From 2466ca13ec80c6181b4ad1b3e6bf66fe95d7f904 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 7 Nov 2018 17:27:30 +0800 Subject: [PATCH] test(Pe): remove unittests for recordio in test_pe_mnist (#14262) recordio is not the official API in Fluid 1.0. Remove unittests for it. test=develop --- .../unittests/test_parallel_executor_mnist.py | 61 +++---------------- 1 file changed, 9 insertions(+), 52 deletions(-) diff --git a/python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py b/python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py index af3745987a..3eecc46701 100644 --- a/python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py +++ b/python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py @@ -14,30 +14,18 @@ from __future__ import print_function -from parallel_executor_test_base import TestParallelExecutorBase -import paddle.fluid as fluid -import paddle.fluid.core as core -import numpy as np -import paddle -import paddle.dataset.mnist as mnist import unittest -import os -MNIST_RECORDIO_FILE = "./mnist_test_pe.recordio" +import numpy as np +import paddle.fluid.core as core +import os +import paddle.fluid as fluid +from parallel_executor_test_base import TestParallelExecutorBase def simple_fc_net(use_feed): - if use_feed: - img = fluid.layers.data(name='image', shape=[784], dtype='float32') - label = fluid.layers.data(name='label', shape=[1], dtype='int64') - else: - reader = fluid.layers.open_files( - filenames=[MNIST_RECORDIO_FILE], - shapes=[[-1, 784], [-1, 1]], - lod_levels=[0, 0], - dtypes=['float32', 'int64']) - reader = fluid.layers.io.double_buffer(reader) - img, label = fluid.layers.read_file(reader) + img = fluid.layers.data(name='image', shape=[784], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') hidden = img for _ in range(4): hidden = fluid.layers.fc( @@ -53,17 +41,8 @@ def simple_fc_net(use_feed): def fc_with_batchnorm(use_feed): - if use_feed: - img = fluid.layers.data(name='image', shape=[784], dtype='float32') - label = fluid.layers.data(name='label', shape=[1], dtype='int64') - else: - reader = fluid.layers.open_files( - filenames=[MNIST_RECORDIO_FILE], - shapes=[[-1, 784], [-1, 1]], - lod_levels=[0, 0], - dtypes=['float32', 'int64']) - reader = fluid.layers.io.double_buffer(reader) - img, label = fluid.layers.read_file(reader) + img = fluid.layers.data(name='image', shape=[784], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') hidden = img for _ in range(1): @@ -88,19 +67,6 @@ class TestMNIST(TestParallelExecutorBase): @classmethod def setUpClass(cls): os.environ['CPU_NUM'] = str(4) - # Convert mnist to recordio file - with fluid.program_guard(fluid.Program(), fluid.Program()): - reader = paddle.batch(mnist.train(), batch_size=4) - feeder = fluid.DataFeeder( - feed_list=[ # order is image and label - fluid.layers.data( - name='image', shape=[784]), - fluid.layers.data( - name='label', shape=[1], dtype='int64'), - ], - place=fluid.CPUPlace()) - fluid.recordio_writer.convert_reader_to_recordio_file( - MNIST_RECORDIO_FILE, reader, feeder) def _init_data(self): np.random.seed(5) @@ -111,10 +77,6 @@ class TestMNIST(TestParallelExecutorBase): def _compare_reduce_and_allreduce(self, model, use_cuda): if use_cuda and not core.is_compiled_with_cuda(): return - self.check_network_convergence( - model, use_cuda=use_cuda, use_reduce=True) - self.check_network_convergence( - model, use_cuda=use_cuda, allow_op_delay=True, use_reduce=True) img, label = self._init_data() @@ -140,9 +102,6 @@ class TestMNIST(TestParallelExecutorBase): def check_simple_fc_convergence(self, use_cuda, use_reduce=False): if use_cuda and not core.is_compiled_with_cuda(): return - self.check_network_convergence(simple_fc_net, use_cuda=use_cuda) - self.check_network_convergence( - simple_fc_net, use_cuda=use_cuda, allow_op_delay=True) img, label = self._init_data() @@ -199,8 +158,6 @@ class TestMNIST(TestParallelExecutorBase): if use_cuda and not core.is_compiled_with_cuda(): return - self.check_network_convergence(fc_with_batchnorm, use_cuda=use_cuda) - img, label = self._init_data() self.check_network_convergence(