wehub-resource-sync
107 lines
1.5 KiB
Python
107 lines
1.5 KiB
Python
from __future__ import absolute_import
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import mxnet as mx
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import mxnet.ndarray as nd
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import numpy as np
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def cuda():
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return mx.gpu()
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def is_cuda_available():
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# TODO: Does MXNet have a convenient function to test GPU availability/compilation?
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try:
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a = nd.array([1, 2, 3], ctx=mx.gpu())
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return True
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except mx.MXNetError:
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return False
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def array_equal(a, b):
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return nd.equal(a, b).asnumpy().all()
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def allclose(a, b, rtol=1e-4, atol=1e-4):
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return np.allclose(a.asnumpy(), b.asnumpy(), rtol=rtol, atol=atol)
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def randn(shape):
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return nd.random.randn(*shape)
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def full(shape, fill_value, dtype, ctx):
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return nd.full(shape, fill_value, dtype=dtype, ctx=ctx)
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def narrow_row_set(x, start, stop, new):
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x[start:stop] = new
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def sparse_to_numpy(x):
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return x.asscipy().todense().A
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def clone(x):
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return x.copy()
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def reduce_sum(x):
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return x.sum()
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def softmax(x, dim):
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return nd.softmax(x, axis=dim)
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def spmm(x, y):
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return nd.dot(x, y)
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def add(a, b):
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return a + b
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def sub(a, b):
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return a - b
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def mul(a, b):
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return a * b
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def div(a, b):
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return a / b
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def sum(x, dim, keepdims=False):
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return x.sum(dim, keepdims=keepdims)
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def max(x, dim):
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return x.max(dim)
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def min(x, dim):
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return x.min(dim)
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def prod(x, dim):
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return x.prod(dim)
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def matmul(a, b):
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return nd.dot(a, b)
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def dot(a, b):
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return nd.sum(mul(a, b), axis=-1)
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def abs(a):
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return nd.abs(a)
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def seed(a):
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return mx.random.seed(a)
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