def create_globalaveragepooling1d(self, klayer, kclayer):
input_shape = klayer.get_input_shape_at(0) # batch, step, dim
b_kw = 1
b_kh = int(input_shape[1])
seq = BLayer.Sequential()
seq.add(
BLayer.View([int(input_shape[1]), 1,
int(input_shape[2])],
num_input_dims=2))
blayer = BLayer.SpatialAveragePooling(kw=b_kw,
kh=b_kh,
dw=0,
dh=0,
pad_w=0,
pad_h=0,
global_pooling=False,
ceil_mode=False,
count_include_pad=False,
divide=True,
format="NHWC",
bigdl_type="float")
seq.add(blayer)
seq.add(BLayer.Squeeze(
2, num_input_dims=2)) # the index start from one but without batch
seq.add(BLayer.Squeeze(1, num_input_dims=1))
return seq
def create_globalmaxpooling2d(self, klayer, kclayer):
bigdl_order = self.get_bdim_order(kclayer)
input_shape = klayer.get_input_shape_at(0)
if bigdl_order == "NCHW":
b_kw = int(input_shape[3])
b_kh = int(input_shape[2])
else:
b_kw = int(input_shape[2])
b_kh = int(input_shape[1])
seq = BLayer.Sequential()
blayer = BLayer.SpatialMaxPooling(kw=b_kw,
kh=b_kh,
dw=b_kw,
dh=b_kh,
pad_w=0,
pad_h=0,
to_ceil=False,
format=bigdl_order,
bigdl_type="float")
seq.add(blayer)
if bigdl_order == "NCHW":
seq.add(BLayer.Squeeze(3, num_input_dims=3))
seq.add(BLayer.Squeeze(2, num_input_dims=2))
else:
seq.add(BLayer.Squeeze(2, num_input_dims=3))
seq.add(BLayer.Squeeze(1, num_input_dims=2))
return seq
def create_globalaveragepooling2d(self, klayer, kclayer):
bigdl_order = self.get_bdim_order(kclayer)
input_shape = klayer.get_input_shape_at(0)
if bigdl_order == "NCHW":
b_kw = int(input_shape[3])
b_kh = int(input_shape[2])
else:
b_kw = int(input_shape[2])
b_kh = int(input_shape[1])
seq = BLayer.Sequential()
blayer = BLayer.SpatialAveragePooling(kw=b_kw,
kh=b_kh,
dw=b_kw,
dh=b_kh,
pad_w=0,
pad_h=0,
global_pooling=False,
ceil_mode=False,
count_include_pad=False,
divide=True,
format=bigdl_order,
bigdl_type="float")
seq.add(blayer)
if bigdl_order == "NCHW":
seq.add(BLayer.Squeeze(3, num_input_dims=3))
seq.add(BLayer.Squeeze(2, num_input_dims=2))
else:
seq.add(BLayer.Squeeze(2, num_input_dims=3))
seq.add(BLayer.Squeeze(1, num_input_dims=2))
return seq
def create_convolution1d(self, klayer, kclayer):
config = kclayer["config"]
input_shape = klayer.get_input_shape_at(0)
# batch, steps, dim, batch is None here, so you cannot use it directly.
stack_size = int(input_shape[2])
bpadW, bpadH = self.to_bigdl_2d_padding(klayer.border_mode)
seq = BLayer.Sequential()
seq.add(
BLayer.Reshape([int(input_shape[1]), 1,
int(input_shape[2])], True))
blayer = BLayer.SpatialConvolution(
n_input_plane=stack_size,
n_output_plane=klayer.nb_filter,
kernel_w=1,
kernel_h=klayer.filter_length,
stride_w=1,
stride_h=klayer.subsample_length,
pad_w=bpadW,
pad_h=bpadH,
n_group=1,
propagate_back=True,
wRegularizer=self.to_bigdl_reg(config["W_regularizer"]),
bRegularizer=self.to_bigdl_reg(config["b_regularizer"]),
init_weight=None,
init_bias=None,
init_grad_weight=None,
init_grad_bias=None,
with_bias=config["bias"],
data_format="NHWC",
bigdl_type="float")
seq.add(blayer)
seq.add(BLayer.Squeeze(3))
return self.combo_parameter_layer(seq, config)
def create_globalmaxpooling1d(self, klayer, kclayer):
input_shape = klayer.get_input_shape_at(0) # batch, step, dim
b_kw = 1
b_kh = int(input_shape[1])
seq = BLayer.Sequential()
seq.add(
BLayer.View([int(input_shape[1]), 1,
int(input_shape[2])],
num_input_dims=2))
blayer = BLayer.SpatialMaxPooling(kw=b_kw,
kh=b_kh,
dw=0,
dh=0,
pad_w=0,
pad_h=0,
to_ceil=False,
format="NHWC",
bigdl_type="float")
seq.add(blayer)
seq.add(BLayer.Squeeze(2, num_input_dims=2))
seq.add(BLayer.Squeeze(1, num_input_dims=1))
return seq