# Copyright 2018 Changan Wang
# 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_v2
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_v2/',
'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', 304,
'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', 0.5, 'Matching threshold in the loss function.')#0.6
tf.app.flags.DEFINE_float(
'neg_threshold', 0.5, 'Matching threshold for the negtive examples in the loss function.')#0.4
# optimizer related configuration
tf.app.flags.DEFINE_float(
'weight_decay', 0.0002, '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', 0.002, 'Initial learning rate.')#0.001
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
tf.app.flags.DEFINE_float(
'learning_rate_decay_factor', 0.96, 'Learning rate decay factor.')
tf.app.flags.DEFINE_float(
'decay_steps', 1000,
'Number of epochs after which learning rate decays.')
# for learning rate piecewise_constant decay
tf.app.flags.DEFINE_string(
'decay_boundaries', '70000, 90000',
'Learning rate decay boundaries by global_step (comma-separated list).')
tf.app.flags.DEFINE_string(
'lr_decay_factors', '1, 0.8, 0.1',
'The values of learning_rate decay factor for each segment between boundaries (comma-separated list).')
# checkpoint related configuration
tf.app.flags.DEFINE_string(
'checkpoint_path', './model/resnet50',#None,
'The path to a checkpoint from which to fine-tune.')
tf.app.flags.DEFINE_string(
'checkpoint_model_scope', '',
'Model scope in the checkpoint. None if the same as the trained model.')
tf.app.flags.DEFINE_string(
'model_scope', 'xdet_resnet',
'Model scope name used to replace the name_scope in checkpoint.')
tf.app.flags.DEFINE_string(
'checkpoint_exclude_scopes', 'xdet_resnet/xdet_head, xdet_resnet/xdet_multi_path, xdet_resnet/xdet_additional_conv',#None
'Comma-separated list of scopes of variables to exclude when restoring from a checkpoint.')
tf.app.flags.DEFINE_boolean(
'ignore_missing_vars', True,
'When restoring a checkpoint would ignore missing variables.')
tf.app.flags.DEFINE_boolean(
'run_on_cloud', True,
'Wether we will train on cloud (pre-trained model will be placed in the "data_dir/cloud_checkpoint_path").')
tf.app.flags.DEFINE_string(
'cloud_checkpoint_path', 'resnet50',
'The path to a checkpoint from which to fine-tune.')
FLAGS = tf.app.flags.FLAGS
def input_pipeline():
image_preprocessing_fn = lambda image_, shape_, glabels_, gbboxes_ : preprocessing_factory.get_preprocessing(
'xdet_resnet', is_training=True)(image_, glabels_, gbboxes_, out_shape=[FLAGS.train_image_size] * 2, data_format=('NCHW' if FLAGS.data_format=='channels_first' else 'NHWC'))
anchor_creator = anchor_manipulator.AnchorCreator([FLAGS.train_image_size] * 2,
layers_shapes = [(38, 38)],
anchor_scales = [[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]],
extra_anchor_scales = [[0.1]],
anchor_ratios = [[1., 2., 3., .5, 0.3333]],
layer_steps = [8])
def input_fn():
all_anchors, num_anchors_list = anchor_creator.get_all_anchors()
anchor_encoder_decoder = anchor_manipulator.AnchorEncoder(all_anchors,
num_classes = FLAGS.num_classes,
allowed_borders = [0.05],
positive_threshold = FLAGS.match_threshold,
ignore_threshold = FLAGS.neg_threshold,
prior_scaling=[0.1, 0.1, 0.2, 0.2])
list_from_batch, _ = dataset_factory.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.data_dir,
image_preprocessing_fn,
file_pattern = None,
reader = None,
batch_size = FLAGS.batch_size,
num_readers = FLAGS.num_readers,
num_preprocessing_threads = FLAGS.num_preprocessing_threads,
num_epochs = FLAGS.train_epochs,
anchor_encoder = anchor_encoder_decoder.encode_all_anchors)
return list_from_batch[-1], {'targets': list_from_batch[:-1],
'decode_fn': lambda pred : anchor_encoder_decoder.decode_all_anchors([pred])[0],
'num_anchors_list': num_anchors_list}
return input_fn
def modified_smooth_l1(bbox_pred, bbox_targets, bbox_inside_weights = 1., bbox_outside_weights = 1., sigma = 1.):
"""
ResultLoss = outside_weights * SmoothL1(inside_weights * (bbox_pred - bbox_targets))
SmoothL1(x) = 0.5 * (sigma * x)^2, if |x| < 1 / sigma^2
|x| - 0.5 / sigma^2, otherwise
"""
sigma2 = sigma * sigma
inside_mul = tf.multiply(bbox_inside_weights, tf.subtract(bbox_pred, bbox_targets))
smooth_l1_sign = tf.cast(tf.less(tf.abs(inside_mul), 1.0 / sigma2), tf.float32)
smooth_l1_option1 = tf.multiply(tf.multiply(inside_mul, inside_mul), 0.5 * sigma2)
smooth_l1_option2 = tf.subtract(tf.abs(inside_mul), 0.5 / sigma2)
smooth_l1_result = tf.add(tf.multiply(smooth_l1_option1, smooth_l1_sign),
tf.multiply(smooth_l1_option2, tf.abs(tf.subtract(smooth_l1_sign, 1.0))))
outside_mul = tf.multiply(bbox_outside_weights, smooth_l1_result)
return outside_mul
def xdet_model_fn(features, labels, mode, params):
"""Our model_fn for ResNet to be used with our Estimator."""
num_anchors_list = labels['num_anchors_list']
num_feature_layers = len(num_anchors_list)
shape = labels['targets'][-1]
glabels = labels['targets'][:num_feature_layers][0]
gtargets = labels['targets'][num_feature_layers : 2 * num_feature_layers][0]
gscores = labels['targets'][2 * num_feature_layers : 3 * num_feature_layers][0]
with tf.variable_scope(params['model_scope'], default_name = None, values = [features], reuse=tf.AUTO_REUSE):
backbone = xdet_body_v2.xdet_resnet_v2(params['resnet_size'], params['data_format'])
body_cls_output, body_regress_output = backbone(inputs=features, is_training=(mode == tf.estimator.ModeKeys.TRAIN))
cls_pred, location_pred = xdet_body_v2.xdet_head(body_cls_output, body_regress_output, params['num_classes'], num_anchors_list[0], (mode == tf.estimator.ModeKeys.TRAIN), data_format=params['data_format'])
if params['data_format'] == 'channels_first':
cls_pred = tf.transpose(cls_pred, [0, 2, 3, 1])
location_pred = tf.transpose(location_pred, [0, 2, 3, 1])
bboxes_pred = labels['decode_fn'](location_pred)#(tf.reshape(location_pred, tf.shape(location_pred).as_list()[0:-1] + [-1, 4]))
cls_pred = tf.reshape(cls_pred, [-1, params['num_classes']])
location_pred = tf.reshape(location_pred, [-1, 4])
glabels = tf.reshape(glabels, [-1])
gscores = tf.reshape(gscores, [-1])
gtargets = tf.reshape(gtargets, [-1, 4])
# raw mask for positive > 0.5, and for negetive < 0.3
# each positive examples has one label
positive_mask = glabels > 0#tf.logical_and(glabels > 0, gscores > params['match_threshold'])
fpositive_mask = tf.cast(positive_mask, tf.float32)
n_positives = tf.reduce_sum(fpositive_mask)
batch_glabels = tf.reshape(glabels, [tf.shape(features)[0], -1])
batch_n_positives = tf.count_nonzero(batch_glabels, -1)
batch_negtive_mask = tf.equal(batch_glabels, 0)
batch_n_negtives = tf.count_nonzero(batch_negtive_mask, -1)
batch_n_neg_select = tf.cast(params['negative_ratio'] * tf.cast(batch_n_positives, tf.float32), tf.int32)
batch_n_neg_select = tf.minimum(batch_n_neg_select, tf.cast(batch_n_negtives, tf.int32))
# hard negative mining for classification
predictions_for_bg = tf.nn.softmax(tf.reshape(cls_pred, [tf.shape(features)[0], -1, params['num_classes']]))[:, :, 0]
prob_for_negtives = tf.where(batch_negtive_mask,
0. - predictions_for_bg,
# ignore all the positives
0. - tf.ones_like(predictions_for_bg))
topk_prob_for_bg, _ = tf.nn.top_k(prob_for_negtives, k=tf.shape(prob_for_negtives)[1])
score_at_k = tf.gather_nd(topk_prob_for_bg, tf.stack([tf.range(tf.shape(features)[0]), batch_n_neg_select - 1], axis=-1))
selected_neg_mask = prob_for_negtives >= tf.expand_dims(score_at_k, axis=-1)
negtive_mask = tf.reshape(tf.logical_and(batch_negtive_mask, selected_neg_mask), [-1])#tf.logical_and(tf.equal(glabels, 0), gscores > 0.)
#negtive_mask = tf.logical_and(tf.logical_and(tf.logical_not(positive_mask), gscores < params['neg_threshold']), gscores > 0.)
#negtive_mask = tf.logical_and(gscores < params['neg_threshold'], tf.logical_not(positive_mask))
# # random select negtive examples for classification
# selected_neg_mask = tf.random_uniform(tf.shape(gscores), minval=0, maxval=1.) < tf.where(
# tf.greater(n_negtives, 0),
# tf.divide(tf.cast(n_neg_to_select, tf.float32), n_negtives),
# tf.zeros_like(tf.cast(n_neg_to_select, tf.float32)),
# name='rand_select_negtive')
# include both selected negtive and all positive examples
final_mask = tf.stop_gradient(tf.logical_or(negtive_mask, positive_mask))
total_examples = tf.reduce_sum(tf.cast(final_mask, tf.float32))
# add mask for glabels and cls_pred here
glabels = tf.boolean_mask(tf.clip_by_value(glabels, 0, params['num_classes']), tf.stop_gradient(final_mask))
cls_pred = tf.boolean_mask(cls_pred, tf.stop_gradient(final_mask))
location_pred = tf.boolean_mask(location_pred, tf.stop_gradient(positive_mask))
gtargets = tf.boolean_mask(gtargets, tf.stop_gradient(positive_mask))
predictions = {
'classes': tf.argmax(cls_pred, axis=-1),
'probabilities': tf.reduce_max(tf.nn.softmax(cls_pred, name='softmax_tensor'), axis=-1),
'bboxes_predict': tf.reshape(bboxes_pred, [-1, 4]) }
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Calculate loss, which includes softmax cross entropy and L2 regularization.
cross_entropy = tf.cond(n_positives > 0., lambda: tf.losses.sparse_softmax_cross_entropy(labels=glabels, logits=cls_pred), lambda: 0.)
#cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=glabels, logits=cls_pred)
# Create a tensor named cross_entropy for logging purposes.
tf.identity(cross_entropy, name='cross_entropy_loss')
tf.summary.scalar('cross_entropy_loss', cross_entropy)
loc_loss = tf.cond(n_positives > 0., lambda: modified_smooth_l1(location_pred, tf.stop_gradient(gtargets), sigma=1.), lambda: tf.zeros_like(location_pred))
#loc_loss = modified_smooth_l1(location_pred, tf.stop_gradient(gtargets))
loc_loss = tf.reduce_mean(tf.reduce_sum(loc_loss, axis=-1))
loc_loss = tf.identity(loc_loss, name='location_loss')
tf.summary.scalar('location_loss', loc_loss)
tf.losses.add_loss(loc_loss)
# Add weight decay to the loss. We exclude the batch norm variables because
# doing so leads to a small improvement in accuracy.
loss = 1.2 * (cross_entropy + loc_loss) + params['weight_decay'] * tf.add_n(
[tf.nn.l2_loss(v) for v in tf.trainable_variables()
if 'batch_normalization' not in v.name])
total_loss = tf.identity(loss, name='total_loss')
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_or_create_global_step()
lr_values = [params['learning_rate'] * decay for decay in params['lr_decay_factors']]
learning_rate = tf.train.piecewise_constant(tf.cast(global_step, tf.int32),
[int(_) for _ in params['decay_boundaries']],
lr_values)
truncated_learning_rate = tf.maximum(learning_rate, tf.constant(params['end_learning_rate'], dtype=learning_rate.dtype))
# Create a tensor named learning_rate for logging purposes.
tf.identity(truncated_learning_rate, name='learning_rate')
tf.summary.scalar('learning_rate', truncated_learning_rate)
optimizer = tf.train.MomentumOptimizer(learning_rate=truncated_learning_rate,
momentum=params['momentum'])
# Batch norm requires update_ops to be added as a train_op dependency.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss, global_step)
else:
train_op = None
cls_accuracy = tf.metrics.accuracy(glabels, predictions['classes'])
metrics = {'cls_accuracy': cls_accuracy}
# Create a tensor named train_accuracy for logging purposes.
tf.identity(cls_accuracy[1], name='cls_accuracy')
tf.summary.scalar('cls_accuracy', cls_accuracy[1])
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=metrics,
scaffold = tf.train.Scaffold(init_fn=train_helper.get_init_fn_for_scaffold(FLAGS)))
def parse_comma_list(args):
return [float(s.strip()) for s in args.split(',')]
def main(_):
# Using the Winograd non-fused algorithms provides a small performance boost.
os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = FLAGS.gpu_memory_fraction)
config = tf.ConfigProto(allow_soft_placement = True, log_device_placement = False, intra_op_parallelism_threads = FLAGS.num_cpu_threads, inter_op_parallelism_threads = FLAGS.num_cpu_threads, gpu_options = gpu_options)
# Set up a RunConfig to only save checkpoints once per training cycle.
run_config = tf.estimator.RunConfig().replace(
save_checkpoints_secs=FLAGS.save_checkpoints_secs).replace(
save_checkpoints_steps=None).replace(
save_summary_steps=FLAGS.save_summary_steps).replace(
keep_checkpoint_max=5).replace(
log_step_count_steps=FLAGS.log_every_n_steps).replace(
session_config=config)
xdetector = tf.estimator.Estimator(
model_fn=xdet_model_fn, model_dir=FLAGS.model_dir, config=run_config,
params={
'resnet_size': FLAGS.resnet_size,
'data_format': FLAGS.data_format,
'model_scope': FLAGS.model_scope,
'num_classes': FLAGS.num_classes,
'negative_ratio': FLAGS.negative_ratio,
'match_threshold': FLAGS.match_threshold,
'neg_threshold': FLAGS.neg_threshold,
'weight_decay': FLAGS.weight_decay,
'momentum': FLAGS.momentum,
'learning_rate': FLAGS.learning_rate,
'end_learning_rate': FLAGS.end_learning_rate,
'learning_rate_decay_factor': FLAGS.learning_rate_decay_factor,
'decay_steps': FLAGS.decay_steps,
'decay_boundaries': parse_comma_list(FLAGS.decay_boundaries),
'lr_decay_factors': parse_comma_list(FLAGS.lr_decay_factors),
})
tensors_to_log = {
'lr': 'learning_rate',
'ce_loss': 'cross_entropy_loss',
'loc_loss': 'location_loss',
'total_loss': 'total_loss',
'cls_acc': 'cls_accuracy',
}
logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=FLAGS.log_every_n_steps)
print('Starting a training cycle.')
xdetector.train(input_fn=input_pipeline(), hooks=[logging_hook])
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run()
# 0.5, 0.5
# INFO:tensorflow:loc_loss = 0.675493, total_loss = 3.02218, ce_loss = 0.556885, cls_acc = 0.803118, lr = 0.002 (14.741 sec)
# 2018-05-06 17:15:47,413 INFO (MainThread-421) loc_loss = 0.675493, total_loss = 3.02218, ce_loss = 0.556885, cls_acc = 0.803118, lr = 0.002 (14.741 sec)
# INFO:tensorflow:step = 119401, loss = 1.61156 (163.394 sec)
# 2018-05-08 19:01:31,997 INFO (MainThread-421) step = 119401, loss = 1.61156 (163.394 sec)
# INFO:tensorflow:global_step/sec: 0.673582
# 2018-05-08 19:01:46,842 INFO (MainThread-421) global_step/sec: 0.673582
# INFO:tensorflow:loc_loss = 0.315306, total_loss = 1.64389, ce_loss = 0.235289, cls_acc = 0.873122, lr = 0.0002 (14.846 sec)
# 2018-05-08 19:01:46,843 INFO (MainThread-421) loc_loss = 0.315306, total_loss = 1.64389, ce_loss = 0.235289, cls_acc = 0.873122, lr = 0.0002 (14.846 sec)
# INFO:tensorflow:global_step/sec: 0.726808
# 2018-05-08 19:02:00,601 INFO (MainThread-421) global_step/sec: 0.726808
# INFO:tensorflow:loc_loss = 0.423853, total_loss = 1.852, ce_loss = 0.300177, cls_acc = 0.873125, lr = 0.0002 (13.759 sec)
# 2018-05-08 19:02:00,602 INFO (MainThread-421) loc_loss = 0.423853, total_loss = 1.852, ce_loss = 0.300177, cls_acc = 0.873125, lr = 0.0002 (13.759 sec)
# INFO:tensorflow:global_step/sec: 0.615656
# 2018-05-08 19:02:16,844 INFO (MainThread-421) global_step/sec: 0.615656
# INFO:tensorflow:loc_loss = 0.218686, total_loss = 1.50142, ce_loss = 0.213196, cls_acc = 0.873128, lr = 0.0002 (16.243 sec)
# 2018-05-08 19:02:16,844 INFO (MainThread-421) loc_loss = 0.218686, total_loss = 1.50142, ce_loss = 0.213196, cls_acc = 0.873128, lr = 0.0002 (16.243 sec)
