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train.py
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train.py
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import argparse
import copy
import cPickle as pickle
import logging
import numpy as np
import pprint
import tensorflow as tf
import tensorflow.contrib.slim as slim
from config import parse_config_file
import train_inputs
#from train_inputs_precomputed import input_nodes
import loss
import model
def train(tfrecords, logdir, cfg, pretrained_model_path=None):
"""
Args:
tfrecords (list)
bbox_priors (np.array)
logdir (str)
cfg (EasyDict)
pretrained_model_path (str) : path to a pretrained Inception Network
"""
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
graph = tf.Graph()
# Force all Variables to reside on the CPU.
with graph.as_default():
# Create a variable to count the number of train() calls.
global_step = slim.get_or_create_global_step()
# Calculate the learning rate schedule.
num_batches_per_epoch = (cfg.NUM_TRAIN_EXAMPLES /
cfg.BATCH_SIZE)
decay_steps = int(num_batches_per_epoch * cfg.NUM_EPOCHS_PER_DELAY)
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(
learning_rate=cfg.INITIAL_LEARNING_RATE,
global_step=global_step,
decay_steps=decay_steps,
decay_rate=cfg.LEARNING_RATE_DECAY_FACTOR,
staircase=cfg.LEARNING_RATE_STAIRCASE
)
# Create an optimizer that performs gradient descent.
optimizer = tf.train.RMSPropOptimizer(
learning_rate=lr,
decay=cfg.RMSPROP_DECAY,
momentum=cfg.RMSPROP_MOMENTUM,
epsilon=cfg.RMSPROP_EPSILON
)
batched_images, batched_heatmaps, batched_parts, batched_part_visibilities, batched_image_ids, batched_background_heatmaps = train_inputs.input_nodes(
tfrecords,
cfg.PARTS.NUM_PARTS,
num_epochs=None,
batch_size=cfg.BATCH_SIZE,
num_threads=cfg.NUM_INPUT_THREADS,
shuffle_batch = True,
capacity = cfg.QUEUE_CAPACITY,
min_after_dequeue = cfg.QUEUE_MIN,
add_summaries = True,
cfg=cfg
)
input_summaries = copy.copy(tf.get_collection(tf.GraphKeys.SUMMARIES))
batch_norm_params = {
'decay': cfg.BATCHNORM_MOVING_AVERAGE_DECAY,
'epsilon': 0.001,
'variables_collections' : [tf.GraphKeys.MOVING_AVERAGE_VARIABLES],
'is_training' : True
}
# Set activation_fn and parameters for batch_norm.
with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
weights_regularizer=slim.l2_regularizer(0.00004),
biases_regularizer=slim.l2_regularizer(0.00004)) as scope:
predicted_heatmaps = model.build(
input = batched_images,
num_parts = cfg.PARTS.NUM_PARTS
)
heatmap_loss, loss_summaries = loss.add_heatmaps_loss(batched_heatmaps, predicted_heatmaps, batched_background_heatmaps, True, cfg)
total_loss = slim.losses.get_total_loss()
# Track the moving averages of all trainable variables.
# At test time we'll restore all variables with the average value
# Note that we maintain a "double-average" of the BatchNormalization
# global statistics. This is more complicated then need be but we employ
# this for backward-compatibility with our previous models.
ema = tf.train.ExponentialMovingAverage(
decay=cfg.MOVING_AVERAGE_DECAY,
num_updates=global_step
)
variables_to_average = (slim.get_model_variables())
maintain_averages_op = ema.apply(variables_to_average)
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, maintain_averages_op)
train_op = slim.learning.create_train_op(total_loss, optimizer)
# Summary operations
summary_op = tf.merge_summary([
tf.scalar_summary('total_loss', total_loss),
tf.scalar_summary('total_heatmap_loss', heatmap_loss),
tf.scalar_summary('learning_rate', lr)
] + input_summaries + loss_summaries)
if pretrained_model_path != None:
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(pretrained_model_path, inception_vars)
else:
init_assign_op = tf.no_op()
init_feed_dict = {}
# Create an initial assignment function.
def InitAssignFn(sess):
sess.run(init_assign_op, init_feed_dict)
sess_config = tf.ConfigProto(
log_device_placement=False,
#device_filters = device_filters,
allow_soft_placement = True,
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=cfg.SESSION_CONFIG.PER_PROCESS_GPU_MEMORY_FRACTION
)
)
saver = tf.train.Saver(
# Save all variables
max_to_keep = cfg.MAX_TO_KEEP,
keep_checkpoint_every_n_hours = cfg.KEEP_CHECKPOINT_EVERY_N_HOURS
)
# Run training.
slim.learning.train(train_op, logdir,
init_fn=InitAssignFn,
number_of_steps=cfg.NUM_TRAIN_ITERATIONS,
save_summaries_secs=cfg.SAVE_SUMMARY_SECS,
save_interval_secs=cfg.SAVE_INTERVAL_SECS,
saver=saver,
session_config=sess_config,
summary_op = summary_op,
log_every_n_steps = cfg.LOG_EVERY_N_STEPS
)
def parse_args():
parser = argparse.ArgumentParser(description='Train the multibox detection system')
parser.add_argument('--tfrecords', dest='tfrecords',
help='paths to tfrecords files that contain the training data', type=str,
nargs='+', required=True)
# parser.add_argument('--priors', dest='priors',
# help='path to the bounding box priors pickle file', type=str,
# required=True)
parser.add_argument('--logdir', dest='logdir',
help='path to directory to store summary files and checkpoint files', type=str,
required=True)
parser.add_argument('--config', dest='config_file',
help='Path to the configuration file',
required=True, type=str)
parser.add_argument('--pretrained_model', dest='pretrained_model',
help='Is this the first iteration? If so pass a full path to a pretrained Inception-v3 model.',
required=False, type=str, default=None)
args = parser.parse_args()
return args
def main():
args = parse_args()
print "Command line arguments:"
pprint.pprint(vars(args))
print
cfg = parse_config_file(args.config_file)
print "Configurations:"
pprint.pprint(cfg)
print
# with open(args.priors) as f:
# bbox_priors = pickle.load(f)
# bbox_priors = np.array(bbox_priors).astype(np.float32)
train(
tfrecords=args.tfrecords,
#bbox_priors=bbox_priors,
logdir=args.logdir,
cfg=cfg,
pretrained_model_path=args.pretrained_model
)
if __name__ == '__main__':
main()