You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
130 lines
3.9 KiB
130 lines
3.9 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed 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.
|
|
|
|
import contextlib
|
|
import unittest
|
|
import numpy as np
|
|
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid import core
|
|
from paddle.fluid.imperative.nn import Conv2D
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def new_program_scope():
|
|
prog = fluid.Program()
|
|
startup_prog = fluid.Program()
|
|
scope = fluid.core.Scope()
|
|
with fluid.scope_guard(scope):
|
|
with fluid.program_guard(prog, startup_prog):
|
|
yield
|
|
|
|
|
|
class MNIST(fluid.imperative.PyLayer):
|
|
def __init__(self):
|
|
super(MNIST, self).__init__()
|
|
|
|
groups = 1
|
|
dilation = [1, 1]
|
|
pad = [0, 0]
|
|
stride = [1, 1]
|
|
input_size = [2, 3, 5, 5] # NCHW
|
|
assert np.mod(input_size[1], groups) == 0
|
|
f_c = input_size[1] // groups
|
|
filter_size = [6, f_c, 3, 3]
|
|
|
|
self._conv2d = Conv2D(
|
|
num_channels=3,
|
|
num_filters=20,
|
|
filter_size=3,
|
|
stride=stride,
|
|
padding=pad,
|
|
dilation=dilation,
|
|
groups=groups,
|
|
use_cudnn=False)
|
|
|
|
def forward(self, inputs):
|
|
x = self._conv2d(inputs)
|
|
return x
|
|
|
|
|
|
class TestImperativeMnist(unittest.TestCase):
|
|
# def test_layer(self):
|
|
# with fluid.imperative.guard():
|
|
# cl = core.Layer()
|
|
# cl.forward([])
|
|
# l = fluid.imperative.PyLayer()
|
|
# l.forward([])
|
|
|
|
# def test_layer_in_out(self):
|
|
# np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
|
|
# with fluid.imperative.guard():
|
|
# l = MyLayer()
|
|
# x = l(np_inp)[0]
|
|
# self.assertIsNotNone(x)
|
|
# dy_out = x._numpy()
|
|
# x._backward()
|
|
# dy_grad = l._x_for_debug._gradient()
|
|
|
|
# with new_program_scope():
|
|
# inp = fluid.layers.data(
|
|
# name="inp", shape=[3], append_batch_size=False)
|
|
# l = MyLayer()
|
|
# x = l(inp)[0]
|
|
# param_grads = fluid.backward.append_backward(
|
|
# x, parameter_list=[l._x_for_debug.name])[0]
|
|
# exe = fluid.Executor(fluid.CPUPlace())
|
|
|
|
# static_out, static_grad = exe.run(
|
|
# feed={inp.name: np_inp},
|
|
# fetch_list=[x.name, param_grads[1].name])
|
|
|
|
# self.assertTrue(np.allclose(dy_out, static_out))
|
|
# self.assertTrue(np.allclose(dy_grad, static_grad))
|
|
|
|
def test_mnist_cpu_float32(self):
|
|
with fluid.imperative.guard():
|
|
mnist = MNIST()
|
|
|
|
data = np.random.rand(2, 3, 5, 5).astype('float32')
|
|
mnist(data)
|
|
# np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
|
|
# with fluid.imperative.guard():
|
|
# mlp = MLP()
|
|
# out = mlp(np_inp)
|
|
# dy_out = out._numpy()
|
|
# out._backward()
|
|
# dy_grad = mlp._fc1._w._gradient()
|
|
|
|
# with new_program_scope():
|
|
# inp = fluid.layers.data(
|
|
# name="inp", shape=[2, 2], append_batch_size=False)
|
|
# mlp = MLP()
|
|
# out = mlp(inp)
|
|
# param_grads = fluid.backward.append_backward(
|
|
# out, parameter_list=[mlp._fc1._w.name])[0]
|
|
# exe = fluid.Executor(fluid.CPUPlace())
|
|
# exe.run(fluid.default_startup_program())
|
|
|
|
# static_out, static_grad = exe.run(
|
|
# feed={inp.name: np_inp},
|
|
# fetch_list=[out.name, param_grads[1].name])
|
|
|
|
# self.assertTrue(np.allclose(dy_out, static_out))
|
|
# self.assertTrue(np.allclose(dy_grad, static_grad))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|