def set_up(self, bottom, bottom_diff, params=None):
self.bottom = bottom
self.bottom_diff = bottom_diff
self.top = hmarray.zeros(bottom.shape)
self.mask = hmarray.random(bottom.shape)
self.top_diff = hmarray.zeros(bottom.shape)
return [(self.top, self.top_diff)]
def get_data():
# data = np.asarray([
# transformer.preprocess('data', im),
# ]).view(hmarray)
data = hmarray.random((10, 3, 227, 227), _range=(0, 255))
# data *= hmarray.random((5, 3, 227, 227), _range=(0, 2))
# data -= hmarray.random((5, 3, 227, 227), _range=(-20, +20))
return data
def get_data():
# data = np.asarray([
# transformer.preprocess('data', im),
# ]).view(hmarray)
data = hmarray.random((10, 3, 227, 227), _range=(0, 255))
# data *= hmarray.random((5, 3, 227, 227), _range=(0, 2))
# data -= hmarray.random((5, 3, 227, 227), _range=(-20, +20))
return data
def set_up(self, bottom, bottom_diff):
self.bottom, self.bottom_diff = bottom, bottom_diff
N = self.num_output
K = np.prod(bottom.shape[1:])
scale = 1.0 / np.sqrt(self.num_output)
if self.weights is None:
self.weights = hmarray.random((N, K), _range=(-scale, scale))
self.weights_diff = hmarray.zeros((N, K))
self.weights_history = hmarray.zeros((N, K))
self.bias_diff = hmarray.zeros((self.num_output, ))
self.bias_history = hmarray.zeros((self.num_output, ))
self.bias_multiplier = hmarray((1, self.bottom.shape[0]))
self.bias_multiplier.fill(1)
self.bias_multiplier.sync_ocl()
top_shape = (bottom.shape[0], N)
self.top = hmarray.zeros(top_shape)
self.top_diff = hmarray.zeros(top_shape)
return [(self.top, self.top_diff)]
def set_up(self, bottom, bottom_diff):
self.bottom = bottom
self.bottom_diff = bottom_diff
num, channels, height, width = bottom.shape
weights_shape = (self.num_output, channels * self.kernel_size *
self.kernel_size)
if self.weights is not None:
self.weights = self.weights.reshape(weights_shape)
else:
n = 1.0 / np.sqrt(self.num_output)
self.weights = hmarray.random(weights_shape, _range=(-n, n))
self.weights_diff = hmarray.zeros(weights_shape)
self.weights_history = hmarray.zeros(weights_shape)
height_out = (height + 2 * self.padding - self.kernel_size) // \
self.stride + 1
width_out = (width + 2 * self.padding - self.kernel_size) // \
self.stride + 1
top_shape = (num, self.num_output, height_out, width_out)
self.top = hmarray.zeros(top_shape)
self.top_diff = hmarray.zeros(top_shape)
return [(self.top, self.top_diff)]
def set_up(self, bottom, bottom_diff):
self.bottom = bottom
self.bottom_diff = bottom_diff
num, channels, height, width = bottom.shape
weights_shape = (self.num_output,
channels * self.kernel_size * self.kernel_size)
if self.weights is not None:
self.weights = self.weights.reshape(weights_shape)
else:
n = 1.0 / np.sqrt(self.num_output)
self.weights = hmarray.random(weights_shape, _range=(-n, n))
self.weights_diff = hmarray.zeros(weights_shape)
self.weights_history = hmarray.zeros(weights_shape)
height_out = (height + 2 * self.padding - self.kernel_size) // \
self.stride + 1
width_out = (width + 2 * self.padding - self.kernel_size) // \
self.stride + 1
top_shape = (num, self.num_output, height_out, width_out)
self.top = hmarray.zeros(top_shape)
self.top_diff = hmarray.zeros(top_shape)
return [(self.top, self.top_diff)]
def get_data():
data = hmarray.random((32, 3, 224, 224), _range=(0, 255))
data.sync_ocl()
return data
