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)
"""
x = layers.conv(img_input,
filters_out=64,
kernel_size=7,
stride=2,
add_bias=False,
name='conv1_conv')
x = layers.norm(x, axis=-1, epsilon=1.001e-5, name='conv1_bn')
x = layers.relu(x, name='conv1_relu')
x = layers.zero_padding(x, padding=((1, 1), (1, 1)), name='pool1_pad')
x = layers.max_pool(x, kernel_size=3, name='pool1')
x = self.conv_block(x, 3, [64, 64, 256], stage=2, block='1', strides=1)
x = self.identity_block(x, 3, [64, 64, 256], stage=2, block='2')
x = self.identity_block(x, 3, [64, 64, 256], stage=2, block='3')
x = self.conv_block(x, 3, [128, 128, 512], stage=3, block='1')
x = self.identity_block(x, 3, [128, 128, 512], stage=3, block='2')
x = self.identity_block(x, 3, [128, 128, 512], stage=3, block='3')
x = self.identity_block(x, 3, [128, 128, 512], stage=3, block='4')
x = self.conv_block(x, 3, [256, 256, 1024], stage=4, block='1')
x = self.identity_block(x, 3, [256, 256, 1024], stage=4, block='2')
x = self.identity_block(x, 3, [256, 256, 1024], stage=4, block='3')
x = self.identity_block(x, 3, [256, 256, 1024], stage=4, block='4')
x = self.identity_block(x, 3, [256, 256, 1024], stage=4, block='5')
x = self.identity_block(x, 3, [256, 256, 1024], stage=4, block='6')
x = self.conv_block(x, 3, [512, 512, 2048], stage=5, block='1')
x = self.identity_block(x, 3, [512, 512, 2048], stage=5, block='2')
x = self.identity_block(x, 3, [512, 512, 2048], stage=5, block='3')
x = layers.avg_pool(x, kernel_size=7, strides=1, name='avg_pool')
x = layers.squeeze(x, axis=[1, 2], name='squeeze')
x = layers.fully_connected(x, self.num_classes, name='probs')
x = layers.softmax(x, name='output-prob')
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 = _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)
"""
x = layers.conv(img_input, filters_out=32, kernel_size=3, stride=2, add_bias=False, name='block1_conv1')
x = layers.norm(x, name='block1_conv1_bn')
x = layers.relu(x, name='block1_conv1_act')
x = layers.conv(x, filters_out=64, kernel_size=3, add_bias=False, name='block1_conv2')
x = layers.norm(x, name='block1_conv2_bn')
x = layers.relu(x, name='block1_conv2_act')
residual = layers.conv(x, filters_out=128, kernel_size=1, stride=2, padding='same', add_bias=False)
residual = layers.norm(residual, name="batch_normalization")
x = layers.separable_conv(x, filters_out=128, kernel_size=3, padding='same', add_bias=False,
name='block2_sepconv1')
x = layers.norm(x, name='block2_sepconv1_bn')
x = layers.relu(x, name='block2_sepconv2_act')
x = layers.separable_conv(x, filters_out=128, kernel_size=3, padding='same', add_bias=False,
name='block2_sepconv2')
x = layers.norm(x, name='block2_sepconv2_bn')
x = layers.max_pool(x, 3, strides=2, padding='same', name='block2_pool')
x += residual
residual = layers.conv(x, filters_out=256, kernel_size=1, stride=2, padding='same', add_bias=False)
residual = layers.norm(residual, name="batch_normalization")
x = layers.relu(x, name='block3_sepconv1_act')
x = layers.separable_conv(x, filters_out=256, kernel_size=3,
padding='same',
add_bias=False,
name='block3_sepconv1')
x = layers.norm(x, name='block3_sepconv1_bn')
x = layers.relu(x, name='block3_sepconv2_act')
x = layers.separable_conv(x, filters_out=256, kernel_size=3,
padding='same',
add_bias=False,
name='block3_sepconv2')
x = layers.norm(x, name='block3_sepconv2_bn')
x = layers.max_pool(x, 3, strides=2,
padding='same',
name='block3_pool')
x += residual
residual = layers.conv(x, filters_out=728, kernel_size=1,
stride=2,
padding='same',
add_bias=False)
residual = layers.norm(residual, name="batch_normalization")
x = layers.relu(x, name='block4_sepconv1_act')
x = layers.separable_conv(x, filters_out=728, kernel_size=3,
padding='same',
add_bias=False,
name='block4_sepconv1')
x = layers.norm(x, name='block4_sepconv1_bn')
x = layers.relu(x, name='block4_sepconv2_act')
x = layers.separable_conv(x, filters_out=728, kernel_size=3,
padding='same',
add_bias=False,
name='block4_sepconv2')
x = layers.norm(x, name='block4_sepconv2_bn')
x = layers.max_pool(x, 3, strides=2,
padding='same',
name='block4_pool')
x += residual
for i in range(8):
residual = x
prefix = 'block' + str(i + 5)
x = layers.relu(x, name=prefix + '_sepconv1_act')
x = layers.separable_conv(x, filters_out=728, kernel_size=3,
padding='same',
add_bias=False,
name=prefix + '_sepconv1')
x = layers.norm(x, name=prefix + '_sepconv1_bn')
x = layers.relu(x, name=prefix + '_sepconv2_act')
x = layers.separable_conv(x, filters_out=728, kernel_size=3,
padding='same',
add_bias=False,
name=prefix + '_sepconv2')
x = layers.norm(x, name=prefix + '_sepconv2_bn')
x = layers.relu(x, name=prefix + '_sepconv3_act')
x = layers.separable_conv(x, filters_out=728, kernel_size=3,
padding='same',
add_bias=False,
name=prefix + '_sepconv3')
x = layers.norm(x, name=prefix + '_sepconv3_bn')
x += residual
residual = layers.conv(x, filters_out=1024, kernel_size=1, stride=2,
padding='same', add_bias=False)
residual = layers.norm(residual, name="batch_normalization")
x = layers.relu(x, name='block13_sepconv1_act')
x = layers.separable_conv(x, filters_out=728, kernel_size=3,
padding='same',
add_bias=False,
name='block13_sepconv1')
x = layers.norm(x, name='block13_sepconv1_bn')
x = layers.relu(x, name='block13_sepconv2_act')
x = layers.separable_conv(x, filters_out=1024, kernel_size=3,
padding='same',
add_bias=False,
name='block13_sepconv2')
x = layers.norm(x, name='block13_sepconv2_bn')
x = layers.max_pool(x, 3,
strides=2,
padding='same',
name='block13_pool')
x += residual
x = layers.separable_conv(x, filters_out=1536, kernel_size=3,
padding='same',
add_bias=False,
name='block14_sepconv1')
x = layers.norm(x, name='block14_sepconv1_bn')
x = layers.relu(x, name='block14_sepconv1_act')
x = layers.separable_conv(x, filters_out=2048, kernel_size=3,
padding='same',
add_bias=False,
name='block14_sepconv2')
x = layers.norm(x, name='block14_sepconv2_bn')
x = layers.relu(x, name='block14_sepconv2_act')
# Classification block
x = layers.avg_pool(x, kernel_size=10, strides=1, name='avg_pool')
x = layers.squeeze(x, axis=[1, 2], name='squeeze')
x = layers.fully_connected(x, self.num_classes, name='predictions')
x = layers.softmax(x, name='output-prob')
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)
"""
x = conv_norm_relu(img_input, 32, 3, strides=2, padding='VALID')
x = conv_norm_relu(x, 32, 3, padding='VALID')
x = conv_norm_relu(
x,
64,
3,
)
x = max_pool(x, 3, strides=2)
x = conv_norm_relu(x, 80, 1, padding='VALID')
x = conv_norm_relu(x, 192, 3, padding='VALID')
x = max_pool(x, 3, strides=2)
# mixed 0: 35 x 35 x 256
branch1x1 = conv_norm_relu(x, 64, 1)
branch5x5 = conv_norm_relu(x, 48, 1)
branch5x5 = conv_norm_relu(branch5x5, 64, 5)
branch3x3dbl = conv_norm_relu(x, 64, 1)
branch3x3dbl = conv_norm_relu(branch3x3dbl, 96, 3)
branch3x3dbl = conv_norm_relu(branch3x3dbl, 96, 3)
branch_pool = avg_pool(x, 3, strides=1, padding='SAME')
branch_pool = conv_norm_relu(branch_pool, 32, 1)
x = concat([branch1x1, branch5x5, branch3x3dbl, branch_pool],
axis=-1,
name='mixed0')
# mixed 1: 35 x 35 x 288
branch1x1 = conv_norm_relu(x, 64, 1)
branch5x5 = conv_norm_relu(x, 48, 1)
branch5x5 = conv_norm_relu(branch5x5, 64, 5)
branch3x3dbl = conv_norm_relu(x, 64, 1)
branch3x3dbl = conv_norm_relu(branch3x3dbl, 96, 3)
branch3x3dbl = conv_norm_relu(branch3x3dbl, 96, 3)
branch_pool = avg_pool(x, 3, strides=1, padding='SAME')
branch_pool = conv_norm_relu(branch_pool, 64, 1)
x = concat([branch1x1, branch5x5, branch3x3dbl, branch_pool],
axis=-1,
name='mixed1')
# mixed 2: 35 x 35 x 288
branch1x1 = conv_norm_relu(x, 64, 1)
branch5x5 = conv_norm_relu(x, 48, 1)
branch5x5 = conv_norm_relu(branch5x5, 64, 5)
branch3x3dbl = conv_norm_relu(x, 64, 1)
branch3x3dbl = conv_norm_relu(branch3x3dbl, 96, 3)
branch3x3dbl = conv_norm_relu(branch3x3dbl, 96, 3)
branch_pool = avg_pool(x, 3, strides=1, padding='SAME')
branch_pool = conv_norm_relu(branch_pool, 64, 1)
x = concat([branch1x1, branch5x5, branch3x3dbl, branch_pool],
axis=-1,
name='mixed2')
# mixed 3: 17 x 17 x 768
branch3x3 = conv_norm_relu(x, 384, 3, strides=2, padding='VALID')
branch3x3dbl = conv_norm_relu(x, 64, 1)
branch3x3dbl = conv_norm_relu(branch3x3dbl, 96, 3)
branch3x3dbl = conv_norm_relu(branch3x3dbl,
96,
3,
strides=2,
padding='VALID')
branch_pool = max_pool(x, 3, 2)
x = concat([branch3x3, branch3x3dbl, branch_pool],
axis=-1,
name='mixed3')
# mixed 4: 17 x 17 x 768
branch1x1 = conv_norm_relu(x, 192, 1)
branch7x7 = conv_norm_relu(x, 128, 1)
branch7x7 = conv_norm_relu(branch7x7, 128, 1, 7)
branch7x7 = conv_norm_relu(branch7x7, 192, 7, 1)
branch7x7dbl = conv_norm_relu(x, 128, 1, 1)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 128, 7, 1)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 128, 1, 7)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 128, 7, 1)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 192, 1, 7)
branch_pool = avg_pool(x, 3, strides=1, padding='SAME')
branch_pool = conv_norm_relu(branch_pool, 192, 1, 1)
x = concat([branch1x1, branch7x7, branch7x7dbl, branch_pool],
axis=-1,
name='mixed4')
# mixed 5, 6: 17 x 17 x 768
for i in range(2):
branch1x1 = conv_norm_relu(x, 192, 1, 1)
branch7x7 = conv_norm_relu(x, 160, 1, 1)
branch7x7 = conv_norm_relu(branch7x7, 160, 1, 7)
branch7x7 = conv_norm_relu(branch7x7, 192, 7, 1)
branch7x7dbl = conv_norm_relu(x, 160, 1, 1)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 160, 7, 1)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 160, 1, 7)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 160, 7, 1)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 192, 1, 7)
branch_pool = avg_pool(x, 3, strides=1, padding='SAME')
branch_pool = conv_norm_relu(branch_pool, 192, 1, 1)
x = concat([branch1x1, branch7x7, branch7x7dbl, branch_pool],
axis=-1,
name='mixed' + str(5 + i))
# mixed 7: 17 x 17 x 768
branch1x1 = conv_norm_relu(x, 192, 1, 1)
branch7x7 = conv_norm_relu(x, 192, 1, 1)
branch7x7 = conv_norm_relu(branch7x7, 192, 1, 7)
branch7x7 = conv_norm_relu(branch7x7, 192, 7, 1)
branch7x7dbl = conv_norm_relu(x, 192, 1, 1)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 192, 7, 1)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 192, 1, 7)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 192, 7, 1)
branch7x7dbl = conv_norm_relu(branch7x7dbl, 192, 1, 7)
branch_pool = avg_pool(x, 3, strides=1, padding='SAME')
branch_pool = conv_norm_relu(branch_pool, 192, 1, 1)
x = concat([branch1x1, branch7x7, branch7x7dbl, branch_pool],
axis=-1,
name='mixed7')
# mixed 8: 8 x 8 x 1280
branch3x3 = conv_norm_relu(x, 192, 1)
branch3x3 = conv_norm_relu(branch3x3,
320,
3,
strides=2,
padding='VALID')
branch7x7x3 = conv_norm_relu(x, 192, 1, 1)
branch7x7x3 = conv_norm_relu(branch7x7x3, 192, 1, 7)
branch7x7x3 = conv_norm_relu(branch7x7x3, 192, 7, 1)
branch7x7x3 = conv_norm_relu(branch7x7x3,
192,
3,
3,
strides=2,
padding='VALID')
branch_pool = max_pool(x, 3, strides=2)
x = concat([branch3x3, branch7x7x3, branch_pool],
axis=-1,
name='mixed8')
# mixed 9: 8 x 8 x 2048
for i in range(2):
branch1x1 = conv_norm_relu(x, 320, 1, 1)
branch3x3 = conv_norm_relu(x, 384, 1, 1)
branch3x3_1 = conv_norm_relu(branch3x3, 384, 1, 3)
branch3x3_2 = conv_norm_relu(branch3x3, 384, 3, 1)
branch3x3 = concat([branch3x3_1, branch3x3_2],
axis=-1,
name='mixed9_' + str(i))
branch3x3dbl = conv_norm_relu(x, 448, 1, 1)
branch3x3dbl = conv_norm_relu(branch3x3dbl, 384, 3, 3)
branch3x3dbl_1 = conv_norm_relu(branch3x3dbl, 384, 1, 3)
branch3x3dbl_2 = conv_norm_relu(branch3x3dbl, 384, 3, 1)
branch3x3dbl = concat([branch3x3dbl_1, branch3x3dbl_2], axis=-1)
branch_pool = avg_pool(x, 3, strides=1, padding='SAME')
branch_pool = conv_norm_relu(branch_pool, 192, 1, 1)
x = concat([branch1x1, branch3x3, branch3x3dbl, branch_pool],
axis=-1,
name='mixed' + str(9 + i))
# Classification block
x = avg_pool(x, kernel_size=8, strides=1, name='avg_pool')
x = squeeze(x, axis=[1, 2], name='squeeze')
x = fully_connected(x, self.num_classes, name='predictions')
x = softmax(x, name='output-prob')
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 = self.penultimate_filters // 24
x = layers.conv(img_input,
filters_out=self.stem_block_filters,
kernel_size=3,
stride=2,
padding='valid',
add_bias=False,
name='stem_conv1')
x = layers.norm(x,
axis=-1,
momentum=0.9997,
epsilon=1e-3,
name='stem_bn1')
p = None
x, p = self.reduction_a_cell(x,
p,
filters // (self.filter_multiplier**2),
block_id='stem_1')
x, p = self.reduction_a_cell(x,
p,
filters // self.filter_multiplier,
block_id='stem_2')
for i in range(self.num_blocks):
x, p = self.normal_a_cell(x, p, filters, block_id='%d' % i)
x, p0 = self.reduction_a_cell(x,
p,
filters * self.filter_multiplier,
block_id='reduce_%d' % self.num_blocks)
p = p0 if not self.skip_reduction else p
for i in range(self.num_blocks):
x, p = self.normal_a_cell(x,
p,
filters * self.filter_multiplier,
block_id='%d' %
(self.num_blocks + i + 1))
x, p0 = self.reduction_a_cell(x,
p,
filters * self.filter_multiplier**2,
block_id='reduce_%d' %
(2 * self.num_blocks))
p = p0 if not self.skip_reduction else p
for i in range(self.num_blocks):
x, p = self.normal_a_cell(x,
p,
filters * self.filter_multiplier**2,
block_id='%d' %
(2 * self.num_blocks + i + 1))
x = layers.relu(x, 'relu')
# Classification block
x = layers.avg_pool(x, kernel_size=7, strides=1, name='avg_pool')
x = layers.squeeze(x, axis=[1, 2], name='squeeze')
x = layers.fully_connected(x, self.num_classes, name='predictions')
x = layers.softmax(x, name='output-prob')
return x