default=None,
- type=str,
- help="Config file of PaddleSlim.")
- parser.add_argument(
- "--output_eval",
- default=None,
- type=str,
- help="Evaluation directory, default is current directory.")
- parser.add_argument(
- "-d",
- "--dataset_dir",
- default=None,
- type=str,
- help="Dataset path, same as DataFeed.dataset.dataset_dir")
- FLAGS = parser.parse_args()
- main()
diff --git a/PaddleCV/PaddleDetection/slim/prune/images/MobileNetV1-YoloV3.pdf b/PaddleCV/PaddleDetection/slim/prune/images/MobileNetV1-YoloV3.pdf
deleted file mode 100644
index f5d3a22db5030ffc6beb1e7f8c92bddd61e366e6..0000000000000000000000000000000000000000
Binary files a/PaddleCV/PaddleDetection/slim/prune/images/MobileNetV1-YoloV3.pdf and /dev/null differ
diff --git a/PaddleCV/PaddleDetection/slim/prune/yolov3_mobilenet_v1_slim.yaml b/PaddleCV/PaddleDetection/slim/prune/yolov3_mobilenet_v1_slim.yaml
deleted file mode 100644
index ff9859eafe5872dee13f33a3703502e334d1c85e..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/prune/yolov3_mobilenet_v1_slim.yaml
+++ /dev/null
@@ -1,23 +0,0 @@
-version: 1.0
-pruners:
- pruner_1:
- class: 'StructurePruner'
- pruning_axis:
- '*': 0
- criterions:
- '*': 'l1_norm'
-strategies:
- uniform_pruning_strategy:
- class: 'UniformPruneStrategy'
- pruner: 'pruner_1'
- start_epoch: 0
- target_ratio: 0.5
- pruned_params: '(conv2_1_sep_weights)|(conv2_2_sep_weights)|(conv3_1_sep_weights)|(conv4_1_sep_weights)|(conv5_1_sep_weights)|(conv5_2_sep_weights)|(conv5_3_sep_weights)|(conv5_4_sep_weights)|(conv5_5_sep_weights)|(conv5_6_sep_weights)|(yolo_block.0.0.0.conv.weights)|(yolo_block.0.0.1.conv.weights)|(yolo_block.0.1.0.conv.weights)|(yolo_block.0.1.1.conv.weights)|(yolo_block.1.0.0.conv.weights)|(yolo_block.1.0.1.conv.weights)|(yolo_block.1.1.0.conv.weights)|(yolo_block.1.1.1.conv.weights)|(yolo_block.1.2.conv.weights)|(yolo_block.2.0.0.conv.weights)|(yolo_block.2.0.1.conv.weights)|(yolo_block.2.1.1.conv.weights)|(yolo_block.2.2.conv.weights)|(yolo_block.2.tip.conv.weights)'
- metric_name: 'acc_top1'
-compressor:
- epoch: 271
- eval_epoch: 10
- #init_model: './checkpoints/0' # Please enable this option for loading checkpoint.
- checkpoint_path: './checkpoints/'
- strategies:
- - uniform_pruning_strategy
diff --git a/PaddleCV/PaddleDetection/slim/quantization/README.md b/PaddleCV/PaddleDetection/slim/quantization/README.md
deleted file mode 100644
index d451e959a8828c24fcafb9ac52b8c5a2a3ce8de5..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/quantization/README.md
+++ /dev/null
@@ -1,245 +0,0 @@
->运行该示例前请安装Paddle1.6或更高版本
-
-# 检测模型量化压缩示例
-
-## 概述
-
-该示例使用PaddleSlim提供的[量化压缩策略](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#1-quantization-aware-training%E9%87%8F%E5%8C%96%E4%BB%8B%E7%BB%8D)对检测模型进行压缩。
-在阅读该示例前，建议您先了解以下内容：
-
-- [检测模型的常规训练方法](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection)
-- [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md)
-
-
-## 配置文件说明
-
-关于配置文件如何编写您可以参考：
-
-- [PaddleSlim配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#122-%E9%85%8D%E7%BD%AE%E6%96%87%E4%BB%B6%E7%9A%84%E4%BD%BF%E7%94%A8)
-- [量化策略配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#21-%E9%87%8F%E5%8C%96%E8%AE%AD%E7%BB%83)
-
-其中save_out_nodes需要得到检测结果的Variable的名称，下面介绍如何确定save_out_nodes的参数
-以MobileNet V1为例，可在compress.py中构建好网络之后，直接打印Variable得到Variable的名称信息。
-代码示例：
-```
- eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
- extra_keys)
- # print(eval_values)
-```
-根据运行结果可看到Variable的名字为：`multiclass_nms_0.tmp_0`。
-## 训练
-
-根据 [PaddleCV/PaddleDetection/tools/train.py](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/tools/train.py) 编写压缩脚本compress.py。
-在该脚本中定义了Compressor对象，用于执行压缩任务。
-
-通过`python compress.py --help`查看可配置参数，简述如下：
-
-- config: 检测库的配置，其中配置了训练超参数、数据集信息等。
-- slim_file: PaddleSlim的配置文件，参见[配置文件说明](#配置文件说明)。
-
-您可以通过运行以下命令运行该示例。
-
-step1: 设置gpu卡
-```
-export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-```
-step2: 开始训练
-
-使用PaddleDetection提供的配置文件用8卡进行训练：
-
-```
-python compress.py \
- -s yolov3_mobilenet_v1_slim.yaml \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- -d "../../dataset/voc" \
- -o max_iters=258 \
- LearningRate.base_lr=0.0001 \
- LearningRate.schedulers="[!PiecewiseDecay {gamma: 0.1, milestones: [258, 516]}]" \
- pretrain_weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar \
- YoloTrainFeed.batch_size=64
-```
-
->通过命令行覆盖设置max_iters选项，因为PaddleDetection中训练是以`batch`为单位迭代的，并没有涉及`epoch`的概念，但是PaddleSlim需要知道当前训练进行到第几个`epoch`, 所以需要将`max_iters`设置为一个`epoch`内的`batch`的数量。
-
-如果要调整训练卡数，需要调整配置文件`yolov3_mobilenet_v1_voc.yml`中的以下参数：
-
-- **max_iters:** 一个`epoch`中batch的数量，需要设置为`total_num / batch_size`, 其中`total_num`为训练样本总数量，`batch_size`为多卡上总的batch size.
-- **YoloTrainFeed.batch_size:** 当使用DataLoader时，表示单张卡上的batch size; 当使用普通reader时，则表示多卡上的总的batch_size。batch_size受限于显存大小。
-- **LeaningRate.base_lr:** 根据多卡的总`batch_size`调整`base_lr`，两者大小正相关，可以简单的按比例进行调整。
-- **LearningRate.schedulers.PiecewiseDecay.milestones：** 请根据batch size的变化对其调整。
-- **LearningRate.schedulers.PiecewiseDecay.LinearWarmup.steps：** 请根据batch size的变化对其进行调整。
-
-
-以下为4卡训练示例，通过命令行覆盖`yolov3_mobilenet_v1_voc.yml`中的参数：
-
-```
-python compress.py \
- -s yolov3_mobilenet_v1_slim.yaml \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- -d "../../dataset/voc" \
- -o max_iters=258 \
- LearningRate.base_lr=0.0001 \
- LearningRate.schedulers="[!PiecewiseDecay {gamma: 0.1, milestones: [258, 516]}]" \
- pretrain_weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar \
- YoloTrainFeed.batch_size=64
-
-```
-
-以下为2卡训练示例，受显存所制，单卡`batch_size`不变, 总`batch_size`减小，`base_lr`减小，一个epoch内batch数量增加，同时需要调整学习率相关参数，如下：
-
-```
-python compress.py \
- -s yolov3_mobilenet_v1_slim.yaml \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- -d "../../dataset/voc" \
- -o max_iters=516 \
- LearningRate.base_lr=0.00005 \
- LearningRate.schedulers="[!PiecewiseDecay {gamma: 0.1, milestones: [516, 1012]}]" \
- pretrain_weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar \
- YoloTrainFeed.batch_size=32
-```
-
-通过`python compress.py --help`查看可配置参数。
-通过`python ../../tools/configure.py ${option_name} help`查看如何通过命令行覆盖配置文件`yolov3_mobilenet_v1_voc.yml`中的参数。
-
-
-
-### 训练时的模型结构
-这部分介绍来源于[量化low-level API介绍](https://github.com/PaddlePaddle/models/tree/develop/PaddleSlim/quant_low_level_api#1-%E9%87%8F%E5%8C%96%E8%AE%AD%E7%BB%83low-level-apis%E4%BB%8B%E7%BB%8D)。
-
-PaddlePaddle框架中和量化相关的IrPass, 分别有QuantizationTransformPass、QuantizationFreezePass、ConvertToInt8Pass。在训练时，对网络应用了QuantizationTransformPass，作用是在网络中的conv2d、depthwise_conv2d、mul等算子的各个输入前插入连续的量化op和反量化op，并改变相应反向算子的某些输入。示例图如下：
-
-
-
-图1：应用QuantizationTransformPass后的结果
-
-
-### 保存断点（checkpoint）
-
-如果在配置文件中设置了`checkpoint_path`, 则在压缩任务执行过程中会自动保存断点，当任务异常中断时，
-重启任务会自动从`checkpoint_path`路径下按数字顺序加载最新的checkpoint文件。如果不想让重启的任务从断点恢复，
-需要修改配置文件中的`checkpoint_path`，或者将`checkpoint_path`路径下文件清空。
-
->注意：配置文件中的信息不会保存在断点中，重启前对配置文件的修改将会生效。
-
-
-### 保存评估和预测模型
-
-如果在配置文件的量化策略中设置了`float_model_save_path`, `int8_model_save_path` 在训练结束后，会保存模型量化压缩之后用于预测的模型。接下来介绍这2种预测模型的区别。
-
-#### FP32模型
-在介绍量化训练时的模型结构时介绍了PaddlePaddle框架中和量化相关的IrPass, 分别是QuantizationTransformPass、QuantizationFreezePass、ConvertToInt8Pass。FP32模型是在应用QuantizationFreezePass并删除eval_program中多余的operators之后，保存的模型。
-
-QuantizationFreezePass主要用于改变IrGraph中量化op和反量化op的顺序，即将类似图1中的量化op和反量化op顺序改变为图2中的布局。除此之外，QuantizationFreezePass还会将`conv2d`、`depthwise_conv2d`、`mul`等算子的权重离线量化为int8_t范围内的值(但数据类型仍为float32)，以减少预测过程中对权重的量化操作，示例如图2：
-
-
-
-图2：应用QuantizationFreezePass后的结果
-
-
-#### 8-bit模型
-在对训练网络进行QuantizationFreezePass之后，执行ConvertToInt8Pass，
-其主要目的是将执行完QuantizationFreezePass后输出的权重类型由`FP32`更改为`INT8`。换言之，用户可以选择将量化后的权重保存为float32类型（不执行ConvertToInt8Pass）或者int8_t类型（执行ConvertToInt8Pass），示例如图3：
-
-
-
-图3：应用ConvertToInt8Pass后的结果
-
-
-> 综上，可得在量化过程中有以下几种模型结构：
-
-1. 原始模型
-2. 经QuantizationTransformPass之后得到的适用于训练的量化模型结构，在${checkpoint_path}下保存的`eval_model`是这种结构，在训练过程中每个epoch结束时也使用这个网络结构进行评估，虽然这个模型结构不是最终想要的模型结构，但是每个epoch的评估结果可用来挑选模型。
-3. 经QuantizationFreezePass之后得到的FP32模型结构，具体结构已在上面进行介绍。本文档中列出的数据集的评估结果是对FP32模型结构进行评估得到的结果。这种模型结构在训练过程中只会保存一次，也就是在量化配置文件中设置的`end_epoch`结束时进行保存，如果想将其他epoch的训练结果转化成FP32模型，可使用脚本 PaddleSlim/classification/quantization/freeze.py进行转化，具体使用方法在[评估](#评估)中介绍。
-4. 经ConvertToInt8Pass之后得到的8-bit模型结构，具体结构已在上面进行介绍。这种模型结构在训练过程中只会保存一次，也就是在量化配置文件中设置的`end_epoch`结束时进行保存，如果想将其他epoch的训练结果转化成8-bit模型，可使用脚本 slim/quantization/freeze.py进行转化，具体使用方法在[评估](#评估)中介绍。
-
-
-## 评估
-
-### 每个epoch保存的评估模型
-因为量化的最终模型只有在end_epoch时保存一次，不能保证保存的模型是最好的，因此
-如果在配置文件中设置了`checkpoint_path`，则每个epoch会保存一个量化后的用于评估的模型，
-该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下，包含`__model__`和`__params__`两个文件。
-其中，`__model__`用于保存模型结构信息，`__params__`用于保存参数（parameters）信息。模型结构和训练时一样。
-
-如果不需要保存评估模型，可以在定义Compressor对象时，将`save_eval_model`选项设置为False（默认为True）。
-
-脚本slim/eval.py中为使用该模型在评估数据集上做评估的示例。
-运行命令为：
-```
-python ../eval.py \
- --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \
- --model_name __model__ \
- --params_name __params__ \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- -d "../../dataset/voc"
-```
-
-在评估之后，选取效果最好的epoch的模型，可使用脚本 slim/quantization/freeze.py将该模型转化为以上介绍的2种模型：FP32模型，int8模型，需要配置的参数为：
-
-- model_path, 加载的模型路径，`为${checkpoint_path}/${epoch_id}/eval_model/`
-- weight_quant_type 模型参数的量化方式，和配置文件中的类型保持一致
-- save_path `FP32`, `8-bit` 模型的保存路径，分别为 `${save_path}/float/`, `${save_path}/int8/`
-
-运行命令示例：
-```
-python freeze.py \
- --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \
- --weight_quant_type ${weight_quant_type} \
- --save_path ${any path you want} \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- -d "../../dataset/voc"
-```
-
-### 最终评估模型
-最终使用的评估模型是FP32模型，使用脚本slim/eval.py中为使用该模型在评估数据集上做评估的示例。
-运行命令为：
-```
-python ../eval.py \
- --model_path ${float_model_path}
- --model_name model \
- --params_name weights \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- -d "../../dataset/voc"
-```
-
-## 预测
-
-### python预测
-FP32模型可直接使用原生PaddlePaddle Fluid预测方法进行预测。
-
-在脚本slim/infer.py中展示了如何使用fluid python API加载使用预测模型进行预测。
-
-运行命令示例:
-```
-python ../infer.py \
- --model_path ${save_path}/float \
- --model_name model \
- --params_name weights \
- -c ../../configs/yolov3_mobilenet_v1_voc.yml \
- --infer_dir ../../demo
-```
-
-
-### PaddleLite预测
-FP32模型可使用PaddleLite进行加载预测，可参见教程[Paddle-Lite如何加载运行量化模型](https://github.com/PaddlePaddle/Paddle-Lite/wiki/model_quantization)
-
-
-## 示例结果
-
->当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。
-
-### MobileNetV1-YOLO-V3
-
-| weight量化方式 | activation量化方式| Box ap |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)|
-|---|---|---|---|---|
-|baseline|- |76.2%|- |-|
-|abs_max|abs_max|- |- |-|
-|abs_max|moving_average_abs_max|74.48%|10.99|3348.68|
-|channel_wise_abs_max|abs_max|- |- |-|
-
-> 注： lite端运行手机信息：Android手机，
-型号：BKL-AL20，运行内存RAM：4GB 6GB，CPU核心数：八核 4*A73 2.36GHz+4*A53 1.8GHz，操作系统：EMUI 8.0，CPU品牌：麒麟970
-
-
-## FAQ
diff --git a/PaddleCV/PaddleDetection/slim/quantization/compress.py b/PaddleCV/PaddleDetection/slim/quantization/compress.py
deleted file mode 100644
index 0e145abcf70c54a3b7960243e20c0cb8cb6d39d9..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/quantization/compress.py
+++ /dev/null
@@ -1,266 +0,0 @@
-# Copyright (c) 2019 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
-import time
-import multiprocessing
-import numpy as np
-import datetime
-from collections import deque
-import sys
-sys.path.append("../../")
-from paddle.fluid.contrib.slim import Compressor
-from paddle.fluid.framework import IrGraph
-from paddle.fluid import core
-
-
-def set_paddle_flags(**kwargs):
- for key, value in kwargs.items():
- if os.environ.get(key, None) is None:
- os.environ[key] = str(value)
-
-
-# NOTE(paddle-dev): All of these flags should be set before
-# `import paddle`. Otherwise, it would not take any effect.
-set_paddle_flags(
- FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
-)
-
-from paddle import fluid
-
-from ppdet.core.workspace import load_config, merge_config, create
-from ppdet.data.data_feed import create_reader
-
-from ppdet.utils.eval_utils import parse_fetches, eval_results
-from ppdet.utils.stats import TrainingStats
-from ppdet.utils.cli import ArgsParser, print_total_cfg
-from ppdet.utils.check import check_gpu
-import ppdet.utils.checkpoint as checkpoint
-from ppdet.modeling.model_input import create_feed
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-
-def eval_run(exe, compile_program, reader, keys, values, cls, test_feed):
- """
- Run evaluation program, return program outputs.
- """
- iter_id = 0
- results = []
- if len(cls) != 0:
- values = []
- for i in range(len(cls)):
- _, accum_map = cls[i].get_map_var()
- cls[i].reset(exe)
- values.append(accum_map)
-
- images_num = 0
- start_time = time.time()
- has_bbox = 'bbox' in keys
- for data in reader():
- data = test_feed.feed(data)
- feed_data = {'image': data['image'], 'im_size': data['im_size']}
- outs = exe.run(compile_program,
- feed=feed_data,
- fetch_list=[values[0]],
- return_numpy=False)
- outs.append(data['gt_box'])
- outs.append(data['gt_label'])
- outs.append(data['is_difficult'])
- res = {
- k: (np.array(v), v.recursive_sequence_lengths())
- for k, v in zip(keys, outs)
- }
- results.append(res)
- if iter_id % 100 == 0:
- logger.info('Test iter {}'.format(iter_id))
- iter_id += 1
- images_num += len(res['bbox'][1][0]) if has_bbox else 1
- logger.info('Test finish iter {}'.format(iter_id))
-
- end_time = time.time()
- fps = images_num / (end_time - start_time)
- if has_bbox:
- logger.info('Total number of images: {}, inference time: {} fps.'.
- format(images_num, fps))
- else:
- logger.info('Total iteration: {}, inference time: {} batch/s.'.format(
- images_num, fps))
-
- return results
-
-
-def main():
- cfg = load_config(FLAGS.config)
- if 'architecture' in cfg:
- main_arch = cfg.architecture
- else:
- raise ValueError("'architecture' not specified in config file.")
-
- merge_config(FLAGS.opt)
- if 'log_iter' not in cfg:
- cfg.log_iter = 20
-
- # check if set use_gpu=True in paddlepaddle cpu version
- check_gpu(cfg.use_gpu)
-
- if cfg.use_gpu:
- devices_num = fluid.core.get_cuda_device_count()
- else:
- devices_num = int(
- os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-
- if 'train_feed' not in cfg:
- train_feed = create(main_arch + 'TrainFeed')
- else:
- train_feed = create(cfg.train_feed)
-
- if 'eval_feed' not in cfg:
- eval_feed = create(main_arch + 'EvalFeed')
- else:
- eval_feed = create(cfg.eval_feed)
-
- place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
- exe = fluid.Executor(place)
-
- lr_builder = create('LearningRate')
- optim_builder = create('OptimizerBuilder')
-
- # build program
- startup_prog = fluid.Program()
- train_prog = fluid.Program()
- with fluid.program_guard(train_prog, startup_prog):
- with fluid.unique_name.guard():
- model = create(main_arch)
- _, feed_vars = create_feed(train_feed, False)
- train_fetches = model.train(feed_vars)
- loss = train_fetches['loss']
- lr = lr_builder()
- optimizer = optim_builder(lr)
- optimizer.minimize(loss)
-
- train_reader = create_reader(train_feed, cfg.max_iters, FLAGS.dataset_dir)
-
- # parse train fetches
- train_keys, train_values, _ = parse_fetches(train_fetches)
- train_values.append(lr)
-
- train_fetch_list = []
- for k, v in zip(train_keys, train_values):
- train_fetch_list.append((k, v))
- print("train_fetch_list: {}".format(train_fetch_list))
-
- eval_prog = fluid.Program()
- with fluid.program_guard(eval_prog, startup_prog):
- with fluid.unique_name.guard():
- model = create(main_arch)
- _, test_feed_vars = create_feed(eval_feed, False)
- fetches = model.eval(test_feed_vars)
- eval_prog = eval_prog.clone(True)
-
- eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
- #eval_pyreader.decorate_sample_list_generator(eval_reader, place)
- test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
-
- # parse eval fetches
- extra_keys = []
- if cfg.metric == 'COCO':
- extra_keys = ['im_info', 'im_id', 'im_shape']
- if cfg.metric == 'VOC':
- extra_keys = ['gt_box', 'gt_label', 'is_difficult']
- eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
- extra_keys)
- # print(eval_values)
-
- eval_fetch_list = []
- for k, v in zip(eval_keys, eval_values):
- eval_fetch_list.append((k, v))
-
- exe.run(startup_prog)
-
- start_iter = 0
-
- checkpoint.load_params(exe, train_prog, cfg.pretrain_weights)
-
- best_box_ap_list = []
-
- def eval_func(program, scope):
-
- #place = fluid.CPUPlace()
- #exe = fluid.Executor(place)
- results = eval_run(exe, program, eval_reader, eval_keys, eval_values,
- eval_cls, test_data_feed)
-
- resolution = None
- if 'mask' in results[0]:
- resolution = model.mask_head.resolution
- box_ap_stats = eval_results(results, eval_feed, cfg.metric,
- cfg.num_classes, resolution, False,
- FLAGS.output_eval)
- if len(best_box_ap_list) == 0:
- best_box_ap_list.append(box_ap_stats[0])
- elif box_ap_stats[0] > best_box_ap_list[0]:
- best_box_ap_list[0] = box_ap_stats[0]
- logger.info("Best test box ap: {}".format(best_box_ap_list[0]))
- return best_box_ap_list[0]
-
- test_feed = [('image', test_feed_vars['image'].name),
- ('im_size', test_feed_vars['im_size'].name)]
-
- com = Compressor(
- place,
- fluid.global_scope(),
- train_prog,
- train_reader=train_reader,
- train_feed_list=[(key, value.name) for key, value in feed_vars.items()],
- train_fetch_list=train_fetch_list,
- eval_program=eval_prog,
- eval_reader=eval_reader,
- eval_feed_list=test_feed,
- eval_func={'map': eval_func},
- eval_fetch_list=[eval_fetch_list[0]],
- prune_infer_model=[["image", "im_size"], ["multiclass_nms_0.tmp_0"]],
- train_optimizer=None)
- com.config(FLAGS.slim_file)
- com.run()
-
-
-if __name__ == '__main__':
- parser = ArgsParser()
- parser.add_argument(
- "-s",
- "--slim_file",
- default=None,
- type=str,
- help="Config file of PaddleSlim.")
- parser.add_argument(
- "--output_eval",
- default=None,
- type=str,
- help="Evaluation directory, default is current directory.")
- parser.add_argument(
- "-d",
- "--dataset_dir",
- default=None,
- type=str,
- help="Dataset path, same as DataFeed.dataset.dataset_dir")
- FLAGS = parser.parse_args()
- main()
diff --git a/PaddleCV/PaddleDetection/slim/quantization/freeze.py b/PaddleCV/PaddleDetection/slim/quantization/freeze.py
deleted file mode 100644
index 42c7bc62fd771366430f3658d9446a0f12fe2125..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/quantization/freeze.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# Copyright (c) 2019 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
-import time
-import multiprocessing
-import numpy as np
-import datetime
-from collections import deque
-import sys
-sys.path.append("../../")
-from paddle.fluid.contrib.slim import Compressor
-from paddle.fluid.framework import IrGraph
-from paddle.fluid import core
-from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
-from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
-from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
-from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
-
-
-def set_paddle_flags(**kwargs):
- for key, value in kwargs.items():
- if os.environ.get(key, None) is None:
- os.environ[key] = str(value)
-
-
-# NOTE(paddle-dev): All of these flags should be set before
-# `import paddle`. Otherwise, it would not take any effect.
-set_paddle_flags(
- FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
-)
-
-from paddle import fluid
-
-from ppdet.core.workspace import load_config, merge_config, create
-from ppdet.data.data_feed import create_reader
-
-from ppdet.utils.eval_utils import parse_fetches, eval_results
-from ppdet.utils.stats import TrainingStats
-from ppdet.utils.cli import ArgsParser
-from ppdet.utils.check import check_gpu
-import ppdet.utils.checkpoint as checkpoint
-from ppdet.modeling.model_input import create_feed
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-
-def eval_run(exe, compile_program, reader, keys, values, cls, test_feed):
- """
- Run evaluation program, return program outputs.
- """
- iter_id = 0
- results = []
-
- images_num = 0
- start_time = time.time()
- has_bbox = 'bbox' in keys
- for data in reader():
- data = test_feed.feed(data)
- feed_data = {'image': data['image'], 'im_size': data['im_size']}
- outs = exe.run(compile_program,
- feed=feed_data,
- fetch_list=values[0],
- return_numpy=False)
- outs.append(data['gt_box'])
- outs.append(data['gt_label'])
- outs.append(data['is_difficult'])
- res = {
- k: (np.array(v), v.recursive_sequence_lengths())
- for k, v in zip(keys, outs)
- }
- results.append(res)
- if iter_id % 100 == 0:
- logger.info('Test iter {}'.format(iter_id))
- iter_id += 1
- images_num += len(res['bbox'][1][0]) if has_bbox else 1
- logger.info('Test finish iter {}'.format(iter_id))
-
- end_time = time.time()
- fps = images_num / (end_time - start_time)
- if has_bbox:
- logger.info('Total number of images: {}, inference time: {} fps.'.
- format(images_num, fps))
- else:
- logger.info('Total iteration: {}, inference time: {} batch/s.'.format(
- images_num, fps))
-
- return results
-
-
-def main():
- cfg = load_config(FLAGS.config)
- if 'architecture' in cfg:
- main_arch = cfg.architecture
- else:
- raise ValueError("'architecture' not specified in config file.")
-
- merge_config(FLAGS.opt)
- if 'log_iter' not in cfg:
- cfg.log_iter = 20
-
- # check if set use_gpu=True in paddlepaddle cpu version
- check_gpu(cfg.use_gpu)
-
- if cfg.use_gpu:
- devices_num = fluid.core.get_cuda_device_count()
- else:
- devices_num = int(
- os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-
- if 'eval_feed' not in cfg:
- eval_feed = create(main_arch + 'EvalFeed')
- else:
- eval_feed = create(cfg.eval_feed)
-
- place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
- exe = fluid.Executor(place)
-
- _, test_feed_vars = create_feed(eval_feed, False)
-
- eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
- #eval_pyreader.decorate_sample_list_generator(eval_reader, place)
- test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
-
- assert os.path.exists(FLAGS.model_path)
- infer_prog, feed_names, fetch_targets = fluid.io.load_inference_model(
- dirname=FLAGS.model_path,
- executor=exe,
- model_filename='__model__.infer',
- params_filename='__params__')
-
- eval_keys = ['bbox', 'gt_box', 'gt_label', 'is_difficult']
- eval_values = [
- 'multiclass_nms_0.tmp_0', 'gt_box', 'gt_label', 'is_difficult'
- ]
- eval_cls = []
- eval_values[0] = fetch_targets[0]
-
- results = eval_run(exe, infer_prog, eval_reader, eval_keys, eval_values,
- eval_cls, test_data_feed)
-
- resolution = None
- if 'mask' in results[0]:
- resolution = model.mask_head.resolution
- box_ap_stats = eval_results(results, eval_feed, cfg.metric, cfg.num_classes,
- resolution, False, FLAGS.output_eval)
-
- logger.info("freeze the graph for inference")
- test_graph = IrGraph(core.Graph(infer_prog.desc), for_test=True)
-
- freeze_pass = QuantizationFreezePass(
- scope=fluid.global_scope(),
- place=place,
- weight_quantize_type=FLAGS.weight_quant_type)
- freeze_pass.apply(test_graph)
- server_program = test_graph.to_program()
- fluid.io.save_inference_model(
- dirname=os.path.join(FLAGS.save_path, 'float'),
- feeded_var_names=feed_names,
- target_vars=fetch_targets,
- executor=exe,
- main_program=server_program,
- model_filename='model',
- params_filename='weights')
-
- logger.info("convert the weights into int8 type")
- convert_int8_pass = ConvertToInt8Pass(
- scope=fluid.global_scope(), place=place)
- convert_int8_pass.apply(test_graph)
- server_int8_program = test_graph.to_program()
- fluid.io.save_inference_model(
- dirname=os.path.join(FLAGS.save_path, 'int8'),
- feeded_var_names=feed_names,
- target_vars=fetch_targets,
- executor=exe,
- main_program=server_int8_program,
- model_filename='model',
- params_filename='weights')
-
-
-if __name__ == '__main__':
- parser = ArgsParser()
- parser.add_argument(
- "-m", "--model_path", default=None, type=str, help="path of checkpoint")
- parser.add_argument(
- "--output_eval",
- default=None,
- type=str,
- help="Evaluation directory, default is current directory.")
- parser.add_argument(
- "-d",
- "--dataset_dir",
- default=None,
- type=str,
- help="Dataset path, same as DataFeed.dataset.dataset_dir")
- parser.add_argument(
- "--weight_quant_type",
- default='abs_max',
- type=str,
- help="quantization type for weight")
- parser.add_argument(
- "--save_path",
- default='./output',
- type=str,
- help="path to save quantization inference model")
-
- FLAGS = parser.parse_args()
- main()
diff --git a/PaddleCV/PaddleDetection/slim/quantization/images/ConvertToInt8Pass.png b/PaddleCV/PaddleDetection/slim/quantization/images/ConvertToInt8Pass.png
deleted file mode 100644
index 8b5849819c0bc8e592dc8f864d8945330df85ab1..0000000000000000000000000000000000000000
Binary files a/PaddleCV/PaddleDetection/slim/quantization/images/ConvertToInt8Pass.png and /dev/null differ
diff --git a/PaddleCV/PaddleDetection/slim/quantization/images/FreezePass.png b/PaddleCV/PaddleDetection/slim/quantization/images/FreezePass.png
deleted file mode 100644
index acd2b0a890a8af85bec6eecdb22e47ad386a178c..0000000000000000000000000000000000000000
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diff --git a/PaddleCV/PaddleDetection/slim/quantization/images/TransformForMobilePass.png b/PaddleCV/PaddleDetection/slim/quantization/images/TransformForMobilePass.png
deleted file mode 100644
index 4104cacc67af0be1c7bc152696e2ae544127aace..0000000000000000000000000000000000000000
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diff --git a/PaddleCV/PaddleDetection/slim/quantization/images/TransformPass.png b/PaddleCV/PaddleDetection/slim/quantization/images/TransformPass.png
deleted file mode 100644
index f29ab62753e0e6ddf28d0c1dda7139705fc24b18..0000000000000000000000000000000000000000
Binary files a/PaddleCV/PaddleDetection/slim/quantization/images/TransformPass.png and /dev/null differ
diff --git a/PaddleCV/PaddleDetection/slim/quantization/yolov3_mobilenet_v1_slim.yaml b/PaddleCV/PaddleDetection/slim/quantization/yolov3_mobilenet_v1_slim.yaml
deleted file mode 100644
index 9d453450d91edf4d10c6aa5fd9fd29f21953e5d3..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/slim/quantization/yolov3_mobilenet_v1_slim.yaml
+++ /dev/null
@@ -1,19 +0,0 @@
-version: 1.0
-strategies:
- quantization_strategy:
- class: 'QuantizationStrategy'
- start_epoch: 0
- end_epoch: 4
- float_model_save_path: './output/yolov3/float'
- int8_model_save_path: './output/yolov3/int8'
- weight_bits: 8
- activation_bits: 8
- weight_quantize_type: 'abs_max'
- activation_quantize_type: 'moving_average_abs_max'
- save_in_nodes: ['image', 'im_size']
- save_out_nodes: ['multiclass_nms_0.tmp_0']
-compressor:
- epoch: 5
- checkpoint_path: './checkpoints/yolov3/'
- strategies:
- - quantization_strategy
diff --git a/PaddleCV/PaddleDetection/tools/__init__.py b/PaddleCV/PaddleDetection/tools/__init__.py
deleted file mode 100644
index d0c32e26092f6ea25771279418582a24ea449ab2..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/tools/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) 2019 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.
diff --git a/PaddleCV/PaddleDetection/tools/configure.py b/PaddleCV/PaddleDetection/tools/configure.py
deleted file mode 100644
index 560d161513ae8f0115d8d3d5f97f6a0695642015..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/tools/configure.py
+++ /dev/null
@@ -1,202 +0,0 @@
-# Copyright (c) 2019 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 print_function
-
-import sys
-from argparse import ArgumentParser, RawDescriptionHelpFormatter
-
-import yaml
-
-from ppdet.core.workspace import get_registered_modules, load_config, dump_value
-from ppdet.utils.cli import ColorTTY, print_total_cfg
-
-color_tty = ColorTTY()
-
-MISC_CONFIG = {
- "architecture": "",
- "max_iters": "",
- "train_feed": "",
- "eval_feed": "",
- "test_feed": "",
- "pretrain_weights": "",
- "save_dir": "",
- "weights": "",
- "metric": "",
- "map_type": "11point",
- "log_smooth_window": 20,
- "snapshot_iter": 10000,
- "log_iter": 20,
- "use_gpu": True,
- "finetune_exclude_pretrained_params": "",
-}
-
-
-def dump_config(module, minimal=False):
- args = module.schema.values()
- if minimal:
- args = [arg for arg in args if not arg.has_default()]
- return yaml.dump(
- {
- module.name: {
- arg.name: arg.default if arg.has_default() else ""
- for arg in args
- }
- },
- default_flow_style=False,
- default_style='')
-
-
-def list_modules(**kwargs):
- target_category = kwargs['category']
- module_schema = get_registered_modules()
- module_by_category = {}
-
- for schema in module_schema.values():
- category = schema.category
- if target_category is not None and schema.category != target_category:
- continue
- if category not in module_by_category:
- module_by_category[category] = [schema]
- else:
- module_by_category[category].append(schema)
-
- for cat, modules in module_by_category.items():
- print("Available modules in the category '{}':".format(cat))
- print("")
- max_len = max([len(mod.name) for mod in modules])
- for mod in modules:
- print(color_tty.green(mod.name.ljust(max_len)),
- mod.doc.split('\n')[0])
- print("")
-
-
-def help_module(**kwargs):
- schema = get_registered_modules()[kwargs['module']]
-
- doc = schema.doc is None and "Not documented" or "{}".format(schema.doc)
- func_args = {arg.name: arg.doc for arg in schema.schema.values()}
- max_len = max([len(k) for k in func_args.keys()])
- opts = "\n".join([
- "{} {}".format(color_tty.green(k.ljust(max_len)), v)
- for k, v in func_args.items()
- ])
- template = dump_config(schema)
- print("{}\n\n{}\n\n{}\n\n{}\n\n{}\n\n{}\n{}\n".format(
- color_tty.bold(color_tty.blue("MODULE DESCRIPTION:")),
- doc,
- color_tty.bold(color_tty.blue("MODULE OPTIONS:")),
- opts,
- color_tty.bold(color_tty.blue("CONFIGURATION TEMPLATE:")),
- template,
- color_tty.bold(color_tty.blue("COMMAND LINE OPTIONS:")), ))
- for arg in schema.schema.values():
- print("--opt {}.{}={}".format(schema.name, arg.name,
- dump_value(arg.default)
- if arg.has_default() else ""))
-
-
-def generate_config(**kwargs):
- minimal = kwargs['minimal']
- modules = kwargs['modules']
- module_schema = get_registered_modules()
- visited = []
- schema = []
-
- def walk(m):
- if m in visited:
- return
- s = module_schema[m]
- schema.append(s)
- visited.append(m)
-
- for mod in modules:
- walk(mod)
-
- # XXX try to be smart about when to add header,
- # if any "architecture" module, is included, head will be added as well
- if any([getattr(m, 'category', None) == 'architecture' for m in schema]):
- # XXX for ordered printing
- header = ""
- for k, v in MISC_CONFIG.items():
- header += yaml.dump(
- {
- k: v
- }, default_flow_style=False, default_style='')
- print(header)
-
- for s in schema:
- print(dump_config(s, minimal))
-
-
-# FIXME this is pretty hackish, maybe implement a custom YAML printer?
-def analyze_config(**kwargs):
- config = load_config(kwargs['file'])
- print_total_cfg(config)
-
-
-if __name__ == '__main__':
- argv = sys.argv[1:]
-
- parser = ArgumentParser(formatter_class=RawDescriptionHelpFormatter)
- subparsers = parser.add_subparsers(help='Supported Commands')
- list_parser = subparsers.add_parser("list", help="list available modules")
- help_parser = subparsers.add_parser(
- "help", help="show detail options for module")
- generate_parser = subparsers.add_parser(
- "generate", help="generate configuration template")
- analyze_parser = subparsers.add_parser(
- "analyze", help="analyze configuration file")
-
- list_parser.set_defaults(func=list_modules)
- help_parser.set_defaults(func=help_module)
- generate_parser.set_defaults(func=generate_config)
- analyze_parser.set_defaults(func=analyze_config)
-
- list_group = list_parser.add_mutually_exclusive_group()
- list_group.add_argument(
- "-c",
- "--category",
- type=str,
- default=None,
- help="list modules for ")
-
- help_parser.add_argument(
- "module",
- help="module to show info for",
- choices=list(get_registered_modules().keys()))
-
- generate_parser.add_argument(
- "modules",
- nargs='+',
- help="include these module in generated configuration template",
- choices=list(get_registered_modules().keys()))
- generate_group = generate_parser.add_mutually_exclusive_group()
- generate_group.add_argument(
- "--minimal", action='store_true', help="only include required options")
- generate_group.add_argument(
- "--full",
- action='store_false',
- dest='minimal',
- help="include all options")
-
- analyze_parser.add_argument("file", help="configuration file to analyze")
-
- if len(sys.argv) < 2:
- parser.print_help()
- sys.exit(1)
-
- args = parser.parse_args(argv)
- if hasattr(args, 'func'):
- args.func(**vars(args))
diff --git a/PaddleCV/PaddleDetection/tools/eval.py b/PaddleCV/PaddleDetection/tools/eval.py
deleted file mode 100644
index 15692d3dd8b13789faaaa6a9a0bb3ed9385f37a6..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/tools/eval.py
+++ /dev/null
@@ -1,183 +0,0 @@
-# Copyright (c) 2019 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
-
-
-def set_paddle_flags(**kwargs):
- for key, value in kwargs.items():
- if os.environ.get(key, None) is None:
- os.environ[key] = str(value)
-
-
-# NOTE(paddle-dev): All of these flags should be set before
-# `import paddle`. Otherwise, it would not take any effect.
-set_paddle_flags(
- FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
-)
-
-import paddle.fluid as fluid
-
-from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results, json_eval_results
-import ppdet.utils.checkpoint as checkpoint
-from ppdet.utils.check import check_gpu
-from ppdet.modeling.model_input import create_feed
-from ppdet.data.data_feed import create_reader
-from ppdet.core.workspace import load_config, merge_config, create
-from ppdet.utils.cli import print_total_cfg
-from ppdet.utils.cli import ArgsParser
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-
-def main():
- """
- Main evaluate function
- """
- cfg = load_config(FLAGS.config)
- if 'architecture' in cfg:
- main_arch = cfg.architecture
- else:
- raise ValueError("'architecture' not specified in config file.")
-
- merge_config(FLAGS.opt)
- # check if set use_gpu=True in paddlepaddle cpu version
- check_gpu(cfg.use_gpu)
- print_total_cfg(cfg)
-
- if 'eval_feed' not in cfg:
- eval_feed = create(main_arch + 'EvalFeed')
- else:
- eval_feed = create(cfg.eval_feed)
-
- multi_scale_test = getattr(cfg, 'MultiScaleTEST', None)
-
- # define executor
- place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
- exe = fluid.Executor(place)
-
- # build program
- model = create(main_arch)
- startup_prog = fluid.Program()
- eval_prog = fluid.Program()
- with fluid.program_guard(eval_prog, startup_prog):
- with fluid.unique_name.guard():
- pyreader, feed_vars = create_feed(eval_feed)
- if multi_scale_test is None:
- fetches = model.eval(feed_vars)
- else:
- fetches = model.eval(feed_vars, multi_scale_test)
- eval_prog = eval_prog.clone(True)
- reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
- pyreader.decorate_sample_list_generator(reader, place)
-
- # eval already exists json file
- if FLAGS.json_eval:
- logger.info(
- "In json_eval mode, PaddleDetection will evaluate json files in "
- "output_eval directly. And proposal.json, bbox.json and mask.json "
- "will be detected by default.")
- json_eval_results(
- eval_feed, cfg.metric, json_directory=FLAGS.output_eval)
- return
-
- compile_program = fluid.compiler.CompiledProgram(
- eval_prog).with_data_parallel()
-
- # load model
- exe.run(startup_prog)
- if 'weights' in cfg:
- checkpoint.load_params(exe, eval_prog, cfg.weights)
-
- assert cfg.metric in ['COCO', 'VOC'], \
- "unknown metric type {}".format(cfg.metric)
- extra_keys = []
- if cfg.metric == 'COCO':
- extra_keys = ['im_info', 'im_id', 'im_shape']
- if cfg.metric == 'VOC':
- extra_keys = ['gt_box', 'gt_label', 'is_difficult']
-
- keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys)
-
- # whether output bbox is normalized in model output layer
- is_bbox_normalized = False
- if hasattr(model, 'is_bbox_normalized') and \
- callable(model.is_bbox_normalized):
- is_bbox_normalized = model.is_bbox_normalized()
-
- sub_eval_prog = None
- sub_keys = None
- sub_values = None
- # build sub-program
- if 'Mask' in main_arch and multi_scale_test:
- sub_eval_prog = fluid.Program()
- with fluid.program_guard(sub_eval_prog, startup_prog):
- with fluid.unique_name.guard():
- _, feed_vars = create_feed(
- eval_feed, use_pyreader=False, sub_prog_feed=True)
- sub_fetches = model.eval(
- feed_vars, multi_scale_test, mask_branch=True)
- extra_keys = []
- if cfg.metric == 'COCO':
- extra_keys = ['im_id', 'im_shape']
- if cfg.metric == 'VOC':
- extra_keys = ['gt_box', 'gt_label', 'is_difficult']
- sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog,
- extra_keys)
- sub_eval_prog = sub_eval_prog.clone(True)
-
- if 'weights' in cfg:
- checkpoint.load_params(exe, sub_eval_prog, cfg.weights)
-
- results = eval_run(exe, compile_program, pyreader, keys, values, cls, cfg,
- sub_eval_prog, sub_keys, sub_values)
-
- # evaluation
- resolution = None
- if 'mask' in results[0]:
- resolution = model.mask_head.resolution
- # if map_type not set, use default 11point, only use in VOC eval
- map_type = cfg.map_type if 'map_type' in cfg else '11point'
- eval_results(results, eval_feed, cfg.metric, cfg.num_classes, resolution,
- is_bbox_normalized, FLAGS.output_eval, map_type)
-
-
-if __name__ == '__main__':
- parser = ArgsParser()
- parser.add_argument(
- "--json_eval",
- action='store_true',
- default=False,
- help="Whether to re eval with already exists bbox.json or mask.json")
- parser.add_argument(
- "-d",
- "--dataset_dir",
- default=None,
- type=str,
- help="Dataset path, same as DataFeed.dataset.dataset_dir")
- parser.add_argument(
- "-f",
- "--output_eval",
- default=None,
- type=str,
- help="Evaluation file directory, default is current directory.")
- FLAGS = parser.parse_args()
- main()
diff --git a/PaddleCV/PaddleDetection/tools/export_model.py b/PaddleCV/PaddleDetection/tools/export_model.py
deleted file mode 100644
index b0c9edac316220ca2b752fad05eec5437f698de8..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/tools/export_model.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# Copyright (c) 2019 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
-
-from paddle import fluid
-
-from ppdet.core.workspace import load_config, merge_config, create
-from ppdet.modeling.model_input import create_feed
-from ppdet.utils.cli import ArgsParser
-import ppdet.utils.checkpoint as checkpoint
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-
-def prune_feed_vars(feeded_var_names, target_vars, prog):
- """
- Filter out feed variables which are not in program,
- pruned feed variables are only used in post processing
- on model output, which are not used in program, such
- as im_id to identify image order, im_shape to clip bbox
- in image.
- """
- exist_var_names = []
- prog = prog.clone()
- prog = prog._prune(targets=target_vars)
- global_block = prog.global_block()
- for name in feeded_var_names:
- try:
- v = global_block.var(name)
- exist_var_names.append(str(v.name))
- except Exception:
- logger.info('save_inference_model pruned unused feed '
- 'variables {}'.format(name))
- pass
- return exist_var_names
-
-
-def save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog):
- cfg_name = os.path.basename(FLAGS.config).split('.')[0]
- save_dir = os.path.join(FLAGS.output_dir, cfg_name)
- feed_var_names = [var.name for var in feed_vars.values()]
- target_vars = list(test_fetches.values())
- feed_var_names = prune_feed_vars(feed_var_names, target_vars, infer_prog)
- logger.info("Export inference model to {}, input: {}, output: "
- "{}...".format(save_dir, feed_var_names,
- [str(var.name) for var in target_vars]))
- fluid.io.save_inference_model(
- save_dir,
- feeded_var_names=feed_var_names,
- target_vars=target_vars,
- executor=exe,
- main_program=infer_prog,
- params_filename="__params__")
-
-
-def main():
- cfg = load_config(FLAGS.config)
-
- if 'architecture' in cfg:
- main_arch = cfg.architecture
- else:
- raise ValueError("'architecture' not specified in config file.")
-
- merge_config(FLAGS.opt)
-
- if 'test_feed' not in cfg:
- test_feed = create(main_arch + 'TestFeed')
- else:
- test_feed = create(cfg.test_feed)
-
- # Use CPU for exporting inference model instead of GPU
- place = fluid.CPUPlace()
- exe = fluid.Executor(place)
-
- model = create(main_arch)
-
- startup_prog = fluid.Program()
- infer_prog = fluid.Program()
- with fluid.program_guard(infer_prog, startup_prog):
- with fluid.unique_name.guard():
- _, feed_vars = create_feed(test_feed, use_pyreader=False)
- test_fetches = model.test(feed_vars)
- infer_prog = infer_prog.clone(True)
-
- exe.run(startup_prog)
- checkpoint.load_params(exe, infer_prog, cfg.weights)
-
- save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog)
-
-
-if __name__ == '__main__':
- parser = ArgsParser()
- parser.add_argument(
- "--output_dir",
- type=str,
- default="output",
- help="Directory for storing the output model files.")
- FLAGS = parser.parse_args()
- main()
diff --git a/PaddleCV/PaddleDetection/tools/face_eval.py b/PaddleCV/PaddleDetection/tools/face_eval.py
deleted file mode 100644
index f74d5ba431e2933e346d8430e9ba9b2ad20af170..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/tools/face_eval.py
+++ /dev/null
@@ -1,298 +0,0 @@
-# Copyright (c) 2019 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
-
-import paddle.fluid as fluid
-import numpy as np
-from PIL import Image
-from collections import OrderedDict
-
-import ppdet.utils.checkpoint as checkpoint
-from ppdet.utils.cli import ArgsParser
-from ppdet.utils.check import check_gpu
-from ppdet.utils.widerface_eval_utils import get_shrink, bbox_vote, \
- save_widerface_bboxes, save_fddb_bboxes, to_chw_bgr
-from ppdet.core.workspace import load_config, merge_config, create
-from ppdet.modeling.model_input import create_feed
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-
-def face_img_process(image,
- mean=[104., 117., 123.],
- std=[127.502231, 127.502231, 127.502231]):
- img = np.array(image)
- img = to_chw_bgr(img)
- img = img.astype('float32')
- img -= np.array(mean)[:, np.newaxis, np.newaxis].astype('float32')
- img /= np.array(std)[:, np.newaxis, np.newaxis].astype('float32')
- img = [img]
- img = np.array(img)
- return img
-
-
-def face_eval_run(exe,
- compile_program,
- fetches,
- img_root_dir,
- gt_file,
- pred_dir='output/pred',
- eval_mode='widerface',
- multi_scale=False):
- # load ground truth files
- with open(gt_file, 'r') as f:
- gt_lines = f.readlines()
- imid2path = []
- pos_gt = 0
- while pos_gt < len(gt_lines):
- name_gt = gt_lines[pos_gt].strip('\n\t').split()[0]
- imid2path.append(name_gt)
- pos_gt += 1
- n_gt = int(gt_lines[pos_gt].strip('\n\t').split()[0])
- pos_gt += 1 + n_gt
- logger.info('The ground truth file load {} images'.format(len(imid2path)))
-
- dets_dist = OrderedDict()
- for iter_id, im_path in enumerate(imid2path):
- image_path = os.path.join(img_root_dir, im_path)
- if eval_mode == 'fddb':
- image_path += '.jpg'
- image = Image.open(image_path).convert('RGB')
- if multi_scale:
- shrink, max_shrink = get_shrink(image.size[1], image.size[0])
- det0 = detect_face(exe, compile_program, fetches, image, shrink)
- det1 = flip_test(exe, compile_program, fetches, image, shrink)
- [det2, det3] = multi_scale_test(exe, compile_program, fetches, image,
- max_shrink)
- det4 = multi_scale_test_pyramid(exe, compile_program, fetches, image,
- max_shrink)
- det = np.row_stack((det0, det1, det2, det3, det4))
- dets = bbox_vote(det)
- else:
- dets = detect_face(exe, compile_program, fetches, image, 1)
- if eval_mode == 'widerface':
- save_widerface_bboxes(image_path, dets, pred_dir)
- else:
- dets_dist[im_path] = dets
- if iter_id % 100 == 0:
- logger.info('Test iter {}'.format(iter_id))
- if eval_mode == 'fddb':
- save_fddb_bboxes(dets_dist, pred_dir)
- logger.info("Finish evaluation.")
-
-
-def detect_face(exe, compile_program, fetches, image, shrink):
- image_shape = [3, image.size[1], image.size[0]]
- if shrink != 1:
- h, w = int(image_shape[1] * shrink), int(image_shape[2] * shrink)
- image = image.resize((w, h), Image.ANTIALIAS)
- image_shape = [3, h, w]
-
- img = face_img_process(image)
- detection, = exe.run(compile_program,
- feed={'image': img},
- fetch_list=[fetches['bbox']],
- return_numpy=False)
- detection = np.array(detection)
- # layout: xmin, ymin, xmax. ymax, score
- if np.prod(detection.shape) == 1:
- logger.info("No face detected")
- return np.array([[0, 0, 0, 0, 0]])
- det_conf = detection[:, 1]
- det_xmin = image_shape[2] * detection[:, 2] / shrink
- det_ymin = image_shape[1] * detection[:, 3] / shrink
- det_xmax = image_shape[2] * detection[:, 4] / shrink
- det_ymax = image_shape[1] * detection[:, 5] / shrink
-
- det = np.column_stack((det_xmin, det_ymin, det_xmax, det_ymax, det_conf))
- return det
-
-
-def flip_test(exe, compile_program, fetches, image, shrink):
- img = image.transpose(Image.FLIP_LEFT_RIGHT)
- det_f = detect_face(exe, compile_program, fetches, img, shrink)
- det_t = np.zeros(det_f.shape)
- # image.size: [width, height]
- det_t[:, 0] = image.size[0] - det_f[:, 2]
- det_t[:, 1] = det_f[:, 1]
- det_t[:, 2] = image.size[0] - det_f[:, 0]
- det_t[:, 3] = det_f[:, 3]
- det_t[:, 4] = det_f[:, 4]
- return det_t
-
-
-def multi_scale_test(exe, compile_program, fetches, image, max_shrink):
- # Shrink detecting is only used to detect big faces
- st = 0.5 if max_shrink >= 0.75 else 0.5 * max_shrink
- det_s = detect_face(exe, compile_program, fetches, image, st)
- index = np.where(
- np.maximum(det_s[:, 2] - det_s[:, 0] + 1, det_s[:, 3] - det_s[:, 1] + 1)
- > 30)[0]
- det_s = det_s[index, :]
- # Enlarge one times
- bt = min(2, max_shrink) if max_shrink > 1 else (st + max_shrink) / 2
- det_b = detect_face(exe, compile_program, fetches, image, bt)
-
- # Enlarge small image x times for small faces
- if max_shrink > 2:
- bt *= 2
- while bt < max_shrink:
- det_b = np.row_stack((det_b, detect_face(exe, compile_program,
- fetches, image, bt)))
- bt *= 2
- det_b = np.row_stack((det_b, detect_face(exe, compile_program, fetches,
- image, max_shrink)))
-
- # Enlarged images are only used to detect small faces.
- if bt > 1:
- index = np.where(
- np.minimum(det_b[:, 2] - det_b[:, 0] + 1,
- det_b[:, 3] - det_b[:, 1] + 1) < 100)[0]
- det_b = det_b[index, :]
- # Shrinked images are only used to detect big faces.
- else:
- index = np.where(
- np.maximum(det_b[:, 2] - det_b[:, 0] + 1,
- det_b[:, 3] - det_b[:, 1] + 1) > 30)[0]
- det_b = det_b[index, :]
- return det_s, det_b
-
-
-def multi_scale_test_pyramid(exe, compile_program, fetches, image, max_shrink):
- # Use image pyramids to detect faces
- det_b = detect_face(exe, compile_program, fetches, image, 0.25)
- index = np.where(
- np.maximum(det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1)
- > 30)[0]
- det_b = det_b[index, :]
-
- st = [0.75, 1.25, 1.5, 1.75]
- for i in range(len(st)):
- if st[i] <= max_shrink:
- det_temp = detect_face(exe, compile_program, fetches, image, st[i])
- # Enlarged images are only used to detect small faces.
- if st[i] > 1:
- index = np.where(
- np.minimum(det_temp[:, 2] - det_temp[:, 0] + 1,
- det_temp[:, 3] - det_temp[:, 1] + 1) < 100)[0]
- det_temp = det_temp[index, :]
- # Shrinked images are only used to detect big faces.
- else:
- index = np.where(
- np.maximum(det_temp[:, 2] - det_temp[:, 0] + 1,
- det_temp[:, 3] - det_temp[:, 1] + 1) > 30)[0]
- det_temp = det_temp[index, :]
- det_b = np.row_stack((det_b, det_temp))
- return det_b
-
-
-def main():
- """
- Main evaluate function
- """
- cfg = load_config(FLAGS.config)
- if 'architecture' in cfg:
- main_arch = cfg.architecture
- else:
- raise ValueError("'architecture' not specified in config file.")
-
- merge_config(FLAGS.opt)
-
- # check if set use_gpu=True in paddlepaddle cpu version
- check_gpu(cfg.use_gpu)
-
- if 'eval_feed' not in cfg:
- eval_feed = create(main_arch + 'EvalFeed')
- else:
- eval_feed = create(cfg.eval_feed)
-
- # define executor
- place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
- exe = fluid.Executor(place)
-
- # build program
- model = create(main_arch)
- startup_prog = fluid.Program()
- eval_prog = fluid.Program()
- with fluid.program_guard(eval_prog, startup_prog):
- with fluid.unique_name.guard():
- _, feed_vars = create_feed(eval_feed, use_pyreader=False)
- fetches = model.eval(feed_vars)
-
- eval_prog = eval_prog.clone(True)
-
- # load model
- exe.run(startup_prog)
- if 'weights' in cfg:
- checkpoint.load_params(exe, eval_prog, cfg.weights)
-
- assert cfg.metric in ['WIDERFACE'], \
- "unknown metric type {}".format(cfg.metric)
-
- annotation_file = getattr(eval_feed.dataset, 'annotation', None)
- dataset_dir = FLAGS.dataset_dir if FLAGS.dataset_dir else \
- getattr(eval_feed.dataset, 'dataset_dir', None)
- img_root_dir = dataset_dir
- if FLAGS.eval_mode == "widerface":
- image_dir = getattr(eval_feed.dataset, 'image_dir', None)
- img_root_dir = os.path.join(dataset_dir, image_dir)
- gt_file = os.path.join(dataset_dir, annotation_file)
- pred_dir = FLAGS.output_eval if FLAGS.output_eval else 'output/pred'
- face_eval_run(
- exe,
- eval_prog,
- fetches,
- img_root_dir,
- gt_file,
- pred_dir=pred_dir,
- eval_mode=FLAGS.eval_mode,
- multi_scale=FLAGS.multi_scale)
-
-
-if __name__ == '__main__':
- parser = ArgsParser()
- parser.add_argument(
- "-d",
- "--dataset_dir",
- default=None,
- type=str,
- help="Dataset path, same as DataFeed.dataset.dataset_dir")
- parser.add_argument(
- "-f",
- "--output_eval",
- default=None,
- type=str,
- help="Evaluation file directory, default is current directory.")
- parser.add_argument(
- "-e",
- "--eval_mode",
- default="widerface",
- type=str,
- help="Evaluation mode, include `widerface` and `fddb`, default is `widerface`."
- )
- parser.add_argument(
- "--multi_scale",
- action='store_true',
- default=False,
- help="If True it will select `multi_scale` evaluation. Default is `False`, it will select `single-scale` evaluation.")
- FLAGS = parser.parse_args()
- main()
diff --git a/PaddleCV/PaddleDetection/tools/infer.py b/PaddleCV/PaddleDetection/tools/infer.py
deleted file mode 100644
index ed10814846037dcabdb301e244370848c647a61b..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/tools/infer.py
+++ /dev/null
@@ -1,269 +0,0 @@
-# Copyright (c) 2019 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
-import glob
-
-import numpy as np
-from PIL import Image
-
-
-def set_paddle_flags(**kwargs):
- for key, value in kwargs.items():
- if os.environ.get(key, None) is None:
- os.environ[key] = str(value)
-
-
-# NOTE(paddle-dev): All of these flags should be set before
-# `import paddle`. Otherwise, it would not take any effect.
-set_paddle_flags(
- FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
-)
-
-from paddle import fluid
-
-from ppdet.utils.cli import print_total_cfg
-from ppdet.core.workspace import load_config, merge_config, create
-from ppdet.modeling.model_input import create_feed
-from ppdet.data.data_feed import create_reader
-
-from ppdet.utils.eval_utils import parse_fetches
-from ppdet.utils.cli import ArgsParser
-from ppdet.utils.check import check_gpu
-from ppdet.utils.visualizer import visualize_results
-import ppdet.utils.checkpoint as checkpoint
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-
-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)
- images = []
-
- # infer_img has a higher priority
- if infer_img and os.path.isfile(infer_img):
- images.append(infer_img)
- return images
-
- 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.extend(glob.glob('{}/*.{}'.format(infer_dir, ext)))
-
- assert len(images) > 0, "no image found in {}".format(infer_dir)
- logger.info("Found {} inference images in total.".format(len(images)))
-
- return images
-
-
-def main():
- cfg = load_config(FLAGS.config)
-
- if 'architecture' in cfg:
- main_arch = cfg.architecture
- else:
- raise ValueError("'architecture' not specified in config file.")
-
- merge_config(FLAGS.opt)
-
- # check if set use_gpu=True in paddlepaddle cpu version
- check_gpu(cfg.use_gpu)
- print_total_cfg(cfg)
-
- if 'test_feed' not in cfg:
- test_feed = create(main_arch + 'TestFeed')
- else:
- test_feed = create(cfg.test_feed)
-
- test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
- test_feed.dataset.add_images(test_images)
-
- place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
- exe = fluid.Executor(place)
-
- model = create(main_arch)
-
- startup_prog = fluid.Program()
- infer_prog = fluid.Program()
- with fluid.program_guard(infer_prog, startup_prog):
- with fluid.unique_name.guard():
- _, feed_vars = create_feed(test_feed, use_pyreader=False)
- test_fetches = model.test(feed_vars)
- infer_prog = infer_prog.clone(True)
-
- reader = create_reader(test_feed)
- feeder = fluid.DataFeeder(place=place, feed_list=feed_vars.values())
-
- exe.run(startup_prog)
- if cfg.weights:
- checkpoint.load_params(exe, infer_prog, cfg.weights)
-
- # parse infer fetches
- assert cfg.metric in ['COCO', 'VOC', 'WIDERFACE'], \
- "unknown metric type {}".format(cfg.metric)
- extra_keys = []
- if cfg['metric'] == 'COCO':
- extra_keys = ['im_info', 'im_id', 'im_shape']
- if cfg['metric'] == 'VOC' or cfg['metric'] == 'WIDERFACE':
- extra_keys = ['im_id', 'im_shape']
- keys, values, _ = parse_fetches(test_fetches, infer_prog, extra_keys)
-
- # parse dataset category
- if cfg.metric == 'COCO':
- from ppdet.utils.coco_eval import bbox2out, mask2out, get_category_info
- if cfg.metric == "VOC":
- from ppdet.utils.voc_eval import bbox2out, get_category_info
- if cfg.metric == "WIDERFACE":
- from ppdet.utils.widerface_eval_utils import bbox2out, get_category_info
-
- anno_file = getattr(test_feed.dataset, 'annotation', None)
- with_background = getattr(test_feed, 'with_background', True)
- use_default_label = getattr(test_feed, 'use_default_label', False)
- clsid2catid, catid2name = get_category_info(anno_file, with_background,
- use_default_label)
-
- # whether output bbox is normalized in model output layer
- is_bbox_normalized = False
- if hasattr(model, 'is_bbox_normalized') and \
- callable(model.is_bbox_normalized):
- is_bbox_normalized = model.is_bbox_normalized()
-
- # use tb-paddle to log image
- if FLAGS.use_tb:
- from tb_paddle import SummaryWriter
- tb_writer = SummaryWriter(FLAGS.tb_log_dir)
- tb_image_step = 0
- tb_image_frame = 0 # each frame can display ten pictures at most.
-
- imid2path = reader.imid2path
- for iter_id, data in enumerate(reader()):
- outs = exe.run(infer_prog,
- feed=feeder.feed(data),
- fetch_list=values,
- return_numpy=False)
- res = {
- k: (np.array(v), v.recursive_sequence_lengths())
- for k, v in zip(keys, outs)
- }
- logger.info('Infer iter {}'.format(iter_id))
-
- bbox_results = None
- mask_results = None
- if 'bbox' in res:
- bbox_results = bbox2out([res], clsid2catid, is_bbox_normalized)
- if 'mask' in res:
- mask_results = mask2out([res], clsid2catid,
- model.mask_head.resolution)
-
- # visualize result
- im_ids = res['im_id'][0]
- for im_id in im_ids:
- image_path = imid2path[int(im_id)]
- image = Image.open(image_path).convert('RGB')
-
- # use tb-paddle to log original image
- if FLAGS.use_tb:
- original_image_np = np.array(image)
- tb_writer.add_image(
- "original/frame_{}".format(tb_image_frame),
- original_image_np,
- tb_image_step,
- dataformats='HWC')
-
- image = visualize_results(image,
- int(im_id), catid2name,
- FLAGS.draw_threshold, bbox_results,
- mask_results)
-
- # use tb-paddle to log image with bbox
- if FLAGS.use_tb:
- infer_image_np = np.array(image)
- tb_writer.add_image(
- "bbox/frame_{}".format(tb_image_frame),
- infer_image_np,
- tb_image_step,
- dataformats='HWC')
- tb_image_step += 1
- if tb_image_step % 10 == 0:
- tb_image_step = 0
- tb_image_frame += 1
-
- 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)
-
-
-if __name__ == '__main__':
- 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_tb",
- type=bool,
- default=False,
- help="whether to record the data to Tensorboard.")
- parser.add_argument(
- '--tb_log_dir',
- type=str,
- default="tb_log_dir/image",
- help='Tensorboard logging directory for image.')
- FLAGS = parser.parse_args()
- main()
diff --git a/PaddleCV/PaddleDetection/tools/train.py b/PaddleCV/PaddleDetection/tools/train.py
deleted file mode 100644
index 6d04c665ecbae873a043624a80661c385194fe9e..0000000000000000000000000000000000000000
--- a/PaddleCV/PaddleDetection/tools/train.py
+++ /dev/null
@@ -1,376 +0,0 @@
-# Copyright (c) 2019 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
-import time
-import numpy as np
-import random
-import datetime
-from collections import deque
-
-
-def set_paddle_flags(**kwargs):
- for key, value in kwargs.items():
- if os.environ.get(key, None) is None:
- os.environ[key] = str(value)
-
-
-# NOTE(paddle-dev): All of these flags should be set before
-# `import paddle`. Otherwise, it would not take any effect.
-set_paddle_flags(
- FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
-)
-
-from paddle import fluid
-from paddle.fluid import profiler
-
-from ppdet.experimental import mixed_precision_context
-from ppdet.core.workspace import load_config, merge_config, create
-from ppdet.data.data_feed import create_reader
-
-from ppdet.utils.cli import print_total_cfg
-from ppdet.utils import dist_utils
-from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results
-from ppdet.utils.stats import TrainingStats
-from ppdet.utils.cli import ArgsParser
-from ppdet.utils.check import check_gpu
-import ppdet.utils.checkpoint as checkpoint
-from ppdet.modeling.model_input import create_feed
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-
-def main():
- env = os.environ
- FLAGS.dist = 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env
- if FLAGS.dist:
- trainer_id = int(env['PADDLE_TRAINER_ID'])
- local_seed = (99 + trainer_id)
- random.seed(local_seed)
- np.random.seed(local_seed)
-
- if FLAGS.enable_ce:
- random.seed(0)
- np.random.seed(0)
-
- cfg = load_config(FLAGS.config)
- if 'architecture' in cfg:
- main_arch = cfg.architecture
- else:
- raise ValueError("'architecture' not specified in config file.")
-
- merge_config(FLAGS.opt)
-
- if 'log_iter' not in cfg:
- cfg.log_iter = 20
-
- # check if set use_gpu=True in paddlepaddle cpu version
- check_gpu(cfg.use_gpu)
- if not FLAGS.dist or trainer_id == 0:
- print_total_cfg(cfg)
-
- if cfg.use_gpu:
- devices_num = fluid.core.get_cuda_device_count()
- else:
- devices_num = int(os.environ.get('CPU_NUM', 1))
-
- if 'train_feed' not in cfg:
- train_feed = create(main_arch + 'TrainFeed')
- else:
- train_feed = create(cfg.train_feed)
-
- if FLAGS.eval:
- if 'eval_feed' not in cfg:
- eval_feed = create(main_arch + 'EvalFeed')
- else:
- eval_feed = create(cfg.eval_feed)
-
- if 'FLAGS_selected_gpus' in env:
- device_id = int(env['FLAGS_selected_gpus'])
- else:
- device_id = 0
- place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
- exe = fluid.Executor(place)
-
- lr_builder = create('LearningRate')
- optim_builder = create('OptimizerBuilder')
-
- # build program
- startup_prog = fluid.Program()
- train_prog = fluid.Program()
- if FLAGS.enable_ce:
- startup_prog.random_seed = 1000
- train_prog.random_seed = 1000
- with fluid.program_guard(train_prog, startup_prog):
- with fluid.unique_name.guard():
- model = create(main_arch)
- train_pyreader, feed_vars = create_feed(train_feed)
-
- if FLAGS.fp16:
- assert (getattr(model.backbone, 'norm_type', None)
- != 'affine_channel'), \
- '--fp16 currently does not support affine channel, ' \
- ' please modify backbone settings to use batch norm'
-
- with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx:
- train_fetches = model.train(feed_vars)
-
- loss = train_fetches['loss']
- if FLAGS.fp16:
- loss *= ctx.get_loss_scale_var()
- lr = lr_builder()
- optimizer = optim_builder(lr)
- optimizer.minimize(loss)
- if FLAGS.fp16:
- loss /= ctx.get_loss_scale_var()
-
- # parse train fetches
- train_keys, train_values, _ = parse_fetches(train_fetches)
- train_values.append(lr)
-
- if FLAGS.eval:
- eval_prog = fluid.Program()
- with fluid.program_guard(eval_prog, startup_prog):
- with fluid.unique_name.guard():
- model = create(main_arch)
- eval_pyreader, feed_vars = create_feed(eval_feed)
- fetches = model.eval(feed_vars)
- eval_prog = eval_prog.clone(True)
-
- eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
- eval_pyreader.decorate_sample_list_generator(eval_reader, place)
-
- # parse eval fetches
- extra_keys = []
- if cfg.metric == 'COCO':
- extra_keys = ['im_info', 'im_id', 'im_shape']
- if cfg.metric == 'VOC':
- extra_keys = ['gt_box', 'gt_label', 'is_difficult']
- if cfg.metric == 'WIDERFACE':
- extra_keys = ['im_id', 'im_shape', 'gt_box']
- eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
- extra_keys)
-
- # compile program for multi-devices
- build_strategy = fluid.BuildStrategy()
- build_strategy.fuse_all_optimizer_ops = False
- build_strategy.fuse_elewise_add_act_ops = True
- # only enable sync_bn in multi GPU devices
- sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
- build_strategy.sync_batch_norm = sync_bn and devices_num > 1 \
- and cfg.use_gpu
-
- exec_strategy = fluid.ExecutionStrategy()
- # iteration number when CompiledProgram tries to drop local execution scopes.
- # Set it to be 1 to save memory usages, so that unused variables in
- # local execution scopes can be deleted after each iteration.
- exec_strategy.num_iteration_per_drop_scope = 1
- if FLAGS.dist:
- dist_utils.prepare_for_multi_process(exe, build_strategy, startup_prog,
- train_prog)
- exec_strategy.num_threads = 1
-
- exe.run(startup_prog)
- compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
- loss_name=loss.name,
- build_strategy=build_strategy,
- exec_strategy=exec_strategy)
-
- if FLAGS.eval:
- compiled_eval_prog = fluid.compiler.CompiledProgram(eval_prog)
-
- fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
-
- ignore_params = cfg.finetune_exclude_pretrained_params \
- if 'finetune_exclude_pretrained_params' in cfg else []
-
- start_iter = 0
- if FLAGS.resume_checkpoint:
- checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
- start_iter = checkpoint.global_step()
- elif cfg.pretrain_weights and fuse_bn and not ignore_params:
- checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
- elif cfg.pretrain_weights:
- checkpoint.load_params(
- exe, train_prog, cfg.pretrain_weights, ignore_params=ignore_params)
-
- train_reader = create_reader(train_feed, (cfg.max_iters - start_iter) *
- devices_num, FLAGS.dataset_dir)
- train_pyreader.decorate_sample_list_generator(train_reader, place)
-
- # whether output bbox is normalized in model output layer
- is_bbox_normalized = False
- if hasattr(model, 'is_bbox_normalized') and \
- callable(model.is_bbox_normalized):
- is_bbox_normalized = model.is_bbox_normalized()
-
- # if map_type not set, use default 11point, only use in VOC eval
- map_type = cfg.map_type if 'map_type' in cfg else '11point'
-
- train_stats = TrainingStats(cfg.log_smooth_window, train_keys)
- train_pyreader.start()
- start_time = time.time()
- end_time = time.time()
-
- cfg_name = os.path.basename(FLAGS.config).split('.')[0]
- save_dir = os.path.join(cfg.save_dir, cfg_name)
- time_stat = deque(maxlen=cfg.log_smooth_window)
- best_box_ap_list = [0.0, 0] #[map, iter]
-
- # use tb-paddle to log data
- if FLAGS.use_tb:
- from tb_paddle import SummaryWriter
- tb_writer = SummaryWriter(FLAGS.tb_log_dir)
- tb_loss_step = 0
- tb_mAP_step = 0
-
- for it in range(start_iter, cfg.max_iters):
- start_time = end_time
- end_time = time.time()
- time_stat.append(end_time - start_time)
- time_cost = np.mean(time_stat)
- eta_sec = (cfg.max_iters - it) * time_cost
- eta = str(datetime.timedelta(seconds=int(eta_sec)))
- outs = exe.run(compiled_train_prog, fetch_list=train_values)
- stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
-
- # use tb-paddle to log loss
- if FLAGS.use_tb:
- if it % cfg.log_iter == 0:
- for loss_name, loss_value in stats.items():
- tb_writer.add_scalar(loss_name, loss_value, tb_loss_step)
- tb_loss_step += 1
-
- train_stats.update(stats)
- logs = train_stats.log()
- if it % cfg.log_iter == 0 and (not FLAGS.dist or trainer_id == 0):
- strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
- it, np.mean(outs[-1]), logs, time_cost, eta)
- logger.info(strs)
-
- #only for continuous evaluation
- if FLAGS.enable_ce and it == cfg.max_iters - 1:
- print("kpis\t{}_train_loss\t{}".format(cfg.architecture, stats['loss']))
- print("kpis\t{}_train_time\t{}".format(cfg.architecture, time_cost))
-
- # profiler tools, used for benchmark
- if FLAGS.is_profiler and it == 5:
- profiler.start_profiler("All")
- elif FLAGS.is_profiler and it == 10:
- profiler.stop_profiler("total", FLAGS.profiler_path)
- return
-
- if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
- and (not FLAGS.dist or trainer_id == 0):
- save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
- checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
-
- if FLAGS.eval:
- # evaluation
- results = eval_run(exe, compiled_eval_prog, eval_pyreader,
- eval_keys, eval_values, eval_cls)
- resolution = None
- if 'mask' in results[0]:
- resolution = model.mask_head.resolution
- box_ap_stats = eval_results(
- results, eval_feed, cfg.metric, cfg.num_classes, resolution,
- is_bbox_normalized, FLAGS.output_eval, map_type)
-
- # use tb_paddle to log mAP
- if FLAGS.use_tb:
- tb_writer.add_scalar("mAP", box_ap_stats[0], tb_mAP_step)
- tb_mAP_step += 1
-
- if box_ap_stats[0] > best_box_ap_list[0]:
- best_box_ap_list[0] = box_ap_stats[0]
- best_box_ap_list[1] = it
- checkpoint.save(exe, train_prog,
- os.path.join(save_dir, "best_model"))
- logger.info("Best test box ap: {}, in iter: {}".format(
- best_box_ap_list[0], best_box_ap_list[1]))
-
- train_pyreader.reset()
-
-
-if __name__ == '__main__':
- parser = ArgsParser()
- parser.add_argument(
- "-r",
- "--resume_checkpoint",
- default=None,
- type=str,
- help="Checkpoint path for resuming training.")
- parser.add_argument(
- "--fp16",
- action='store_true',
- default=False,
- help="Enable mixed precision training.")
- parser.add_argument(
- "--loss_scale",
- default=8.,
- type=float,
- help="Mixed precision training loss scale.")
- parser.add_argument(
- "--eval",
- action='store_true',
- default=False,
- help="Whether to perform evaluation in train")
- parser.add_argument(
- "--output_eval",
- default=None,
- type=str,
- help="Evaluation directory, default is current directory.")
- parser.add_argument(
- "-d",
- "--dataset_dir",
- default=None,
- type=str,
- help="Dataset path, same as DataFeed.dataset.dataset_dir")
- parser.add_argument(
- "--use_tb",
- type=bool,
- default=False,
- help="whether to record the data to Tensorboard.")
- parser.add_argument(
- '--tb_log_dir',
- type=str,
- default="tb_log_dir/scalar",
- help='Tensorboard logging directory for scalar.')
- parser.add_argument(
- '--enable_ce',
- type=bool,
- default=False,
- help="If set True, enable continuous evaluation job."
- "This flag is only used for internal test.")
-
- #NOTE:args for profiler tools, used for benchmark
- parser.add_argument(
- '--is_profiler',
- type=int,
- default=0,
- help='The switch of profiler tools. (used for benchmark)')
- parser.add_argument(
- '--profiler_path',
- type=str,
- default="./",
- help='The profiler output file path. (used for benchmark)')
- FLAGS = parser.parse_args()
- main()
diff --git a/PaddleCV/Research/AGEchallenge/Classification/1. PrepareData.ipynb b/PaddleCV/Research/AGEchallenge/Classification/1. PrepareData.ipynb
deleted file mode 100644
index 438786023ccd0e83bf29163079ab0c5b2f931e12..0000000000000000000000000000000000000000
--- a/PaddleCV/Research/AGEchallenge/Classification/1. PrepareData.ipynb
+++ /dev/null
@@ -1,368 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# [Angle closure Glaucoma Evaluation Challenge](https://age.grand-challenge.org/Details/)\n",
- "## Angle closure classification Baseline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## requirement install"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install xlrd"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Zip File Extract\n",
- "\n",
- "Assume `Training100.zip` and `Validation_ASOCT_Image.zip` are stored @ `./AGE_challenge Baseline/datasets/`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!unzip -q ../datasets/Training100.zip -d ../datasets/\n",
- "!unzip -q ../datasets/Validation_ASOCT_Image.zip -d ../datasets/"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Explore Data & Train/Val split"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import csv\n",
- "import matplotlib.pyplot as plt\n",
- "import cv2\n",
- "import os, shutil\n",
- "import pprint\n",
- "import pandas as pd\n",
- "\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "data_root_path = \"../datasets/Training100/\"\n",
- "xlsx_file_path = os.path.join(data_root_path, \"Training100_Location.xlsx\")\n",
- "\n",
- "# Load\n",
- "image_path = os.path.join(data_root_path, \"ASOCT_Image\")\n",
- "label_file_path = os.path.join(data_root_path, \"train_cls.csv\")\n",
- "\n",
- "# Save\n",
- "train_file_path = os.path.join(data_root_path, \"cls_train_split.csv\")\n",
- "val_file_path = os.path.join(data_root_path, \"cls_val_split.csv\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
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- " \n",
- " \n",
- " | \n",
- " ASOCT_Name | \n",
- " Left_Label | \n",
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- " Right_Label | \n",
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- " \n",
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