def squeeze_excitation_layer(
inputs,
ratio,
training=True,
data_format='NCHW',
kernel_initializer=tf.compat.v1.variance_scaling_initializer(),
bias_initializer=tf.zeros_initializer(),
name="squeeze_excitation_layer"):
if data_format not in ['NHWC', 'NCHW']:
raise ValueError(
"Unknown data format: `%s` (accepted: ['NHWC', 'NCHW'])" %
data_format)
in_shape = inputs.get_shape()
num_channels = in_shape[1] if data_format == "NCHW" else in_shape[-1]
with tf.variable_scope(name):
net = inputs
# squeeze
squeeze = layers.reduce_mean(net,
keepdims=False,
data_format=data_format,
name='squeeze_spatial_mean')
# fc + relu
excitation = layers.dense(inputs=squeeze,
units=num_channels // ratio,
use_bias=True,
trainable=training,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer)
excitation = layers.relu(excitation)
# fc + sigmoid
excitation = layers.dense(inputs=excitation,
units=num_channels,
use_bias=True,
trainable=training,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer)
excitation = layers.sigmoid(excitation)
out_shape = [-1, num_channels, 1, 1
] if data_format == "NCHW" else [-1, 1, 1, num_channels]
excitation = tf.reshape(excitation, out_shape)
net = net * excitation
return net
def build_model(self, inputs, training=True, reuse=False):
with var_storage.model_variable_scope(
self.model_hparams.model_name,
reuse=reuse,
dtype=self.model_hparams.dtype):
with tf.variable_scope("input_reshape"):
if self.model_hparams.input_format == 'NHWC' and self.model_hparams.compute_format == 'NCHW':
# Reshape inputs: NHWC => NCHW
inputs = tf.transpose(inputs, [0, 3, 1, 2])
elif self.model_hparams.input_format == 'NCHW' and self.model_hparams.compute_format == 'NHWC':
# Reshape inputs: NCHW => NHWC
inputs = tf.transpose(inputs, [0, 2, 3, 1])
if self.model_hparams.dtype != inputs.dtype:
inputs = tf.cast(inputs, self.model_hparams.dtype)
net = blocks.conv2d_block(
inputs,
n_channels=64,
kernel_size=(7, 7),
strides=(2, 2),
mode='SAME',
use_batch_norm=True,
activation='relu',
is_training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
name='conv2d'
)
net = layers.max_pooling2d(
net,
pool_size=(3, 3),
strides=(2, 2),
padding='SAME',
data_format=self.model_hparams.compute_format,
name="max_pooling2d",
)
model_bottlenecks = self.model_hparams.layers_depth
for block_id, block_bottleneck in enumerate(model_bottlenecks):
for layer_id in range(self.model_hparams.layers_count[block_id]):
stride = 2 if (layer_id == 0 and block_id != 0) else 1
net = blocks.bottleneck_block(
inputs=net,
depth=block_bottleneck * self.model_hparams.expansions,
depth_bottleneck=block_bottleneck,
cardinality=self.model_hparams.cardinality,
stride=stride,
training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
block_name="btlnck_block_%d_%d" % (block_id, layer_id),
use_se=self.model_hparams.use_se,
ratio=self.model_hparams.se_ratio)
with tf.variable_scope("output"):
net = layers.reduce_mean(
net, keepdims=False, data_format=self.model_hparams.compute_format, name='spatial_mean')
logits = layers.dense(
inputs=net,
units=self.model_hparams.n_classes,
use_bias=True,
trainable=training,
kernel_initializer=self.dense_hparams.kernel_initializer,
bias_initializer=self.dense_hparams.bias_initializer)
if logits.dtype != tf.float32:
logits = tf.cast(logits, tf.float32)
probs = layers.softmax(logits, name="softmax", axis=1)
return probs, logits
def build_model(self, inputs, training=True, reuse=False):
with var_storage.model_variable_scope(self.model_hparams.model_name,
reuse=reuse,
dtype=self.model_hparams.dtype):
with tf.variable_scope("input_reshape"):
if self.model_hparams.input_format == 'NHWC' and self.model_hparams.compute_format == 'NCHW':
# Reshape inputs: NHWC => NCHW
inputs = tf.transpose(inputs, [0, 3, 1, 2])
elif self.model_hparams.input_format == 'NCHW' and self.model_hparams.compute_format == 'NHWC':
# Reshape inputs: NCHW => NHWC
inputs = tf.transpose(inputs, [0, 2, 3, 1])
if self.model_hparams.dtype != inputs.dtype:
inputs = tf.cast(inputs, self.model_hparams.dtype)
net = blocks.conv2d_block(
inputs,
n_channels=64,
# n_channels=16,
kernel_size=(7, 7),
strides=(2, 2),
mode='SAME_RESNET',
use_batch_norm=True,
activation='relu',
is_training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
name='conv2d')
net = layers.max_pooling2d(
net,
pool_size=(3, 3),
strides=(2, 2),
padding='SAME',
data_format=self.model_hparams.compute_format,
name="max_pooling2d",
)
for block_id, _ in enumerate(
range(self.model_hparams.layer_counts[0])):
net = blocks.bottleneck_block(
inputs=net,
depth=256,
depth_bottleneck=64,
stride=1,
training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
block_name="btlnck_block_1_%d" % (block_id + 1))
for block_id, i in enumerate(
range(self.model_hparams.layer_counts[1])):
stride = 2 if i == 0 else 1
net = blocks.bottleneck_block(
inputs=net,
depth=512,
depth_bottleneck=128,
stride=stride,
training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
block_name="btlnck_block_2_%d" % (block_id + 1))
for block_id, i in enumerate(
range(self.model_hparams.layer_counts[2])):
block_id += 1
stride = 2 if i == 0 else 1
net = blocks.bottleneck_block(
inputs=net,
depth=1024,
depth_bottleneck=256,
stride=stride,
training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
block_name="btlnck_block_3_%d" % (block_id + 1))
for block_id, i in enumerate(
range(self.model_hparams.layer_counts[3])):
stride = 2 if i == 0 else 1
net = blocks.bottleneck_block(
inputs=net,
depth=2048,
depth_bottleneck=512,
stride=stride,
training=training,
data_format=self.model_hparams.compute_format,
conv2d_hparams=self.conv2d_hparams,
batch_norm_hparams=self.batch_norm_hparams,
block_name="btlnck_block_4_%d" % (block_id + 1))
with tf.variable_scope("output"):
net = layers.reduce_mean(
net,
keepdims=False,
data_format=self.model_hparams.compute_format,
name='spatial_mean')
logits = layers.dense(
inputs=net,
units=self.model_hparams.n_classes,
use_bias=True,
trainable=training,
kernel_initializer=self.dense_hparams.kernel_initializer,
bias_initializer=self.dense_hparams.bias_initializer)
if logits.dtype != tf.float32:
logits = tf.cast(logits, tf.float32, name="logits")
probs = layers.softmax(logits, name="softmax", axis=1)
return probs, logits