def build_frozen_conv6_lottery(args, init_values, mask_values):
return SequentialNetwork([
FreezeConv2D(64, 3, init_values[0], init_values[1], mask_values[0], mask_values[1], kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
Activation('relu'),
FreezeConv2D(64, 3, init_values[2], init_values[3], mask_values[2], mask_values[3], kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_2'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
FreezeConv2D(128, 3, init_values[4], init_values[5], mask_values[4], mask_values[5], kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_3'),
Activation('relu'),
FreezeConv2D(128, 3, init_values[6], init_values[7], mask_values[6], mask_values[7], kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_4'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
FreezeConv2D(256, 3, init_values[8], init_values[9], mask_values[8], mask_values[9], kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_5'),
Activation('relu'),
FreezeConv2D(256, 3, init_values[10], init_values[11], mask_values[10], mask_values[11], kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_6'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Flatten(),
# Dropout(0.5),
FreezeDense(256, init_values[12], init_values[13], mask_values[12], mask_values[13], kernel_initializer=glorot_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
# Dropout(0.5),
FreezeDense(256, init_values[14], init_values[15], mask_values[14], mask_values[15], kernel_initializer=glorot_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_2'),
# Dropout(0.5),
FreezeDense(10, init_values[16], init_values[17], mask_values[16], mask_values[17], kernel_initializer=glorot_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_3')
])
def build_conv6_lottery(args):
return SequentialNetwork([
Conv2D(64, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
Activation('relu'),
Conv2D(64, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_2'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(128, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_3'),
Activation('relu'),
Conv2D(128, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_4'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(256, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_5'),
Activation('relu'),
Conv2D(256, 3, kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_6'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Flatten(),
# Dropout(0.5),
Dense(256, kernel_initializer=glorot_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
# Dropout(0.5),
Dense(256, kernel_initializer=glorot_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_2'),
# Dropout(0.5),
Dense(10, kernel_initializer=glorot_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_3')
])
def build_vgg_mini(args):
return SequentialNetwork([
Conv2D(64, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(128, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_2'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(256, (3, 3), kernel_initializer=he_normal, padding='same', activation=relu, kernel_regularizer=l2reg(args.l2), name='conv2D_3'),
MaxPooling2D((2, 2), (2, 2)),
Flatten(),
Dense(512, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
Dropout(0.5),
Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_2')
])
def build_lenet_conv(args): # ok this is a slightly modified lenet
return SequentialNetwork([
Conv2D(20, 5, kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
# BatchNormalization(momentum=0.0, name='batch_norm_1'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(40, 5, kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_2'),
# BatchNormalization(momentum=0.0, name='batch_norm_2'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Flatten(),
Dropout(0.25),
Dense(400, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
Dropout(0.5),
Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_2')
])
def build_conv2_supermask(args):
kwargs = {}
if args.signed_constant:
kwargs['signed_constant'] = True
kwargs['const_multiplier'] = args.signed_constant_multiplier
if args.dynamic_scaling:
kwargs['dynamic_scaling'] = True
return SequentialNetwork([
MaskedConv2D(64,
3,
kernel_initializer=glorot_normal,
sigmoid_bias=args.sigmoid_bias,
round_mask=args.round_mask,
padding='same',
kernel_regularizer=l2reg(args.l2),
name='conv2D_1',
**kwargs),
Activation('relu'),
MaskedConv2D(64,
3,
kernel_initializer=glorot_normal,
sigmoid_bias=args.sigmoid_bias,
round_mask=args.round_mask,
padding='same',
kernel_regularizer=l2reg(args.l2),
name='conv2D_2',
**kwargs),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Flatten(),
MaskedDense(256,
kernel_initializer=glorot_normal,
sigmoid_bias=args.sigmoid_bias,
round_mask=args.round_mask,
activation=relu,
kernel_regularizer=l2reg(args.l2),
name='fc_1',
**kwargs),
MaskedDense(256,
kernel_initializer=glorot_normal,
sigmoid_bias=args.sigmoid_bias,
round_mask=args.round_mask,
activation=relu,
kernel_regularizer=l2reg(args.l2),
name='fc_2',
**kwargs),
MaskedDense(10,
kernel_initializer=glorot_normal,
sigmoid_bias=args.sigmoid_bias,
round_mask=args.round_mask,
activation=None,
kernel_regularizer=l2reg(args.l2),
name='fc_3',
**kwargs)
])
def build_linknet_2(args):
layers = conv_bn_relu(32, 3, stride=1, name="block1_conv1")
for layer in conv_bn_relu(32, 3, stride=1, name="block1_conv2"):
layers.append(layer)
layers.append(MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="block1_pool"))
layers.append(Activation('relu'))
layers.append(Flatten())
layers.append(Dense(400, kernel_initializer=he_normal, activation=relu, kernel_regularizer=l2reg(0), name='fc_1'))
layers.append(Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(0), name='fc_2'))
return SequentialNetwork(layers)
def build_basic_model(args):
return SequentialNetwork([
Conv2D(16, 2, padding='same', name='conv2D_1'),
# BatchNormalization(momentum=0.0, name='batch_norm_1'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(32, 2, padding='same', name='conv2D_2'),
# BatchNormalization(momentum=0.0, name='batch_norm_2'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Conv2D(64, 2, padding='same', name='conv2D_3'),
Activation('relu'),
MaxPooling2D((2,2), (2,2)),
GlobalAveragePooling2D(),
Dense(1000, activation=relu),
Dropout(0.2),
Dense(1000, activation=relu, name='fc_1'),
Dropout(0.2),
Dense(5, activation= None, name='fc_2')
])
def build_masked_conv2_lottery(args, mask_values):
return SequentialNetwork([
MaskedConv2D(64, 3, mask_values[0], mask_values[1], kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
Activation('relu'),
MaskedConv2D(64, 3, mask_values[2], mask_values[3], kernel_initializer=glorot_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_2'),
Activation('relu'),
MaxPooling2D((2, 2), (2, 2)),
Flatten(),
MaskedDense(256, mask_values[4], mask_values[5], kernel_initializer=glorot_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_1'),
MaskedDense(256, mask_values[6], mask_values[7], kernel_initializer=glorot_normal, activation=relu, kernel_regularizer=l2reg(args.l2), name='fc_2'),
MaskedDense(10, mask_values[8], mask_values[9], kernel_initializer=glorot_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_3')
])
def __init__(self, classes=1, dropout=0.5, feature_scale=4):
super(LinkNet, self).__init__()
self.conv_bn_relu_1 = self.track_layers(conv_bn_relu(32, 3, stride=1, name="block1_conv1"))
self.conv_bn_relu_2 = self.track_layers(conv_bn_relu(32, 3, stride=1, name="block1_conv2"))
self.maxPool = MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="block1_pool")
layers = [2, 2, 2, 2, 2]
filters = [64, 128, 256, 512, 32]
enc1 = self.track_layers(encoder(m=32, n=filters[0], blocks=layers[0], stride=1, name='encoder1'))
enc2 = self.track_layers(encoder(m=filters[0], n=filters[1], blocks=layers[1], stride=2, name='encoder2'))
enc3 = self.track_layers(encoder(m=filters[1], n=filters[2], blocks=layers[2], stride=2, name='encoder3'))
enc4 = self.track_layers(encoder(m=filters[2], n=filters[3], blocks=layers[3], stride=2, name='encoder4'))
enc5 = self.track_layers(encoder(m=filters[3], n=filters[4], blocks=layers[4], stride=2, name='encoder5'))
self.decoder = self.track_layer(LinkNetDecoder(enc1, enc2, enc3, enc4, enc5, filters, feature_scale))
# self.dropout = tfkeras.layers.SpatialDropout2D(dropout)
self.conv1 = self.track_layer(Conv2D(filters=classes, kernel_size=(1, 1), padding='same', name='prediction'))
self.act = self.track_layer(Activation('sigmoid', name='mask'))
def __init__(self,
rpn_params,
bsamp_params,
nms_params,
l2=0,
im_h=64,
im_w=64,
coordconv=False,
clip=True,
filtersame=False):
super(RegionProposalSampler, self).__init__()
self.rpn_params = rpn_params
self.bsamp_params = bsamp_params
self.nms_params = nms_params
self.im_h = im_h
self.im_w = im_w
self.clip = clip
_pad = 'same' if filtersame else 'valid'
_dim = 16 if filtersame else 13
if coordconv:
self.l(
'bottom_conv',
SequentialNetwork(
[
AddCoords(x_dim=im_w,
y_dim=im_h,
with_r=False,
skiptile=True), # (batch, 64, 64, 4 or 5)
Conv2D(32, (5, 5),
padding=_pad,
kernel_initializer=he_normal,
kernel_regularizer=l2reg(l2)),
ReLu,
MaxPooling2D(pool_size=2, strides=2),
Conv2D(64, (5, 5),
padding=_pad,
kernel_initializer=he_normal,
kernel_regularizer=l2reg(l2)),
ReLu,
MaxPooling2D(pool_size=2, strides=2),
],
name='bottom_conv'))
self.l(
'another_conv',
SequentialNetwork([
AddCoords(
x_dim=_dim, y_dim=_dim, with_r=False, skiptile=True),
Conv2D(rpn_params.rpn_hidden_dim, (3, 3),
padding='same',
kernel_initializer=he_normal,
kernel_regularizer=l2reg(l2)), ReLu
],
name='another_conv'))
self.l(
'box_mover',
SequentialNetwork([
Conv2D(rpn_params.rpn_hidden_dim, (3, 3),
padding='same',
kernel_initializer=he_normal,
kernel_regularizer=l2reg(l2)), ReLu,
AddCoords(
x_dim=_dim, y_dim=_dim, with_r=False, skiptile=True),
Conv2D(4 * rpn_params.num_anchors, (1, 1),
kernel_initializer=tf.zeros_initializer,
bias_initializer=tf.constant_initializer([0.]),
kernel_regularizer=l2reg(l2))
],
name='box_mover')) # (13,13,4*k)
else:
self.l(
'bottom_conv',
SequentialNetwork([
Conv2D(32, (5, 5),
padding=_pad,
kernel_initializer=he_normal,
kernel_regularizer=l2reg(l2)),
ReLu,
MaxPooling2D(pool_size=2, strides=2),
Conv2D(64, (5, 5),
padding=_pad,
kernel_initializer=he_normal,
kernel_regularizer=l2reg(l2)),
ReLu,
MaxPooling2D(pool_size=2, strides=2),
],
name='bottom_conv'))
self.l(
'another_conv',
SequentialNetwork([
Conv2D(rpn_params.rpn_hidden_dim, (3, 3),
padding='same',
kernel_initializer=he_normal,
kernel_regularizer=l2reg(l2)), ReLu
],
name='another_conv'))
self.l('box_mover',
SequentialNetwork([
Conv2D(rpn_params.rpn_hidden_dim, (3, 3),
padding='same',
kernel_initializer=he_normal,
kernel_regularizer=l2reg(l2)), ReLu,
Conv2D(4 * rpn_params.num_anchors, (1, 1),
kernel_initializer=tf.zeros_initializer,
bias_initializer=tf.constant_initializer([0.]),
kernel_regularizer=l2reg(l2))
],
name='box_mover')) # (13,13,4*k)
self.l('box_scorer',
SequentialNetwork([
Conv2D(2 * rpn_params.num_anchors, (1, 1),
kernel_initializer=he_normal,
kernel_regularizer=l2reg(l2)),
],
name='box_scorer')) # (13,13,2*k)
return