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提交 824cb369 authored 作者: ballasn's avatar ballasn 提交者： GitHub
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Merge pull request #5458 from bscellier/import_numpy_gpuarray

Import numpy gpuarray
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theano/gpuarray/basic_ops.py
浏览文件 @ 824cb369
......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import os
import copy
import re
import numpy
import numpy as np
from theano import Op, Apply, Type, Variable
from theano import tensor, config
......@@ -206,7 +206,7 @@ class Kernel(object):
def get_flags(*types):
def get_dtype(t):
if isinstance(t, string_types):
return numpy.dtype(t)
return np.dtype(t)
elif isinstance(t, Type):
return t.dtype
elif isinstance(t, Variable):
......@@ -215,13 +215,13 @@ class Kernel(object):
raise TypeError("can't get a dtype from %s" % (type(t),))
dtypes = [get_dtype(t) for t in types]
flags = dict(cluda=True)
if any(d == numpy.float64 for d in dtypes):
if any(d == np.float64 for d in dtypes):
flags['have_double'] = True
if any(d.itemsize < 4 for d in dtypes):
flags['have_small'] = True
if any(d.kind == 'c' for d in dtypes):
flags['have_complex'] = True
if any(d == numpy.float16 for d in dtypes):
if any(d == np.float16 for d in dtypes):
flags['have_half'] = True
return flags
......@@ -275,8 +275,8 @@ def get_ctype(dtype):
elif dtype == gpuarray.SSIZE:
return "ssize_t"
else:
if not isinstance(dtype, numpy.dtype):
dtype = numpy.dtype(dtype)
if not isinstance(dtype, np.dtype):
dtype = np.dtype(dtype)
return 'npy_' + dtype.name
......@@ -311,7 +311,7 @@ class GpuKernelBase(object):
except MethodNotDefined:
o = []
# We rely on the input types for the directory to gpuarray includes
return o + [numpy.get_include()]
return o + [np.get_include()]
def _generate_kernel_bin(self, k, ctx):
gk = gpuarray.GpuKernel(k.code, k.name, k.params, context=ctx,
......@@ -466,7 +466,7 @@ def get_dtype(s):
if s == 'ssize':
return gpuarray.SSIZE
else:
return numpy.dtype(s)
return np.dtype(s)
class CGpuKernelBase(COp, GpuKernelBase):
......@@ -565,7 +565,7 @@ class HostFromGpu(Op):
def perform(self, node, inp, out):
x, = inp
z, = out
z[0] = numpy.asarray(x)
z[0] = np.asarray(x)
def c_code(self, node, name, inputs, outputs, sub):
return """
......@@ -1285,7 +1285,7 @@ class GpuJoin(HideC, Join):
if axis < 0:
axis += axis_and_tensors[1].ndim
# we check these tensors for being empty.
if (view != -1) and numpy.all(
if (view != -1) and np.all(
[tensor.shape[axis] == 0 for tensor in
tensors[0:view] + tensors[view + 1:]]):
out[0] = tensors[view]
......
theano/gpuarray/blocksparse.py
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......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import logging
import os
import numpy
import numpy as np
from theano import Apply, tensor
from theano.gof import COp
from theano.tensor import discrete_dtypes, as_tensor_variable
......@@ -121,7 +121,7 @@ class GpuSparseBlockOuter(COp):
def make_node(self, o, x, y, xIdx, yIdx, alpha=None):
ctx = infer_context_name(o, x, y)
one = tensor.constant(numpy.asarray(1.0, dtype='float32'))
one = tensor.constant(np.asarray(1.0, dtype='float32'))
o = as_gpuarray_variable(o, ctx)
x = as_gpuarray_variable(x, ctx)
y = as_gpuarray_variable(y, ctx)
......
theano/gpuarray/dnn.py
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......@@ -4,7 +4,7 @@ import os
import sys
import warnings
import numpy
import numpy as np
from six import integer_types
import theano
......@@ -457,8 +457,8 @@ gpu_dnn_conv_desc.cache = {}
# scalar constants
_zero = constant(numpy.asarray(0.0, dtype='float64'))
_one = constant(numpy.asarray(1.0, dtype='float64'))
_zero = constant(np.asarray(0.0, dtype='float64'))
_one = constant(np.asarray(1.0, dtype='float64'))
def ensure_dt(val, default, name, dtype):
......@@ -2482,8 +2482,8 @@ class RNNBlock(object):
bytesize = _get_param_size(self.desc, input_size, self.dtype,
self.context_name)
bytesize = int(bytesize)
assert bytesize % numpy.dtype(self.dtype).itemsize == 0
return bytesize // numpy.dtype(self.dtype).itemsize
assert bytesize % np.dtype(self.dtype).itemsize == 0
return bytesize // np.dtype(self.dtype).itemsize
def split_params(self, w, layer, input_size):
if not isinstance(w, GpuArraySharedVariable):
......
theano/gpuarray/elemwise.py
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from __future__ import absolute_import, print_function, division
import copy
import numpy
import numpy as np
import theano
from theano import Apply, scalar, config, Op
......@@ -27,7 +27,7 @@ from .fp16_help import load_w, write_w
def make_argument(v, name):
return ArrayArg(numpy.dtype(v.type.dtype), name)
return ArrayArg(np.dtype(v.type.dtype), name)
def as_C_string_const(s):
......
theano/gpuarray/linalg.py
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......@@ -7,7 +7,7 @@ import warnings
from theano import Op
from theano.gpuarray import basic_ops, GpuArrayType
import numpy
import numpy as np
from numpy.linalg.linalg import LinAlgError
try:
......@@ -107,7 +107,7 @@ class GpuCusolverSolve(Op):
ctx.cusolver_handle = cusolver.cusolverDnCreate()
def check_dev_info(self, dev_info):
val = numpy.asarray(dev_info)[0]
val = np.asarray(dev_info)[0]
if val > 0:
raise LinAlgError('A is singular')
......
theano/gpuarray/neighbours.py
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from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
from theano import Op, Apply, config
from theano.tensor.nnet.neighbours import Images2Neibs
......@@ -256,8 +256,8 @@ class GpuImages2Neibs(GpuKernelBase, Images2Neibs, Op):
dtype_neib_shape = node.inputs[1].dtype
dtype_neib_step = node.inputs[2].dtype
dtype_z = node.outputs[0].dtype
itemsize_ten4 = numpy.dtype(dtype_ten4).itemsize
itemsize_z = numpy.dtype(dtype_z).itemsize
itemsize_ten4 = np.dtype(dtype_ten4).itemsize
itemsize_z = np.dtype(dtype_z).itemsize
typecode_z = pygpu.gpuarray.dtype_to_typecode(node.outputs[0].dtype)
ten4, neib_shape, neib_step = inp
z, = out
......
theano/gpuarray/nnet.py
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from __future__ import absolute_import, print_function, division
import os
import numpy
import numpy as np
from theano import Op, Apply, config
from six import StringIO
......@@ -195,13 +195,13 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias(GpuKernelBase, Op):
def c_code(self, node, nodename, inp, out, sub):
if node.inputs[0].type.context.kind != b'cuda':
raise NotImplementedError('cuda only')
itemsize_x = numpy.dtype(node.inputs[0].dtype).itemsize
worksize_x = numpy.dtype(work_dtype(node.inputs[0].dtype)).itemsize
itemsize_b = numpy.dtype(node.inputs[1].dtype).itemsize
itemsize_y_idx = numpy.dtype(node.inputs[2].dtype).itemsize
itemsize_nll = numpy.dtype(node.outputs[0].dtype).itemsize
itemsize_sm = numpy.dtype(node.outputs[1].dtype).itemsize
itemsize_am = numpy.dtype(node.outputs[2].dtype).itemsize
itemsize_x = np.dtype(node.inputs[0].dtype).itemsize
worksize_x = np.dtype(work_dtype(node.inputs[0].dtype)).itemsize
itemsize_b = np.dtype(node.inputs[1].dtype).itemsize
itemsize_y_idx = np.dtype(node.inputs[2].dtype).itemsize
itemsize_nll = np.dtype(node.outputs[0].dtype).itemsize
itemsize_sm = np.dtype(node.outputs[1].dtype).itemsize
itemsize_am = np.dtype(node.outputs[2].dtype).itemsize
x, b, y_idx = inp
nll, sm, am = out
fail = sub['fail']
......@@ -307,15 +307,15 @@ class GpuCrossentropySoftmax1HotWithBiasDx(GpuKernelBase, Op):
if node.inputs[0].type.context.kind != b'cuda':
raise NotImplementedError("cuda only")
typecode_dx = pygpu.gpuarray.dtype_to_typecode(node.outputs[0].dtype)
itemsize_dnll = numpy.dtype(node.inputs[0].dtype).itemsize
itemsize_sm = numpy.dtype(node.inputs[1].dtype).itemsize
itemsize_y_idx = numpy.dtype(node.inputs[2].dtype).itemsize
itemsize_dx = numpy.dtype(node.outputs[0].dtype).itemsize
itemsize_dnll = np.dtype(node.inputs[0].dtype).itemsize
itemsize_sm = np.dtype(node.inputs[1].dtype).itemsize
itemsize_y_idx = np.dtype(node.inputs[2].dtype).itemsize
itemsize_dx = np.dtype(node.outputs[0].dtype).itemsize
dtype_dnll = node.inputs[0].dtype
dtype_sm = node.inputs[1].dtype
dtype_y_idx = node.inputs[2].dtype
dtype_dx = node.outputs[0].dtype
type_intp = gpuarray.dtype_to_ctype(numpy.intp)
type_intp = gpuarray.dtype_to_ctype(np.intp)
dnll, sm, y_idx = inp
dx, = out
fail = sub['fail']
......@@ -519,8 +519,8 @@ class GpuSoftmax(GpuKernelBase, Op):
dtype_x = node.inputs[0].dtype
work_x = work_dtype(dtype_x)
dtype_z = node.outputs[0].dtype
itemsize_x = numpy.dtype(dtype_x).itemsize
itemsize_z = numpy.dtype(dtype_z).itemsize
itemsize_x = np.dtype(dtype_x).itemsize
itemsize_z = np.dtype(dtype_z).itemsize
typecode = pygpu.gpuarray.dtype_to_typecode(node.outputs[0].dtype)
x, = inp
z, = out
......@@ -719,9 +719,9 @@ class GpuSoftmaxWithBias(GpuKernelBase, Op):
dtype_b = node.inputs[1].dtype
dtype_z = node.outputs[0].dtype
work_x = work_dtype(dtype_x)
itemsize_x = numpy.dtype(dtype_x).itemsize
itemsize_b = numpy.dtype(dtype_b).itemsize
itemsize_z = numpy.dtype(dtype_z).itemsize
itemsize_x = np.dtype(dtype_x).itemsize
itemsize_b = np.dtype(dtype_b).itemsize
itemsize_z = np.dtype(dtype_z).itemsize
typecode = pygpu.gpuarray.dtype_to_typecode(node.outputs[0].dtype)
x, b = inp
z, = out
......
theano/gpuarray/opt.py
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from __future__ import absolute_import, print_function, division
import copy
import numpy
import numpy as np
import logging
import pdb
import time
......@@ -622,7 +622,7 @@ def local_gpualloc_memset_0(node):
inp = node.inputs[0]
if (isinstance(inp, GpuArrayConstant) and
inp.data.size == 1 and
(numpy.asarray(inp.data) == 0).all()):
(np.asarray(inp.data) == 0).all()):
new_op = gpu_alloc(node.op.context_name, memset_0=True)
return [new_op(*node.inputs)]
......@@ -632,7 +632,7 @@ def local_gpualloc_memset_0(node):
def local_gpua_alloc_empty_to_zeros(node):
if isinstance(node.op, GpuAllocEmpty):
context_name = infer_context_name(*node.inputs)
z = numpy.asarray(0, dtype=node.outputs[0].dtype)
z = np.asarray(0, dtype=node.outputs[0].dtype)
return [gpu_alloc(context_name)(as_gpuarray_variable(z, context_name),
*node.inputs)]
optdb.register('local_gpua_alloc_empty_to_zeros',
......@@ -830,7 +830,7 @@ def local_gpua_shape_graph(op, context_name, inputs, outputs):
def gpu_print_wrapper(op, cnda):
op.old_op.global_fn(op.old_op, numpy.asarray(cnda))
op.old_op.global_fn(op.old_op, np.asarray(cnda))
@register_opt('fast_compile')
......
theano/gpuarray/opt_util.py
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from __future__ import absolute_import, print_function, division
from functools import wraps
import numpy
import numpy as np
from theano import tensor, scalar as scal, Constant
from theano.gof import local_optimizer
......@@ -11,7 +11,7 @@ from theano.tensor import (DimShuffle, get_scalar_constant_value,
from .basic_ops import GpuFromHost, HostFromGpu, GpuAllocEmpty, GpuReshape, gpu_alloc_empty
from .elemwise import GpuDimShuffle, GpuElemwise
_one = scal.constant(numpy.asarray(1.0, dtype='float32'))
_one = scal.constant(np.asarray(1.0, dtype='float32'))
def grab_cpu_scalar(v, nd):
......
theano/gpuarray/subtensor.py
浏览文件 @ 824cb369
......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import os
import numpy
import numpy as np
from six import integer_types
from six.moves import StringIO
......@@ -128,7 +128,7 @@ class GpuSubtensor(HideC, Subtensor):
def fix_idx(idx):
if idx is None:
return "0", 1
elif isinstance(idx, (numpy.integer, integer_types)):
elif isinstance(idx, (np.integer, integer_types)):
return str(idx), 0
elif isinstance(idx, gof.Type):
return indices.pop(0), 0
......@@ -155,7 +155,7 @@ class GpuSubtensor(HideC, Subtensor):
else:
if isinstance(idx, gof.Type):
start = indices.pop(0)
elif isinstance(idx, (numpy.integer, integer_types)):
elif isinstance(idx, (np.integer, integer_types)):
start = idx
else:
assert 0, idx
......@@ -511,7 +511,7 @@ class GpuAdvancedSubtensor(HideC, tensor.AdvancedSubtensor):
# if there are more than one (narray > 1) it should be ignored.
ap = 0
for k, i in enumerate(list(nidx)):
if (isinstance(i, numpy.ndarray) and
if (isinstance(i, np.ndarray) and
i.ndim != 0):
transp.remove(k)
transp.insert(p, k)
......@@ -545,7 +545,7 @@ class GpuAdvancedSubtensor(HideC, tensor.AdvancedSubtensor):
x = x.__getitem__(idx_)
# flatten the array-indexed dimensions
shape = ((numpy.prod(x.shape[0: p]),) +
shape = ((np.prod(x.shape[0: p]),) +
x.shape[p:])
input_flat = x.reshape(shape)
......@@ -644,7 +644,7 @@ class GpuAdvancedIncSubtensor1(Op):
# content to index x and y (This is because we serve as
# fallback for _dev20).
if isinstance(idx, gpuarray.GpuArray):
idx = numpy.asarray(idx)
idx = np.asarray(idx)
# If `y` has as many dimensions as `x`, then we want to iterate
# jointly on `x` and `y`. Otherwise, it means `y` should be
......@@ -877,10 +877,10 @@ if (GpuArray_vector_add_fast(%(out)s, %(y)s, %(ind)s, %(set_instead_of_inc)s)) {
dtype_y = node.inputs[1].dtype
dtype_ind = node.inputs[2].dtype
dtype_out = node.outputs[0].dtype
itemsize_x = numpy.dtype(dtype_x).itemsize
itemsize_y = numpy.dtype(dtype_y).itemsize
itemsize_ind = numpy.dtype(dtype_ind).itemsize
itemsize_out = numpy.dtype(dtype_out).itemsize
itemsize_x = np.dtype(dtype_x).itemsize
itemsize_y = np.dtype(dtype_y).itemsize
itemsize_ind = np.dtype(dtype_ind).itemsize
itemsize_out = np.dtype(dtype_out).itemsize
flags = Kernel.get_flags(dtype_x, dtype_y, dtype_ind)
type_x = gpuarray.dtype_to_ctype(dtype_x)
type_y = gpuarray.dtype_to_ctype(dtype_y)
......@@ -1007,10 +1007,10 @@ __device__ ga_half atomicExch(ga_half *addr, ga_half val) {
dtype_y = node.inputs[1].dtype
dtype_ind = node.inputs[2].dtype
dtype_out = node.outputs[0].dtype
itemsize_x = numpy.dtype(dtype_x).itemsize
itemsize_y = numpy.dtype(dtype_y).itemsize
itemsize_ind = numpy.dtype(dtype_ind).itemsize
itemsize_out = numpy.dtype(dtype_out).itemsize
itemsize_x = np.dtype(dtype_x).itemsize
itemsize_y = np.dtype(dtype_y).itemsize
itemsize_ind = np.dtype(dtype_ind).itemsize
itemsize_out = np.dtype(dtype_out).itemsize
k_var = "k_vector_add_fast_" + nodename
return super(GpuAdvancedIncSubtensor1_dev20, self).c_support_code_struct(node, nodename) + """
......@@ -1112,7 +1112,7 @@ class GpuDiagonal(Subtensor):
if x.size == 0:
out_shape = [d for i, d in enumerate(x.shape)
if i not in (self.axis1, self.axis2)]
diag_size = numpy.min((x.shape[self.axis1], x.shape[self.axis2]))
diag_size = np.min((x.shape[self.axis1], x.shape[self.axis2]))
out_shape.append(diag_size)
z[0] = node.outputs[0].type.value_zeros(tuple(out_shape))
return
......@@ -1128,15 +1128,15 @@ class GpuDiagonal(Subtensor):
if x.shape[stride_axis] < x.shape[slice_axis]:
# in the bigger triangle
numstride = small_axis - numpy.max((
0, small_axis + numpy.abs(self.offset) - large_axis))
numstride = small_axis - np.max((
0, small_axis + np.abs(self.offset) - large_axis))
else:
# in the smaller triangle
numstride = small_axis - numpy.abs(self.offset)
numstride = small_axis - np.abs(self.offset)
slicer = [numpy.s_[:], ] * x.ndim
slicer[stride_axis] = numpy.s_[:numstride]
slicer[slice_axis] = numpy.abs(self.offset)
slicer = [np.s_[:], ] * x.ndim
slicer[stride_axis] = np.s_[:numstride]
slicer[slice_axis] = np.abs(self.offset)
slicer = tuple(slicer)
# step 2) Swap stride_axis to the last dim because we want the dim on
......
theano/gpuarray/tests/rnn_support.py
浏览文件 @ 824cb369
......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import theano
import theano.tensor as T
import numpy
import numpy as np
class Model(object):
......@@ -27,9 +27,9 @@ class Model(object):
def uniform(stdev, size):
"""uniform distribution with the given stdev and size"""
return numpy.random.uniform(
low=-stdev * numpy.sqrt(3),
high=stdev * numpy.sqrt(3),
return np.random.uniform(
low=-stdev * np.sqrt(3),
high=stdev * np.sqrt(3),
size=size
).astype(theano.config.floatX)
......@@ -37,7 +37,7 @@ def uniform(stdev, size):
def linear_transform_weights(input_dim, output_dim,
param_list=None, name=""):
"theano shared variable given input and output dimension"
weight_inialization = uniform(numpy.sqrt(2.0 / input_dim),
weight_inialization = uniform(np.sqrt(2.0 / input_dim),
(input_dim, output_dim))
W = theano.shared(weight_inialization, name=name)
......@@ -49,7 +49,7 @@ def linear_transform_weights(input_dim, output_dim,
def bias_weights(length, param_list=None, name=""):
"theano shared variable for bias unit, given length"
bias_initialization = numpy.zeros(length).astype(theano.config.floatX)
bias_initialization = np.zeros(length).astype(theano.config.floatX)
bias = theano.shared(
bias_initialization,
......
theano/gpuarray/tests/test_abstractconv.py
浏览文件 @ 824cb369
......@@ -3,7 +3,7 @@ from __future__ import absolute_import, print_function, division
from nose.plugins.skip import SkipTest
from nose.tools import assert_raises
import numpy
import numpy as np
from theano.tensor.nnet.tests import test_abstract_conv
from ..type import GpuArrayType, gpuarray_shared_constructor, get_context
......@@ -250,7 +250,7 @@ class TestDnnConvTypes(test_abstract_conv.TestConvTypes):
self.filters = gpu_ftensor4()
self.topgrad = gpu_ftensor4()
self.constant_tensor = gpuarray.array(
numpy.zeros((3, 5, 7, 11), dtype='float32'),
np.zeros((3, 5, 7, 11), dtype='float32'),
context=get_context(test_ctx_name))
......
theano/gpuarray/tests/test_basic_ops.py
浏览文件 @ 824cb369
......@@ -4,7 +4,7 @@ from theano.compat import izip
from six import iteritems
import numpy
import numpy as np
import theano
import theano.tensor as T
from theano.tensor import TensorType
......@@ -29,7 +29,7 @@ from .config import mode_with_gpu, mode_without_gpu, test_ctx_name
from pygpu import gpuarray
utt.seed_rng()
rng = numpy.random.RandomState(seed=utt.fetch_seed())
rng = np.random.RandomState(seed=utt.fetch_seed())
def inplace_func(inputs, outputs, mode=None, allow_input_downcast=False,
......@@ -92,8 +92,8 @@ def makeTester(name, op, gpu_op, cases, checks=None, mode_gpu=mode_with_gpu,
for testname, inputs in iteritems(cases):
for _ in range(len(inputs)):
if type(inputs[_]) is float:
inputs[_] = numpy.asarray(inputs[_],
dtype=theano.config.floatX)
inputs[_] = np.asarray(inputs[_],
dtype=theano.config.floatX)
self.run_case(testname, inputs)
def run_case(self, testname, inputs):
......@@ -177,7 +177,7 @@ def test_transfer_cpu_gpu():
a = T.fmatrix('a')
g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
av = numpy.asarray(rng.rand(5, 4), dtype='float32')
av = np.asarray(rng.rand(5, 4), dtype='float32')
gv = gpuarray.array(av, context=get_context(test_ctx_name))
f = theano.function([a], GpuFromHost(test_ctx_name)(a))
......@@ -186,14 +186,14 @@ def test_transfer_cpu_gpu():
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert numpy.all(fv == av)
assert np.all(fv == av)
def test_transfer_gpu_gpu():
g = GpuArrayType(dtype='float32', broadcastable=(False, False),
context_name=test_ctx_name)()
av = numpy.asarray(rng.rand(5, 4), dtype='float32')
av = np.asarray(rng.rand(5, 4), dtype='float32')
gv = gpuarray.array(av, context=get_context(test_ctx_name))
mode = mode_with_gpu.excluding('cut_gpua_host_transfers', 'local_cut_gpua_host_gpua')
f = theano.function([g], GpuToGpu(test_ctx_name)(g), mode=mode)
......@@ -211,7 +211,7 @@ def test_transfer_strided():
a = T.fmatrix('a')
g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
av = numpy.asarray(rng.rand(5, 8), dtype='float32')
av = np.asarray(rng.rand(5, 8), dtype='float32')
gv = gpuarray.array(av, context=get_context(test_ctx_name))
av = av[:, ::2]
......@@ -223,7 +223,7 @@ def test_transfer_strided():
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert numpy.all(fv == av)
assert np.all(fv == av)
def gpu_alloc_expected(x, *shp):
......@@ -237,16 +237,16 @@ GpuAllocTester = makeTester(
op=alloc,
gpu_op=GpuAlloc(test_ctx_name),
cases=dict(
correct01=(rand(), numpy.int32(7)),
correct01=(rand(), np.int32(7)),
# just gives a DeepCopyOp with possibly wrong results on the CPU
# correct01_bcast=(rand(1), numpy.int32(7)),
correct02=(rand(), numpy.int32(4), numpy.int32(7)),
correct12=(rand(7), numpy.int32(4), numpy.int32(7)),
correct13=(rand(7), numpy.int32(2), numpy.int32(4),
numpy.int32(7)),
correct23=(rand(4, 7), numpy.int32(2), numpy.int32(4),
numpy.int32(7)),
bad_shape12=(rand(7), numpy.int32(7), numpy.int32(5)),
# correct01_bcast=(rand(1), np.int32(7)),
correct02=(rand(), np.int32(4), np.int32(7)),
correct12=(rand(7), np.int32(4), np.int32(7)),
correct13=(rand(7), np.int32(2), np.int32(4),
np.int32(7)),
correct23=(rand(4, 7), np.int32(2), np.int32(4),
np.int32(7)),
bad_shape12=(rand(7), np.int32(7), np.int32(5)),
)
)
......@@ -282,7 +282,7 @@ def test_shape():
v = gpuarray.zeros((3, 4, 5), dtype='float32', context=get_context(test_ctx_name))
f = theano.function([x], x.shape)
topo = f.maker.fgraph.toposort()
assert numpy.all(f(v) == (3, 4, 5))
assert np.all(f(v) == (3, 4, 5))
if theano.config.mode != 'FAST_COMPILE':
assert len(topo) == 4
assert isinstance(topo[0].op, T.opt.Shape_i)
......@@ -292,7 +292,7 @@ def test_shape():
mode = mode_with_gpu.excluding("local_shape_to_shape_i")
f = theano.function([x], x.shape, mode=mode)
topo = f.maker.fgraph.toposort()
assert numpy.all(f(v) == (3, 4, 5))
assert np.all(f(v) == (3, 4, 5))
assert len(topo) == 1
assert isinstance(topo[0].op, T.Shape)
......@@ -300,7 +300,7 @@ def test_shape():
def test_gpu_contiguous():
a = T.fmatrix('a')
i = T.iscalar('i')
a_val = numpy.asarray(numpy.random.rand(4, 5), dtype='float32')
a_val = np.asarray(np.random.rand(4, 5), dtype='float32')
# The reshape is needed otherwise we make the subtensor on the CPU
# to transfer less data.
f = theano.function([a, i], gpu_contiguous(a.reshape((5, 4))[::i]),
......@@ -353,22 +353,22 @@ class G_Join_and_Split(test_basic.T_Join_and_Split):
self.shared = gpuarray_shared_constructor
def test_gpusplit_opt(self):
rng = numpy.random.RandomState(seed=utt.fetch_seed())
rng = np.random.RandomState(seed=utt.fetch_seed())
m = self.shared(rng.rand(4, 6).astype(self.floatX))
o = T.Split(2)(m, 0, [2, 2])
f = theano.function([], o, mode=self.mode)
assert any([isinstance(node.op, self.split_op_class)
for node in f.maker.fgraph.toposort()])
o1, o2 = f()
assert numpy.allclose(o1, m.get_value(borrow=True)[:2])
assert numpy.allclose(o2, m.get_value(borrow=True)[2:])
assert np.allclose(o1, m.get_value(borrow=True)[:2])
assert np.allclose(o2, m.get_value(borrow=True)[2:])
def test_gpujoin_gpualloc():
a = T.fmatrix('a')
a_val = numpy.asarray(numpy.random.rand(4, 5), dtype='float32')
a_val = np.asarray(np.random.rand(4, 5), dtype='float32')
b = T.fmatrix('b')
b_val = numpy.asarray(numpy.random.rand(3, 5), dtype='float32')
b_val = np.asarray(np.random.rand(3, 5), dtype='float32')
f = theano.function([a, b], T.join(0, T.zeros_like(a), T.ones_like(b)) + 4,
mode=mode_without_gpu)
......@@ -387,7 +387,7 @@ def test_gpujoin_gpualloc():
for node in f_gpu2.maker.fgraph.toposort()]) == 2
assert sum([node.op == gpu_join
for node in f_gpu2.maker.fgraph.toposort()]) == 1
assert numpy.allclose(f(a_val, b_val), f_gpu2(a_val, b_val))
assert np.allclose(f(a_val, b_val), f_gpu2(a_val, b_val))
def test_gpueye():
......@@ -401,14 +401,14 @@ def test_gpueye():
M = N
N_symb = T.iscalar()
M_symb = T.iscalar()
k_symb = numpy.asarray(0)
k_symb = np.asarray(0)
out = T.eye(N_symb, M_symb, k_symb, dtype=dtype)
f = theano.function([N_symb, M_symb],
T.stack(out),
mode=mode_with_gpu)
result = numpy.asarray(f(N, M))
assert numpy.allclose(result, numpy.eye(N, M_, dtype=dtype))
assert result.dtype == numpy.dtype(dtype)
result = np.asarray(f(N, M))
assert np.allclose(result, np.eye(N, M_, dtype=dtype))
assert result.dtype == np.dtype(dtype)
assert any([isinstance(node.op, GpuEye)
for node in f.maker.fgraph.toposort()])
......@@ -429,8 +429,8 @@ def test_hostfromgpu_shape_i():
'specialize')
a = T.fmatrix('a')
ca = theano.gpuarray.type.GpuArrayType('float32', (False, False))()
av = numpy.asarray(numpy.random.rand(5, 4), dtype='float32')
cv = gpuarray.asarray(numpy.random.rand(5, 4),
av = np.asarray(np.random.rand(5, 4), dtype='float32')
cv = gpuarray.asarray(np.random.rand(5, 4),
dtype='float32',
context=get_context(test_ctx_name))
......@@ -464,7 +464,7 @@ def test_Gpujoin_inplace():
non-empty element.
"""
s = T.lscalar()
data = numpy.array([3, 4, 5], dtype=theano.config.floatX)
data = np.array([3, 4, 5], dtype=theano.config.floatX)
x = gpuarray_shared_constructor(data, borrow=True)
z = T.zeros((s,))
......@@ -473,4 +473,4 @@ def test_Gpujoin_inplace():
f = theano.function([s], theano.Out(c, borrow=True))
assert x.get_value(borrow=True, return_internal_type=True) is f(0)
assert numpy.allclose(f(0), [3, 4, 5])
assert np.allclose(f(0), [3, 4, 5])
theano/gpuarray/tests/test_blas.py
浏览文件 @ 824cb369
......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
from unittest import TestCase
from nose.plugins.skip import SkipTest
import itertools
import numpy
import numpy as np
import theano
from theano import tensor
......@@ -138,8 +138,8 @@ def test_gemv_zeros():
# Apply to an empty matrix shape (5,0) and an empty vector shape (0,)
dim = 1000
A = numpy.zeros((dim, 0), dtype=theano.config.floatX)
b = numpy.zeros((0,), dtype=theano.config.floatX)
A = np.zeros((dim, 0), dtype=theano.config.floatX)
b = np.zeros((0,), dtype=theano.config.floatX)
tmp = f(A, b)
assert numpy.allclose(tmp,
numpy.zeros((dim,)))
assert np.allclose(tmp,
np.zeros((dim,)))
theano/gpuarray/tests/test_blocksparse.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import theano
from theano import tensor
......@@ -39,7 +39,7 @@ class BlockSparse_Gemv_and_Outer(test_blocksparse.BlockSparse_Gemv_and_Outer):
o = gpu_sparse_block_gemv(b.take(oIdx, axis=0), W, h, iIdx, oIdx)
gW = theano.grad(o.sum(), W)
lr = numpy.asarray(0.05, dtype='float32')
lr = np.asarray(0.05, dtype='float32')
upd = W - lr * gW
......
theano/gpuarray/tests/test_cgpukernelbase.py
浏览文件 @ 824cb369
from __future__ import division, absolute_import, print_function
import numpy
import numpy as np
from six.moves import xrange
import theano
......@@ -69,4 +69,4 @@ def test_cgpukernelbase():
r = f()
assert (numpy.asarray(r) == numpy.eye(4, 5, dtype='int32')).all()
assert (np.asarray(r) == np.eye(4, 5, dtype='int32')).all()
theano/gpuarray/tests/test_elemwise.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import scipy.special
import theano
......@@ -45,8 +45,8 @@ def test_elemwise_pow():
output = base ** exp
f = theano.function([base, exp], output)
base_val = numpy.random.randint(0, 5, size=10).astype(dtype_base)
exp_val = numpy.random.randint(0, 3, size=10).astype(dtype_exp)
base_val = np.random.randint(0, 5, size=10).astype(dtype_base)
exp_val = np.random.randint(0, 3, size=10).astype(dtype_exp)
# Call the function to make sure the output is valid
out = f(base_val, exp_val)
......@@ -68,7 +68,7 @@ class TestMathErrorFunctions(TestCase):
# to have the GPU ops run on large data.
default_array = [x / 10.0 for x in range(-50, 50)] * 1000
for dtype in self.dtypes:
numpy_array = numpy.asarray(default_array, dtype=dtype)
numpy_array = np.asarray(default_array, dtype=dtype)
self.default_arrays[dtype] = numpy_array
self.expected_erfinv_outputs[dtype] = scipy.special.erfinv(numpy_array)
self.expected_erfcinv_outputs[dtype] = scipy.special.erfcinv(numpy_array)
......@@ -127,7 +127,7 @@ class test_float16():
o = (cz - cz**2 +
tensor.cast(x, 'int16') + tensor.cast(x, 'float32') +
tensor.cast(w, 'float16') -
tensor.constant(numpy.float16(1.0)))
tensor.constant(np.float16(1.0)))
theano.function([w, x, y], o, mode=mode_with_gpu)
......@@ -154,9 +154,9 @@ class test_float16():
i8.astype('float32')],
mode=mode_with_gpu)
d1 = (numpy.random.rand(4) * 10).astype('float16')
d2 = (numpy.random.rand(5) * 10).astype('float32')
d3 = (numpy.random.rand(6) * 10).astype('int8')
d1 = (np.random.rand(4) * 10).astype('float16')
d2 = (np.random.rand(5) * 10).astype('float32')
d3 = (np.random.rand(6) * 10).astype('int8')
res = f(d1, d2, d3)
for i, out in enumerate(f.outputs):
......@@ -337,9 +337,7 @@ class T_gpureduce_dtype(test_elemwise.T_reduce_dtype):
def speed_reduce10():
import numpy
import theano
data = numpy.random.rand(1000, 1000).astype("float32")
data = np.random.rand(1000, 1000).astype("float32")
m = theano.tensor.fmatrix()
f = theano.function([m], [m.sum(axis=0), m.T.sum(axis=0)],
mode=mode_with_gpu)
......
theano/gpuarray/tests/test_fft.py
浏览文件 @ 824cb369
......@@ -7,7 +7,6 @@ import theano.tensor as T
from theano.tests import unittest_tools as utt
import theano.gpuarray.fft
import numpy.fft
from .config import mode_with_gpu
......@@ -37,7 +36,7 @@ class TestFFT(unittest.TestCase):
res_rfft_comp = (np.asarray(res_rfft[:, :, 0]) +
1j * np.asarray(res_rfft[:, :, 1]))
rfft_ref = numpy.fft.rfft(inputs_val, axis=1)
rfft_ref = np.fft.rfft(inputs_val, axis=1)
utt.assert_allclose(rfft_ref, res_rfft_comp)
......@@ -72,7 +71,7 @@ class TestFFT(unittest.TestCase):
res_rfft_comp = (np.asarray(res_rfft[:, :, :, 0]) +
1j * np.asarray(res_rfft[:, :, :, 1]))
rfft_ref = numpy.fft.rfftn(inputs_val, axes=(1, 2))
rfft_ref = np.fft.rfftn(inputs_val, axes=(1, 2))
utt.assert_allclose(rfft_ref, res_rfft_comp, atol=1e-4, rtol=1e-4)
......@@ -91,7 +90,7 @@ class TestFFT(unittest.TestCase):
utt.assert_allclose(inputs_val, np.asarray(res_ifft))
inputs_val = numpy.random.random((1, N, N, 2)).astype('float32')
inputs_val = np.random.random((1, N, N, 2)).astype('float32')
inputs = theano.shared(inputs_val)
irfft = theano.gpuarray.fft.cuirfft(inputs)
......@@ -123,7 +122,7 @@ class TestFFT(unittest.TestCase):
res_rfft_comp = (np.asarray(res_rfft[:, :, :, 0]) +
1j * np.asarray(res_rfft[:, :, :, 1]))
rfft_ref = numpy.fft.rfftn(inputs_val, axes=(1, 2))
rfft_ref = np.fft.rfftn(inputs_val, axes=(1, 2))
utt.assert_allclose(rfft_ref / N, res_rfft_comp, atol=1e-4, rtol=1e-4)
......@@ -146,7 +145,7 @@ class TestFFT(unittest.TestCase):
f_irfft = theano.function([], irfft, mode=mode_with_gpu)
res_irfft = f_irfft()
irfft_ref = numpy.fft.irfftn(inputs_ref, axes=(1, 2))
irfft_ref = np.fft.irfftn(inputs_ref, axes=(1, 2))
utt.assert_allclose(irfft_ref * N, res_irfft, atol=1e-4, rtol=1e-4)
......@@ -195,7 +194,7 @@ class TestFFT(unittest.TestCase):
res_rfft_comp = (np.asarray(res_rfft[:, :, :, 0]) +
1j * np.asarray(res_rfft[:, :, :, 1]))
rfft_ref = numpy.fft.rfftn(inputs_val, s=(M, M), axes=(1, 2))
rfft_ref = np.fft.rfftn(inputs_val, s=(M, M), axes=(1, 2))
utt.assert_allclose(rfft_ref, res_rfft_comp, atol=1e-4, rtol=1e-4)
......@@ -214,7 +213,7 @@ class TestFFT(unittest.TestCase):
res_irfft = f_irfft()
inputs_ref = inputs_val[:, :, :, 0] + 1j * inputs_val[:, :, :, 1]
irfft_ref = numpy.fft.irfftn(inputs_ref, s=(M, M), axes=(1, 2)) * M
irfft_ref = np.fft.irfftn(inputs_ref, s=(M, M), axes=(1, 2)) * M
utt.assert_allclose(irfft_ref, res_irfft, atol=1e-4, rtol=1e-4)
......@@ -243,12 +242,12 @@ class TestFFT(unittest.TestCase):
utt.verify_grad(f_irfft, [inputs_val], eps=eps)
def test_params(self):
inputs_val = numpy.random.random((1, N)).astype('float32')
inputs_val = np.random.random((1, N)).astype('float32')
inputs = theano.shared(inputs_val)
self.assertRaises(ValueError, theano.gpuarray.fft.curfft, inputs, norm=123)
inputs_val = numpy.random.random((1, N // 2 + 1, 2)).astype('float32')
inputs_val = np.random.random((1, N // 2 + 1, 2)).astype('float32')
inputs = theano.shared(inputs_val)
self.assertRaises(ValueError, theano.gpuarray.fft.cuirfft, inputs, norm=123)
......
theano/gpuarray/tests/test_gemmcorr.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
import unittest
import numpy
import numpy as np
import theano
from theano import config
......@@ -23,8 +23,8 @@ class TestCorrMM(unittest.TestCase):
inputs_shape = [inputs_shape[i] for i in (0, 3, 1, 2)]
filters_shape = [filters_shape[i] for i in (0, 3, 1, 2)]
inputs_val = numpy.random.random(inputs_shape).astype(config.floatX)
filters_val = numpy.random.random(filters_shape).astype(config.floatX)
inputs_val = np.random.random(inputs_shape).astype(config.floatX)
filters_val = np.random.random(filters_shape).astype(config.floatX)
inputs = gpuarray_shared_constructor(inputs_val)
filters = gpuarray_shared_constructor(filters_val)
......@@ -122,11 +122,11 @@ class TestCorrMM(unittest.TestCase):
filters_shape = [filters_shape[i] for i in (0, 3, 1, 2)]
dCdH_shape = [dCdH_shape[i] for i in (0, 3, 1, 2)]
inputs_val = numpy.random.random(inputs_shape).astype(config.floatX)
dCdH_val = numpy.random.random(dCdH_shape).astype(config.floatX)
inputs_val = np.random.random(inputs_shape).astype(config.floatX)
dCdH_val = np.random.random(dCdH_shape).astype(config.floatX)
inputs = gpuarray_shared_constructor(inputs_val)
dCdH = gpuarray_shared_constructor(dCdH_val)
shape = gpuarray_shared_constructor(numpy.array(filters_shape[2:]))
shape = gpuarray_shared_constructor(np.array(filters_shape[2:]))
if (subsample == (1, 1)):
conv_ref = CorrMM_gradWeights(subsample=subsample)(
......@@ -169,14 +169,14 @@ class TestCorrMM(unittest.TestCase):
inputs_shape = [inputs_shape[i] for i in (0, 3, 1, 2)]
filters_shape = [filters_shape[i] for i in (0, 3, 1, 2)]
inputs_val = numpy.random.random(inputs_shape).astype(config.floatX)
filters_val = numpy.random.random(filters_shape).astype(config.floatX)
inputs_val = np.random.random(inputs_shape).astype(config.floatX)
filters_val = np.random.random(filters_shape).astype(config.floatX)
inputs = gpuarray_shared_constructor(inputs_val)
filters = gpuarray_shared_constructor(filters_val)
bottom_height = (inputs_shape[2] - 1) * subsample[0] + filters_shape[2]
bottom_width = (inputs_shape[3] - 1) * subsample[1] + filters_shape[3]
bottom_shape = gpuarray_shared_constructor(numpy.array([bottom_height, bottom_width]))
bottom_shape = gpuarray_shared_constructor(np.array([bottom_height, bottom_width]))
if (subsample == (1, 1)):
conv_ref = CorrMM_gradInputs(subsample=subsample)(
......
theano/gpuarray/tests/test_gemmcorr3d.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
import unittest
import numpy
import numpy as np
import theano
from theano import config
......@@ -23,8 +23,8 @@ class TestCorr3dMM(unittest.TestCase):
inputs_shape = [inputs_shape[i] for i in (0, 4, 1, 2, 3)]
filters_shape = [filters_shape[i] for i in (0, 4, 1, 2, 3)]
inputs_val = numpy.random.random(inputs_shape).astype(config.floatX)
filters_val = numpy.random.random(filters_shape).astype(config.floatX)
inputs_val = np.random.random(inputs_shape).astype(config.floatX)
filters_val = np.random.random(filters_shape).astype(config.floatX)
inputs = gpuarray_shared_constructor(inputs_val)
filters = gpuarray_shared_constructor(filters_val)
......@@ -121,11 +121,11 @@ class TestCorr3dMM(unittest.TestCase):
filters_shape = [filters_shape[i] for i in (0, 4, 1, 2, 3)]
dCdH_shape = [dCdH_shape[i] for i in (0, 4, 1, 2, 3)]
inputs_val = numpy.random.random(inputs_shape).astype(config.floatX)
dCdH_val = numpy.random.random(dCdH_shape).astype(config.floatX)
inputs_val = np.random.random(inputs_shape).astype(config.floatX)
dCdH_val = np.random.random(dCdH_shape).astype(config.floatX)
inputs = gpuarray_shared_constructor(inputs_val)
dCdH = gpuarray_shared_constructor(dCdH_val)
shape = gpuarray_shared_constructor(numpy.array(filters_shape[2:]))
shape = gpuarray_shared_constructor(np.array(filters_shape[2:]))
if (subsample == (1, 1, 1)):
conv_ref = Corr3dMM_gradWeights(subsample=subsample)(
......@@ -168,15 +168,15 @@ class TestCorr3dMM(unittest.TestCase):
inputs_shape = [inputs_shape[i] for i in (0, 4, 1, 2, 3)]
filters_shape = [filters_shape[i] for i in (0, 4, 1, 2, 3)]
inputs_val = numpy.random.random(inputs_shape).astype(config.floatX)
filters_val = numpy.random.random(filters_shape).astype(config.floatX)
inputs_val = np.random.random(inputs_shape).astype(config.floatX)
filters_val = np.random.random(filters_shape).astype(config.floatX)
inputs = gpuarray_shared_constructor(inputs_val)
filters = gpuarray_shared_constructor(filters_val)
bottom_height = (inputs_shape[2] - 1) * subsample[0] + filters_shape[2]
bottom_width = (inputs_shape[3] - 1) * subsample[1] + filters_shape[3]
bottom_depth = (inputs_shape[4] - 1) * subsample[2] + filters_shape[4]
bottom_shape = gpuarray_shared_constructor(numpy.array([bottom_height, bottom_width, bottom_depth]))
bottom_shape = gpuarray_shared_constructor(np.array([bottom_height, bottom_width, bottom_depth]))
if (subsample == (1, 1, 1)):
conv_ref = Corr3dMM_gradInputs(subsample=subsample)(
......
theano/gpuarray/tests/test_linalg.py
浏览文件 @ 824cb369
from __future__ import absolute_import, division, print_function
import unittest
import numpy
import numpy as np
import theano
from theano.tests import unittest_tools as utt
......@@ -19,8 +19,8 @@ if not cusolver_available:
class TestCusolver(unittest.TestCase):
def run_gpu_solve(self, A_val, x_val, A_struct=None):
b_val = numpy.dot(A_val, x_val)
b_val_trans = numpy.dot(A_val.T, x_val)
b_val = np.dot(A_val, x_val)
b_val_trans = np.dot(A_val.T, x_val)
A = theano.tensor.matrix("A", dtype="float32")
b = theano.tensor.matrix("b", dtype="float32")
......@@ -35,59 +35,59 @@ class TestCusolver(unittest.TestCase):
fn = theano.function([A, b, b_trans], [solver, solver_trans], mode=mode_with_gpu)
res = fn(A_val, b_val, b_val_trans)
x_res = numpy.array(res[0])
x_res_trans = numpy.array(res[1])
x_res = np.array(res[0])
x_res_trans = np.array(res[1])
utt.assert_allclose(x_val, x_res)
utt.assert_allclose(x_val, x_res_trans)
def test_diag_solve(self):
numpy.random.seed(1)
A_val = numpy.asarray([[2, 0, 0], [0, 1, 0], [0, 0, 1]],
dtype="float32")
x_val = numpy.random.uniform(-0.4, 0.4, (A_val.shape[1],
1)).astype("float32")
np.random.seed(1)
A_val = np.asarray([[2, 0, 0], [0, 1, 0], [0, 0, 1]],
dtype="float32")
x_val = np.random.uniform(-0.4, 0.4, (A_val.shape[1],
1)).astype("float32")
self.run_gpu_solve(A_val, x_val)
def test_bshape_solve(self):
"""
Test when shape of b (k, m) is such as m > k
"""
numpy.random.seed(1)
A_val = numpy.asarray([[2, 0, 0], [0, 1, 0], [0, 0, 1]],
dtype="float32")
x_val = numpy.random.uniform(-0.4, 0.4, (A_val.shape[1],
A_val.shape[1] + 1)).astype("float32")
np.random.seed(1)
A_val = np.asarray([[2, 0, 0], [0, 1, 0], [0, 0, 1]],
dtype="float32")
x_val = np.random.uniform(-0.4, 0.4, (A_val.shape[1],
A_val.shape[1] + 1)).astype("float32")
self.run_gpu_solve(A_val, x_val)
def test_sym_solve(self):
numpy.random.seed(1)
A_val = numpy.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
A_sym = numpy.dot(A_val, A_val.T)
x_val = numpy.random.uniform(-0.4, 0.4, (A_val.shape[1],
1)).astype("float32")
np.random.seed(1)
A_val = np.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
A_sym = np.dot(A_val, A_val.T)
x_val = np.random.uniform(-0.4, 0.4, (A_val.shape[1],
1)).astype("float32")
self.run_gpu_solve(A_sym, x_val, 'symmetric')
def test_orth_solve(self):
numpy.random.seed(1)
A_val = numpy.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
A_orth = numpy.linalg.svd(A_val)[0]
x_val = numpy.random.uniform(-0.4, 0.4, (A_orth.shape[1],
1)).astype("float32")
np.random.seed(1)
A_val = np.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
A_orth = np.linalg.svd(A_val)[0]
x_val = np.random.uniform(-0.4, 0.4, (A_orth.shape[1],
1)).astype("float32")
self.run_gpu_solve(A_orth, x_val)
def test_uni_rand_solve(self):
numpy.random.seed(1)
A_val = numpy.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
x_val = numpy.random.uniform(-0.4, 0.4,
(A_val.shape[1], 4)).astype("float32")
np.random.seed(1)
A_val = np.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
x_val = np.random.uniform(-0.4, 0.4,
(A_val.shape[1], 4)).astype("float32")
self.run_gpu_solve(A_val, x_val)
def test_linalgerrsym_solve(self):
numpy.random.seed(1)
A_val = numpy.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
x_val = numpy.random.uniform(-0.4, 0.4,
(A_val.shape[1], 4)).astype("float32")
A_val = numpy.dot(A_val.T, A_val)
np.random.seed(1)
A_val = np.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
x_val = np.random.uniform(-0.4, 0.4,
(A_val.shape[1], 4)).astype("float32")
A_val = np.dot(A_val.T, A_val)
# make A singular
A_val[:, 2] = A_val[:, 1] + A_val[:, 3]
......@@ -99,10 +99,10 @@ class TestCusolver(unittest.TestCase):
self.assertRaises(LinAlgError, fn, A_val, x_val)
def test_linalgerr_solve(self):
numpy.random.seed(1)
A_val = numpy.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
x_val = numpy.random.uniform(-0.4, 0.4,
(A_val.shape[1], 4)).astype("float32")
np.random.seed(1)
A_val = np.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
x_val = np.random.uniform(-0.4, 0.4,
(A_val.shape[1], 4)).astype("float32")
# make A singular
A_val[:, 2] = 0
......
theano/gpuarray/tests/test_multinomial.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import unittest
......@@ -62,10 +62,10 @@ def test_multinomial_large():
assert any([type(node.op) is GPUAMultinomialFromUniform
for node in f.maker.fgraph.toposort()])
pval = numpy.arange(10000 * 4,
dtype='float32').reshape((10000, 4)) + 0.1
pval = np.arange(10000 * 4,
dtype='float32').reshape((10000, 4)) + 0.1
pval = pval / pval.sum(axis=1)[:, None]
uval = numpy.ones_like(pval[:, 0]) * 0.5
uval = np.ones_like(pval[:, 0]) * 0.5
mval = f(pval, uval)
assert mval.shape == pval.shape
......@@ -78,7 +78,7 @@ def test_multinomial_large():
else:
raise NotImplementedError(config.cast_policy)
utt.assert_allclose(mval.sum(axis=1), 2)
asdf = numpy.asarray([0, 0, 2, 0]) + 0 * pval
asdf = np.asarray([0, 0, 2, 0]) + 0 * pval
utt.assert_allclose(mval, asdf) # broadcast over all rows
......@@ -92,9 +92,9 @@ def test_gpu_opt_dtypes():
f = function([p, u], m, allow_input_downcast=True, mode=mode_with_gpu)
assert any([type(node.op) is GPUAMultinomialFromUniform
for node in f.maker.fgraph.toposort()])
pval = numpy.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
pval = np.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
pval = pval / pval.sum(axis=1)[:, None]
uval = numpy.ones_like(pval[:, 0]) * 0.5
uval = np.ones_like(pval[:, 0]) * 0.5
samples = f(pval, uval)
assert samples.dtype == dtype, "%s != %s" % (samples.dtype, dtype)
......@@ -112,9 +112,9 @@ def test_gpu_opt():
f = function([p, u], m, allow_input_downcast=True, mode=mode_with_gpu)
assert any([type(node.op) is GPUAMultinomialFromUniform
for node in f.maker.fgraph.toposort()])
pval = numpy.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
pval = np.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
pval = pval / pval.sum(axis=1)[:, None]
uval = numpy.ones_like(pval[:, 0]) * 0.5
uval = np.ones_like(pval[:, 0]) * 0.5
f(pval, uval)
# Test with a row, it was failing in the past.
......@@ -125,9 +125,9 @@ def test_gpu_opt():
f = function([r, u], m, allow_input_downcast=True, mode=mode_with_gpu)
assert any([type(node.op) is GPUAMultinomialFromUniform
for node in f.maker.fgraph.toposort()])
pval = numpy.arange(1 * 4, dtype='float32').reshape((1, 4)) + 0.1
pval = np.arange(1 * 4, dtype='float32').reshape((1, 4)) + 0.1
pval = pval / pval.sum(axis=1)[:, None]
uval = numpy.ones_like(pval[:, 0]) * 0.5
uval = np.ones_like(pval[:, 0]) * 0.5
f(pval, uval)
......@@ -146,15 +146,15 @@ class test_OP_wor(unittest.TestCase):
n_elements = 1000
all_indices = range(n_elements)
numpy.random.seed(12345)
np.random.seed(12345)
for i in [5, 10, 50, 100, 500, n_elements]:
uni = numpy.random.rand(i).astype(config.floatX)
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
uni = np.random.rand(i).astype(config.floatX)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
res = f(pvals, uni, i)
res = numpy.squeeze(res)
res = np.squeeze(res)
assert len(res) == i, res
assert numpy.all(numpy.in1d(numpy.unique(res), all_indices)), res
assert np.all(np.in1d(np.unique(res), all_indices)), res
def test_fail_select_alot(self):
"""
......@@ -170,9 +170,9 @@ class test_OP_wor(unittest.TestCase):
n_elements = 100
n_selected = 200
numpy.random.seed(12345)
uni = numpy.random.rand(n_selected).astype(config.floatX)
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
np.random.seed(12345)
uni = np.random.rand(n_selected).astype(config.floatX)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
self.assertRaises(ValueError, f, pvals, uni, n_selected)
......@@ -191,18 +191,18 @@ class test_OP_wor(unittest.TestCase):
n_elements = 100
n_selected = 10
mean_rtol = 0.0005
numpy.random.seed(12345)
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
np.random.seed(12345)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
avg_pvals = numpy.zeros((n_elements,), dtype=config.floatX)
avg_pvals = np.zeros((n_elements,), dtype=config.floatX)
for rep in range(10000):
uni = numpy.random.rand(n_selected).astype(config.floatX)
uni = np.random.rand(n_selected).astype(config.floatX)
res = f(pvals, uni, n_selected)
res = numpy.squeeze(res)
res = np.squeeze(res)
avg_pvals[res] += 1
avg_pvals /= avg_pvals.sum()
avg_diff = numpy.mean(abs(avg_pvals - pvals))
avg_diff = np.mean(abs(avg_pvals - pvals))
assert avg_diff < mean_rtol, avg_diff
......@@ -222,14 +222,14 @@ class test_function_wor(unittest.TestCase):
n_elements = 1000
all_indices = range(n_elements)
numpy.random.seed(12345)
np.random.seed(12345)
for i in [5, 10, 50, 100, 500, n_elements]:
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
res = f(pvals, i)
res = numpy.squeeze(res)
res = np.squeeze(res)
assert len(res) == i
assert numpy.all(numpy.in1d(numpy.unique(res), all_indices)), res
assert np.all(np.in1d(np.unique(res), all_indices)), res
def test_fail_select_alot(self):
"""
......@@ -246,8 +246,8 @@ class test_function_wor(unittest.TestCase):
n_elements = 100
n_selected = 200
numpy.random.seed(12345)
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
np.random.seed(12345)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
self.assertRaises(ValueError, f, pvals, n_selected)
......@@ -267,17 +267,17 @@ class test_function_wor(unittest.TestCase):
n_elements = 100
n_selected = 10
mean_rtol = 0.0005
numpy.random.seed(12345)
pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
np.random.seed(12345)
pvals = np.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
avg_pvals = numpy.zeros((n_elements,), dtype=config.floatX)
avg_pvals = np.zeros((n_elements,), dtype=config.floatX)
for rep in range(10000):
res = f(pvals, n_selected)
res = numpy.squeeze(res)
res = np.squeeze(res)
avg_pvals[res] += 1
avg_pvals /= avg_pvals.sum()
avg_diff = numpy.mean(abs(avg_pvals - pvals))
avg_diff = np.mean(abs(avg_pvals - pvals))
assert avg_diff < mean_rtol
......@@ -294,9 +294,9 @@ def test_gpu_opt_wor():
assert any([type(node.op) is GPUAMultinomialWOReplacementFromUniform
for node in f.maker.fgraph.toposort()])
n_samples = 3
pval = numpy.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
pval = np.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
pval = pval / pval.sum(axis=1)[:, None]
uval = numpy.ones(pval.shape[0] * n_samples) * 0.5
uval = np.ones(pval.shape[0] * n_samples) * 0.5
f(pval, uval, n_samples)
# Test with a row, it was failing in the past.
......@@ -307,7 +307,7 @@ def test_gpu_opt_wor():
f = function([r, u, n], m, allow_input_downcast=True, mode=mode_with_gpu)
assert any([type(node.op) is GPUAMultinomialWOReplacementFromUniform
for node in f.maker.fgraph.toposort()])
pval = numpy.arange(1 * 4, dtype='float32').reshape((1, 4)) + 0.1
pval = np.arange(1 * 4, dtype='float32').reshape((1, 4)) + 0.1
pval = pval / pval.sum(axis=1)[:, None]
uval = numpy.ones_like(pval[:, 0]) * 0.5
uval = np.ones_like(pval[:, 0]) * 0.5
f(pval, uval, 1)
theano/gpuarray/tests/test_nerv.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
from nose.plugins.skip import SkipTest
import numpy
import numpy as np
from theano import function
from theano.tests import unittest_tools as utt
......@@ -40,10 +40,10 @@ def test_gemm16_value():
f = function([m, m2], dot(m, m2), mode=mode_with_gpu)
v1 = numpy.random.random((3, 4)).astype('float16')
v2 = numpy.random.random((4, 2)).astype('float16')
v1 = np.random.random((3, 4)).astype('float16')
v2 = np.random.random((4, 2)).astype('float16')
of = f(v1, v2)
on = numpy.dot(v1, v2)
on = np.dot(v1, v2)
utt.assert_allclose(of, on)
theano/gpuarray/tests/test_nnet.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import unittest
import theano
......@@ -46,13 +46,13 @@ def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
# Seed numpy.random with config.unittests.rseed
utt.seed_rng()
xx = numpy.asarray(numpy.random.rand(batch_size, n_in),
dtype=numpy.float32)
yy = numpy.ones((batch_size,), dtype='int32')
b_values = numpy.zeros((n_out,), dtype='float32')
W_values = numpy.asarray(numpy.random.rand(n_in, n_out), dtype='float32')
xx = np.asarray(np.random.rand(batch_size, n_in),
dtype=np.float32)
yy = np.ones((batch_size,), dtype='int32')
b_values = np.zeros((n_out,), dtype='float32')
W_values = np.asarray(np.random.rand(n_in, n_out), dtype='float32')
dot_value = numpy.asarray(numpy.dot(xx, W_values), dtype='float32')
dot_value = np.asarray(np.dot(xx, W_values), dtype='float32')
del W_values
p_y_given_x = T.nnet.softmax(dot_result + b)
y_pred = T.argmax(p_y_given_x, axis=-1)
......@@ -97,10 +97,10 @@ def test_GpuCrossentropySoftmax1HotWithBiasDx():
# Seed numpy.random with config.unittests.rseed
utt.seed_rng()
softmax_output_value = numpy.random.rand(batch_size,
n_out).astype('float32')
dnll_value = numpy.asarray(numpy.random.rand(batch_size), dtype='float32')
y_idx_value = numpy.random.randint(low=0, high=5, size=batch_size)
softmax_output_value = np.random.rand(batch_size,
n_out).astype('float32')
dnll_value = np.asarray(np.random.rand(batch_size), dtype='float32')
y_idx_value = np.random.randint(low=0, high=5, size=batch_size)
softmax_output = T.fmatrix()
softmax_output /= softmax_output.sum(axis=1).reshape(
......@@ -174,8 +174,8 @@ def softmax_with_bias_unittest_template(dtypeInput, dtypeBias):
GpuSoftmaxWithBias)
def cmp(n, m):
data = numpy.random.uniform(1e-7, 1, (n, m)).astype(dtype=dtypeInput)
b_data = numpy.random.uniform(1e-7, 1, (m,)).astype(dtype=dtypeBias)
data = np.random.uniform(1e-7, 1, (n, m)).astype(dtype=dtypeInput)
b_data = np.random.uniform(1e-7, 1, (m,)).astype(dtype=dtypeBias)
out = f(data, b_data)
gout = f_gpu(data, b_data)
......@@ -227,7 +227,7 @@ def softmax_unittest_template(dtypeInput):
GpuSoftmax)
def cmp(n, m):
data = numpy.random.uniform(0, 1, (n, m)).astype(dtype=dtypeInput)
data = np.random.uniform(0, 1, (n, m)).astype(dtype=dtypeInput)
out = f(data)
gout = f_gpu(data)
......@@ -301,7 +301,7 @@ class test_SoftMax(unittest.TestCase):
return f, f_gpu
def _cmp(self, n, m, f, f_gpu):
data = numpy.arange(n * m, dtype='float32').reshape(n, m)
data = np.arange(n * m, dtype='float32').reshape(n, m)
out = f(data)
gout = f_gpu(data)
utt.assert_allclose(out, gout)
......
theano/gpuarray/tests/test_opt.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
from nose.tools import assert_raises
import theano
......@@ -79,13 +79,13 @@ def test_local_gpu_contiguous():
def test_flatten():
m = theano.tensor.fmatrix()
f = theano.function([m], m.flatten(), mode=mode_with_gpu)
val = numpy.random.rand(10, 11).astype("float32")
val = np.random.rand(10, 11).astype("float32")
res = f(val)
utt.assert_allclose(res, val.flatten())
assert res.shape == val.flatten().shape
assert GpuReshape in [type(node.op)
for node in f.maker.fgraph.toposort()]
val = numpy.random.rand(10, 11).astype("float32")
val = np.random.rand(10, 11).astype("float32")
res = f(val)
utt.assert_allclose(res, val.flatten())
assert res.shape == val.flatten().shape
......@@ -93,7 +93,7 @@ def test_flatten():
for node in f.maker.fgraph.toposort()]
f = theano.function([m], m.flatten(ndim=2), mode=mode_with_gpu)
val = numpy.random.rand(10, 11).astype("float32")
val = np.random.rand(10, 11).astype("float32")
res = f(val)
utt.assert_allclose(res, val)
assert res.shape == val.shape
......@@ -102,7 +102,7 @@ def test_flatten():
m = theano.tensor.tensor3()
f = theano.function([m], m.flatten(ndim=2), mode=mode_with_gpu)
val = numpy.random.rand(10, 11, 12).astype("float32")
val = np.random.rand(10, 11, 12).astype("float32")
res = f(val)
utt.assert_allclose(res, val.reshape(10, -1))
assert res.shape == val.reshape(10, -1).shape
......@@ -120,7 +120,7 @@ def test_reduce():
f = theano.function([m], getattr(m, method)(axis=0,
**param),
mode=mode_with_gpu)
val = numpy.random.rand(10, 11).astype("float32")
val = np.random.rand(10, 11).astype("float32")
res = f(val)
utt.assert_allclose(res, getattr(val, method)(axis=0))
assert res.shape == (11,)
......@@ -135,9 +135,9 @@ def test_reduce():
def test_local_gpualloc_memset_0():
i = theano.tensor.iscalar()
z = numpy.zeros((1,), dtype='float32')
o = numpy.ones((1,), dtype='float32')
ones = numpy.ones((2,), dtype='float32')
z = np.zeros((1,), dtype='float32')
o = np.ones((1,), dtype='float32')
ones = np.ones((2,), dtype='float32')
# Test with 0 from CPU op.
# Should not be transfered as the only client is the output
......@@ -146,7 +146,7 @@ def test_local_gpualloc_memset_0():
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert isinstance(topo[0].op, theano.tensor.Alloc)
assert (numpy.asarray(f(6)) == 0).all()
assert (np.asarray(f(6)) == 0).all()
# Test with 0 from CPU op.
# Should be transfered as it is used by another op.
......@@ -155,7 +155,7 @@ def test_local_gpualloc_memset_0():
topo = f.maker.fgraph.toposort()
assert len(topo) == 3
assert isinstance(topo[0].op, GpuAlloc)
assert (numpy.asarray(f(6)) == 0).all()
assert (np.asarray(f(6)) == 0).all()
# Test with 0
a = GpuAlloc(test_ctx_name)(z, i)
......@@ -163,7 +163,7 @@ def test_local_gpualloc_memset_0():
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert isinstance(topo[0].op, GpuAlloc) and topo[0].op.memset_0
assert (numpy.asarray(f(6)) == 0).all()
assert (np.asarray(f(6)) == 0).all()
# Test with 1
a = GpuAlloc(test_ctx_name)(o, i)
......@@ -172,7 +172,7 @@ def test_local_gpualloc_memset_0():
assert len(topo) == 1
assert isinstance(topo[0].op, GpuAlloc)
assert not topo[0].op.memset_0
assert (numpy.asarray(f(6)) == 1).all()
assert (np.asarray(f(6)) == 1).all()
# Test with 1, 1
a = GpuAlloc(test_ctx_name)(ones, i)
......@@ -181,7 +181,7 @@ def test_local_gpualloc_memset_0():
assert len(topo) == 1
assert isinstance(topo[0].op, GpuAlloc)
assert not topo[0].op.memset_0
assert (numpy.asarray(f(2)) == 1).all()
assert (np.asarray(f(2)) == 1).all()
def test_local_gpualloc_empty():
......@@ -219,7 +219,7 @@ def test_local_gpualloc_empty():
def test_rebroadcast():
d = numpy.random.rand(10, 10).astype('float32')
d = np.random.rand(10, 10).astype('float32')
v = theano.tensor.fmatrix()
up = tensor.unbroadcast(v.sum().dimshuffle('x', 'x'), 0, 1)
f = theano.function([v], [up], mode=mode_with_gpu)
......@@ -257,14 +257,14 @@ class test_gpu_ifelse(test_ifelse.test_ifelse):
f = theano.function([x, cond],
theano.ifelse.ifelse(cond, x.mean(), x.sum()),
mode=mode_with_gpu)
assert f(numpy.float32([1, 2, 3]), 0) == 6
assert f(np.float32([1, 2, 3]), 0) == 6
x = tensor.vector()
cond = tensor.scalar()
f = theano.function([x, cond],
theano.ifelse.ifelse(cond, x.mean(), x.sum()),
mode=mode_with_gpu)
assert f(numpy.float32([1, 2, 3]), 0) == 6
assert f(np.float32([1, 2, 3]), 0) == 6
def test_print_op():
......@@ -277,7 +277,7 @@ def test_print_op():
assert isinstance(topo[1].op, theano.printing.Print)
assert isinstance(topo[2].op, GpuElemwise)
assert topo[3].op == host_from_gpu
f(numpy.random.random((5, 5)).astype('float32'))
f(np.random.random((5, 5)).astype('float32'))
def test_pdbbreakpoint_op():
......@@ -306,7 +306,7 @@ def test_local_gpu_elemwise_careduce():
topo = f.maker.fgraph.toposort()
assert len(topo) == 3
assert topo[1].op.pre_scalar_op == theano.scalar.sqr
data = numpy.random.rand(3, 4).astype(theano.config.floatX)
data = np.random.rand(3, 4).astype(theano.config.floatX)
utt.assert_allclose(f(data), (data * data).sum())
o = (x * x).sum(axis=1)
......@@ -328,15 +328,15 @@ def test_local_lift_dot22scalar():
for n in f_gpu.maker.fgraph.apply_nodes)
assert any(isinstance(n.op, GpuGemm)
for n in f_gpu.maker.fgraph.apply_nodes)
x_val = numpy.random.random((2, 3)).astype(theano.config.floatX)
y_val = numpy.random.random((3, 4)).astype(theano.config.floatX)
x_val = np.random.random((2, 3)).astype(theano.config.floatX)
y_val = np.random.random((3, 4)).astype(theano.config.floatX)
a_val = 0.5
utt.assert_allclose(f_cpu(x_val, y_val, a_val), f_gpu(x_val, y_val, a_val))
def test_local_gpu_subtensor():
# Test shared forced on CPU.
t = tensor._shared(numpy.zeros(20, "float32"))
t = tensor._shared(np.zeros(20, "float32"))
f = theano.function([], t[3:4], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
assert any([type(node.op) is tensor.Subtensor for node in topo])
......@@ -367,7 +367,7 @@ def test_local_gpu_subtensor():
# Test shared forced on CPU end we do computation on the output of
# the subtensor.
t = tensor._shared(numpy.zeros(20, "float32"))
t = tensor._shared(np.zeros(20, "float32"))
f = theano.function([], t[3:4] + 1, mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
assert any([type(node.op) is tensor.Subtensor for node in topo])
......@@ -386,9 +386,9 @@ def test_local_gpu_elemwise():
b = tensor.fmatrix()
c = tensor.fmatrix()
a_v = (numpy.random.rand(4, 5) * 10).astype("int8")
b_v = (numpy.random.rand(4, 5) * 10).astype("float32")
c_v = (numpy.random.rand(4, 5) * 10).astype("float32")
a_v = (np.random.rand(4, 5) * 10).astype("int8")
b_v = (np.random.rand(4, 5) * 10).astype("float32")
c_v = (np.random.rand(4, 5) * 10).astype("float32")
# Due to optimization order, this composite is created when all
# the op are on the gpu.
......@@ -440,7 +440,7 @@ def test_local_gpu_elemwise():
utt.assert_allclose(out[1], a_v * c_v)
# Test non-contiguous input
c = gpuarray_shared_constructor(numpy.asarray(c_v, dtype='float32'))
c = gpuarray_shared_constructor(np.asarray(c_v, dtype='float32'))
f = theano.function([a, b], outs_op(a[::2], b[::2], c[::2]),
mode=mode_with_gpu)
out = f(a_v, b_v)
......@@ -462,7 +462,7 @@ def test_local_lift_abstractconv_gpu_shape():
def test_local_assert_no_cpu_op():
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
m = rng.uniform(-1, 1, (10, 10)).astype("float32")
ms = gpuarray_shared_constructor(m, name="m_shared")
out = theano.tensor.tanh(ms).dot(ms.T)
......@@ -512,6 +512,6 @@ def test_local_lift_solve():
for n in f_gpu.maker.fgraph.apply_nodes)
assert any(isinstance(n.op, GpuCusolverSolve)
for n in f_gpu.maker.fgraph.apply_nodes)
A_val = numpy.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
b_val = numpy.random.uniform(-0.4, 0.4, (5, 3)).astype("float32")
A_val = np.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
b_val = np.random.uniform(-0.4, 0.4, (5, 3)).astype("float32")
utt.assert_allclose(f_cpu(A_val, b_val), f_gpu(A_val, b_val))
theano/gpuarray/tests/test_pickle.py
浏览文件 @ 824cb369
......@@ -12,7 +12,7 @@ from six import reraise
from nose.plugins.skip import SkipTest
from nose.tools import assert_raises
import numpy
import numpy as np
from theano.compat import PY3
from theano import config
......@@ -75,7 +75,7 @@ def test_unpickle_gpuarray_as_numpy_ndarray_flag2():
reraise(SkipTest, exc_value, exc_trace)
raise
assert isinstance(mat, numpy.ndarray)
assert isinstance(mat, np.ndarray)
assert mat[0] == -42.0
finally:
......
theano/gpuarray/tests/test_pool.py
浏览文件 @ 824cb369
......@@ -4,7 +4,7 @@ import unittest
import copy
import itertools
import numpy
import numpy as np
import theano
from theano import gradient
from theano import tensor
......@@ -81,7 +81,7 @@ def test_pool2d():
(3, 2, 6, 6, 6, 5),
(3, 2, 6, 6, 6, 5, 7), ]
numpy.random.RandomState(utt.fetch_seed()).shuffle(shps)
np.random.RandomState(utt.fetch_seed()).shuffle(shps)
test_ws = (2, 2), (3, 2), (1, 1)
test_st = (2, 2), (3, 2), (1, 1)
test_mode = ['max', 'sum', 'average_inc_pad', 'average_exc_pad']
......@@ -113,7 +113,7 @@ def test_pool2d():
for node in f.maker.fgraph.toposort()])
assert any([isinstance(node.op, Pool)
for node in f2.maker.fgraph.toposort()])
assert numpy.allclose(f(), f2()), (shp, ws, st, pad, mode, ignore_border)
assert np.allclose(f(), f2()), (shp, ws, st, pad, mode, ignore_border)
a_pooled_grad = tensor.grad(a_pooled.sum(), a)
......@@ -131,7 +131,7 @@ def test_pool2d():
assert any([isinstance(node.op, gop2)
for node in g2.maker.fgraph.toposort()])
assert numpy.allclose(g(), g2()), (shp, ws, st, pad, mode, ignore_border)
assert np.allclose(g(), g2()), (shp, ws, st, pad, mode, ignore_border)
# test rop and grad grad for max pooling
# for average pooling grad grad is just average pooling grad
......@@ -151,7 +151,7 @@ def test_pool2d():
isinstance(node.op, DownsampleFactorMaxGradGrad)
for node in gr2.maker.fgraph.toposort()
])
assert numpy.allclose(gr(), gr2()), (shp, ws, st, pad, mode, ignore_border)
assert np.allclose(gr(), gr2()), (shp, ws, st, pad, mode, ignore_border)
ggf = gradient.Lop(tensor.grad((a_pooled**2).sum(), a), a, a)
......@@ -166,7 +166,7 @@ def test_pool2d():
isinstance(node.op, DownsampleFactorMaxGradGrad)
for node in gg2.maker.fgraph.toposort()
])
assert numpy.allclose(gg(), gg2()), (shp, ws, st, pad, mode, ignore_border)
assert np.allclose(gg(), gg2()), (shp, ws, st, pad, mode, ignore_border)
def test_pool3d():
......@@ -191,7 +191,7 @@ def test_pool3d():
(3, 2, 6, 6, 6, 5),
(3, 2, 6, 6, 6, 5, 7), ]
numpy.random.RandomState(utt.fetch_seed()).shuffle(shps)
np.random.RandomState(utt.fetch_seed()).shuffle(shps)
test_ws = (2, 2, 2), (3, 2, 3), (1, 1, 1)
test_st = (2, 2, 2), (2, 3, 2), (1, 1, 1)
test_mode = ['max', 'sum', 'average_inc_pad', 'average_exc_pad']
......@@ -223,7 +223,7 @@ def test_pool3d():
for node in f.maker.fgraph.toposort()])
assert any([isinstance(node.op, Pool)
for node in f2.maker.fgraph.toposort()])
assert numpy.allclose(f(), f2()), (shp, ws, st, pad, mode, ignore_border)
assert np.allclose(f(), f2()), (shp, ws, st, pad, mode, ignore_border)
a_pooled_grad = tensor.grad(a_pooled.sum(), a)
......@@ -241,7 +241,7 @@ def test_pool3d():
assert any([isinstance(node.op, gop2)
for node in g2.maker.fgraph.toposort()])
assert numpy.allclose(g(), g2()), (shp, ws, st, pad, mode, ignore_border)
assert np.allclose(g(), g2()), (shp, ws, st, pad, mode, ignore_border)
# test rop and grad grad for max pooling
# for average pooling grad grad is just average pooling grad
......@@ -261,7 +261,7 @@ def test_pool3d():
isinstance(node.op, DownsampleFactorMaxGradGrad)
for node in gr2.maker.fgraph.toposort()
])
assert numpy.allclose(gr(), gr2()), (shp, ws, st, pad, mode, ignore_border)
assert np.allclose(gr(), gr2()), (shp, ws, st, pad, mode, ignore_border)
ggf = gradient.Lop(tensor.grad((a_pooled**2).sum(), a), a, a)
......@@ -276,4 +276,4 @@ def test_pool3d():
isinstance(node.op, DownsampleFactorMaxGradGrad)
for node in gg2.maker.fgraph.toposort()
])
assert numpy.allclose(gg(), gg2()), (shp, ws, st, pad, mode, ignore_border)
assert np.allclose(gg(), gg2()), (shp, ws, st, pad, mode, ignore_border)
theano/gpuarray/tests/test_scan.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
from unittest import TestCase
import numpy
import numpy as np
from six.moves import xrange
import theano
......@@ -43,19 +43,19 @@ class T_Scan(TestCase):
allow_input_downcast=True,
mode=mode)
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
v_u = numpy.asarray(v_u, dtype='float32')
v_x0 = numpy.asarray(v_x0, dtype='float32')
W = numpy.asarray(W, dtype='float32')
W_in = numpy.asarray(W_in, dtype='float32')
v_u = np.asarray(v_u, dtype='float32')
v_x0 = np.asarray(v_x0, dtype='float32')
W = np.asarray(W, dtype='float32')
W_in = np.asarray(W_in, dtype='float32')
# compute the output in numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[0] * W_in + v_x0 * W
for step in xrange(1, 4):
v_out[step] = v_u[step] * W_in + v_out[step - 1] * W
......@@ -115,14 +115,14 @@ class T_Scan(TestCase):
mode=mode_with_gpu)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
# compute the output in numpy
v_out = numpy.zeros((4,))
v_out = np.zeros((4,))
v_out[0] = v_u[0] * W_in + v_x0 * W
for step in xrange(1, 4):
v_out[step] = v_u[step] * W_in + v_out[step - 1] * W
......@@ -177,20 +177,20 @@ class T_Scan(TestCase):
mode=mode_with_gpu)
# get random initial values
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
v_u = rng.uniform(size=(4,), low=-5., high=5.)
v_x0 = rng.uniform()
W = rng.uniform()
W_in = rng.uniform()
# compute the output in numpy
v_out1 = numpy.zeros((4,))
v_out2 = numpy.zeros((4,), dtype='int64')
v_out1 = np.zeros((4,))
v_out2 = np.zeros((4,), dtype='int64')
v_out1[0] = v_u[0] * W_in + v_x0 * W
v_out2[0] = v_u[0] + v_x0
for step in xrange(1, 4):
v_out1[step] = v_u[step] * W_in + v_out1[step - 1] * W
v_out2[step] = numpy.int64(v_u[step] + v_out1[step - 1])
v_out2[step] = np.int64(v_u[step] + v_out1[step - 1])
theano_out1, theano_out2 = f2(v_u, v_x0, W_in, W)
utt.assert_allclose(theano_out1, v_out1)
......@@ -212,9 +212,9 @@ class T_Scan(TestCase):
for node in scan_node_topo])
def test_gpu4_gibbs_chain(self):
rng = numpy.random.RandomState(utt.fetch_seed())
v_vsample = numpy.array(rng.binomial(1, .5, size=(3, 20),),
dtype='float32')
rng = np.random.RandomState(utt.fetch_seed())
v_vsample = np.array(rng.binomial(1, .5, size=(3, 20),),
dtype='float32')
vsample = theano.shared(v_vsample)
trng = theano.sandbox.rng_mrg.MRG_RandomStreams(
utt.fetch_seed())
......
theano/gpuarray/tests/test_subtensor.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import unittest
import theano
......@@ -47,8 +47,8 @@ def test_advinc_subtensor1():
# Test the second case in the opt local_gpu_advanced_incsubtensor1
for shp in [(3, 3), (3, 3, 3)]:
shared = gpuarray_shared_constructor
xval = numpy.arange(numpy.prod(shp), dtype='float32').reshape(shp) + 1
yval = numpy.empty((2,) + shp[1:], dtype='float32')
xval = np.arange(np.prod(shp), dtype='float32').reshape(shp) + 1
yval = np.empty((2,) + shp[1:], dtype='float32')
yval[:] = 10
x = shared(xval, name='x')
y = tensor.tensor(dtype='float32',
......@@ -61,7 +61,7 @@ def test_advinc_subtensor1():
rval = f(yval)
rep = xval.copy()
rep[[0, 2]] += yval
assert numpy.allclose(rval, rep)
assert np.allclose(rval, rep)
def test_advinc_subtensor1_dtype():
......@@ -69,8 +69,8 @@ def test_advinc_subtensor1_dtype():
shp = (3, 4)
for dtype1, dtype2 in [('float32', 'int8'), ('float32', 'float64')]:
shared = gpuarray_shared_constructor
xval = numpy.arange(numpy.prod(shp), dtype=dtype1).reshape(shp) + 1
yval = numpy.empty((2,) + shp[1:], dtype=dtype2)
xval = np.arange(np.prod(shp), dtype=dtype1).reshape(shp) + 1
yval = np.empty((2,) + shp[1:], dtype=dtype2)
yval[:] = 10
x = shared(xval, name='x')
y = tensor.tensor(dtype=yval.dtype,
......@@ -83,7 +83,7 @@ def test_advinc_subtensor1_dtype():
rval = f(yval)
rep = xval.copy()
rep[[0, 2]] += yval
assert numpy.allclose(rval, rep)
assert np.allclose(rval, rep)
def test_advinc_subtensor1_vector_scalar():
......@@ -91,8 +91,8 @@ def test_advinc_subtensor1_vector_scalar():
shp = (3,)
for dtype1, dtype2 in [('float32', 'int8'), ('float32', 'float64')]:
shared = gpuarray_shared_constructor
xval = numpy.arange(numpy.prod(shp), dtype=dtype1).reshape(shp) + 1
yval = numpy.asarray(10, dtype=dtype2)
xval = np.arange(np.prod(shp), dtype=dtype1).reshape(shp) + 1
yval = np.asarray(10, dtype=dtype2)
x = shared(xval, name='x')
y = tensor.tensor(dtype=yval.dtype,
broadcastable=(False,) * len(yval.shape),
......@@ -104,14 +104,14 @@ def test_advinc_subtensor1_vector_scalar():
rval = f(yval)
rep = xval.copy()
rep[[0, 2]] += yval
assert numpy.allclose(rval, rep)
assert np.allclose(rval, rep)
def test_incsub_f16():
shp = (3, 3)
shared = gpuarray_shared_constructor
xval = numpy.arange(numpy.prod(shp), dtype='float16').reshape(shp) + 1
yval = numpy.empty((2,) + shp[1:], dtype='float16')
xval = np.arange(np.prod(shp), dtype='float16').reshape(shp) + 1
yval = np.empty((2,) + shp[1:], dtype='float16')
yval[:] = 2
x = shared(xval, name='x')
y = tensor.tensor(dtype='float16',
......@@ -124,7 +124,7 @@ def test_incsub_f16():
rval = f(yval)
rep = xval.copy()
rep[[0, 2]] += yval
assert numpy.allclose(rval, rep)
assert np.allclose(rval, rep)
expr = tensor.inc_subtensor(x[1:], y)
f = theano.function([y], expr, mode=mode_with_gpu)
......@@ -133,7 +133,7 @@ def test_incsub_f16():
rval = f(yval)
rep = xval.copy()
rep[1:] += yval
assert numpy.allclose(rval, rep)
assert np.allclose(rval, rep)
class G_advancedsubtensor(test_subtensor.TestAdvancedSubtensor):
......@@ -159,7 +159,7 @@ def test_adv_subtensor():
# Test the advancedsubtensor on gpu.
shp = (2, 3, 4)
shared = gpuarray_shared_constructor
xval = numpy.arange(numpy.prod(shp), dtype=theano.config.floatX).reshape(shp)
xval = np.arange(np.prod(shp), dtype=theano.config.floatX).reshape(shp)
idx1, idx2 = tensor.ivectors('idx1', 'idx2')
idxs = [idx1, None, slice(0, 2, 1), idx2, None]
x = shared(xval, name='x')
......@@ -171,27 +171,27 @@ def test_adv_subtensor():
idx2_val = [0, 1]
rval = f(idx1_val, idx2_val)
rep = xval[idx1_val, None, slice(0, 2, 1), idx2_val, None]
assert numpy.allclose(rval, rep)
assert np.allclose(rval, rep)
class test_gpudiagonal(unittest.TestCase):
def test_matrix(self):
x = tensor.matrix()
np_x = numpy.arange(77).reshape(7, 11).astype(theano.config.floatX)
np_x = np.arange(77).reshape(7, 11).astype(theano.config.floatX)
fn = theano.function([x], GpuDiagonal()(x), mode=mode_with_gpu)
assert numpy.allclose(fn(np_x), np_x.diagonal())
assert np.allclose(fn(np_x), np_x.diagonal())
fn = theano.function([x], GpuDiagonal(2)(x), mode=mode_with_gpu)
assert numpy.allclose(fn(np_x), np_x.diagonal(2))
assert np.allclose(fn(np_x), np_x.diagonal(2))
fn = theano.function([x], GpuDiagonal(-3)(x), mode=mode_with_gpu)
assert numpy.allclose(fn(np_x), np_x.diagonal(-3))
assert np.allclose(fn(np_x), np_x.diagonal(-3))
def test_tensor(self):
x = tensor.tensor4()
np_x = numpy.arange(30107).reshape(7, 11, 17, 23).astype(theano.config.floatX)
np_x = np.arange(30107).reshape(7, 11, 17, 23).astype(theano.config.floatX)
for offset, axis1, axis2 in [
(1, 0, 1), (-1, 0, 1), (0, 1, 0), (-2, 1, 0),
(-3, 1, 0), (-2, 2, 0), (3, 3, 0), (-1, 3, 2),
(2, 2, 3), (-1, 2, 1), (1, 3, 1), (-1, 1, 3)]:
assert numpy.allclose(
assert np.allclose(
GpuDiagonal(offset, axis1, axis2)(x).eval({x: np_x}),
np_x.diagonal(offset, axis1, axis2))
theano/gpuarray/tests/test_type.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
import os
import numpy
import numpy as np
import theano
from theano.compat import PY3
......@@ -33,10 +33,10 @@ def test_values_eq_approx():
a = rand_gpuarray(20, dtype='float32')
assert GpuArrayType.values_eq_approx(a, a)
b = a.copy()
b[0] = numpy.asarray(b[0]) + 1.
b[0] = np.asarray(b[0]) + 1.
assert not GpuArrayType.values_eq_approx(a, b)
b = a.copy()
b[0] = -numpy.asarray(b[0])
b[0] = -np.asarray(b[0])
assert not GpuArrayType.values_eq_approx(a, b)
......@@ -50,7 +50,7 @@ def test_specify_shape():
def test_filter_float():
theano.compile.shared_constructor(gpuarray_shared_constructor)
try:
s = theano.shared(numpy.array(0.0, dtype='float32'),
s = theano.shared(np.array(0.0, dtype='float32'),
target=test_ctx_name)
theano.function([], updates=[(s, 0.0)])
finally:
......@@ -73,6 +73,6 @@ def test_unpickle_gpuarray_as_numpy_ndarray_flag0():
u = CompatUnpickler(fp)
mat = u.load()
assert isinstance(mat, pygpu.gpuarray.GpuArray)
assert numpy.asarray(mat)[0] == -42.0
assert np.asarray(mat)[0] == -42.0
finally:
config.experimental.unpickle_gpu_on_cpu = oldflag
theano/gpuarray/type.py
浏览文件 @ 824cb369
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import six.moves.copyreg as copyreg
from six import iteritems
import warnings
......@@ -226,7 +226,7 @@ class GpuArrayType(Type):
converted_data = theano._asarray(data, self.dtype)
# We use the `values_eq` static function from TensorType
# to handle NaN values.
if TensorType.values_eq(numpy.asarray(data),
if TensorType.values_eq(np.asarray(data),
converted_data,
force_same_dtype=False):
data = converted_data
......@@ -293,18 +293,18 @@ class GpuArrayType(Type):
return False
if force_same_dtype and a.typecode != b.typecode:
return False
a_eq_b = numpy.asarray(compare(a, '==', b))
a_eq_b = np.asarray(compare(a, '==', b))
if a_eq_b.all():
return True
# maybe the trouble is that there are NaNs
a = numpy.asarray(a)
b = numpy.asarray(b)
a = np.asarray(a)
b = np.asarray(b)
a_missing = numpy.isnan(a)
a_missing = np.isnan(a)
if a_missing.any():
b_missing = numpy.isnan(b)
return numpy.all(a_eq_b + (a_missing == b_missing))
b_missing = np.isnan(b)
return np.all(a_eq_b + (a_missing == b_missing))
else:
return False
......@@ -326,16 +326,16 @@ class GpuArrayType(Type):
rtol_ = rtol
if atol is not None:
atol_ = atol
res = elemwise2(a, '', b, a, odtype=numpy.dtype('bool'),
res = elemwise2(a, '', b, a, odtype=np.dtype('bool'),
op_tmpl="res = (fabs(a - b) <"
"(%(atol_)s + %(rtol_)s * fabs(b)))" %
locals())
ret = numpy.asarray(res).all()
ret = np.asarray(res).all()
if ret:
return True
# maybe the trouble is that there are NaNs
an = numpy.asarray(a)
bn = numpy.asarray(b)
an = np.asarray(a)
bn = np.asarray(b)
return tensor.TensorType.values_eq_approx(
an, bn, allow_remove_inf=allow_remove_inf,
allow_remove_nan=allow_remove_nan, rtol=rtol, atol=atol)
......@@ -408,9 +408,9 @@ class GpuArrayType(Type):
def get_size(self, shape_info):
if shape_info:
return numpy.prod(shape_info) * numpy.dtype(self.dtype).itemsize
return np.prod(shape_info) * np.dtype(self.dtype).itemsize
else:
return numpy.dtype(self.dtype).itemsize
return np.dtype(self.dtype).itemsize
def c_declare(self, name, sub, check_input=True):
return """
......@@ -470,7 +470,7 @@ class GpuArrayType(Type):
'<gpuarray_api.h>']
def c_header_dirs(self):
return [pygpu.get_include(), numpy.get_include()]
return [pygpu.get_include(), np.get_include()]
def c_libraries(self):
return ['gpuarray']
......@@ -509,7 +509,7 @@ class GpuArrayVariable(_operators, Variable):
# override the default
def __repr_test_value__(self):
return repr(numpy.array(theano.gof.op.get_test_value(self)))
return repr(np.array(theano.gof.op.get_test_value(self)))
GpuArrayType.Variable = GpuArrayVariable
......@@ -534,13 +534,13 @@ class GpuArrayConstant(_operators, Constant):
"""
def signature(self):
return GpuArraySignature((self.type, numpy.asarray(self.data)))
return GpuArraySignature((self.type, np.asarray(self.data)))
def __str__(self):
if self.name is not None:
return self.name
try:
np_data = numpy.asarray(self.data)
np_data = np.asarray(self.data)
except gpuarray.GpuArrayException:
np_data = self.data
return "GpuArrayConstant{%s}" % np_data
......@@ -568,7 +568,7 @@ class GpuArraySharedVariable(_operators, SharedVariable):
else:
return self.container.value.copy()
else:
return numpy.asarray(self.container.value)
return np.asarray(self.container.value)
def set_value(self, value, borrow=False):
if isinstance(value, pygpu.gpuarray.GpuArray):
......@@ -601,7 +601,7 @@ def gpuarray_shared_constructor(value, name=None, strict=False,
if target == 'gpu' or target == 'cpu':
raise TypeError('not for me')
if not isinstance(value, (numpy.ndarray, pygpu.gpuarray.GpuArray)):
if not isinstance(value, (np.ndarray, pygpu.gpuarray.GpuArray)):
raise TypeError('ndarray or GpuArray required')
if target is notset:
......@@ -809,7 +809,7 @@ copyreg.constructor(GpuArray_unpickler)
def GpuArray_pickler(cnda):
ctx_name = _name_for_ctx(cnda.context)
return (GpuArray_unpickler, (numpy.asarray(cnda), ctx_name))
return (GpuArray_unpickler, (np.asarray(cnda), ctx_name))
# In case pygpu is not imported.
if pygpu is not None:
......
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