def setup(self, bottom, top):
conv_param = self.layer_param.convolution_param
channels, height, width = bottom[0].shape
num_output = conv_param.num_output
assert channels % self.group == 0, \
"Number of channels should be a multiple of group."
assert num_output % self.group == 0, \
"Number of outputs should be a multiple of group."
self.bias_term = conv_param.bias_term
if self.weights is not None:
logging.debug("Skipping parameter initialization")
else:
weights_shape = (num_output, channels // self.group,
self.kernel_h, self.kernel_w)
weight_filler = conv_param.weight_filler
if weight_filler.type == 'gaussian':
self.weights = weight_filler.mean + weight_filler.std * \
Array.standard_normal(
weights_shape).astype(np.float32)
self.weight_diff = Array.empty_like(self.weights)
else:
raise Exception("Filler not implemented for weight filler \
type {}".format(weight_filler.type))
if self.bias_term:
self.bias = Array((num_output, ), np.float32)
self.bias_diff = Array.empty_like(self.bias)
filler = conv_param.bias_filler
if filler.type == 'constant':
self.bias.fill(filler.value)
else:
raise Exception("Filler not implemented for bias filler \
type {}".format(filler.type))
def setup(self, bottom, top):
conv_param = self.layer_param.convolution_param
channels, height, width = bottom[0].shape
num_output = conv_param.num_output
assert channels % self.group == 0, \
"Number of channels should be a multiple of group."
assert num_output % self.group == 0, \
"Number of outputs should be a multiple of group."
self.bias_term = conv_param.bias_term
if self.weights is not None:
logging.debug("Skipping parameter initialization")
else:
weights_shape = (num_output, channels // self.group, self.kernel_h,
self.kernel_w)
weight_filler = conv_param.weight_filler
if weight_filler.type == 'gaussian':
self.weights = weight_filler.mean + weight_filler.std * \
Array.standard_normal(
weights_shape).astype(np.float32)
self.weight_diff = Array.empty_like(self.weights)
else:
raise Exception("Filler not implemented for weight filler \
type {}".format(weight_filler.type))
if self.bias_term:
self.bias = Array((num_output, ), np.float32)
self.bias_diff = Array.empty_like(self.bias)
filler = conv_param.bias_filler
if filler.type == 'constant':
self.bias.fill(filler.value)
else:
raise Exception("Filler not implemented for bias filler \
type {}".format(filler.type))
def setup(self, bottom, top):
weights_shape = (self.num_output, bottom.shape[0])
weight_filler = self.layer_param.inner_product_param.weight_filler
if weight_filler.type == 'gaussian':
self.weights = weight_filler.mean + weight_filler.std * \
Array.standard_normal(
weights_shape).astype(np.float32)
else:
raise Exception("Filler not implemented for weight filler"
"type {}".format(weight_filler.type))
