def initialize():
tf.compat.v1.disable_eager_execution()
options.initialize_with_logfiles(get_parser())
logging.info(f'-- Starting --')
logging.info(f'Host: {socket.gethostname()}')
logging.info(f'Process id (pid): {os.getpid()}')
if FLAGS.comment:
logging.info(f'Comment: {FLAGS.comment}')
logging.info(f'Raw command: {" ".join(map(shlex.quote, sys.argv))}')
logging.info(f'Parsed flags: {FLAGS}')
tfu.set_data_format(FLAGS.data_format)
tfu.set_dtype(tf.float32 if FLAGS.dtype == 'float32' else tf.float16)
if FLAGS.batch_size_test is None:
FLAGS.batch_size_test = FLAGS.batch_size
if FLAGS.checkpoint_dir is None:
FLAGS.checkpoint_dir = FLAGS.logdir
FLAGS.checkpoint_dir = util.ensure_absolute_path(
FLAGS.checkpoint_dir, root=f'{paths.DATA_ROOT}/experiments')
os.makedirs(FLAGS.checkpoint_dir, exist_ok=True)
if not FLAGS.pred_path:
FLAGS.pred_path = f'predictions_{FLAGS.dataset}.npz'
base = os.path.dirname(
FLAGS.load_path) if FLAGS.load_path else FLAGS.checkpoint_dir
FLAGS.pred_path = util.ensure_absolute_path(FLAGS.pred_path, base)
if FLAGS.bone_length_dataset is None:
FLAGS.bone_length_dataset = FLAGS.dataset
if FLAGS.load_path:
if FLAGS.load_path.endswith('.index') or FLAGS.load_path.endswith(
'.meta'):
FLAGS.load_path = os.path.splitext(FLAGS.load_path)[0]
FLAGS.load_path = util.ensure_absolute_path(FLAGS.load_path,
FLAGS.checkpoint_dir)
# Override the default data format in slim layers
enter_context(
slim.arg_scope([
slim.conv2d, slim.conv3d, slim.conv3d_transpose,
slim.conv2d_transpose, slim.avg_pool2d, slim.separable_conv2d,
slim.max_pool2d, slim.batch_norm, slim.spatial_softmax
],
data_format=tfu.data_format()))
# Override default paddings to SAME
enter_context(
slim.arg_scope([slim.avg_pool2d, slim.max_pool2d], padding='SAME'))
tf.compat.v2.random.set_seed(FLAGS.seed)
if FLAGS.gui:
plt.switch_backend('TkAgg')
def resnet_arg_scope():
batch_norm_params = dict(decay=0.997,
epsilon=1e-5,
scale=True,
is_training=tfu.is_training(),
fused=True,
data_format=tfu.data_format())
with slim.arg_scope(
[slim.conv2d, slim.conv3d],
weights_regularizer=slim.l2_regularizer(1e-4),
weights_initializer=slim.variance_scaling_initializer(),
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
with slim.arg_scope([slim.batch_norm], **batch_norm_params):
with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:
return arg_sc
def initialize():
global FLAGS
parse_and_set_global_flags()
setup_logging()
logging.info(f'-- Starting --')
logging.info(f'Host: {socket.gethostname()}')
logging.info(f'Process id (pid): {os.getpid()}')
if FLAGS.comment:
logging.info(f'Comment: {FLAGS.comment}')
logging.info(f'Raw command: {" ".join(map(shlex.quote, sys.argv))}')
logging.info(f'Parsed flags: {FLAGS}')
tfu.set_data_format(FLAGS.data_format)
if FLAGS.dtype == 'float32':
tfu.set_dtype(tf.float32)
elif FLAGS.dtype == 'float16':
tfu.set_dtype(tf.float16)
else:
raise ValueError(f'Training dtype {FLAGS.dtype} not supported, only float16/32.')
# We parallelize on a coarser level already, openmp just makes things slower
# Override the default data format in slim layers
enter_context(slim.arg_scope(
[slim.conv2d, slim.conv3d, slim.conv3d_transpose, slim.conv2d_transpose, slim.avg_pool2d,
slim.separable_conv2d, slim.max_pool2d, slim.batch_norm, slim.spatial_softmax],
data_format=tfu.data_format()))
# Override default paddings to SAME
enter_context(slim.arg_scope([slim.avg_pool2d, slim.max_pool2d], padding='SAME'))
if FLAGS.gui:
plt.switch_backend('TkAgg')
tf.set_random_seed(FLAGS.seed)
def conv2d_same(inputs,
num_outputs,
kernel_size,
stride=1,
rate=1,
centered_stride=False,
scope=None,
**kwargs):
"""Strided 2-D convolution with 'SAME' padding.
When stride > 1, then we do explicit zero-padding, followed by conv2d with
'VALID' padding.
Note that
net = conv2d_same(inputs, num_outputs, 3, stride=stride)
is equivalent to
net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=1,
padding='SAME')
net = subsample(net, factor=stride)
whereas
net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=stride,
padding='SAME')
is different when the input's height or width is even, which is why we add the
current function. For more details, see ResnetUtilsTest.testConv2DSameEven().
Args:
inputs: A 4-D tensor of size [batch, height_in, width_in, channels].
num_outputs: An integer, the number of output filters.
kernel_size: An int with the kernel_size of the filters.
stride: An integer, the output stride.
rate: An integer, rate for atrous convolution.
scope: Scope.
Returns:
output: A 4-D tensor of size [batch, height_out, width_out, channels] with
the convolution output.
"""
if stride == 1 or centered_stride:
return layers_lib.conv2d(inputs,
num_outputs,
kernel_size,
stride=stride,
rate=rate,
padding='SAME',
scope=scope,
**kwargs)
else:
kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
pad_total = kernel_size_effective - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
if tfu.data_format() == 'NHWC':
inputs = array_ops.pad(
inputs,
[[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
else:
inputs = array_ops.pad(
inputs,
[[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]])
return layers_lib.conv2d(inputs,
num_outputs,
kernel_size,
stride=stride,
rate=rate,
padding='VALID',
scope=scope,
**kwargs)