def TranslateData(self, batch, i):
"""Applies translations to data at index i in batch."""
sizeX = self.sizeX
sizex = self.sizex
batchsize = batch[i].shape[1]
shift = (sizeX - sizex) / 2
offset_x = np.array([
random.choice(self.translate_range_x) + shift
for k in range(batchsize)
]).reshape(1, -1)
offset_y = np.array([
random.choice(self.translate_range_y) + shift
for k in range(batchsize)
]).reshape(1, -1)
num_channels = self.num_channels
d = batch[i]
if self.offset_x is None:
self.offset_x = cm.CUDAMatrix(offset_x)
else:
self.offset_x.overwrite(offset_x)
if self.offset_y is None:
self.offset_y = cm.CUDAMatrix(offset_y)
else:
self.offset_y.overwrite(offset_y)
if self.translated_d is None or self.translated_d.shape[1] != batchsize:
self.translated_d = cm.empty((sizex**2 * num_channels, batchsize))
d.generate_translations(sizeX,
sizex,
self.offset_x,
self.offset_y,
target=self.translated_d)
batch[i] = self.translated_d
def log(self, inplace=False):
if self.typ == 'numpy':
return PMAT(np.log(self.mat))
elif self.typ == 'cuda':
if inplace:
self.mat = cm.log(self.mat)
return self
else:
target = cm.empty(self.mat.shape)
cm.log(self.mat, target=target)
return PMAT(target)
def element_add(self, mat, inplace=False):
if self.typ == 'numpy':
return PMAT(self.mat + mat.mat)
elif self.typ == 'cuda':
if inplace:
self.mat = self.mat.add(mat.mat)
return self
else:
target = cm.empty(self.mat.shape)
self.mat.add(mat.mat, target=target)
return PMAT(target)
def add_col_vec(self, colvec, inplace=False):
if self.typ == 'numpy':
return PMAT(self.mat + colvec.mat)
elif self.typ == 'cuda':
if inplace:
self.mat.add_col_vec(colvec.mat)
return self
else:
target = cm.empty(self.mat.shape)
self.mat.add_col_vec(colvec.mat, target=target)
return PMAT(target)
def multiply_by_col(self, colvec, inplace=False):
if self.typ == 'numpy':
return PMAT(self.mat * colvec.mat)
elif self.typ == 'cuda':
if inplace:
self.mat.mult_by_col(colvec.mat)
return self
else:
target = cm.empty(self.mat.shape)
self.mat.mult_by_col(colvec.mat, target=target)
return PMAT(target)
def subtract(self, mat, inplace=False):
if self.typ == 'numpy':
return PMAT(self.mat - mat.mat)
if self.typ == 'cuda':
if inplace:
self.mat.subtract(mat.mat)
return self
else:
target = cm.empty(self.mat.shape)
self.mat.subtract(mat.mat, target=target)
return PMAT(target)
def exp(self, inplace=False):
if self.typ == "numpy":
return PMAT(np.exp(self.mat))
elif self.typ == "cuda":
if inplace:
self.mat = cm.exp(self.mat)
return self
else:
target = cm.empty(self.mat.shape)
cm.exp(self.mat, target=target)
return PMAT(target)
def element_multiply(self, mat, inplace=False):
if self.typ == "numpy":
return PMAT(self.mat * mat.mat)
elif self.typ == "cuda":
if inplace:
self.mat = self.mat.mult(mat.mat)
return self
else:
target = cm.empty(self.mat.shape)
self.mat.mult(mat.mat, target=target)
return PMAT(target)
def add_row_vec(self, rowvec, inplace=False):
if self.typ == "numpy":
return PMAT(self.mat + rowvec.mat)
elif self.typ == "cuda":
if inplace:
self.mat.add_row_vec(rowvec.mat)
return self
else:
target = cm.empty(self.mat.shape)
self.mat.add_row_vec(rowvec.mat, target=target)
return PMAT(target)
def multiply_by_row(self, rowvec, inplace=False):
if self.typ == "numpy":
return PMAT(self.mat * rowvec.mat)
elif self.typ == "cuda":
if inplace:
self.mat.mult_by_row(rowvec.mat)
return self
else:
target = cm.empty(self.mat.shape)
self.mat.mult_by_row(rowvec.mat, target=target)
return PMAT(target)
def divide_by_row(self, rowvec, inplace=False):
if self.typ == 'numpy':
return PMAT(self.mat / rowvec.mat)
elif self.typ == 'cuda':
if inplace:
rowvec.mat.reciprocal()
self.mat.mult_by_row(rowvec.mat)
return self
else:
target = cm.empty(self.mat.shape)
rowvec.mat.reciprocal()
self.mat.mult_by_row(rowvec.mat, target=target)
return PMAT(target)
