def get_shallow_convnet(window_size=4096, channels=2, output_size=84):
inputs = Input(shape=(window_size, channels))
conv = ComplexConv1D(32, 512, strides=16, activation='relu')(inputs)
pool = AveragePooling1D(pool_size=4, strides=2)(conv)
pool = Permute([2, 1])(pool)
flattened = Flatten()(pool)
dense = ComplexDense(2048, activation='relu')(flattened)
predictions = ComplexDense(output_size,
activation='sigmoid',
bias_initializer=Constant(value=-5))(dense)
predictions = GetReal(predictions)
model = Model(inputs=inputs, outputs=predictions)
model.compile(optimizer=Adam(lr=1e-4),
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def getResidualBlock(I, filter_size, featmaps, stage, block, shortcut,
convArgs, bnArgs, d):
"""Get residual block."""
activation = d.act
drop_prob = d.dropout
nb_fmaps1, nb_fmaps2 = featmaps
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
if K.image_data_format() == 'channels_first' and K.ndim(I) != 3:
channel_axis = 1
else:
channel_axis = -1
if d.model == "real":
O = BatchNormalization(name=bn_name_base + '_2a', **bnArgs)(I)
elif d.model == "complex":
O = ComplexBN(name=bn_name_base + '_2a', **bnArgs)(I)
O = Activation(activation)(O)
if shortcut == 'regular' or d.spectral_pool_scheme == "nodownsample":
if d.model == "real":
O = Conv2D(nb_fmaps1,
filter_size,
name=conv_name_base + '2a',
**convArgs)(O)
elif d.model == "complex":
O = ComplexConv2D(nb_fmaps1,
filter_size,
name=conv_name_base + '2a',
**convArgs)(O)
elif shortcut == 'projection':
if d.spectral_pool_scheme == "proj":
O = applySpectralPooling(O, d)
if d.model == "real":
O = Conv2D(nb_fmaps1,
filter_size,
name=conv_name_base + '2a',
strides=(2, 2),
**convArgs)(O)
elif d.model == "complex":
O = ComplexConv2D(nb_fmaps1,
filter_size,
name=conv_name_base + '2a',
strides=(2, 2),
**convArgs)(O)
if d.model == "real":
O = BatchNormalization(name=bn_name_base + '_2b', **bnArgs)(O)
O = Activation(activation)(O)
O = Conv2D(nb_fmaps2,
filter_size,
name=conv_name_base + '2b',
**convArgs)(O)
elif d.model == "complex":
O = ComplexBN(name=bn_name_base + '_2b', **bnArgs)(O)
O = Activation(activation)(O)
O = ComplexConv2D(nb_fmaps2,
filter_size,
name=conv_name_base + '2b',
**convArgs)(O)
if shortcut == 'regular':
O = Add()([O, I])
elif shortcut == 'projection':
if d.spectral_pool_scheme == "proj":
I = applySpectralPooling(I, d)
if d.model == "real":
X = Conv2D(
nb_fmaps2, (1, 1),
name=conv_name_base + '1',
strides=(2, 2) if d.spectral_pool_scheme != "nodownsample" else
(1, 1),
**convArgs)(I)
O = Concatenate(channel_axis)([X, O])
elif d.model == "complex":
X = ComplexConv2D(
nb_fmaps2, (1, 1),
name=conv_name_base + '1',
strides=(2, 2) if d.spectral_pool_scheme != "nodownsample" else
(1, 1),
**convArgs)(I)
O_real = Concatenate(channel_axis)([GetReal()(X), GetReal()(O)])
O_imag = Concatenate(channel_axis)([GetImag()(X), GetImag()(O)])
O = Concatenate(1)([O_real, O_imag])
return O
def getResidualBlock(I, filter_size, featmaps, stage, block, shortcut,
convArgs, convArgs_real, bnArgs, d):
"""Get residual block."""
activation = d.act
drop_prob = d.dropout
nb_fmaps1, nb_fmaps2 = featmaps
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
if K.image_data_format() == 'channels_first' and K.ndim(I) != 3:
channel_axis = 1
else:
channel_axis = -1
# if d.model == "real":
if "real" in d.model:
O = BatchNormalization(name=bn_name_base + '_2a', **bnArgs)(I)
# elif d.model == "complex":
elif "complex" in d.model:
O = ComplexBN(name=bn_name_base + '_2a', **bnArgs)(I)
if d.aact == "complex_joint_relu":
O = Lambda(ComplexJointReLU)(O)
else:
O = Activation(activation)(O)
if shortcut == 'regular' or d.spectral_pool_scheme == "nodownsample":
if d.model == "real":
O = Conv2D(nb_fmaps1,
filter_size,
name=conv_name_base + '2a',
**convArgs)(O)
elif d.model == "real_dws":
O = SeparableConv2D(nb_fmaps1,
filter_size,
name=conv_name_base + '2a',
**convArgs)(O)
elif (d.model
== "real_group") or (d.model == "real_group_pwc_full") or (
d.model == "real_group_pwc_group"):
O_g0 = Lambda(lambda O: O[:, :(O.shape[1] // 2), :, :])(O)
O_g1 = Lambda(lambda O: O[:, (O.shape[1] // 2):, :, :])(O)
O_g0 = Conv2D(nb_fmaps1 // 2,
filter_size,
name=conv_name_base + '2a_g0',
**convArgs)(O_g0)
O_g1 = Conv2D(nb_fmaps1 // 2,
filter_size,
name=conv_name_base + '2a_g1',
**convArgs)(O_g1)
O_g00 = Lambda(lambda O_g0: O_g0[:, :(O_g0.shape[1] // 2), :, :])(
O_g0)
O_g01 = Lambda(lambda O_g0: O_g0[:, (O_g0.shape[1] // 2):, :, :])(
O_g0)
O_g10 = Lambda(lambda O_g1: O_g1[:, :(O_g1.shape[1] // 2), :, :])(
O_g1)
O_g11 = Lambda(lambda O_g1: O_g1[:, (O_g1.shape[1] // 2):, :, :])(
O_g1)
O = Concatenate(axis=1)(
[O_g00, O_g11, O_g01, O_g10]
) # This ordering allows permutation of odd-numbered outputs (O_g0, O_g1).
if d.model == "real_group_pwc_full":
O = Conv2D(nb_fmaps1, (1, 1),
name=conv_name_base + '2a_pwc',
**convArgs)(O)
elif d.model == "real_group_pwc_group":
O_g0 = Lambda(lambda O: O[:, :(O.shape[1] // 2), :, :])(O)
O_g1 = Lambda(lambda O: O[:, (O.shape[1] // 2):, :, :])(O)
O_g0 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2a_pwc_g0',
**convArgs_real)(O_g0)
O_g1 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2a_pwc_g1',
**convArgs_real)(O_g1)
O = Concatenate(axis=1)([O_g0, O_g1])
elif d.model == "complex":
O = ComplexConv2D(nb_fmaps1,
filter_size,
name=conv_name_base + '2a',
**convArgs)(O)
elif (d.model == "complex_concat") or (d.model
== "complex_concat_pwc_group"):
O = ComplexConvConcat2D(nb_fmaps1 // 2,
filter_size,
name=conv_name_base + '2a',
**convArgs)(O)
if d.model == "complex_concat_pwc_group":
O_g0 = Lambda(lambda O: O[:, :(O.shape[1] // 2), :, :])(O)
O_g1 = Lambda(lambda O: O[:, (O.shape[1] // 2):, :, :])(O)
O_g0 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2a_pwc_g0',
**convArgs_real)(O_g0)
O_g1 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2a_pwc_g1',
**convArgs_real)(O_g1)
O = Concatenate(axis=1)([O_g0, O_g1])
else:
print("Error: unknown model type")
exit(-1)
elif shortcut == 'projection':
if d.spectral_pool_scheme == "proj":
O = applySpectralPooling(O, d)
if d.model == "real":
O = Conv2D(nb_fmaps1,
filter_size,
name=conv_name_base + '2a',
strides=(2, 2),
**convArgs)(O)
elif d.model == "real_dws":
O = SeparableConv2D(nb_fmaps1,
filter_size,
name=conv_name_base + '2a',
strides=(2, 2),
**convArgs)(O)
elif (d.model
== "real_group") or (d.model == "real_group_pwc_full") or (
d.model == "real_group_pwc_group"):
O_g0 = Lambda(lambda O: O[:, :(O.shape[1] // 2), :, :])(O)
O_g1 = Lambda(lambda O: O[:, (O.shape[1] // 2):, :, :])(O)
O_g0 = Conv2D(nb_fmaps1 // 2,
filter_size,
name=conv_name_base + '2a_g0',
strides=(2, 2),
**convArgs)(O_g0)
O_g1 = Conv2D(nb_fmaps1 // 2,
filter_size,
name=conv_name_base + '2a_g1',
strides=(2, 2),
**convArgs)(O_g1)
O_g00 = Lambda(lambda O_g0: O_g0[:, :(O_g0.shape[1] // 2), :, :])(
O_g0)
O_g01 = Lambda(lambda O_g0: O_g0[:, (O_g0.shape[1] // 2):, :, :])(
O_g0)
O_g10 = Lambda(lambda O_g1: O_g1[:, :(O_g1.shape[1] // 2), :, :])(
O_g1)
O_g11 = Lambda(lambda O_g1: O_g1[:, (O_g1.shape[1] // 2):, :, :])(
O_g1)
O = Concatenate(axis=1)([O_g00, O_g11, O_g01, O_g10])
if d.model == "real_group_pwc_full":
O = Conv2D(nb_fmaps1, (1, 1),
name=conv_name_base + '2a_pwc',
**convArgs)(O)
elif d.model == "real_group_pwc_group":
O_g0 = Lambda(lambda O: O[:, :(O.shape[1] // 2), :, :])(O)
O_g1 = Lambda(lambda O: O[:, (O.shape[1] // 2):, :, :])(O)
O_g0 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2a_pwc_g0',
**convArgs_real)(O_g0)
O_g1 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2a_pwc_g1',
**convArgs_real)(O_g1)
O = Concatenate(axis=1)([O_g0, O_g1])
elif d.model == "complex":
O = ComplexConv2D(nb_fmaps1,
filter_size,
name=conv_name_base + '2a',
strides=(2, 2),
**convArgs)(O)
elif (d.model == "complex_concat") or (d.model
== "complex_concat_pwc_group"):
O = ComplexConvConcat2D(nb_fmaps1 // 2,
filter_size,
name=conv_name_base + '2a',
strides=(2, 2),
**convArgs)(O)
if d.model == "complex_concat_pwc_group":
O_g0 = Lambda(lambda O: O[:, :(O.shape[1] // 2), :, :])(O)
O_g1 = Lambda(lambda O: O[:, (O.shape[1] // 2):, :, :])(O)
O_g0 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2a_pwc_g0',
**convArgs_real)(O_g0)
O_g1 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2a_pwc_g1',
**convArgs_real)(O_g1)
O = Concatenate(axis=1)([O_g0, O_g1])
else:
print("Error: unknown model type")
exit(-1)
if d.model == "real":
O = BatchNormalization(name=bn_name_base + '_2b', **bnArgs)(O)
O = Activation(activation)(O)
O = Conv2D(nb_fmaps2,
filter_size,
name=conv_name_base + '2b',
**convArgs)(O)
elif d.model == "real_dws":
O = BatchNormalization(name=bn_name_base + '_2b', **bnArgs)(O)
O = Activation(activation)(O)
O = SeparableConv2D(nb_fmaps2,
filter_size,
name=conv_name_base + '2b',
**convArgs)(O)
elif (d.model == "real_group") or (d.model == "real_group_pwc_full") or (
d.model == "real_group_pwc_group"):
O = BatchNormalization(name=bn_name_base + '_2b', **bnArgs)(O)
O = Activation(activation)(O)
O_g0 = Lambda(lambda O: O[:, :(O.shape[1] // 2), :, :])(O)
O_g1 = Lambda(lambda O: O[:, (O.shape[1] // 2):, :, :])(O)
O_g0 = Conv2D(nb_fmaps2 // 2,
filter_size,
name=conv_name_base + '2b_g0',
**convArgs)(O_g0)
O_g1 = Conv2D(nb_fmaps2 // 2,
filter_size,
name=conv_name_base + '2b_g1',
**convArgs)(O_g1)
O_g00 = Lambda(lambda O_g0: O_g0[:, :(O_g0.shape[1] // 2), :, :])(O_g0)
O_g01 = Lambda(lambda O_g0: O_g0[:, (O_g0.shape[1] // 2):, :, :])(O_g0)
O_g10 = Lambda(lambda O_g1: O_g1[:, :(O_g1.shape[1] // 2), :, :])(O_g1)
O_g11 = Lambda(lambda O_g1: O_g1[:, (O_g1.shape[1] // 2):, :, :])(O_g1)
O = Concatenate(axis=1)([O_g00, O_g11, O_g01, O_g10])
if d.model == "real_group_pwc_full":
O = Conv2D(nb_fmaps2, (1, 1),
name=conv_name_base + '2b_pwc',
**convArgs)(O)
elif d.model == "real_group_pwc_group":
O_g0 = Lambda(lambda O: O[:, :(O.shape[1] // 2), :, :])(O)
O_g1 = Lambda(lambda O: O[:, (O.shape[1] // 2):, :, :])(O)
O_g0 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2b_pwc_g0',
**convArgs_real)(O_g0)
O_g1 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2b_pwc_g1',
**convArgs_real)(O_g1)
O = Concatenate(axis=1)([O_g0, O_g1])
elif d.model == "complex":
O = ComplexBN(name=bn_name_base + '_2b', **bnArgs)(O)
if d.aact == "complex_joint_relu":
O = Lambda(ComplexJointReLU)(O)
else:
O = Activation(activation)(O)
O = ComplexConv2D(nb_fmaps2,
filter_size,
name=conv_name_base + '2b',
**convArgs)(O)
elif (d.model == "complex_concat") or (d.model
== "complex_concat_pwc_group"):
O = ComplexBN(name=bn_name_base + '_2b', **bnArgs)(O)
if d.aact == "complex_joint_relu":
O = Lambda(ComplexJointReLU)(O)
else:
O = Activation(activation)(O)
O = ComplexConvConcat2D(nb_fmaps2 // 2,
filter_size,
name=conv_name_base + '2b',
**convArgs)(O)
if d.model == "complex_concat_pwc_group":
O_g0 = Lambda(lambda O: O[:, :(O.shape[1] // 2), :, :])(O)
O_g1 = Lambda(lambda O: O[:, (O.shape[1] // 2):, :, :])(O)
O_g0 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2b_pwc_g0',
**convArgs_real)(O_g0)
O_g1 = Conv2D(int(O.shape[1] // 2), (1, 1),
name=conv_name_base + '2b_pwc_g1',
**convArgs_real)(O_g1)
O = Concatenate(axis=1)([O_g0, O_g1])
else:
print("Error: unknown model type")
exit(-1)
if shortcut == 'regular':
O = Add()([O, I])
elif shortcut == 'projection':
if d.spectral_pool_scheme == "proj":
I = applySpectralPooling(I, d)
if d.model == "real":
X = Conv2D(
nb_fmaps2, (1, 1),
name=conv_name_base + '1',
strides=(2, 2) if d.spectral_pool_scheme != "nodownsample" else
(1, 1),
**convArgs)(I)
O = Concatenate(channel_axis)([X, O])
elif d.model == "real_dws":
X = SeparableConv2D(
nb_fmaps2, (1, 1),
name=conv_name_base + '1',
strides=(2, 2) if d.spectral_pool_scheme != "nodownsample" else
(1, 1),
**convArgs)(I)
O = Concatenate(channel_axis)([X, O])
elif (d.model
== "real_group") or (d.model == "real_group_pwc_full") or (
d.model == "real_group_pwc_group"):
I_g0 = Lambda(lambda I: I[:, :(I.shape[1] // 2), :, :])(I)
I_g1 = Lambda(lambda I: I[:, (I.shape[1] // 2):, :, :])(I)
X_g0 = Conv2D(
nb_fmaps2 // 2, (1, 1),
name=conv_name_base + '1_g0',
strides=(2, 2) if d.spectral_pool_scheme != "nodownsample" else
(1, 1),
**convArgs)(I_g0)
X_g1 = Conv2D(
nb_fmaps2 // 2, (1, 1),
name=conv_name_base + '1_g1',
strides=(2, 2) if d.spectral_pool_scheme != "nodownsample" else
(1, 1),
**convArgs)(I_g1)
X_g00 = Lambda(lambda X_g0: X_g0[:, :(X_g0.shape[1] // 2), :, :])(
X_g0)
X_g01 = Lambda(lambda X_g0: X_g0[:, (X_g0.shape[1] // 2):, :, :])(
X_g0)
X_g10 = Lambda(lambda X_g1: X_g1[:, :(X_g1.shape[1] // 2), :, :])(
X_g1)
X_g11 = Lambda(lambda X_g1: X_g1[:, (X_g1.shape[1] // 2):, :, :])(
X_g1)
X = Concatenate(axis=1)([X_g00, X_g11, X_g01, X_g10])
if d.model == "real_group_pwc_full":
X = Conv2D(nb_fmaps2, (1, 1),
name=conv_name_base + '1_pwc',
**convArgs)(X)
elif d.model == "real_group_pwc_group":
X_g0 = Lambda(lambda X: X[:, :(X.shape[1] // 2), :, :])(X)
X_g1 = Lambda(lambda X: X[:, (X.shape[1] // 2):, :, :])(X)
X_g0 = Conv2D(int(X.shape[1] // 2), (1, 1),
name=conv_name_base + '1_pwc_g0',
**convArgs_real)(X_g0)
X_g1 = Conv2D(int(X.shape[1] // 2), (1, 1),
name=conv_name_base + '1_pwc_g1',
**convArgs_real)(X_g1)
X = Concatenate(axis=1)([X_g0, X_g1])
O = Concatenate(channel_axis)([X, O])
elif d.model == "complex":
X = ComplexConv2D(
nb_fmaps2, (1, 1),
name=conv_name_base + '1',
strides=(2, 2) if d.spectral_pool_scheme != "nodownsample" else
(1, 1),
**convArgs)(I)
O_real = Concatenate(channel_axis)([GetReal()(X), GetReal()(O)])
O_imag = Concatenate(channel_axis)([GetImag()(X), GetImag()(O)])
O = Concatenate(1)([O_real, O_imag])
elif (d.model == "complex_concat") or (d.model
== "complex_concat_pwc_group"):
X = ComplexConvConcat2D(
nb_fmaps2 // 2, (1, 1),
name=conv_name_base + '1',
strides=(2, 2) if d.spectral_pool_scheme != "nodownsample" else
(1, 1),
**convArgs)(I)
if d.model == "complex_concat_pwc_group":
X_g0 = Lambda(lambda X: X[:, :(X.shape[1] // 2), :, :])(X)
X_g1 = Lambda(lambda X: X[:, (X.shape[1] // 2):, :, :])(X)
X_g0 = Conv2D(int(X.shape[1] // 2), (1, 1),
name=conv_name_base + '1_pwc_g0',
**convArgs_real)(X_g0)
X_g1 = Conv2D(int(X.shape[1] // 2), (1, 1),
name=conv_name_base + '1_pwc_g1',
**convArgs_real)(X_g1)
X = Concatenate(axis=1)([X_g0, X_g1])
O_real = Concatenate(channel_axis)([GetReal()(X), GetReal()(O)])
O_imag = Concatenate(channel_axis)([GetImag()(X), GetImag()(O)])
O = Concatenate(1)([O_real, O_imag])
else:
print("Error: unknown model type")
exit(-1)
return O
