wehub-resource-sync
120 lines
3.3 KiB
Python
120 lines
3.3 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import numpy as np
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os.environ["FLAGS_check_nan_inf"] = "1"
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import paddle
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from paddle import base
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from paddle.base import core
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paddle.enable_static()
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np.random.seed(0)
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def generator():
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batch_size = 5
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for i in range(5):
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curr_train_x = np.random.randint(
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batch_size, size=(batch_size, 3)
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).astype("float32")
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if i >= 2:
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curr_train_x[0, :] = np.nan
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curr_train_x[-1, :] = np.inf
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res = []
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for i in range(batch_size):
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y = i % 3
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res.append([y])
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y_label = np.array(res).astype('int64')
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yield [curr_train_x, y_label]
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def net():
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x = paddle.static.data(name="x", shape=[-1, 3], dtype='float32')
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y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
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# test int64 value
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zero = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
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# test float16 value
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fp16_zero = paddle.cast(zero, dtype='float16')
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y = y + zero
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hidden = x
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hidden = paddle.static.nn.fc(x=hidden, size=400, activation="sigmoid")
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hidden = paddle.static.nn.fc(x=hidden, size=3)
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cost, y_predict = paddle.nn.functional.softmax_with_cross_entropy(
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hidden, y, return_softmax=True
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)
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acc_top1 = paddle.static.accuracy(input=y_predict, label=y, k=1)
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avg_cost = paddle.mean(cost)
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sgd_optimizer = paddle.optimizer.SGD(learning_rate=0.05)
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sgd_optimizer.minimize(avg_cost)
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return y_predict, avg_cost, acc_top1
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def check(use_cuda):
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main = base.Program()
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startup = base.Program()
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scope = base.core.Scope()
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with (
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base.scope_guard(scope),
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base.program_guard(main, startup),
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):
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y_predict, avg_cost, acc_top1 = net()
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place = base.CUDAPlace(0) if use_cuda else base.CPUPlace()
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exe = base.Executor(place)
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exe.run(startup)
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step = 0.0
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for train_data, y_label in generator():
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outs = exe.run(
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main,
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feed={'x': train_data, 'y': y_label},
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fetch_list=[y_predict, avg_cost, acc_top1],
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)
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step += 1
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print(f'iter={step:.0f},cost={outs[1]},acc1={outs[2]}')
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if __name__ == '__main__':
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try:
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check(use_cuda=False)
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raise AssertionError
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except Exception as e:
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print(e)
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print(type(e))
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assert type(e) == RuntimeError
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if core.is_compiled_with_cuda():
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try:
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check(use_cuda=True)
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raise AssertionError
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except Exception as e:
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print(e)
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print(type(e))
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# Note. Enforce in cuda kernel may not catch in paddle, and
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# Exception type will be RuntimeError
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assert type(e) == OSError or type(e) == RuntimeError
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