wehub-resource-sync
111 lines
1.8 KiB
Python
111 lines
1.8 KiB
Python
from __future__ import absolute_import
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import numpy as np
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import tensorflow as tf
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from scipy.sparse import coo_matrix
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def cuda():
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return "/gpu:0"
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def is_cuda_available():
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return tf.test.is_gpu_available(cuda_only=True)
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def array_equal(a, b):
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return np.array_equal(a.numpy(), b.numpy())
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def allclose(a, b, rtol=1e-4, atol=1e-4):
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return np.allclose(
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tf.convert_to_tensor(a).numpy(),
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tf.convert_to_tensor(b).numpy(),
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rtol=rtol,
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atol=atol,
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)
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def randn(shape):
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return tf.random.normal(shape)
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def full(shape, fill_value, dtype, ctx):
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with tf.device(ctx):
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t = tf.constant(fill_value, shape=shape, dtype=dtype)
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return t
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def narrow_row_set(x, start, stop, new):
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# x[start:stop] = new
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raise NotImplementedError("TF doesn't support inplace update")
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def sparse_to_numpy(x):
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# tf.sparse.to_dense assume sorted indices, need to turn off validate_indices in our cases
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return tf.sparse.to_dense(x, validate_indices=False).numpy()
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def clone(x):
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return tf.identity(x)
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def reduce_sum(x):
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return tf.reduce_sum(x)
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def softmax(x, dim):
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return tf.math.softmax(x, axis=dim)
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def spmm(x, y):
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return tf.sparse.sparse_dense_matmul(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 tf.reduce_sum(x, axis=dim, keepdims=keepdims)
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def max(x, dim):
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return tf.reduce_max(x, axis=dim)
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def min(x, dim):
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return tf.reduce_min(x, axis=dim)
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def prod(x, dim):
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return tf.reduce_prod(x, axis=dim)
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def matmul(a, b):
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return tf.linalg.matmul(a, b)
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def dot(a, b):
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return sum(mul(a, b), dim=-1)
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def abs(a):
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return tf.abs(a)
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def seed(a):
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return tf.random.set_seed(a)
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