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custom.py
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custom.py
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"""Loads a sample video and classifies using a trained Kinetics checkpoint."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import os
import i3d
_IMAGE_SIZE = 224
_CHECKPOINT_PATHS = {
'rgb': 'data/checkpoints/rgb_scratch/model.ckpt',
'rgb600': 'data/checkpoints/rgb_scratch_kin600/model.ckpt',
'flow': 'data/checkpoints/flow_scratch/model.ckpt',
'rgb_imagenet': 'data/checkpoints/rgb_imagenet/model.ckpt',
'flow_imagenet': 'data/checkpoints/flow_imagenet/model.ckpt',
}
_LABEL_MAP_PATH = 'data/label_map.txt'
_LABEL_MAP_PATH_600 = 'data/label_map_600.txt'
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string('eval_type', 'joint', 'rgb, rgb600, flow, or joint')
tf.flags.DEFINE_boolean('imagenet_pretrained', True, '')
tf.flags.DEFINE_string('final_endpoint', 'Logits', 'Mixed_4f, Logits, Predictions, etc.')
tf.flags.DEFINE_string('path', 'temp', '')
tf.flags.DEFINE_string('flow_path', 'temp', '')
tf.flags.DEFINE_string('save_path', 'temp', '')
tf.flags.DEFINE_integer('frames', 0, '')
_PATHS = {
'rgb': FLAGS.path,
'flow': FLAGS.flow_path,
}
_SAVE_PATH = FLAGS.save_path
_SAMPLE_VIDEO_FRAMES = FLAGS.frames
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
eval_type = FLAGS.eval_type
imagenet_pretrained = FLAGS.imagenet_pretrained
final_endpoint = FLAGS.final_endpoint
NUM_CLASSES = 400
if eval_type == 'rgb600':
NUM_CLASSES = 600
if eval_type not in ['rgb', 'rgb600', 'flow', 'joint']:
raise ValueError('Bad `eval_type`, must be one of rgb, rgb600, flow, joint')
if final_endpoint not in ['Mixed_4f', 'Logits', 'Predictions']:
raise ValueError('Bad `final_endpoint`, must be one of Mixed_4f, Logits, Predictions')
if eval_type == 'rgb600':
kinetics_classes = [x.strip() for x in open(_LABEL_MAP_PATH_600)]
else:
kinetics_classes = [x.strip() for x in open(_LABEL_MAP_PATH)]
if eval_type in ['rgb', 'rgb600', 'joint']:
# RGB input has 3 channels.
rgb_input = tf.placeholder(
tf.float32,
shape=(1, _SAMPLE_VIDEO_FRAMES, _IMAGE_SIZE, _IMAGE_SIZE, 3))
with tf.variable_scope('RGB'):
rgb_model = i3d.InceptionI3d(
NUM_CLASSES, spatial_squeeze=True, final_endpoint=final_endpoint)
rgb_output, _ = rgb_model(
rgb_input, is_training=False, dropout_keep_prob=1.0)
rgb_variable_map = {}
for variable in tf.global_variables():
if variable.name.split('/')[0] == 'RGB':
if eval_type == 'rgb600':
rgb_variable_map[variable.name.replace(':0', '')[len('RGB/inception_i3d/'):]] = variable
else:
rgb_variable_map[variable.name.replace(':0', '')] = variable
rgb_saver = tf.train.Saver(var_list=rgb_variable_map, reshape=True)
if eval_type in ['flow', 'joint']:
# Flow input has only 2 channels.
flow_input = tf.placeholder(
tf.float32,
shape=(1, _SAMPLE_VIDEO_FRAMES, _IMAGE_SIZE, _IMAGE_SIZE, 2))
with tf.variable_scope('Flow'):
flow_model = i3d.InceptionI3d(
NUM_CLASSES, spatial_squeeze=True, final_endpoint=final_endpoint)
flow_output, _ = flow_model(
flow_input, is_training=False, dropout_keep_prob=1.0)
flow_variable_map = {}
for variable in tf.global_variables():
if variable.name.split('/')[0] == 'Flow':
flow_variable_map[variable.name.replace(':0', '')] = variable
flow_saver = tf.train.Saver(var_list=flow_variable_map, reshape=True)
if eval_type == 'rgb' or eval_type == 'rgb600':
model_output = rgb_output
elif eval_type == 'flow':
model_output = flow_output
else:
model_output = rgb_output + flow_output
with tf.Session() as sess:
feed_dict = {}
if eval_type in ['rgb', 'rgb600', 'joint']:
if imagenet_pretrained:
rgb_saver.restore(sess, _CHECKPOINT_PATHS['rgb_imagenet'])
else:
rgb_saver.restore(sess, _CHECKPOINT_PATHS[eval_type])
tf.logging.info('RGB checkpoint restored')
rgb_sample = np.load(_PATHS['rgb'])
rgb_sample = rgb_sample[np.newaxis, ...]
tf.logging.info('RGB data loaded, shape=%s', str(rgb_sample.shape))
feed_dict[rgb_input] = rgb_sample
if eval_type in ['flow', 'joint']:
if imagenet_pretrained:
flow_saver.restore(sess, _CHECKPOINT_PATHS['flow_imagenet'])
else:
flow_saver.restore(sess, _CHECKPOINT_PATHS['flow'])
tf.logging.info('Flow checkpoint restored')
flow_sample = np.load(_PATHS['flow'])
flow_sample = flow_sample[np.newaxis, ...]
tf.logging.info('Flow data loaded, shape=%s', str(flow_sample.shape))
feed_dict[flow_input] = flow_sample
output = sess.run(model_output, feed_dict=feed_dict)
np.save(_SAVE_PATH, output)
if __name__ == '__main__':
tf.app.run(main)