minqiyang
7 years ago
parent
8d88c5a87d
commit
3cd10a7c4f
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# Copyright (c) 2018 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 contextlib
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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from paddle.fluid import core
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from paddle.fluid.layers.nn import FC
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@contextlib.contextmanager
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def new_program_scope():
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prog = fluid.Program()
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startup_prog = fluid.Program()
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scope = fluid.core.Scope()
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with fluid.scope_guard(scope):
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with fluid.program_guard(prog, startup_prog):
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yield
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class MyLayer(fluid.imperative.PyLayer):
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def __init__(self):
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super(MyLayer, self).__init__()
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def forward(self, inputs):
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x = fluid.layers.relu(inputs[0])
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self._x_for_debug = x
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return [fluid.layers.elementwise_mul(x, x)]
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class MLP(fluid.imperative.PyLayer):
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def __init__(self):
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super(MLP, self).__init__()
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self._fc1 = FC(3,
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fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.1)))
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self._fc2 = FC(4,
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fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.1)))
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def forward(self, inputs):
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x = self._fc1(inputs[0])
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x = self._fc2(x)
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x = fluid.layers.reduce_sum(x)
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return x
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class TestImperative(unittest.TestCase):
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def test_layer(self):
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with fluid.imperative.guard():
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cl = core.Layer()
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cl.forward([])
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l = fluid.imperative.PyLayer()
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l.forward([])
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def test_layer_in_out(self):
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np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
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with fluid.imperative.guard():
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l = MyLayer()
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x = l(np_inp)[0]
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self.assertIsNotNone(x)
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dy_out = x._numpy()
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x._backward()
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dy_grad = l._x_for_debug._gradient()
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with new_program_scope():
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inp = fluid.layers.data(
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name="inp", shape=[3], append_batch_size=False)
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l = MyLayer()
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x = l(inp)[0]
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param_grads = fluid.backward.append_backward(
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x, parameter_list=[l._x_for_debug.name])[0]
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exe = fluid.Executor(fluid.CPUPlace())
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static_out, static_grad = exe.run(
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feed={inp.name: np_inp},
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fetch_list=[x.name, param_grads[1].name])
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self.assertTrue(np.allclose(dy_out, static_out))
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self.assertTrue(np.allclose(dy_grad, static_grad))
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def test_mlp(self):
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np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
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with fluid.imperative.guard():
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mlp = MLP()
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out = mlp(np_inp)
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dy_out = out._numpy()
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out._backward()
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dy_grad = mlp._fc1._w._gradient()
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with new_program_scope():
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inp = fluid.layers.data(
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name="inp", shape=[2, 2], append_batch_size=False)
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mlp = MLP()
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out = mlp(inp)
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param_grads = fluid.backward.append_backward(
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out, parameter_list=[mlp._fc1._w.name])[0]
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exe = fluid.Executor(fluid.CPUPlace())
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exe.run(fluid.default_startup_program())
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static_out, static_grad = exe.run(
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feed={inp.name: np_inp},
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fetch_list=[out.name, param_grads[1].name])
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self.assertTrue(np.allclose(dy_out, static_out))
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self.assertTrue(np.allclose(dy_grad, static_grad))
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if __name__ == '__main__':
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unittest.main()
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@ -0,0 +1,129 @@
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# Copyright (c) 2018 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 contextlib
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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from paddle.fluid import core
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from paddle.fluid.imperative.nn import Conv2D
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@contextlib.contextmanager
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def new_program_scope():
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prog = fluid.Program()
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startup_prog = fluid.Program()
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scope = fluid.core.Scope()
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with fluid.scope_guard(scope):
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with fluid.program_guard(prog, startup_prog):
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yield
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class MNIST(fluid.imperative.PyLayer):
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def __init__(self):
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super(MNIST, self).__init__()
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groups = 1
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dilation = [1, 1]
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pad = [0, 0]
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stride = [1, 1]
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input_size = [2, 3, 5, 5] # NCHW
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assert np.mod(input_size[1], groups) == 0
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f_c = input_size[1] // groups
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filter_size = [6, f_c, 3, 3]
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self._conv2d = Conv2D(
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num_channels=3,
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num_filters=20,
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filter_size=3,
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stride=stride,
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padding=pad,
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dilation=dilation,
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groups=groups,
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use_cudnn=False)
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def forward(self, inputs):
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x = self._conv2d(inputs)
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return x
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class TestImperativeMnist(unittest.TestCase):
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# def test_layer(self):
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# with fluid.imperative.guard():
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# cl = core.Layer()
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# cl.forward([])
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# l = fluid.imperative.PyLayer()
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# l.forward([])
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# def test_layer_in_out(self):
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# np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
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# with fluid.imperative.guard():
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# l = MyLayer()
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# x = l(np_inp)[0]
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# self.assertIsNotNone(x)
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# dy_out = x._numpy()
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# x._backward()
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# dy_grad = l._x_for_debug._gradient()
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# with new_program_scope():
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# inp = fluid.layers.data(
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# name="inp", shape=[3], append_batch_size=False)
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# l = MyLayer()
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# x = l(inp)[0]
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# param_grads = fluid.backward.append_backward(
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# x, parameter_list=[l._x_for_debug.name])[0]
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# exe = fluid.Executor(fluid.CPUPlace())
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# static_out, static_grad = exe.run(
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# feed={inp.name: np_inp},
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# fetch_list=[x.name, param_grads[1].name])
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# self.assertTrue(np.allclose(dy_out, static_out))
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# self.assertTrue(np.allclose(dy_grad, static_grad))
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def test_mnist_cpu_float32(self):
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with fluid.imperative.guard():
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mnist = MNIST()
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data = np.random.rand(2, 3, 5, 5).astype('float32')
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mnist(data)
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# np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
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# with fluid.imperative.guard():
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# mlp = MLP()
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# out = mlp(np_inp)
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# dy_out = out._numpy()
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# out._backward()
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# dy_grad = mlp._fc1._w._gradient()
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# with new_program_scope():
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# inp = fluid.layers.data(
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# name="inp", shape=[2, 2], append_batch_size=False)
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# mlp = MLP()
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# out = mlp(inp)
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# param_grads = fluid.backward.append_backward(
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# out, parameter_list=[mlp._fc1._w.name])[0]
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# exe = fluid.Executor(fluid.CPUPlace())
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# exe.run(fluid.default_startup_program())
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# static_out, static_grad = exe.run(
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# feed={inp.name: np_inp},
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# fetch_list=[out.name, param_grads[1].name])
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# self.assertTrue(np.allclose(dy_out, static_out))
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# self.assertTrue(np.allclose(dy_grad, static_grad))
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if __name__ == '__main__':
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unittest.main()
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