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#
# 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.
# ==============================================================================
"""Eval checkpoint driver.
This is an example evaluation script for users to understand the EfficientNet
model checkpoints on CPU. To serve EfficientNet, please consider to export a
`SavedModel` from checkpoints and use tf-serving to serve.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import sys
from absl import app
from absl import flags
import numpy as np
import tensorflow as tf
import efficientnet_builder
import preprocessing
flags.DEFINE_string('model_name', 'efficientnet-b0', 'Model name to eval.')
flags.DEFINE_string('runmode', 'examples', 'Running mode: examples or imagenet')
flags.DEFINE_string('imagenet_eval_glob', None,
'Imagenet eval image glob, '
'such as /imagenet/ILSVRC2012*.JPEG')
flags.DEFINE_string('imagenet_eval_label', None,
'Imagenet eval label file path, '
'such as /imagenet/ILSVRC2012_validation_ground_truth.txt')
flags.DEFINE_string('ckpt_dir', '/tmp/ckpt/', 'Checkpoint folders')
flags.DEFINE_string('example_img', '/tmp/panda.jpg',
'Filepath for a single example image.')
flags.DEFINE_string('labels_map_file', '/tmp/labels_map.txt',
'Labels map from label id to its meaning.')
flags.DEFINE_integer('num_images', 5000,
'Number of images to eval. Use -1 to eval all images.')
FLAGS = flags.FLAGS
MEAN_RGB = [0.485 * 255, 0.456 * 255, 0.406 * 255]
STDDEV_RGB = [0.229 * 255, 0.224 * 255, 0.225 * 255]
class EvalCkptDriver(object):
"""A driver for running eval inference.
Attributes:
model_name: str. Model name to eval.
batch_size: int. Eval batch size.
num_classes: int. Number of classes, default to 1000 for ImageNet.
image_size: int. Input image size, determined by model name.
"""
def __init__(self, model_name='efficientnet-b0', batch_size=1):
"""Initialize internal variables."""
self.model_name = model_name
self.batch_size = batch_size
self.num_classes = 1000
# Model Scaling parameters
_, _, self.image_size, _ = efficientnet_builder.efficientnet_params(
model_name)
def restore_model(self, sess, ckpt_dir):
"""Restore variables from checkpoint dir."""
checkpoint = tf.train.latest_checkpoint(ckpt_dir)
ema = tf.train.ExponentialMovingAverage(decay=0.9999)
ema_vars = tf.trainable_variables() + tf.get_collection('moving_vars')
for v in tf.global_variables():
if 'moving_mean' in v.name or 'moving_variance' in v.name:
ema_vars.append(v)
ema_vars = list(set(ema_vars))
var_dict = ema.variables_to_restore(ema_vars)
saver = tf.train.Saver(var_dict, max_to_keep=1)
saver.restore(sess, checkpoint)
def build_model(self, features, is_training):
"""Build model with input features."""
features -= tf.constant(MEAN_RGB, shape=[1, 1, 3], dtype=features.dtype)
features /= tf.constant(STDDEV_RGB, shape=[1, 1, 3], dtype=features.dtype)
logits, _ = efficientnet_builder.build_model(
features, self.model_name, is_training)
probs = tf.nn.softmax(logits)
probs = tf.squeeze(probs)
return probs
def build_dataset(self, filenames, labels, is_training):
"""Build input dataset."""
filenames = tf.constant(filenames)
labels = tf.constant(labels)
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
def _parse_function(filename, label):
image_string = tf.read_file(filename)
image_decoded = preprocessing.preprocess_image(
image_string, is_training, self.image_size)
image = tf.cast(image_decoded, tf.float32)
return image, label
dataset = dataset.map(_parse_function)
dataset = dataset.batch(self.batch_size)
iterator = dataset.make_one_shot_iterator()
images, labels = iterator.get_next()
return images, labels
def run_inference(self, ckpt_dir, image_files, labels):
"""Build and run inference on the target images and labels."""
with tf.Graph().as_default(), tf.Session() as sess:
images, labels = self.build_dataset(image_files, labels, False)
probs = self.build_model(images, is_training=False)
sess.run(tf.global_variables_initializer())
self.restore_model(sess, ckpt_dir)
prediction_idx = []
prediction_prob = []
for _ in range(len(image_files) // self.batch_size):
out_probs = sess.run(probs)
idx = np.argsort(out_probs)[::-1]
prediction_idx.append(idx[:5])
prediction_prob.append([out_probs[pid] for pid in idx[:5]])
# Return the top 5 predictions (idx and prob) for each image.
return prediction_idx, prediction_prob
def eval_example_images(model_name, ckpt_dir, image_files, labels_map_file):
"""Eval a list of example images.
Args:
model_name: str. The name of model to eval.
ckpt_dir: str. Checkpoint directory path.
image_files: List[str]. A list of image file paths.
labels_map_file: str. The labels map file path.
Returns:
A tuple (pred_idx, and pred_prob), where pred_idx is the top 5 prediction
index and pred_prob is the top 5 prediction probability.
"""
eval_ckpt_driver = EvalCkptDriver(model_name)
classes = json.loads(tf.gfile.Open(labels_map_file).read())
pred_idx, pred_prob = eval_ckpt_driver.run_inference(
ckpt_dir, image_files, [0] * len(image_files))
for i in range(len(image_files)):
print('predicted class for image {}: '.format(image_files[i]))
for j, idx in enumerate(pred_idx[i]):
print(' -> top_{} ({:4.2f}%): {} '.format(
j, pred_prob[i][j] * 100, classes[str(idx)]))
return pred_idx, pred_prob
def eval_imagenet(model_name,
ckpt_dir,
imagenet_eval_glob,
imagenet_eval_label,
num_images):
"""Eval ImageNet images and report top1/top5 accuracy.
Args:
model_name: str. The name of model to eval.
ckpt_dir: str. Checkpoint directory path.
imagenet_eval_glob: str. File path glob for all eval images.
imagenet_eval_label: str. File path for eval label.
num_images: int. Number of images to eval: -1 means eval the whole dataset.
Returns:
A tuple (top1, top5) for top1 and top5 accuracy.
"""
eval_ckpt_driver = EvalCkptDriver(model_name)
imagenet_val_labels = [int(i) for i in tf.gfile.GFile(imagenet_eval_label)]
imagenet_filenames = sorted(tf.gfile.Glob(imagenet_eval_glob))
if num_images < 0:
num_images = len(imagenet_filenames)
image_files = imagenet_filenames[:num_images]
labels = imagenet_val_labels[:num_images]
pred_idx, _ = eval_ckpt_driver.run_inference(ckpt_dir, image_files, labels)
top1_cnt, top5_cnt = 0.0, 0.0
for i, label in enumerate(labels):
top1_cnt += label in pred_idx[i][:1]
top5_cnt += label in pred_idx[i][:5]
if i % 100 == 0:
print('Step {}: top1_acc = {:4.2f}% top5_acc = {:4.2f}%'.format(
i, 100 * top1_cnt / (i + 1), 100 * top5_cnt / (i + 1)))
sys.stdout.flush()
top1, top5 = 100 * top1_cnt / num_images, 100 * top5_cnt / num_images
print('Final: top1_acc = {:4.2f}% top5_acc = {:4.2f}%'.format(top1, top5))
return top1, top5
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.ERROR)
if FLAGS.runmode == 'examples':
# Run inference for an example image.
eval_example_images(FLAGS.model_name, FLAGS.ckpt_dir, [FLAGS.example_img],
FLAGS.labels_map_file)
elif FLAGS.runmode == 'imagenet':
# Run inference for imagenet.
eval_imagenet(FLAGS.model_name, FLAGS.ckpt_dir, FLAGS.imagenet_eval_glob,
FLAGS.imagenet_eval_label, FLAGS.num_images)
else:
print('must specify runmode: examples or imagenet')
if __name__ == '__main__':
app.run(main)
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