def construct_ls_vo_rt_model(inputs,
cropping=((0, 0), (0, 0)),
output_size=500,
regularization=0,
kernel_initializer='glorot_normal'):
inputs = concat(inputs)
features, bottleneck = construct_encoder(
inputs, kernel_initializer=kernel_initializer)
reconstructed_flow = construct_flow_decoder(
bottleneck, cropping=cropping, output_channels=inputs.shape[-1].value)
fc_rotation = dense(features,
output_size=output_size,
layers_num=2,
regularization=regularization,
kernel_initializer=kernel_initializer,
name='rotation')
fc_translation = dense(features,
output_size=output_size,
layers_num=2,
regularization=regularization,
kernel_initializer=kernel_initializer,
name='translation')
outputs = construct_outputs(
[fc_rotation] * 3 + [fc_translation] * 3,
regularization=regularization) + [reconstructed_flow]
return outputs
def construct_simple_model(inputs,
conv_layers=3,
conv_filters=64,
kernel_sizes=3,
strides=1,
paddings='same',
fc_layers=2,
output_sizes=500,
activations='elu',
regularizations=0,
batch_norms=True):
if type(conv_filters) != list:
conv_filters = [conv_filters] * conv_layers
if type(kernel_sizes) != list:
kernel_sizes = [kernel_sizes] * conv_layers
if type(strides) != list:
strides = [strides] * conv_layers
if type(paddings) != list:
paddings = [paddings] * conv_layers
if type(output_sizes) != list:
output_sizes = [output_sizes] * fc_layers
if type(activations) != list:
activations = [activations] * (conv_layers + fc_layers)
if type(regularizations) != list:
regularizations = [regularizations] * (conv_layers + fc_layers)
if type(batch_norms) != list:
batch_norms = [batch_norms] * (conv_layers + fc_layers)
inputs = concat(inputs)
conv = inputs
for i in range(conv_layers):
conv = conv2d(conv,
conv_filters[i],
kernel_size=kernel_sizes[i],
batch_norm=batch_norms[i],
padding=paddings[i],
kernel_initializer='glorot_normal',
strides=strides[i],
activation=activations[i],
activity_regularizer=l2(regularizations[i]))
flatten = Flatten()(conv)
fc = flatten
for i in range(fc_layers):
fc = Dense(output_sizes[i],
kernel_initializer='glorot_normal',
activation=activations[i + conv_layers],
activity_regularizer=l2(regularizations[i]))(fc)
outputs = construct_outputs([fc] * 6)
return outputs
def construct_st_vo_model(inputs, kernel_initializer='glorot_normal'):
inputs = concat(inputs)
conv1 = Conv2D(64,
kernel_size=3,
strides=2,
kernel_initializer=kernel_initializer,
name='conv1')(inputs)
pool1 = MaxPooling2D(pool_size=4, strides=4, name='pool1')(conv1)
conv2 = Conv2D(20,
kernel_size=3,
kernel_initializer=kernel_initializer,
name='conv2')(pool1)
pool2 = MaxPooling2D(pool_size=2, strides=2, name='pool2')(conv2)
flatten1 = Flatten(name='flatten1')(pool1)
flatten2 = Flatten(name='flatten2')(pool2)
merged = concatenate([flatten1, flatten2], axis=1)
activation = Activation('relu')(merged)
fc = dense(activation, kernel_initializer=kernel_initializer, name='fc')
outputs = construct_outputs([fc] * 6)
return outputs
def construct_ls_vo_rt_no_decoder_model(inputs,
output_size=500,
regularization=0,
kernel_initializer='glorot_normal'):
inputs = concat(inputs)
features, _ = construct_encoder(inputs,
kernel_initializer=kernel_initializer)
fc_rotation = dense(features,
output_size=output_size,
layers_num=2,
regularization=regularization,
kernel_initializer=kernel_initializer,
name='rotation')
fc_translation = dense(features,
output_size=output_size,
layers_num=2,
regularization=regularization,
kernel_initializer=kernel_initializer,
name='translation')
outputs = construct_outputs([fc_rotation] * 3 + [fc_translation] * 3,
regularization=regularization)
return outputs
def construct_resnet50_model(inputs,
weights='imagenet',
kernel_initializer='glorot_normal'):
inputs = concat(inputs)
conv0 = Conv2D(3,
kernel_size=7,
padding='same',
activation='relu',
kernel_initializer=kernel_initializer,
name='conv0')(inputs)
features = ResNet50(weights=weights, include_top=False,
pooling=None)(conv0)
flatten = Flatten()(features)
fc = dense(flatten,
output_size=500,
num_layers=2,
activation='relu',
kernel_initializer=kernel_initializer)
outputs = construct_outputs([fc] * 6)
return outputs
def construct_encoder(inputs,
layers=4,
filters=[[16, 16, 32]] * 4,
kernel_sizes=[[7, 5, 3]] * 4,
strides=2,
dilation_rates=None,
kernel_initializer='glorot_normal',
use_gated_convolutions=False):
conv = gated_conv2d if use_gated_convolutions else conv2d
makelist = lambda x: [x] if isinstance(x, int) else x
if isinstance(filters, int):
filters = [filters] * layers
if isinstance(kernel_sizes, int):
kernel_sizes = [kernel_sizes] * layers
if isinstance(strides, int):
strides = [strides] * layers
if dilation_rates is None:
dilation_rates = [1] * layers
for i in range(layers):
layer_filters = makelist(filters[i])
layer_kernel_sizes = makelist(kernel_sizes[i])
layer_dilation_rates = makelist(dilation_rates[i])
layer_stride = strides[i]
convs = max(len(layer_filters), max(len(layer_kernel_sizes), len(layer_dilation_rates)))
assert len(layer_filters) in (1, convs)
assert len(layer_kernel_sizes) in (1, convs)
assert len(layer_dilation_rates) in (1, convs)
if len(layer_filters) == 1:
layer_filters *= convs
if len(layer_kernel_sizes) == 1:
layer_kernel_sizes *= convs
if len(layer_dilation_rates) == 1:
layer_dilation_rates *= convs
print(f'Layer {i + 1}: {convs} convolutions')
outputs = []
for flt, kernel_size, dilation_rate in zip(layer_filters, layer_kernel_sizes, layer_dilation_rates):
print(f'\tfilters={flt}, kernel size={kernel_size}, stride={layer_stride}, dilation rate={dilation_rate}')
outputs.append(
conv(inputs,
flt,
kernel_size=kernel_size,
strides=layer_stride,
dilation_rate=dilation_rate,
padding='same',
activation='relu',
kernel_initializer=kernel_initializer)
)
inputs = concat(outputs)
merged = conv(inputs,
64,
kernel_size=1,
padding='same',
activation='relu',
kernel_initializer=kernel_initializer)
flatten = Flatten()(merged)
return flatten
def construct_encoder(inputs,
use_depth=True,
use_flow=True,
use_association_layer=True,
use_grid=False,
concat_axis=3,
filters=256,
stride=2,
f_x=1,
f_y=1,
c_x=0.5,
c_y=0.5,
kernel_initializer='glorot_normal'):
# flow convolutional branch
if use_flow:
flow = concat(inputs[:2])
if use_grid:
flow = add_grid(flow, f_x=f_x, f_y=f_y, c_x=c_x, c_y=c_y)
for i in range(1, 5):
flow = conv2d(flow,
2 ** (i + 5),
kernel_size=3,
strides=2,
kernel_initializer=kernel_initializer,
name=f'conv{i}_flow')
# depth convolutional branch
if use_depth:
if use_association_layer: # pass flow_z as input
depth = depth_flow(concat(inputs))
else:
depth = concat(inputs[2:])
if use_grid:
depth = add_grid(depth, f_x=f_x, f_y=f_y, c_x=c_x, c_y=c_y)
for i in range(1, 5):
depth = conv2d(depth,
2 ** (i + 5),
kernel_size=3,
strides=2,
kernel_initializer=kernel_initializer,
name=f'conv{i}_depth')
if use_flow and use_depth:
concatenated = concat([flow, depth])
elif use_flow:
concatenated = flow
elif use_depth:
concatenated = depth
merged = conv2d(concatenated,
filters,
kernel_size=1,
strides=stride,
kernel_initializer=kernel_initializer,
name='merge')
flatten = Flatten()(merged)
return flatten
def construct_flexible_model(inputs,
kernel_sizes=[7, 5, 3, 3, 3, 3],
strides=[2, 1, 4, 1, 2, 1],
dilation_rates=None,
output_size=500,
regularization=0,
activation='relu',
kernel_initializer='glorot_normal',
use_gated_convolutions=False,
use_batch_norm=False,
split=False,
transform=None,
agnostic=True,
channel_wise=False,
concat_scale_to_fc=False,
multiply_outputs_by_scale=False,
confidence_mode=None):
inputs, scale = transform_inputs(inputs,
transform=transform,
agnostic=agnostic,
channel_wise=channel_wise)
features = construct_encoder(inputs,
kernel_sizes=kernel_sizes,
strides=strides,
dilation_rates=dilation_rates,
activation=activation,
kernel_initializer=kernel_initializer,
use_gated_convolutions=use_gated_convolutions,
use_batch_norm=use_batch_norm)
if concat_scale_to_fc:
fc_rotation = features
fc_translation = features
for i in range(2):
fc_rotation = concat([fc_rotation, scale])
fc_translation = concat([fc_translation, scale])
fc_rotation = dense(fc_rotation,
output_size=output_size,
layers_num=1,
regularization=regularization,
activation=activation,
kernel_initializer=kernel_initializer)
fc_translation = dense(fc_translation,
output_size=output_size,
layers_num=1,
regularization=regularization,
activation=activation,
kernel_initializer=kernel_initializer)
else:
fc_rotation = dense(features,
output_size=output_size,
layers_num=2,
regularization=regularization,
activation=activation,
kernel_initializer=kernel_initializer,
name='rotation')
fc_translation = dense(features,
output_size=output_size,
layers_num=2,
regularization=regularization,
activation=activation,
kernel_initializer=kernel_initializer,
name='translation')
if split:
fc = chunk(fc_rotation, n=3) + chunk(fc_translation, n=3)
else:
fc = [fc_rotation] * 3 + [fc_translation] * 3
outputs = construct_outputs(
fc,
regularization=regularization,
scale=scale if multiply_outputs_by_scale else None,
confidence_mode=confidence_mode)
return outputs
def construct_sequential_rt_model(inputs,
intrinsics,
use_input_flow=False,
use_diff_flow=False,
use_rotation_flow=False,
kernel_sizes=[7, 5, 3, 3, 3, 3],
strides=[2, 1, 4, 1, 2, 1],
dilation_rates=None,
hidden_size=500,
regularization=0,
activation='relu',
kernel_initializer='glorot_normal',
use_gated_convolutions=False,
use_batch_norm=False,
return_confidence=False):
assert use_input_flow or use_diff_flow or use_rotation_flow
inputs = concat(inputs)
features_rotation = construct_encoder(
inputs,
kernel_sizes=kernel_sizes,
strides=strides,
dilation_rates=dilation_rates,
kernel_initializer=kernel_initializer,
use_gated_convolutions=use_gated_convolutions,
use_batch_norm=use_batch_norm)
fc_rotation = dense(features_rotation,
output_size=hidden_size,
regularization=regularization,
activation=activation,
kernel_initializer=kernel_initializer,
layers_num=2,
name='rotation')
output_rotation = construct_output(fc_rotation,
name='rotation',
regularization=regularization)
rotation_flow = flow_composer(output_rotation, intrinsics=intrinsics)
inputs_for_translation = []
if use_input_flow:
inputs_for_translation.append(inputs)
if use_diff_flow:
inputs_for_translation.append(Subtract()([inputs, rotation_flow]))
if use_rotation_flow:
inputs_for_translation.append(rotation_flow)
features_translation = construct_encoder(
concat(inputs_for_translation),
kernel_sizes=kernel_sizes,
strides=strides,
dilation_rates=dilation_rates,
kernel_initializer=kernel_initializer,
use_gated_convolutions=use_gated_convolutions,
use_batch_norm=use_batch_norm)
fc_translation = dense(features_translation,
output_size=hidden_size,
regularization=regularization,
activation=activation,
kernel_initializer=kernel_initializer,
layers_num=2,
name='translation')
output_translation = construct_output(fc_translation,
name='translation',
regularization=regularization)
return output_rotation + output_translation
