def decoder_baseline(label_sizes, nb_bits=12, data_shape=(1, 64, 64),
depth=1, nb_filter=16, optimizer='adam'):
n = nb_filter
input = Input(shape=data_shape)
x = sequential([
conv2d_block(n, depth=depth, pooling='max'), # 32x32
conv2d_block(2*n, depth=depth, pooling='max'), # 16x16
conv2d_block(4*n, depth=depth, pooling='max'), # 8x8
conv2d_block(8*n, depth=depth, pooling='max'), # 4x4
])(input)
outputs, losses = decoder_end_block(x, label_sizes, nb_bits,
activation=lambda: ELU())
model = Model(input, list(outputs.values()))
model.compile(optimizer, loss=list(losses.values()),)
return model
def test_resnet():
n = 4
x = Input(shape=(1, 8, 8))
y = sequential([
conv2d_block(n),
resnet(n)
])(x)
model = Model(x, y)
assert model.get_output_shape_for((None, 1, 8, 8)) == (None, n, 8, 8)
def test_conv2d_block():
x = Input(shape=(1, 8, 8))
y = sequential(
conv2d_block(4)
)(x)
model = Model(x, y)
assert model.get_output_shape_for((None, 1, 8, 8)) == (None, 4, 8, 8)
x = Input(shape=(1, 8, 8))
y = sequential(
conv2d_block(4, pooling='avg')
)(x)
model = Model(x, y)
assert model.get_output_shape_for((None, 1, 8, 8)) == (None, 4, 4, 4)
x = Input(shape=(1, 8, 8))
y = sequential(
conv2d_block(4, up=True)
)(x)
model = Model(x, y)
assert model.get_output_shape_for((None, 1, 8, 8)) == (None, 4, 16, 16)
def decoder_baseline(label_sizes,
nb_bits=12,
data_shape=(1, 64, 64),
depth=1,
nb_filter=16,
optimizer='adam'):
n = nb_filter
input = Input(shape=data_shape)
x = sequential([
conv2d_block(n, depth=depth, pooling='max'), # 32x32
conv2d_block(2 * n, depth=depth, pooling='max'), # 16x16
conv2d_block(4 * n, depth=depth, pooling='max'), # 8x8
conv2d_block(8 * n, depth=depth, pooling='max'), # 4x4
])(input)
outputs, losses = decoder_end_block(x,
label_sizes,
nb_bits,
activation=lambda: ELU())
model = Model(input, list(outputs.values()))
model.compile(
optimizer,
loss=list(losses.values()),
)
return model
def simple_gan_generator(nb_units, z, labels, depth_map, tag3d, depth=2):
n = nb_units
depth_map_features = sequential([
conv2d_block(n),
conv2d_block(2 * n),
])(depth_map)
tag3d_features = sequential([
conv2d_block(n, subsample=2),
conv2d_block(2 * n, subsample=2),
])(tag3d)
x = sequential([
Dense(5 * n),
BatchNormalization(mode=2),
Activation('relu'),
Dense(5 * n),
BatchNormalization(mode=2),
Activation('relu'),
])(concat([z, labels]))
blur = InBounds(0, 1, clip=True)(Dense(1)(x))
x = sequential([
Dense(8 * 4 * 4 * n),
Activation('relu'),
BatchNormalization(mode=2),
Reshape((8 * n, 4, 4)),
])(x)
x = sequential([
conv2d_block(8 * n, filters=1, depth=1, up=True), # 4x4 -> 8x8
conv2d_block(8 * n, depth=depth, up=True), # 8x8 -> 16x16
])(x)
off_depth_map = sequential([
conv2d_block(2 * n, depth=depth),
])(concat([x, depth_map_features]))
light = sequential([
conv2d_block(2 * n, depth=depth, up=True), # 16x16 -> 32x32
conv2d_block(n, depth=depth, up=True), # 32x32 -> 64x64
])(off_depth_map)
def get_light(x):
return sequential([
conv2d_block(1, filters=1, batchnorm=False),
GaussianBlur(sigma=4),
InBounds(0, 1, clip=True),
])(x)
light_sb = get_light(light)
light_sw = get_light(light)
light_t = get_light(light)
background = sequential([
conv2d_block(2 * n, depth=depth, up=True), # 16x16 -> 32x32
conv2d_block(n, depth=depth, up=True), # 32x32 -> 64x64
conv2d_block(1, batchnorm=False),
InBounds(-1, 1, clip=True),
])(off_depth_map)
details = sequential([
conv2d_block(2 * n, depth=depth, up=True), # 16x16 -> 32x32
conv2d_block(n, depth=depth, up=True), # 32x32 -> 64x64
conv2d_block(1, depth=1, batchnorm=False),
InBounds(-1, 1, clip=True)
])(concat(tag3d_features, off_depth_map))
return blur, [light_sb, light_sw, light_t], background, details
def get_light(x):
return sequential([
conv2d_block(1, filters=1, batchnorm=False),
GaussianBlur(sigma=4),
InBounds(0, 1, clip=True),
])(x)
def get_light(x):
return sequential([
conv2d_block(1, filters=1, batchnorm=False),
GaussianBlur(sigma=4),
InBounds(0, 1, clip=True),
])(x)
def simple_gan_generator(nb_units, z, labels, depth_map,
tag3d, depth=2):
n = nb_units
depth_map_features = sequential([
conv2d_block(n),
conv2d_block(2*n),
])(depth_map)
tag3d_features = sequential([
conv2d_block(n, subsample=2),
conv2d_block(2*n, subsample=2),
])(tag3d)
x = sequential([
Dense(5*n),
BatchNormalization(mode=2),
Activation('relu'),
Dense(5*n),
BatchNormalization(mode=2),
Activation('relu'),
])(concat([z, labels]))
blur = InBounds(0, 1, clip=True)(Dense(1)(x))
x = sequential([
Dense(8*4*4*n),
Activation('relu'),
BatchNormalization(mode=2),
Reshape((8*n, 4, 4)),
])(x)
x = sequential([
conv2d_block(8*n, filters=1, depth=1, up=True), # 4x4 -> 8x8
conv2d_block(8*n, depth=depth, up=True), # 8x8 -> 16x16
])(x)
off_depth_map = sequential([
conv2d_block(2*n, depth=depth),
])(concat([x, depth_map_features]))
light = sequential([
conv2d_block(2*n, depth=depth, up=True), # 16x16 -> 32x32
conv2d_block(n, depth=depth, up=True), # 32x32 -> 64x64
])(off_depth_map)
def get_light(x):
return sequential([
conv2d_block(1, filters=1, batchnorm=False),
GaussianBlur(sigma=4),
InBounds(0, 1, clip=True),
])(x)
light_sb = get_light(light)
light_sw = get_light(light)
light_t = get_light(light)
background = sequential([
conv2d_block(2*n, depth=depth, up=True), # 16x16 -> 32x32
conv2d_block(n, depth=depth, up=True), # 32x32 -> 64x64
conv2d_block(1, batchnorm=False),
InBounds(-1, 1, clip=True),
])(off_depth_map)
details = sequential([
conv2d_block(2*n, depth=depth, up=True), # 16x16 -> 32x32
conv2d_block(n, depth=depth, up=True), # 32x32 -> 64x64
conv2d_block(1, depth=1, batchnorm=False),
InBounds(-1, 1, clip=True)
])(concat(tag3d_features, off_depth_map))
return blur, [light_sb, light_sw, light_t], background, details
