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| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you under the Apache License, Version 2.0 (the | ||
| # "License"); you may not use this file except in compliance | ||
| # with the License. You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, | ||
| # software distributed under the License is distributed on an | ||
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
| # KIND, either express or implied. See the License for the | ||
| # specific language governing permissions and limitations | ||
| # under the License. | ||
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| import os | ||
| import sys | ||
| import tempfile | ||
| import math | ||
| import numpy as np | ||
| import mxnet as mx | ||
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| curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__))) | ||
| sys.path.append(os.path.join(curr_path, '../python/unittest/')) | ||
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| from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context, check_symbolic_forward, create_2d_tensor, use_np | ||
| from mxnet import gluon, nd | ||
| from common import with_seed | ||
| import pytest | ||
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| # dimension constants | ||
| SMALL_X = 100 | ||
| MEDIUM_X = 10000 | ||
| LARGE_X = 100000000 | ||
| VLARGE_X = 4300000000 | ||
| SMALL_Y = 50 | ||
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| @use_np | ||
| def test_gluon_embedding(): | ||
| m = gluon.nn.Embedding(SMALL_Y, MEDIUM_X) | ||
| m.initialize() | ||
| a = mx.np.zeros((MEDIUM_X, SMALL_Y)) | ||
| b = m(a) | ||
| assert b.shape == (MEDIUM_X, SMALL_Y, MEDIUM_X) | ||
| assert b.asnumpy().size == LARGE_X * SMALL_Y | ||
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| @use_np | ||
| def test_fully_connected(): | ||
| a = mx.np.ones(shape=(LARGE_X, SMALL_Y)) | ||
| b = mx.np.ones(shape=(SMALL_Y, SMALL_Y)) | ||
| c = mx.np.ones(shape=(b.shape[0],)) | ||
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| # w/o bias | ||
| res = mx.npx.FullyConnected(a, b, num_hidden=b.shape[0], no_bias=True) | ||
| assert np.sum(res[-1].asnumpy() == a.shape[1]) == b.shape[0] | ||
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| # w/ bias | ||
| res = mx.npx.FullyConnected(a, b, c, num_hidden=b.shape[0], no_bias=False) | ||
| assert np.sum(res[-1].asnumpy() == a.shape[1] + 1) == b.shape[0] | ||
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| @use_np | ||
| def test_dense(): | ||
| data = mx.np.ones(shape=(50*1000*1000, 100)) | ||
| linear = gluon.nn.Dense(100) | ||
| linear.initialize() | ||
| res = linear(data) | ||
| assert res.shape == (50000000, 100) | ||
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| @use_np | ||
| def test_softmax(): | ||
| input_data = mx.np.ones((SMALL_Y, LARGE_X)) | ||
| for axis in [0, 1]: | ||
| true_output = np.full((SMALL_Y, LARGE_X), (1 / input_data.shape[axis])) | ||
| output = npx.softmax(input_data, axis=axis) | ||
| assert_almost_equal(output.asnumpy(), true_output, rtol=1e-5, atol=1e-5) | ||
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| @use_np | ||
| def test_softmax_cross_entropy(): | ||
| # dtype of input data, mxnet cross entropy set explicitly to float64 | ||
| # numpy implicitly takes care of double precision | ||
| batch_size = SMALL_Y | ||
| num_labels = LARGE_X | ||
| input_data = mx.np.ones((batch_size, num_labels), dtype="float64") | ||
| input_label = mx.np.zeros((batch_size,), dtype="float64") | ||
| true_softmax = np.full((batch_size, num_labels), (1 / num_labels)) | ||
| # use 1/batch_size when softmax axis=0 | ||
| # here 1/num_labels since softmax_cross_entropy uses default axis | ||
| # by default axis=1 | ||
| np_one_hot_label = np.zeros((batch_size, num_labels)) | ||
| np_one_hot_label[:, 0] = 1 | ||
| true_softmax_cross_entropy = np.sum(-np.log(true_softmax) * | ||
| np_one_hot_label) | ||
| mx_softmax_cross_entropy = mx.npx.softmax_cross_entropy(input_data, | ||
| input_label, | ||
| dtype="float64") | ||
| assert_almost_equal(mx_softmax_cross_entropy.asnumpy(), | ||
| true_softmax_cross_entropy, rtol=1e-3, atol=1e-5) |