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evaluate.py
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evaluate.py
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"""
Copyright (c) College of Mechatronics and Control Engineering, Shenzhen University.
All rights reserved.
Description :
Author:Team Li
"""
"""
Copyright (c) College of Mechatronics and Control Engineering, Shenzhen University.
All rights reserved.
Description :
Author:Team Li
"""
from nets.catch_net import factory
from utils import net_tools
from utils import data_pileline_tools
from utils.common_tools import *
from utils.tf_extended import tf_utils
import utils.tf_extended as tfe
from dataset import dataset_factory
import math
import time
import tensorflow as tf
slim = tf.contrib.slim
from config import *
# =========================================================================== #
# model Flags.
# =========================================================================== #
tf.app.flags.DEFINE_string(
'backbone_name', 'mobilenet_v2',
'The name of the architecture to train.')
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', 4,
'The number of threads used to create the batches.')
tf.app.flags.DEFINE_string(
'checkpoint_path', 'checkpoint/',
'checkpoint(for all net) full name from which to fine-tune.')
tf.app.flags.DEFINE_string(
'eval_dir', 'evaluation/', 'Directory where the results are saved to.')
tf.app.flags.DEFINE_integer(
'batch_size', 1, 'The number of samples in each batch.')
# =========================================================================== #
# evaluation Flags.
# =========================================================================== #
tf.app.flags.DEFINE_float(
'select_threshold', 0.3, 'Selection threshold.')
tf.app.flags.DEFINE_integer(
'select_top_k', 400, 'Select top-k detected bounding boxes.')
tf.app.flags.DEFINE_integer(
'keep_top_k', 200, 'Keep top-k detected objects.')
tf.app.flags.DEFINE_float(
'nms_threshold', 0.4, 'Non-Maximum Selection threshold.')
tf.app.flags.DEFINE_float(
'matching_threshold', 0.5, 'Matching threshold with groundtruth objects.')
tf.app.flags.DEFINE_float(
'gpu_memory_fraction', 0.8, 'GPU memory fraction to use.')
FLAGS = tf.app.flags.FLAGS
DTYPE = tf.float32
global_step = tf.Variable(0, trainable=False, name='global_step')
def flatten(x):
result = []
for el in x:
if isinstance(el, tuple):
result.extend(flatten(el))
else:
result.append(el)
return result
def main(_):
## assert ##
logger.info('Asserting parameters')
assert FLAGS.backbone_name in supported_backbone_name
## translate the anchor box config to x,y,h,w in all layers ##
layer_n = len(list(extract_feat_name[FLAGS.backbone_name]))
anchors_all = net_tools.anchors_all_layer(img_size,
feat_size_all_layers[FLAGS.backbone_name],
net_tools.init_anchor(layer_n))
## building data pileline ##
logger.info('Building data pileline, using dataset---%s' % ('bdd100k_train'))
with tf.device('/cpu:0'): ## use cpu to read data and batch data
dataset = dataset_factory.get_dataset(
'bdd100k', 'train', './dataset/bdd100k_TfRecord/')
img, labels, bboxes = data_pileline_tools.prepare_data_test(dataset, num_readers=FLAGS.num_readers,
batch_size=FLAGS.batch_size, shuffle=False)
difficults = tf.zeros(tf.shape(labels), dtype=tf.int64)
list_shape = [1] * 4
batch_info_for_refine = tf.train.batch(
tf_utils.reshape_list([img, labels, bboxes, difficults]),
batch_size=FLAGS.batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=FLAGS.batch_size,
dynamic_pad=True)
## the batch img, gt for loss1, and responsible index ##
b_imgs, b_glables, b_gbboxes, b_difficults = \
tf_utils.reshape_list(batch_info_for_refine, list_shape)
norm_img = (2.0 / 255.0) * b_imgs - 1.0
norm_img = tf.cast(norm_img, dtype=DTYPE)
logger.info('Building model, using backbone---%s' % (FLAGS.backbone_name))
config_dict = {'process_backbone_method': process_backbone_method.NONE,
'deconv_method': deconv_method.LEARN_HALF,
'merge_method': merge_method.ADD,
'train_range':train_range.ALL}
net = factory(inputs=norm_img, backbone_name=FLAGS.backbone_name,
is_training=False, dtype=DTYPE, config_dict=config_dict)
refine_out, det_out, clf_out = net.get_output()
locations_all_layers = [] ##encode by [ymin, xmin, ymax, xmax]
predition_all_layers = []
for clf in clf_out:
predition_all_layers.append(slim.softmax(clf))
for refine_out, det_out, anchors_one_layer in \
zip(refine_out, det_out, anchors_all):
center_locations = net_tools.decode_locations_one_layer(anchors_one_layer, (refine_out+det_out))
corner_locations = centerBboxes_2_cornerBboxes(center_locations)
locations_all_layers.append(corner_locations) ## [h,w,anchor_num,4]
# Performing post-processing on CPU: loop-intensive, usually more efficient.
with tf.device('/device:CPU:0'):
rscores, rbboxes = net_tools.detected_bboxes(predition_all_layers, locations_all_layers,
select_threshold=FLAGS.select_threshold,
nms_threshold=FLAGS.nms_threshold,
top_k=FLAGS.select_top_k,
keep_top_k=FLAGS.keep_top_k)
# Compute TP and FP statistics.
num_gbboxes, tp, fp, rscores = \
tfe.bboxes_matching_batch(rscores.keys(), rscores, rbboxes,
b_glables, b_gbboxes, b_difficults,
matching_threshold=FLAGS.matching_threshold)
variables_to_restore = slim.get_variables_to_restore()
# =================================================================== #
# Evaluation metrics.
# =================================================================== #
with tf.device('/device:CPU:0'):
dict_metrics = {}
# FP and TP metrics.
tp_fp_metric = tfe.streaming_tp_fp_arrays(num_gbboxes, tp, fp, rscores)
for c in tp_fp_metric[0].keys():
dict_metrics['tp_fp_%s' % c] = (tp_fp_metric[0][c],
tp_fp_metric[1][c])
# Add to summaries precision/recall values.
aps_voc07 = {}
aps_voc12 = {}
for c in tp_fp_metric[0].keys():
# Precison and recall values.
prec, rec = tfe.precision_recall(*tp_fp_metric[0][c])
# Average precision VOC07.
v = tfe.average_precision_voc07(prec, rec)
summary_name = 'AP_VOC07/%s' % c
op = tf.summary.scalar(summary_name, v, collections=[])
# op = tf.Print(op, [v], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
aps_voc07[c] = v
# Average precision VOC12.
v = tfe.average_precision_voc12(prec, rec)
summary_name = 'AP_VOC12/%s' % c
op = tf.summary.scalar(summary_name, v, collections=[])
# op = tf.Print(op, [v], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
aps_voc12[c] = v
# Mean average precision VOC07.
summary_name = 'AP_VOC07/mAP'
mAP = tf.add_n(list(aps_voc07.values())) / len(aps_voc07)
op = tf.summary.scalar(summary_name, mAP, collections=[])
op = tf.Print(op, [mAP], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
# Mean average precision VOC12.
summary_name = 'AP_VOC12/mAP'
mAP = tf.add_n(list(aps_voc12.values())) / len(aps_voc12)
op = tf.summary.scalar(summary_name, mAP, collections=[])
op = tf.Print(op, [mAP], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
# Split into values and updates ops.
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map(dict_metrics)
# =================================================================== #
# Evaluation loop.
# =================================================================== #
config_ = tf.ConfigProto()
config_.gpu_options.allow_growth = True
num_batches = math.ceil(3000 / float(FLAGS.batch_size))
if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
else:
checkpoint_path = FLAGS.checkpoint_path
tf.logging.info('Evaluating %s' % checkpoint_path)
# Standard evaluation loop.
start = time.time()
slim.evaluation.evaluate_once(
master='',
checkpoint_path=checkpoint_path,
logdir=FLAGS.eval_dir,
num_evals=num_batches,
eval_op=flatten(list(names_to_updates.values())),
variables_to_restore=variables_to_restore,
session_config=config_)
# Log time spent.
elapsed = time.time()
elapsed = elapsed - start
print('Time spent : %.3f seconds.' % elapsed)
print('Time spent per BATCH: %.3f seconds.' % (elapsed / num_batches))
if __name__ == '__main__':
tf.app.run()