def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format):
"""Strided 2-D convolution with explicit padding."""
# The padding is consistent and is based only on `kernel_size`, not on the
# dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
inputs_for_logging = inputs
if strides > 1:
inputs = fixed_padding(inputs, kernel_size, data_format)
outputs = tf.layers.conv2d(
inputs=inputs,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=('SAME' if strides == 1 else 'VALID'),
use_bias=False,
kernel_initializer=tf.variance_scaling_initializer(
distribution="truncated_normal"),
data_format=data_format)
resnet_log_helper.log_conv2d(input_tensor=inputs_for_logging,
output_tensor=outputs,
stride=strides,
filters=filters,
initializer=mlperf_log.TRUNCATED_NORMAL,
use_bias=False)
return outputs
def conv2d_fixed_padding(inputs,
filters,
kernel_size,
strides,
is_training,
data_format='channels_first'):
"""Strided 2-D convolution with explicit padding.
The padding is consistent and is based only on `kernel_size`, not on the
dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
Args:
inputs: `Tensor` of size `[batch, channels, height_in, width_in]`.
filters: `int` number of filters in the convolution.
kernel_size: `int` size of the kernel to be used in the convolution.
strides: `int` strides of the convolution.
is_training: `bool` for whether the model is in training.
data_format: `str` either "channels_first" for `[batch, channels, height,
width]` or "channels_last for `[batch, height, width, channels]`.
Returns:
A `Tensor` of shape `[batch, filters, height_out, width_out]`.
"""
inputs_for_logging = inputs
if strides > 1:
inputs = fixed_padding(inputs, kernel_size, data_format=data_format)
outputs = tf.layers.conv2d(
inputs=inputs,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=('SAME' if strides == 1 else 'VALID'),
use_bias=False,
kernel_initializer=tf.variance_scaling_initializer(),
data_format=data_format)
if is_training and FLAGS.mlperf_logging:
resnet_log_helper.log_conv2d(
input_tensor=inputs_for_logging,
output_tensor=outputs,
stride=strides,
filters=filters,
initializer=mlperf_log.TRUNCATED_NORMAL,
use_bias=False)
return outputs
def log_conv2d(self, input_tensor, output_tensor, stride_height,
stride_width, filters, initializer, use_bias):
"""Log a conv2d call."""
if self.model == 'resnet50':
assert stride_height == stride_width, (
'--ml_perf_compliance_logging does not support convolutions where '
'the stride height is not equal to the stride width. '
'stride_height=%d, stride_width=%d' %
(stride_height, stride_width))
if isinstance(initializer, tf.truncated_normal_initializer):
initializer = tags.TRUNCATED_NORMAL
elif (isinstance(initializer, tf.glorot_uniform_initializer)
or initializer is None):
initializer = 'glorot_uniform'
resnet_log_helper.log_conv2d(input_tensor, output_tensor,
stride_width, filters, initializer,
use_bias)