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jblitzar
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github-python-corpus

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# 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
#from scipy.misc import imread, imsave, imshow, imresize
import tensorflow as tf
from net import xdet_body
from utility import train_helper
from dataset import dataset_factory
from preprocessing import preprocessing_factory
from preprocessing import anchor_manipulator
# hardware related configuration
tf.app.flags.DEFINE_integer(
'num_readers', 16,
'The number of parallel readers that read data from the dataset.')
tf.app.flags.DEFINE_integer(
'num_preprocessing_threads', 48,
'The number of threads used to create the batches.')
tf.app.flags.DEFINE_integer(
'num_cpu_threads', 0,
'The number of cpu cores used to train.')
tf.app.flags.DEFINE_float(
'gpu_memory_fraction', 1., 'GPU memory fraction to use.')
# scaffold related configuration
tf.app.flags.DEFINE_string(
'data_dir', '../PASCAL/VOC_TF/VOC0712TF/',
'The directory where the dataset input data is stored.')
tf.app.flags.DEFINE_string(
'dataset_name', 'pascalvoc_0712', 'The name of the dataset to load.')
tf.app.flags.DEFINE_integer(
'num_classes', 21, 'Number of classes to use in the dataset.')
tf.app.flags.DEFINE_string(
'dataset_split_name', 'train', 'The name of the train/test split.')
tf.app.flags.DEFINE_string(
'model_dir', './logs/',
'The directory where the model will be stored.')
tf.app.flags.DEFINE_integer(
'log_every_n_steps', 10,
'The frequency with which logs are print.')
tf.app.flags.DEFINE_integer(
'save_summary_steps', 500,
'The frequency with which summaries are saved, in seconds.')
tf.app.flags.DEFINE_integer(
'save_checkpoints_secs', 7200,
'The frequency with which the model is saved, in seconds.')
# model related configuration
tf.app.flags.DEFINE_integer(
'train_image_size', 320,
'The size of the input image for the model to use.')
tf.app.flags.DEFINE_integer(
'resnet_size', 50,
'The size of the ResNet model to use.')
tf.app.flags.DEFINE_integer(
'train_epochs', None,
'The number of epochs to use for training.')
tf.app.flags.DEFINE_integer(
'batch_size', 16,
'Batch size for training and evaluation.')
tf.app.flags.DEFINE_string(
'data_format', 'channels_first', # 'channels_first' or 'channels_last'
'A flag to override the data format used in the model. channels_first '
'provides a performance boost on GPU but is not always compatible '
'with CPU. If left unspecified, the data format will be chosen '
'automatically based on whether TensorFlow was built for CPU or GPU.')
tf.app.flags.DEFINE_float(
'negative_ratio', 3., 'Negative ratio in the loss function.')
tf.app.flags.DEFINE_float(
'match_threshold', 0.6, 'Matching threshold in the loss function.')
tf.app.flags.DEFINE_float(
'neg_threshold', 0.4, 'Matching threshold for the negtive examples in the loss function.')
# optimizer related configuration
tf.app.flags.DEFINE_float(
'weight_decay', 0.0005, 'The weight decay on the model weights.')
tf.app.flags.DEFINE_float(
'momentum', 0.9,
'The momentum for the MomentumOptimizer and RMSPropOptimizer.')
tf.app.flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')
tf.app.flags.DEFINE_float(
'end_learning_rate', 0.0001,
'The minimal end learning rate used by a polynomial decay learning rate.')
# for learning rate exponential_decay