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347 lines
16 KiB
347 lines
16 KiB
5 years ago
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Generic evaluation script that evaluates a SSD model
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on a given dataset."""
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import math
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import sys
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import six
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import time
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import numpy as np
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import tensorflow as tf
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import tf_extended as tfe
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import tf_utils
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from tensorflow.python.framework import ops
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from datasets import dataset_factory
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from nets import nets_factory
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from preprocessing import preprocessing_factory
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slim = tf.contrib.slim
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# =========================================================================== #
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# Some default EVAL parameters
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# =========================================================================== #
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# List of recalls values at which precision is evaluated.
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LIST_RECALLS = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.85,
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0.90, 0.95, 0.96, 0.97, 0.98, 0.99]
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DATA_FORMAT = 'NHWC'
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# =========================================================================== #
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# SSD evaluation Flags.
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# =========================================================================== #
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tf.app.flags.DEFINE_float(
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'select_threshold', 0.01, 'Selection threshold.')
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tf.app.flags.DEFINE_integer(
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'select_top_k', 400, 'Select top-k detected bounding boxes.')
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tf.app.flags.DEFINE_integer(
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'keep_top_k', 200, 'Keep top-k detected objects.')
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tf.app.flags.DEFINE_float(
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'nms_threshold', 0.45, 'Non-Maximum Selection threshold.')
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tf.app.flags.DEFINE_float(
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'matching_threshold', 0.5, 'Matching threshold with groundtruth objects.')
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tf.app.flags.DEFINE_integer(
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'eval_resize', 4, 'Image resizing: None / CENTRAL_CROP / PAD_AND_RESIZE / WARP_RESIZE.')
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tf.app.flags.DEFINE_integer(
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'eval_image_size', None, 'Eval image size.')
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tf.app.flags.DEFINE_boolean(
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'remove_difficult', True, 'Remove difficult objects from evaluation.')
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# =========================================================================== #
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# Main evaluation flags.
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# =========================================================================== #
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tf.app.flags.DEFINE_integer(
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'num_classes', 21, 'Number of classes to use in the dataset.')
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tf.app.flags.DEFINE_integer(
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'batch_size', 1, 'The number of samples in each batch.')
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tf.app.flags.DEFINE_integer(
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'max_num_batches', None,
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'Max number of batches to evaluate by default use all.')
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tf.app.flags.DEFINE_string(
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'master', '', 'The address of the TensorFlow master to use.')
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tf.app.flags.DEFINE_string(
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'checkpoint_path', '/tmp/tfmodel/',
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'The directory where the model was written to or an absolute path to a '
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'checkpoint file.')
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tf.app.flags.DEFINE_string(
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'eval_dir', '/tmp/tfmodel/', 'Directory where the results are saved to.')
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tf.app.flags.DEFINE_integer(
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'num_preprocessing_threads', 4,
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'The number of threads used to create the batches.')
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tf.app.flags.DEFINE_string(
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'dataset_name', 'imagenet', 'The name of the dataset to load.')
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tf.app.flags.DEFINE_string(
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'dataset_split_name', 'test', 'The name of the train/test split.')
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tf.app.flags.DEFINE_string(
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'dataset_dir', None, 'The directory where the dataset files are stored.')
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tf.app.flags.DEFINE_string(
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'model_name', 'inception_v3', 'The name of the architecture to evaluate.')
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tf.app.flags.DEFINE_string(
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'preprocessing_name', None, 'The name of the preprocessing to use. If left '
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'as `None`, then the model_name flag is used.')
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tf.app.flags.DEFINE_float(
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'moving_average_decay', None,
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'The decay to use for the moving average.'
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'If left as None, then moving averages are not used.')
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tf.app.flags.DEFINE_float(
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'gpu_memory_fraction', 0.1, 'GPU memory fraction to use.')
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tf.app.flags.DEFINE_boolean(
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'wait_for_checkpoints', False, 'Wait for new checkpoints in the eval loop.')
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FLAGS = tf.app.flags.FLAGS
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def main(_):
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if not FLAGS.dataset_dir:
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raise ValueError('You must supply the dataset directory with --dataset_dir')
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tf.logging.set_verbosity(tf.logging.INFO)
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with tf.Graph().as_default():
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tf_global_step = slim.get_or_create_global_step()
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# =================================================================== #
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# Dataset + SSD model + Pre-processing
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# =================================================================== #
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dataset = dataset_factory.get_dataset(
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FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
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# Get the SSD network and its anchors.
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ssd_class = nets_factory.get_network(FLAGS.model_name)
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ssd_params = ssd_class.default_params._replace(num_classes=FLAGS.num_classes)
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ssd_net = ssd_class(ssd_params)
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# Evaluation shape and associated anchors: eval_image_size
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ssd_shape = ssd_net.params.img_shape
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ssd_anchors = ssd_net.anchors(ssd_shape)
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# Select the preprocessing function.
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preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
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image_preprocessing_fn = preprocessing_factory.get_preprocessing(
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preprocessing_name, is_training=False)
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tf_utils.print_configuration(FLAGS.__flags, ssd_params,
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dataset.data_sources, FLAGS.eval_dir)
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# =================================================================== #
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# Create a dataset provider and batches.
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# =================================================================== #
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with tf.device('/cpu:0'):
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with tf.name_scope(FLAGS.dataset_name + '_data_provider'):
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provider = slim.dataset_data_provider.DatasetDataProvider(
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dataset,
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common_queue_capacity=2 * FLAGS.batch_size,
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common_queue_min=FLAGS.batch_size,
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shuffle=False)
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# Get for SSD network: image, labels, bboxes.
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[image, shape, glabels, gbboxes] = provider.get(['image', 'shape',
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'object/label',
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'object/bbox'])
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if FLAGS.remove_difficult:
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[gdifficults] = provider.get(['object/difficult'])
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else:
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gdifficults = tf.zeros(tf.shape(glabels), dtype=tf.int64)
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# Pre-processing image, labels and bboxes.
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image, glabels, gbboxes, gbbox_img = \
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image_preprocessing_fn(image, glabels, gbboxes,
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out_shape=ssd_shape,
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data_format=DATA_FORMAT,
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resize=FLAGS.eval_resize,
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difficults=None)
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# Encode groundtruth labels and bboxes.
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gclasses, glocalisations, gscores = \
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ssd_net.bboxes_encode(glabels, gbboxes, ssd_anchors)
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batch_shape = [1] * 5 + [len(ssd_anchors)] * 3
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# Evaluation batch.
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r = tf.train.batch(
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tf_utils.reshape_list([image, glabels, gbboxes, gdifficults, gbbox_img,
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gclasses, glocalisations, gscores]),
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batch_size=FLAGS.batch_size,
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num_threads=FLAGS.num_preprocessing_threads,
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capacity=5 * FLAGS.batch_size,
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dynamic_pad=True)
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(b_image, b_glabels, b_gbboxes, b_gdifficults, b_gbbox_img, b_gclasses,
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b_glocalisations, b_gscores) = tf_utils.reshape_list(r, batch_shape)
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# =================================================================== #
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# SSD Network + Ouputs decoding.
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# =================================================================== #
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dict_metrics = {}
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arg_scope = ssd_net.arg_scope(data_format=DATA_FORMAT)
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with slim.arg_scope(arg_scope):
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predictions, localisations, logits, end_points = \
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ssd_net.net(b_image, is_training=False)
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# Add losses functions.
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ssd_net.losses(logits, localisations,
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b_gclasses, b_glocalisations, b_gscores)
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# Performing post-processing on CPU: loop-intensive, usually more efficient.
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with tf.device('/device:CPU:0'):
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# Detected objects from SSD output.
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localisations = ssd_net.bboxes_decode(localisations, ssd_anchors)
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rscores, rbboxes = \
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ssd_net.detected_bboxes(predictions, localisations,
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select_threshold=FLAGS.select_threshold,
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nms_threshold=FLAGS.nms_threshold,
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clipping_bbox=None,
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top_k=FLAGS.select_top_k,
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keep_top_k=FLAGS.keep_top_k)
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# Compute TP and FP statistics.
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num_gbboxes, tp, fp, rscores = \
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tfe.bboxes_matching_batch(rscores.keys(), rscores, rbboxes,
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b_glabels, b_gbboxes, b_gdifficults,
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matching_threshold=FLAGS.matching_threshold)
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# Variables to restore: moving avg. or normal weights.
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if FLAGS.moving_average_decay:
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variable_averages = tf.train.ExponentialMovingAverage(
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FLAGS.moving_average_decay, tf_global_step)
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variables_to_restore = variable_averages.variables_to_restore(
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slim.get_model_variables())
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variables_to_restore[tf_global_step.op.name] = tf_global_step
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else:
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variables_to_restore = slim.get_variables_to_restore()
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# =================================================================== #
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# Evaluation metrics.
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# =================================================================== #
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with tf.device('/device:CPU:0'):
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dict_metrics = {}
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# First add all losses.
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for loss in tf.get_collection(tf.GraphKeys.LOSSES):
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dict_metrics[loss.op.name] = slim.metrics.streaming_mean(loss)
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# Extra losses as well.
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for loss in tf.get_collection('EXTRA_LOSSES'):
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dict_metrics[loss.op.name] = slim.metrics.streaming_mean(loss)
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# Add metrics to summaries and Print on screen.
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for name, metric in dict_metrics.items():
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# summary_name = 'eval/%s' % name
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summary_name = name
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op = tf.summary.scalar(summary_name, metric[0], collections=[])
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# op = tf.Print(op, [metric[0]], summary_name)
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tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
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# FP and TP metrics.
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tp_fp_metric = tfe.streaming_tp_fp_arrays(num_gbboxes, tp, fp, rscores)
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for c in tp_fp_metric[0].keys():
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dict_metrics['tp_fp_%s' % c] = (tp_fp_metric[0][c],
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tp_fp_metric[1][c])
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# Add to summaries precision/recall values.
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aps_voc07 = {}
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aps_voc12 = {}
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for c in tp_fp_metric[0].keys():
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# Precison and recall values.
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prec, rec = tfe.precision_recall(*tp_fp_metric[0][c])
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# Average precision VOC07.
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v = tfe.average_precision_voc07(prec, rec)
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summary_name = 'AP_VOC07/%s' % c
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op = tf.summary.scalar(summary_name, v, collections=[])
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# op = tf.Print(op, [v], summary_name)
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tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
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aps_voc07[c] = v
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# Average precision VOC12.
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v = tfe.average_precision_voc12(prec, rec)
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summary_name = 'AP_VOC12/%s' % c
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op = tf.summary.scalar(summary_name, v, collections=[])
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# op = tf.Print(op, [v], summary_name)
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tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
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aps_voc12[c] = v
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# Mean average precision VOC07.
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summary_name = 'AP_VOC07/mAP'
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mAP = tf.add_n(list(aps_voc07.values())) / len(aps_voc07)
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op = tf.summary.scalar(summary_name, mAP, collections=[])
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op = tf.Print(op, [mAP], summary_name)
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tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
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# Mean average precision VOC12.
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summary_name = 'AP_VOC12/mAP'
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mAP = tf.add_n(list(aps_voc12.values())) / len(aps_voc12)
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op = tf.summary.scalar(summary_name, mAP, collections=[])
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op = tf.Print(op, [mAP], summary_name)
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tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
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# for i, v in enumerate(l_precisions):
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# summary_name = 'eval/precision_at_recall_%.2f' % LIST_RECALLS[i]
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# op = tf.summary.scalar(summary_name, v, collections=[])
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# op = tf.Print(op, [v], summary_name)
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# tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
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# Split into values and updates ops.
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names_to_values, names_to_updates = slim.metrics.aggregate_metric_map(dict_metrics)
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# =================================================================== #
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# Evaluation loop.
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# =================================================================== #
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gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
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config = tf.ConfigProto(log_device_placement=False, gpu_options=gpu_options)
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# config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
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# Number of batches...
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if FLAGS.max_num_batches:
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num_batches = FLAGS.max_num_batches
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else:
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num_batches = math.ceil(dataset.num_samples / float(FLAGS.batch_size))
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if not FLAGS.wait_for_checkpoints:
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if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
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checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
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else:
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checkpoint_path = FLAGS.checkpoint_path
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tf.logging.info('Evaluating %s' % checkpoint_path)
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# Standard evaluation loop.
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start = time.time()
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slim.evaluation.evaluate_once(
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master=FLAGS.master,
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checkpoint_path=checkpoint_path,
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logdir=FLAGS.eval_dir,
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num_evals=num_batches,
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eval_op=list(names_to_updates.values()),
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variables_to_restore=variables_to_restore,
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session_config=config)
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# Log time spent.
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elapsed = time.time()
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elapsed = elapsed - start
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print('Time spent : %.3f seconds.' % elapsed)
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print('Time spent per BATCH: %.3f seconds.' % (elapsed / num_batches))
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else:
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checkpoint_path = FLAGS.checkpoint_path
|
||
|
|
tf.logging.info('Evaluating %s' % checkpoint_path)
|
||
|
|
|
||
|
|
# Waiting loop.
|
||
|
|
slim.evaluation.evaluation_loop(
|
||
|
|
master=FLAGS.master,
|
||
|
|
checkpoint_dir=checkpoint_path,
|
||
|
|
logdir=FLAGS.eval_dir,
|
||
|
|
num_evals=num_batches,
|
||
|
|
eval_op=list(names_to_updates.values()),
|
||
|
|
variables_to_restore=variables_to_restore,
|
||
|
|
eval_interval_secs=60,
|
||
|
|
max_number_of_evaluations=np.inf,
|
||
|
|
session_config=config,
|
||
|
|
timeout=None)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
tf.app.run()
|