# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # #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. """ Contains common utility functions. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys import distutils.util import numpy as np import six import collections from collections import deque import datetime from paddle.fluid import core import argparse import functools from config import * def print_arguments(args): """Print argparse's arguments. Usage: .. code-block:: python parser = argparse.ArgumentParser() parser.add_argument("name", default="Jonh", type=str, help="User name.") args = parser.parse_args() print_arguments(args) :param args: Input argparse.Namespace for printing. :type args: argparse.Namespace """ print("----------- Configuration Arguments -----------") for arg, value in sorted(six.iteritems(vars(args))): print("%s: %s" % (arg, value)) print("------------------------------------------------") def add_arguments(argname, type, default, help, argparser, **kwargs): """Add argparse's argument. Usage: .. code-block:: python parser = argparse.ArgumentParser() add_argument("name", str, "Jonh", "User name.", parser) args = parser.parse_args() """ type = distutils.util.strtobool if type == bool else type argparser.add_argument( "--" + argname, default=default, type=type, help=help + ' Default: %(default)s.', **kwargs) 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 = deque(maxlen=window_size) def add_value(self, value): self.deque.append(value) def get_median_value(self): return np.median(self.deque) def now_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] = round(v.get_median_value(), 3) 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 def parse_args(): """return all args """ parser = argparse.ArgumentParser(description=__doc__) add_arg = functools.partial(add_arguments, argparser=parser) # yapf: disable # ENV add_arg('parallel', bool, True, "Whether use parallel.") add_arg('use_gpu', bool, True, "Whether use GPU.") add_arg('model_save_dir', str, 'output', "The path to save model.") add_arg('pretrained_model', str, 'imagenet_resnet50_fusebn', "The init model path.") add_arg('dataset', str, 'coco2017', "coco2014, coco2017.") add_arg('class_num', int, 81, "Class number.") add_arg('data_dir', str, 'dataset/coco', "The data root path.") add_arg('use_pyreader', bool, True, "Use pyreader.") add_arg('use_profile', bool, False, "Whether use profiler.") add_arg('padding_minibatch',bool, False, "If False, only resize image and not pad, image shape is different between" " GPUs in one mini-batch. If True, image shape is the same in one mini-batch.") #SOLVER add_arg('learning_rate', float, 0.01, "Learning rate.") add_arg('max_iter', int, 180000, "Iter number.") add_arg('log_window', int, 20, "Log smooth window, set 1 for debug, set 20 for train.") # RCNN # RPN add_arg('anchor_sizes', int, [32,64,128,256,512], "The size of anchors.") add_arg('aspect_ratios', float, [0.5,1.0,2.0], "The ratio of anchors.") add_arg('variance', float, [1.,1.,1.,1.], "The variance of anchors.") add_arg('rpn_stride', float, [16.,16.], "Stride of the feature map that RPN is attached.") add_arg('rpn_nms_thresh', float, 0.7, "NMS threshold used on RPN proposals") # TRAIN VAL INFER add_arg('MASK_ON', bool, False, "Option for different models. If False, choose faster_rcnn. If True, choose mask_rcnn") add_arg('im_per_batch', int, 1, "Minibatch size.") add_arg('max_size', int, 1333, "The resized image height.") add_arg('scales', int, [800], "The resized image height.") add_arg('batch_size_per_im',int, 512, "fast rcnn head batch size") add_arg('pixel_means', float, [102.9801, 115.9465, 122.7717], "pixel mean") add_arg('nms_thresh', float, 0.5, "NMS threshold.") add_arg('score_thresh', float, 0.05, "score threshold for NMS.") add_arg('snapshot_stride', int, 10000, "save model every snapshot stride.") # SINGLE EVAL AND DRAW add_arg('draw_threshold', float, 0.8, "Confidence threshold to draw bbox.") add_arg('image_path', str, 'dataset/coco/val2017', "The image path used to inference and visualize.") add_arg('image_name', str, '', "The single image used to inference and visualize.") # ce parser.add_argument( '--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.') # yapf: enable args = parser.parse_args() file_name = sys.argv[0] if 'train' in file_name or 'profile' in file_name: merge_cfg_from_args(args, 'train') else: merge_cfg_from_args(args, 'val') return args