rm eval_det_utils

deploy/slim/prune/eval_det_utils.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import sys
-import logging
-import numpy as np
-import paddle.fluid as fluid
-
-__dir__ = os.path.dirname(__file__)
-sys.path.append(__dir__)
-sys.path.append(os.path.join(__dir__, '..', '..', '..'))
-
-__all__ = ['eval_det_run']
-
-import logging
-FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
-logging.basicConfig(level=logging.INFO, format=FORMAT)
-logger = logging.getLogger(__name__)
-
-import cv2
-import json
-from copy import deepcopy
-from ppocr.utils.utility import create_module
-from ppocr.data.reader_main import reader_main
-from tools.eval_utils.eval_det_iou import DetectionIoUEvaluator
-
-
-def cal_det_res(exe, config, eval_info_dict):
-    global_params = config['Global']
-    save_res_path = global_params['save_res_path']
-    postprocess_params = deepcopy(config["PostProcess"])
-    postprocess_params.update(global_params)
-    postprocess = create_module(postprocess_params['function']) \
-        (params=postprocess_params)
-    if not os.path.exists(os.path.dirname(save_res_path)):
-        os.makedirs(os.path.dirname(save_res_path))
-    with open(save_res_path, "wb") as fout:
-        tackling_num = 0
-        for data in eval_info_dict['reader']():
-            img_num = len(data)
-            tackling_num = tackling_num + img_num
-            logger.info("test tackling num:%d", tackling_num)
-            img_list = []
-            ratio_list = []
-            img_name_list = []
-            for ino in range(img_num):
-                img_list.append(data[ino][0])
-                ratio_list.append(data[ino][1])
-                img_name_list.append(data[ino][2])
-            try:
-                img_list = np.concatenate(img_list, axis=0)
-            except:
-                err = "concatenate error usually caused by different input image shapes in evaluation or testing.\n \
-                Please set \"test_batch_size_per_card\" in main yml as 1\n \
-                or add \"test_image_shape: [h, w]\" in reader yml for EvalReader."
-
-                raise Exception(err)
-            outs = exe.run(eval_info_dict['program'], \
-                           feed={'image': img_list}, \
-                           fetch_list=eval_info_dict['fetch_varname_list'])
-            outs_dict = {}
-            for tno in range(len(outs)):
-                fetch_name = eval_info_dict['fetch_name_list'][tno]
-                fetch_value = np.array(outs[tno])
-                outs_dict[fetch_name] = fetch_value
-            dt_boxes_list = postprocess(outs_dict, ratio_list)
-            for ino in range(img_num):
-                dt_boxes = dt_boxes_list[ino]
-                img_name = img_name_list[ino]
-                dt_boxes_json = []
-                for box in dt_boxes:
-                    tmp_json = {"transcription": ""}
-                    tmp_json['points'] = box.tolist()
-                    dt_boxes_json.append(tmp_json)
-                otstr = img_name + "\t" + json.dumps(dt_boxes_json) + "\n"
-                fout.write(otstr.encode())
-    return
-
-
-def load_label_infor(label_file_path, do_ignore=False):
-    img_name_label_dict = {}
-    with open(label_file_path, "rb") as fin:
-        lines = fin.readlines()
-        for line in lines:
-            substr = line.decode().strip("\n").split("\t")
-            bbox_infor = json.loads(substr[1])
-            bbox_num = len(bbox_infor)
-            for bno in range(bbox_num):
-                text = bbox_infor[bno]['transcription']
-                ignore = False
-                if text == "###" and do_ignore:
-                    ignore = True
-                bbox_infor[bno]['ignore'] = ignore
-            img_name_label_dict[os.path.basename(substr[0])] = bbox_infor
-    return img_name_label_dict
-
-
-def cal_det_metrics(gt_label_path, save_res_path):
-    """
-    calculate the detection metrics
-    Args:
-        gt_label_path(string): The groundtruth detection label file path
-        save_res_path(string): The saved predicted detection label path
-    return:
-        claculated metrics including Hmean, precision and recall
-    """
-    evaluator = DetectionIoUEvaluator()
-    gt_label_infor = load_label_infor(gt_label_path, do_ignore=True)
-    dt_label_infor = load_label_infor(save_res_path)
-    results = []
-    for img_name in gt_label_infor:
-        gt_label = gt_label_infor[img_name]
-        if img_name not in dt_label_infor:
-            dt_label = []
-        else:
-            dt_label = dt_label_infor[img_name]
-        result = evaluator.evaluate_image(gt_label, dt_label)
-        results.append(result)
-    methodMetrics = evaluator.combine_results(results)
-    return methodMetrics
-
-
-def eval_det_run(eval_args, mode='eval'):
-    exe = eval_args['exe']
-    config = eval_args['config']
-    eval_info_dict = eval_args['eval_info_dict']
-    cal_det_res(exe, config, eval_info_dict)
-
-    save_res_path = config['Global']['save_res_path']
-    if mode == "eval":
-        gt_label_path = config['EvalReader']['label_file_path']
-        metrics = cal_det_metrics(gt_label_path, save_res_path)
-    else:
-        gt_label_path = config['TestReader']['label_file_path']
-        do_eval = config['TestReader']['do_eval']
-        if do_eval:
-            metrics = cal_det_metrics(gt_label_path, save_res_path)
-        else:
-            metrics = {}
-    return metrics['hmean']

deploy/slim/prune/pruning_and_finetune.py

@@ -104,14 +104,6 @@ def main():
     # compile program for multi-devices
     init_model(config, train_program, exe)
 
-    # params = get_pruned_params(train_program)
-    '''
-    sens_file = ['sensitivities_'+ str(x) for x in range(0,4)]
-    sens = []
-    for f in sens_file:
-        sens.append(load_sensitivities(f+'.data'))
-    sen = merge_sensitive(sens)
-    '''
     sen = load_sensitivities("sensitivities_0.data")
     for i in skip_list:
         sen.pop(i)
@@ -161,28 +153,7 @@ def main():
         program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
 
 
-def test_reader():
-    config = program.load_config(FLAGS.config)
-    program.merge_config(FLAGS.opt)
-    print(config)
-    train_reader = reader_main(config=config, mode="train")
-    import time
-    starttime = time.time()
-    count = 0
-    try:
-        for data in train_reader():
-            count += 1
-            if count % 1 == 0:
-                batch_time = time.time() - starttime
-                starttime = time.time()
-                print("reader:", count, len(data), batch_time)
-    except Exception as e:
-        logger.info(e)
-    logger.info("finish reader: {}, Success!".format(count))
-
-
 if __name__ == '__main__':
     parser = program.ArgsParser()
     FLAGS = parser.parse_args()
     main()
-#     test_reader()

deploy/slim/prune/sensitivity_anal.py

@@ -42,7 +42,7 @@ import cv2
 from paddle import fluid
 import paddleslim as slim
 from copy import deepcopy
-from eval_det_utils import eval_det_run
+from tools.eval_utils.eval_det_utils import eval_det_run
 
 from tools import program
 from ppocr.utils.utility import initial_logger
@@ -65,6 +65,14 @@ def get_pruned_params(program):
     return params
 
 
+def eval_function(eval_args, mode='eval'):
+    exe = eval_args['exe']
+    config = eval_args['config']
+    eval_info_dict = eval_args['eval_info_dict']
+    metrics = eval_det_run(exe, config, eval_info_dict, mode=mode)
+    return metrics['hmean']
+
+
 def main():
     config = program.load_config(FLAGS.config)
     program.merge_config(FLAGS.opt)
@@ -99,7 +107,7 @@ def main():
         'fetch_varname_list':eval_fetch_varname_list}
     eval_args = dict()
     eval_args = {'exe': exe, 'config': config, 'eval_info_dict': eval_info_dict}
-    metrics = eval_det_run(eval_args)
+    metrics = eval_function(eval_args)
     print("Baseline: {}".format(metrics))
 
     params = get_pruned_params(eval_program)
@@ -108,7 +116,7 @@ def main():
         eval_program,
         place,
         params,
-        eval_det_run,
+        eval_function,
         sensitivities_file="sensitivities_0.data",
         pruned_ratios=[0.1],
         eval_args=eval_args,