import inputs
import argparse
import my_utils as mu
import tensorflow as tf
from model import model_fn, input_fn
logger = mu.get_default_logger()
parser = argparse.ArgumentParser()
parser.add_argument('--data',
default='cv',
type=str,
help='Dataset for evaluation')
def main():
def eval_input_fn():
return input_fn(*data[config['data']],
batch_size=config['batch_size'],
shuffle=False)
data = inputs.load_data(config['n_examples_for_cv'])
estimator = tf.estimator.Estimator(model_fn=model_fn,
params=config,
model_dir=config['model_dir'])
for ckpt in tf.train.get_checkpoint_state(
config['model_dir']).all_model_checkpoint_paths:
with mu.Timer() as timer:
result = estimator.evaluate(eval_input_fn, checkpoint_path=ckpt)
result['data'] = config['data']
logger.info('Done in %.fs', timer.eclipsed)
import net
import my_utils
import tensorflow as tf
import tensorflow.contrib.slim as slim
logger = my_utils.get_default_logger()
def model_placeholder(config):
height, width = config['input_size']
image = tf.placeholder(tf.uint8, name='image_ph', shape=(height, width, 3))
label = tf.placeholder(tf.int32, name='label_ph', shape=(height, width))
bbox = tf.placeholder(tf.int32, name='bbox_ph', shape=(4,))
return image, label, bbox
class Model:
def __init__(self, image, input_size):
"""
:param image: tf.placeholder or tf.Tensor, one single image with shape(None, None, 3) and dtype=tf.uint8
:param input_size: list or tuple,
"""
self.input_size = input_size
logger.info('Building model graph...')
self.net = net.FCN(image)
with tf.name_scope('bbox'):
conv6 = slim.conv2d(self.net.endpoints['conv5'],
num_outputs=64,
kernel_size=(5, 5),
stride=1)
