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)
"""
channel_axis = -1
x = conv_norm_relu(img_input,
64,
7,
strides=2,
padding='SAME',
name='InceptionV1/Conv2d_1a_7x7',
norm_suffix="/BatchNorm",
weight_suffix="weights",
conv_suffix="")
x = max_pool(x, 3, strides=2, padding='same', name='MaxPool_2a_3x3')
x = conv_norm_relu(x,
64,
1,
padding='same',
name='InceptionV1/Conv2d_2b_1x1',
weight_suffix="weights",
conv_suffix="",
norm_suffix="/BatchNorm")
x = conv_norm_relu(x,
192,
3,
padding='same',
name='InceptionV1/Conv2d_2c_3x3',
weight_suffix="weights",
conv_suffix="",
norm_suffix="/BatchNorm")
x = max_pool(x, 3, strides=2, padding='same', name='MaxPool_3a_3x3')
# Now the '3' level inception units
x = self.inception_block(x, ((64, ), (96, 128), (16, 32), (32, )),
channel_axis, 'InceptionV1/Mixed_3b')
x = self.inception_block(x, ((128, ), (128, 192), (32, 96), (64, )),
channel_axis, 'InceptionV1/Mixed_3c')
x = max_pool(x, 3, strides=2, padding='same', name='MaxPool_4a_3x3')
# Now the '4' level inception units
x = self.inception_block(x, ((192, ), (96, 208), (16, 48), (64, )),
channel_axis, 'InceptionV1/Mixed_4b')
x = self.inception_block(x, ((160, ), (112, 224), (24, 64), (64, )),
channel_axis, 'InceptionV1/Mixed_4c')
x = self.inception_block(x, ((128, ), (128, 256), (24, 64), (64, )),
channel_axis, 'InceptionV1/Mixed_4d')
x = self.inception_block(x, ((112, ), (144, 288), (32, 64), (64, )),
channel_axis, 'InceptionV1/Mixed_4e')
x = self.inception_block(x, ((256, ), (160, 320), (32, 128), (128, )),
channel_axis, 'InceptionV1/Mixed_4f')
x = max_pool(x, 2, strides=2, padding='same', name='MaxPool_5a_2x2')
# Now the '5' level inception units
x = self.inception_block(x, ((256, ), (160, 320), (32, 128), (128, )),
channel_axis, 'InceptionV1/Mixed_5b')
x = self.inception_block(x, ((384, ), (192, 384), (48, 128), (128, )),
channel_axis, 'InceptionV1/Mixed_5c')
# Classification block
x = avg_pool(x,
kernel_size=7,
strides=1,
name='avg_pool',
padding='valid')
x = conv(x,
filters_out=self.num_classes + 1,
kernel_size=1,
padding='valid',
add_bias=True,
name='InceptionV1/Logits/Conv2d_0c_1x1',
weight_suffix="weights",
bias_suffix="biases")
x = squeeze(x, axis=[1, 2], name='squeeze')
x = softmax(x, name='output-prob')
return x
def inception_block(
x: TensorType,
specs: Tuple,
channel_axis: int,
name: str,
weight_suffix: Optional[str] = "weights",
conv_suffix: Optional[str] = "",
norm_suffix: Optional[str] = "/BatchNorm") -> TensorType:
"""Inception block.
Args:
x: input_tensor
specs: Number of filters per branch.
channel_axis: channel dimension
name: Prefix for ths block.
weight_suffix: Name of learn-able parameters in conv.
conv_suffix: Suffix for conv layer.
norm_suffix: Suffix for batch norm.
Returns: Concatenated output of inception block.
"""
(br0, br1, br2, br3) = specs # ((64,), (96,128), (16,32), (32,))
branch_0 = conv_norm_relu(x,
br0[0],
1,
1,
name=name + "/Branch_0/Conv2d_0a_1x1",
weight_suffix=weight_suffix,
conv_suffix=conv_suffix,
norm_suffix=norm_suffix)
branch_1 = conv_norm_relu(x,
br1[0],
1,
1,
name=name + "/Branch_1/Conv2d_0a_1x1",
weight_suffix=weight_suffix,
conv_suffix=conv_suffix,
norm_suffix=norm_suffix)
branch_1 = conv_norm_relu(branch_1,
br1[1],
3,
3,
name=name + "/Branch_1/Conv2d_0b_3x3",
weight_suffix=weight_suffix,
conv_suffix=conv_suffix,
norm_suffix=norm_suffix)
branch_2 = conv_norm_relu(x,
br2[0],
1,
1,
name=name + "/Branch_2/Conv2d_0a_1x1",
weight_suffix=weight_suffix,
conv_suffix=conv_suffix,
norm_suffix=norm_suffix)
if name == "InceptionV1/Mixed_5b":
branch_2b_name = name + "/Branch_2/Conv2d_0a_3x3"
else:
branch_2b_name = name + "/Branch_2/Conv2d_0b_3x3"
branch_2 = conv_norm_relu(branch_2,
br2[1],
3,
3,
name=branch_2b_name,
weight_suffix=weight_suffix,
conv_suffix=conv_suffix,
norm_suffix=norm_suffix)
branch_3 = max_pool(x,
3,
strides=1,
padding='same',
name=name + "/Branch_3/Conv2d_0a_max")
branch_3 = conv_norm_relu(branch_3,
br3[0],
1,
1,
name=name + "/Branch_3/Conv2d_0b_1x1",
weight_suffix=weight_suffix,
conv_suffix=conv_suffix,
norm_suffix=norm_suffix)
x = concat([branch_0, branch_1, branch_2, branch_3],
axis=channel_axis,
name=name + "_Concatenated")
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