def forward(self, from_btm, to_top, phase):
narrays = []
self.concat_dim = len(from_btm[self.btm_names[0]].shape) - 1 - self.concat_dim_caffe
for i in range(len(self.btm_names)):
narrays.append(from_btm[self.btm_names[i]])
self.slice_count.append(from_btm[self.btm_names[i]].shape[self.concat_dim])
to_top[self.top_names[0]] = owl.concat(narrays, self.concat_dim)
def ff(self, act, phase):
if self.group == 1:
self.ff_act = act
return self.convolver.ff(act, self.weight, self.bias)
else:
#slice data
self.group_data = []
group_result = []
self.group_filter = []
self.group_bias = []
data_concat_dim = 2
filter_concat_dim = 3
bias_concat_dim = 0
data_slice_count = act.shape[data_concat_dim] / self.group
filter_slice_count = self.weight.shape[
filter_concat_dim] / self.group
for i in xrange(self.group):
self.group_data.append(
owl.slice(act, data_concat_dim, data_slice_count * i,
data_slice_count))
self.group_filter.append(
owl.slice(self.weight, filter_concat_dim,
filter_slice_count * i, filter_slice_count))
self.group_bias.append(
owl.slice(self.bias, bias_concat_dim,
filter_slice_count * i, filter_slice_count))
group_result.append(
self.convolver.ff(self.group_data[i], self.group_filter[i],
self.group_bias[i]))
#concat
return owl.concat(group_result, data_concat_dim)
def forward(self, from_btm, to_top, phase):
narrays = []
self.concat_dim = len(
from_btm[self.btm_names[0]].shape) - 1 - self.concat_dim_caffe
for i in range(len(self.btm_names)):
narrays.append(from_btm[self.btm_names[i]])
self.slice_count.append(
from_btm[self.btm_names[i]].shape[self.concat_dim])
to_top[self.top_names[0]] = owl.concat(narrays, self.concat_dim)
def bp(self, sen):
if self.group == 1:
self.weightgrad = self.convolver.weight_grad(
sen, self.ff_act, self.weight)
self.biasgrad = self.convolver.bias_grad(sen)
return self.convolver.bp(sen, self.ff_act, self.weight)
else:
#slice data
group_sen = []
group_wgrad = []
group_bgrad = []
group_result = []
data_concat_dim = 2
filter_concat_dim = 3
bias_concat_dim = 0
data_slice_count = sen.shape[data_concat_dim] / self.group
filter_slice_count = self.weight.shape[
filter_concat_dim] / self.group
for i in xrange(self.group):
group_sen.append(
owl.slice(sen, data_concat_dim, data_slice_count * i,
data_slice_count))
group_wgrad.append(
self.convolver.weight_grad(group_sen[i],
self.group_data[i],
self.group_filter[i]))
group_bgrad.append(self.convolver.bias_grad(group_sen[i]))
group_result.append(
self.convolver.bp(group_sen[i], self.group_data[i],
self.group_filter[i]))
#concat
self.weightgrad = owl.concat(group_wgrad, filter_concat_dim)
self.biasgrad = owl.concat(group_bgrad, bias_concat_dim)
#free space
self.group_data = []
self.group_filter = []
self.group_bias = []
return owl.concat(group_result, data_concat_dim)