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eval_seglink.py
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eval_seglink.py
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#encoding = utf-8
import numpy as np
import math
import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
from tensorflow.contrib.training.python.training import evaluation
from datasets import dataset_factory
from preprocessing import ssd_vgg_preprocessing
from tf_extended import seglink, metrics as tfe_metrics, bboxes as tfe_bboxes
import util
import cv2
from nets import seglink_symbol, anchor_layer
slim = tf.contrib.slim
import config
# =========================================================================== #
# model threshold parameters
# =========================================================================== #
tf.app.flags.DEFINE_string('train_with_ignored', False,
'whether to use ignored bbox (in ic15) in training.')
tf.app.flags.DEFINE_boolean('do_grid_search', False,
'whether to do grid search to find a best combinations of \
seg_conf_threshold and link_conf_threshold.')
tf.app.flags.DEFINE_float('seg_loc_loss_weight', 1.0,
'the loss weight of segment localization')
tf.app.flags.DEFINE_float('link_cls_loss_weight', 1.0,
'the loss weight of linkage classification loss')
tf.app.flags.DEFINE_float('seg_conf_threshold', 0.9,
'the threshold on the confidence of segment')
tf.app.flags.DEFINE_float('link_conf_threshold', 0.7,
'the threshold on the confidence of linkage')
# =========================================================================== #
# Checkpoint and running Flags
# =========================================================================== #
tf.app.flags.DEFINE_string('checkpoint_path', None,
'the path of checkpoint to be evaluated. \
If it is a directory containing many checkpoints, \
the lastest will be evaluated.')
tf.app.flags.DEFINE_float('gpu_memory_fraction', 0.1,
'the gpu memory fraction to be used. \
If less than 0, allow_growth = True is used.')
tf.app.flags.DEFINE_bool('using_moving_average', False,
'Whether to use ExponentionalMovingAverage')
tf.app.flags.DEFINE_float('moving_average_decay', 0.9999,
'The decay rate of ExponentionalMovingAverage')
# =========================================================================== #
# I/O and preprocessing Flags.
# =========================================================================== #
tf.app.flags.DEFINE_integer(
'num_readers', 4,
'The number of parallel readers that read data from the dataset.')
tf.app.flags.DEFINE_integer(
'num_preprocessing_threads', 1,
'The number of threads used to create the batches.')
# =========================================================================== #
# Dataset Flags.
# =========================================================================== #
tf.app.flags.DEFINE_string(
'dataset_name', None, 'The name of the dataset to load.')
tf.app.flags.DEFINE_string(
'dataset_split_name', 'train', 'The name of the train/test split.')
tf.app.flags.DEFINE_string(
'dataset_dir', None, 'The directory where the dataset files are stored.')
tf.app.flags.DEFINE_string(
'model_name', 'seglink_vgg', 'The name of the architecture to train.')
tf.app.flags.DEFINE_integer('eval_image_width', 1280, 'Train image size')
tf.app.flags.DEFINE_integer('eval_image_height', 768, 'Train image size')
FLAGS = tf.app.flags.FLAGS
def config_initialization():
# image shape and feature layers shape inference
image_shape = (FLAGS.eval_image_height, FLAGS.eval_image_width)
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
tf.logging.set_verbosity(tf.logging.DEBUG)
config.init_config(image_shape,
batch_size = 1,
seg_conf_threshold = FLAGS.seg_conf_threshold,
link_conf_threshold = FLAGS.link_conf_threshold,
train_with_ignored = FLAGS.train_with_ignored,
seg_loc_loss_weight = FLAGS.seg_loc_loss_weight,
link_cls_loss_weight = FLAGS.link_cls_loss_weight,
)
util.proc.set_proc_name('eval_' + FLAGS.model_name + '_' + FLAGS.dataset_name )
dataset = dataset_factory.get_dataset(FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
config.print_config(FLAGS, dataset, print_to_file = False)
return dataset
def read_dataset(dataset):
with tf.name_scope(FLAGS.dataset_name +'_' + FLAGS.dataset_split_name + '_data_provider'):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
shuffle=False)
[image, shape, filename, gignored, gbboxes, x1, x2, x3, x4, y1, y2, y3, y4] = provider.get([
'image', 'shape', 'filename',
'object/ignored',
'object/bbox',
'object/oriented_bbox/x1',
'object/oriented_bbox/x2',
'object/oriented_bbox/x3',
'object/oriented_bbox/x4',
'object/oriented_bbox/y1',
'object/oriented_bbox/y2',
'object/oriented_bbox/y3',
'object/oriented_bbox/y4'
])
gxs = tf.transpose(tf.stack([x1, x2, x3, x4])) #shape = (N, 4)
gys = tf.transpose(tf.stack([y1, y2, y3, y4]))
image = tf.identity(image, 'input_image')
# Pre-processing image, labels and bboxes.
image, gignored, gbboxes, gxs, gys = ssd_vgg_preprocessing.preprocess_image(
image, gignored, gbboxes, gxs, gys,
out_shape = config.image_shape,
data_format = config.data_format,
is_training = False)
image = tf.identity(image, 'processed_image')
# calculate ground truth
seg_label, seg_loc, link_gt = seglink.tf_get_all_seglink_gt(gxs, gys, gignored)
return image, seg_label, seg_loc, link_gt, filename, shape, gignored, gxs, gys
def eval(dataset):
dict_metrics = {}
checkpoint_dir = util.io.get_dir(FLAGS.checkpoint_path)
logdir = util.io.join_path(checkpoint_dir,
'eval',
"%s_%s"%(FLAGS.dataset_name, FLAGS.dataset_split_name))
global_step = slim.get_or_create_global_step()
with tf.name_scope('evaluation_%dx%d'%(FLAGS.eval_image_height, FLAGS.eval_image_width)):
with tf.variable_scope(tf.get_variable_scope(), reuse = True):# the variables has been created in config.init_config
# get input tensor
image, seg_label, seg_loc, link_gt, filename, shape, gignored, gxs, gys = read_dataset(dataset)
# expand dim if needed
b_image = tf.expand_dims(image, axis = 0);
b_seg_label = tf.expand_dims(seg_label, axis = 0)
b_seg_loc = tf.expand_dims(seg_loc, axis = 0)
b_link_gt = tf.expand_dims(link_gt, axis = 0)
b_shape = tf.expand_dims(shape, axis = 0)
# build seglink loss
net = seglink_symbol.SegLinkNet(inputs = b_image, data_format = config.data_format)
net.build_loss(seg_labels = b_seg_label,
seg_offsets = b_seg_loc,
link_labels = b_link_gt,
do_summary = False) # the summary will be added in the following lines
# gather seglink losses
losses = tf.get_collection(tf.GraphKeys.LOSSES)
assert len(losses) == 3 # 3 is the number of seglink losses: seg_cls, seg_loc, link_cls
for loss in tf.get_collection(tf.GraphKeys.LOSSES):
dict_metrics[loss.op.name] = slim.metrics.streaming_mean(loss)
seglink_loss = tf.add_n(losses)
dict_metrics['seglink_loss'] = slim.metrics.streaming_mean(seglink_loss)
# Add metrics to summaries.
for name, metric in dict_metrics.items():
tf.summary.scalar(name, metric[0])
# shape = (height, width, channels) when format = NHWC TODO
gxs = gxs * tf.cast(shape[1], gxs.dtype)
gys = gys * tf.cast(shape[0], gys.dtype)
if FLAGS.do_grid_search:
# grid search
seg_ths = np.arange(0.5, 0.91, 0.1)
link_ths = seg_ths
else:
seg_ths = [FLAGS.seg_conf_threshold]
link_ths = [FLAGS.link_conf_threshold]
eval_result_path = util.io.join_path(logdir, 'eval_on_%s_%s.log'%(FLAGS.dataset_name, FLAGS.dataset_split_name))
for seg_th in seg_ths:
for link_th in link_ths:
config._set_det_th(seg_th, link_th)
eval_result_msg = 'seg_conf_threshold=%f, link_conf_threshold = %f, '\
%(config.seg_conf_threshold, config.link_conf_threshold)
eval_result_msg += 'iter = %r, recall = %r, precision = %f, fmean = %r'
with tf.name_scope('seglink_conf_th_%f_%f'\
%(config.seg_conf_threshold, config.link_conf_threshold)):
# decode seglink to bbox output, with absolute length, instead of being within [0,1]
bboxes_pred = seglink.tf_seglink_to_bbox(net.seg_scores, net.link_scores, net.seg_offsets,
b_shape, seg_conf_threshold = seg_th, link_conf_threshold = link_th)
# bboxes_pred = tf.Print(bboxes_pred, [tf.shape(bboxes_pred)], '%f_%f, shape of bboxes = '%(seg_th, link_th))
# calculate true positive and false positive
# the xs and ys from tfrecord is 0~1, resize them to absolute length before matching.
num_gt_bboxes, tp, fp = tfe_bboxes.bboxes_matching(bboxes_pred, gxs, gys, gignored)
tp_fp_metric = tfe_metrics.streaming_tp_fp_arrays(num_gt_bboxes, tp, fp)
dict_metrics['tp_fp_%f_%f'%(config.seg_conf_threshold, config.link_conf_threshold)] = (tp_fp_metric[0], tp_fp_metric[1])
# precision and recall
precision, recall = tfe_metrics.precision_recall(*tp_fp_metric[0])
fmean = tfe_metrics.fmean(precision, recall)
fmean = util.tf.Print(fmean, data = [global_step, recall, precision, fmean],
msg = eval_result_msg,
file = eval_result_path, mode = 'a')
fmean = tf.Print(fmean, [recall, precision, fmean], '%f_%f, Recall, Precision, Fmean = '%(seg_th, link_th))
tf.summary.scalar('Precision', precision)
tf.summary.scalar('Recall', recall)
tf.summary.scalar('F-mean', fmean)
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map(dict_metrics)
sess_config = tf.ConfigProto(log_device_placement = False, allow_soft_placement = True)
if FLAGS.gpu_memory_fraction < 0:
sess_config.gpu_options.allow_growth = True
elif FLAGS.gpu_memory_fraction > 0:
sess_config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_memory_fraction;
# Variables to restore: moving avg. or normal weights.
if FLAGS.using_moving_average:
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay)
variables_to_restore = variable_averages.variables_to_restore(
slim.get_model_variables())
variables_to_restore[global_step.op.name] = global_step
else:
variables_to_restore = slim.get_variables_to_restore()
if util.io.is_dir(FLAGS.checkpoint_path):
slim.evaluation.evaluation_loop(
master = '',
eval_op=list(names_to_updates.values()),
num_evals=dataset.num_samples,
variables_to_restore=variables_to_restore,
checkpoint_dir = checkpoint_dir,
logdir = logdir,
session_config=sess_config)
else:
slim.evaluation.evaluate_once(
master = '',
eval_op=list(names_to_updates.values()),
variables_to_restore=variables_to_restore,
num_evals=2,#dataset.num_samples,
checkpoint_path = FLAGS.checkpoint_path,
logdir = logdir,
session_config=sess_config)
def main(_):
eval(config_initialization())
if __name__ == '__main__':
tf.app.run()