From 0f6ef8edba17736ced024c62e773f001299f84fb Mon Sep 17 00:00:00 2001 From: minqiyang Date: Sat, 29 Dec 2018 18:17:05 +0800 Subject: [PATCH] Add MNIST test=develop --- python/paddle/fluid/imperative/nn.py | 2 +- .../unittests/test_imperative_optimizer.py | 60 ++++++++++++------- 2 files changed, 38 insertions(+), 24 deletions(-) diff --git a/python/paddle/fluid/imperative/nn.py b/python/paddle/fluid/imperative/nn.py index 7f3be204635..8757670ef82 100644 --- a/python/paddle/fluid/imperative/nn.py +++ b/python/paddle/fluid/imperative/nn.py @@ -99,7 +99,7 @@ class Conv2D(layers.PyLayer): self._bias_param = self._helper.create_parameter( attr=self._helper.bias_attr, - shape=[num_filter_channels], + shape=[num_filters], dtype=self._dtype, is_bias=True) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py index 775b10e6dc7..e9dd158295e 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_optimizer.py @@ -29,8 +29,8 @@ from test_imperative_base import new_program_scope class SimpleImgConvPool(fluid.imperative.PyLayer): def __init__(self, num_channels, - filter_size, num_filters, + filter_size, pool_size, pool_stride, pool_padding=0, @@ -77,10 +77,10 @@ class MNIST(fluid.imperative.PyLayer): super(MNIST, self).__init__(param_attr=param_attr, bias_attr=bias_attr) self._simple_img_conv_pool_1 = SimpleImgConvPool( - 1, 5, 20, 2, 2, act="relu") + 1, 20, 5, 2, 2, act="relu") self._simple_img_conv_pool_2 = SimpleImgConvPool( - 20, 5, 50, 2, 2, act="relu") + 20, 50, 5, 2, 2, act="relu") pool_2_shape = 50 * 8 * 8 SIZE = 10 @@ -106,18 +106,15 @@ class TestImperativeMnist(unittest.TestCase): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed - mnist = Conv2D(1, 20, 5) + # mnist = Conv2D(1, 20, 5) + mnist = MNIST() sgd = SGDOptimizer(learning_rate=1e-3) train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=128) - dy_param_value = {} - for param in fluid.default_main_program().global_block( - ).all_parameters(): - dy_param_value[param.name] = param._numpy() - + dy_param_init_value = {} for batch_id, data in enumerate(train_reader()): - if batch_id >= 1: + if batch_id >= 2: break x_data = np.array( @@ -133,9 +130,17 @@ class TestImperativeMnist(unittest.TestCase): loss = fluid.layers.reduce_mean(cost) dy_out = loss._numpy() + if batch_id == 0: + for param in fluid.default_main_program().global_block( + ).all_parameters(): + dy_param_init_value[param.name] = param._numpy() + loss._backward() sgd.minimize(loss) - dy_filter_param = mnist._filter_param._numpy() + dy_param_value = {} + for param in fluid.default_main_program().global_block( + ).all_parameters(): + dy_param_value[param.name] = param._numpy() with new_program_scope(): fluid.default_startup_program().random_seed = seed @@ -143,7 +148,8 @@ class TestImperativeMnist(unittest.TestCase): exe = fluid.Executor(fluid.CPUPlace()) - mnist = Conv2D(1, 20, 5) + # mnist = Conv2D(1, 20, 5) + mnist = MNIST() sgd = SGDOptimizer(learning_rate=1e-3) train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=128) @@ -156,7 +162,7 @@ class TestImperativeMnist(unittest.TestCase): sgd.minimize(loss) # initialize params and fetch them - static_param_value = {} + static_param_init_value = {} static_param_name_list = [] for param in fluid.default_startup_program().global_block( ).all_parameters(): @@ -166,27 +172,35 @@ class TestImperativeMnist(unittest.TestCase): fetch_list=static_param_name_list) for i in range(len(static_param_name_list)): - static_param_value[static_param_name_list[i]] = out[i] + static_param_init_value[static_param_name_list[i]] = out[i] for batch_id, data in enumerate(train_reader()): - if batch_id >= 1: + if batch_id >= 2: break x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array([x[1] for x in data]).astype('int64').reshape( [128, 1]) - static_out, static_filter_param = exe.run( - fluid.default_main_program(), - feed={"pixel": x_data, - "label": y_data}, - fetch_list=[loss.name, mnist._filter_param.name]) + fetch_list = [loss.name] + fetch_list.extend(static_param_name_list) + out = exe.run(fluid.default_main_program(), + feed={"pixel": x_data, + "label": y_data}, + fetch_list=fetch_list) + + static_param_value = {} + static_out = out[0] + for i in range(1, len(out)): + static_param_value[static_param_name_list[i - 1]] = out[i] + + for key, value in six.iteritems(static_param_init_value): + self.assertTrue( + np.allclose(value.all(), dy_param_init_value[key].all())) + self.assertTrue(np.allclose(static_out.all(), dy_out.all())) for key, value in six.iteritems(static_param_value): self.assertTrue(np.allclose(value.all(), dy_param_value[key].all())) - self.assertTrue(np.allclose(static_out.all(), dy_out.all())) - self.assertTrue( - np.allclose(static_filter_param.all(), dy_filter_param.all())) if __name__ == '__main__': -- GitLab