def __init__(self, data, labels, cv_percent, test_percent, batch_size, mode='sequential'):
if len(labels.shape) ==1: labels = u.create_t_matrix(labels)
elif labels.shape[0]==1 or labels.shape[1]==1: labels = np.float32(u.create_t_matrix(labels))
self.size = labels.shape[0]
self.shapes = [data.shape[1],labels.shape[1]]
shape = u.handle_shape(data.shape)
shape_label = u.handle_shape(labels.shape)
self.arrays = {}
self.mode = mode
#data = np.float32(np.asanyarray(data,order='F'))
#data = np.float32(data.reshape(shape))
#labels = np.float32(np.asanyarray(labels,order='F'))
#labels = np.float32(labels.reshape(shape_label))
self.pinned_buffers = {}
self.batch_size = batch_size
self.cv_percent = cv_percent
self.test_percent = test_percent
self.current = None
self.next_X = None
self.offsize_X = []
self.offsize_y = []
self.set_type = 'train'
self.set_type_prev = 'train'
self.next_batch_idx = 0
#self.X = array(None, lib.funcs.fto_pinned(shape[0],shape[1],shape[2],shape[3],data.ctypes.data_as(ct.POINTER(ct.c_float))))
#self.y = array(None, lib.funcs.fto_pinned(shape_label[0],shape_label[1],shape_label[2],shape_label[3],
# labels.ctypes.data_as(ct.POINTER(ct.c_float))))
self.X = lib.funcs.fto_pinned(shape[0], shape[1], shape[2], shape[3], data.ctypes.data_as(ct.POINTER(ct.c_float)))
self.y = lib.funcs.fto_pinned(shape_label[0], shape_label[1], shape_label[2], shape_label[3], labels.ctypes.data_as(ct.POINTER(ct.c_float)))
#del data
#del labels
self.set_batch_sizes()
self.init_buffers()
self.net = None
self.sample_size = None
self.relative_error = None
self.start_batching = True
self.peer_access_enabled = False
self.batch_buffers = {}
def forward(self, data=None, target=None, inTrainingMode=True):
if data is not None:
shape = u.handle_shape(data.shape)
self.unitcount = shape[3]
self.handle_input_size(shape[2])
self.root.target = target
self.funcs.activation(data, self.activation, self.out,
inTrainingMode)
#if inTrainingMode: self.handle_parallelism()
else:
#if inTrainingMode: self.handle_parallelism()
#cool
gpu.dot(self.prev_layer.out, self.prev_layer.w_next,
self.activation)
#not cool activation, dot problem? -> nope, memory problem (wrong buffer size)?
#print self.prev_layer.out.sum()
#print self.prev_layer.w_next.sum()
#print self.activation.sum()
#print 'a'
#sleep(0.5)
gpu.add(self.activation, self.prev_layer.b_next, self.activation)
self.funcs.activation(self.activation, self.activation, self.out,
inTrainingMode)
if self.next_layer: self.next_layer.forward(None, None, inTrainingMode)
def __init__(self,
npArray=None,
mat_pointer=None,
split_idx=-1,
dtype=np.float32):
self.shape = None
self.dummy = False
self.id = uuid.uuid4()
self.split_idx = split_idx
if type(npArray) == type(np.array(1)):
npArray = np.float32(npArray)
shape = u.handle_shape(npArray.shape)
mat_pointer = lib.funcs.fempty_split(shape[0], shape[1], shape[2],
shape[3], split_idx)
lib.funcs.ftogpu_split(
mat_pointer, npArray.ctypes.data_as(ct.POINTER(ct.c_float)),
split_idx)
self.shape = npArray.shape
if mat_pointer:
self.shape_tensor = (mat_pointer.contents.batches,
mat_pointer.contents.maps,
mat_pointer.contents.rows,
mat_pointer.contents.cols)
if not self.shape: self.shape = self.shape_tensor
self.pt = mat_pointer
self.npArray = npArray
mem.arrays[self.id] = [self.shape_tensor, self.pt, dtype]
pass
def empty(shape, split_idx=-1):
shape = u.handle_shape(shape)
out = array(
None,
lib.funcs.fempty_split(shape[0], shape[1], ct.c_int32(shape[2]),
ct.c_int32(shape[3]), split_idx), split_idx)
return out
def zeros(shape, split_idx=-1):
shape = u.handle_shape(shape)
out = array(
None,
lib.funcs.fzeros_split(shape[0], shape[1], shape[2], shape[3],
split_idx))
return out
def normal(self, loc=0.0, scale=1.0, size=None):
d0, d1, d2, d3 = u.handle_shape(size)
assert (d0 * d1 * d2 * d3) % 2 == 0, "Size must be a multiple of 2!"
assert size, "Size must be greater than zero!"
return array(
None,
lib.funcs.fnormal(self.p_gpupy, d0, d1, d2, d3, ct.c_float(loc),
ct.c_float(scale)))
def to_col_major_pinned_pointer(x1, pt_out=None):
if x1.dtype != np.float32: x1 = np.float32(x1)
shape = u.handle_shape(x1.shape)
if not pt_out: pt_out = empty_pinned_pointer(shape)
lib.funcs.inp_to_col_major_pinned(
x1.ctypes.data_as(ct.POINTER(ct.c_float)), pt_out, shape[0], shape[1],
shape[2], shape[3])
return pt_out
def pointer2ndarray(pt, shape, dtype=np.float32):
shape_tensor = u.handle_shape(shape)
size = shape_tensor[0] * shape_tensor[1] * shape_tensor[2] * shape_tensor[3]
str_buffer = ct.string_at(pt, ct.sizeof(ct.c_float) * size)
return np.fromstring(str_buffer, dtype=dtype).reshape(shape)
def empty_pinned_pointer(shape):
out = np.empty(shape)
shape_tensor = u.handle_shape(shape)
return lib.funcs.fto_pinned(shape_tensor[0], shape_tensor[1],
shape_tensor[2], shape_tensor[3],
out.ctypes.data_as(ct.POINTER(ct.c_float)))
def to_pinned_pointer(x1):
if x1.dtype != np.float32: x1 = np.float32(x1)
shape = u.handle_shape(x1.shape)
return lib.funcs.fto_pinned(shape[0], shape[1], shape[2], shape[3],
x1.ctypes.data_as(ct.POINTER(ct.c_float)))
def ones(shape):
shape = u.handle_shape(shape)
out = array(None, lib.funcs.fones(shape[0], shape[1], shape[2], shape[3]))
return out
