def build_model(self, img_input: TensorType) -> TensorType:
"""Build graph using img_input as input.
Args:
img_input: 4D Image input tensor of shape (batch, height, width, channels)
Returns:
`Tensor` holding output probabilities per class, shape (batch, num_classes)
"""
filters = _make_divisible(32 * self.alpha, 8)
# Conv 1 block
x = layers.zero_padding(img_input,
padding=((0, 1), (0, 1)),
name='Conv1_pad')
x = layers.conv(x,
filters_out=filters,
kernel_size=3,
padding='valid',
add_bias=False,
stride=2,
name='Conv1')
x = layers.norm(x,
axis=-1,
epsilon=1e-3,
momentum=0.999,
name='bn_Conv1')
x = layers.relu(x,
name='Conv1_relu',
max_value=tf.constant(6, tf.float16))
# Depthwise separable convolutions
x = self._inverted_res_block(x,
filters=16,
alpha=self.alpha,
stride=1,
expansion=1,
block_id=0)
x = self._inverted_res_block(x,
filters=24,
alpha=self.alpha,
stride=2,
expansion=6,
block_id=1)
x = self._inverted_res_block(x,
filters=24,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=2)
x = self._inverted_res_block(x,
filters=32,
alpha=self.alpha,
stride=2,
expansion=6,
block_id=3)
x = self._inverted_res_block(x,
filters=32,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=4)
x = self._inverted_res_block(x,
filters=32,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=5)
x = self._inverted_res_block(x,
filters=64,
alpha=self.alpha,
stride=2,
expansion=6,
block_id=6)
x = self._inverted_res_block(x,
filters=64,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=7)
x = self._inverted_res_block(x,
filters=64,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=8)
x = self._inverted_res_block(x,
filters=64,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=9)
x = self._inverted_res_block(x,
filters=96,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=10)
x = self._inverted_res_block(x,
filters=96,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=11)
x = self._inverted_res_block(x,
filters=96,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=12)
x = self._inverted_res_block(x,
filters=160,
alpha=self.alpha,
stride=2,
expansion=6,
block_id=13)
x = self._inverted_res_block(x,
filters=160,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=14)
x = self._inverted_res_block(x,
filters=160,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=15)
x = self._inverted_res_block(x,
filters=320,
alpha=self.alpha,
stride=1,
expansion=6,
block_id=16)
# no alpha applied to last conv as stated in the paper:
# if the width multiplier is greater than 1 we
# increase the number of output channels
if self.alpha > 1.0:
last_block_filters = _make_divisible(1280 * self.alpha, 8)
else:
last_block_filters = 1280
x = layers.conv(x,
filters_out=last_block_filters,
kernel_size=1,
add_bias=False,
name='Conv_1')
x = layers.norm(x, epsilon=1e-3, momentum=0.999, name='Conv_1_bn')
x = layers.relu(x,
max_value=tf.constant(6, tf.float16),
name='out_relu')
# Include top
x = layers.global_avg_pool(x)
x = layers.fully_connected(x, self.num_classes, name='Logits')
x = layers.softmax(x, name='act_softmax')
return x
def build_model(self, img_input: TensorType) -> TensorType:
"""Build graph using img_input as input.
Args:
img_input: 4D Image input tensor of shape (batch, height, width, channels)
Returns:
`Tensor` holding output probabilities per class, shape (batch, num_classes)
"""
filters = int(32 * self.alpha)
shape = (-1, 1, 1, int(1024 * self.alpha))
# Conv 1 block
x = layers.zero_padding(img_input,
padding=((0, 1), (0, 1)),
name='conv1_pad')
x = layers.conv(x,
filters_out=filters,
kernel_size=3,
padding='valid',
add_bias=False,
stride=2,
name='conv1')
x = layers.norm(x, axis=-1, name='conv1_bn')
x = layers.relu(x, name='conv1_relu')
# Depthwise convolutions
x = self._depthwise_conv_block(x,
64,
self.alpha,
depth_multiplier=1,
block_id=1)
x = self._depthwise_conv_block(x,
128,
self.alpha,
depth_multiplier=1,
strides=2,
block_id=2)
x = self._depthwise_conv_block(x,
128,
self.alpha,
depth_multiplier=1,
block_id=3)
x = self._depthwise_conv_block(x,
256,
self.alpha,
depth_multiplier=1,
strides=2,
block_id=4)
x = self._depthwise_conv_block(x,
256,
self.alpha,
depth_multiplier=1,
block_id=5)
x = self._depthwise_conv_block(x,
512,
self.alpha,
depth_multiplier=1,
strides=2,
block_id=6)
x = self._depthwise_conv_block(x,
512,
self.alpha,
depth_multiplier=1,
block_id=7)
x = self._depthwise_conv_block(x,
512,
self.alpha,
depth_multiplier=1,
block_id=8)
x = self._depthwise_conv_block(x,
512,
self.alpha,
depth_multiplier=1,
block_id=9)
x = self._depthwise_conv_block(x,
512,
self.alpha,
depth_multiplier=1,
block_id=10)
x = self._depthwise_conv_block(x,
512,
self.alpha,
depth_multiplier=1,
block_id=11)
x = self._depthwise_conv_block(x,
1024,
self.alpha,
depth_multiplier=1,
strides=2,
block_id=12)
x = self._depthwise_conv_block(x,
1024,
self.alpha,
depth_multiplier=1,
block_id=13)
# Include top
x = layers.global_avg_pool(x)
x = layers.reshape(x, shape=shape, name='reshape_1')
x = layers.conv(x,
filters_out=self.num_classes,
kernel_size=1,
padding='same',
name='conv_preds',
add_bias=False)
x = layers.reshape(x, shape=(-1, self.num_classes), name='reshape_2')
x = layers.softmax(x, name='act_softmax')
return x