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PaddleDetection
提交 cce766d7 编写于 作者: M minqiyang
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Reverse iterator op's input 

test=develop
上级 1a55f7d3
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Showing with 209 addition and 234 deletion
paddle/fluid/imperative/layer.cc
浏览文件 @ cce766d7
......@@ -81,10 +81,6 @@ class TensorAddToFunctor : public boost::static_visitor<> {
} // namespace detail
template <int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<float, MajorType, IndexType>;
void AddTo(Variable* src, Variable* dst, platform::Place place) {
framework::Tensor* dst_tensor = dst->GetMutable<framework::LoDTensor>();
framework::Tensor* src_tensor = src->GetMutable<framework::LoDTensor>();
......@@ -99,18 +95,10 @@ void AddTo(Variable* src, Variable* dst, platform::Place place) {
"dst_numel %lld vs. src_numel %lld", dst_tensor->numel(),
src_tensor->numel());
auto result = EigenVector<>::Flatten(*dst_tensor);
auto in_0_e = EigenVector<>::Flatten(*dst_tensor);
auto in_1_e = EigenVector<>::Flatten(*src_tensor);
platform::DeviceContext* dev_ctx =
platform::DeviceContextPool::Instance().Get(place);
platform::CPUDeviceContext* x =
reinterpret_cast<platform::CPUDeviceContext*>(dev_ctx);
result.device(*x->eigen_device()) = in_0_e + in_1_e;
// detail::TensorAddToFunctor<float> func(
// src_tensor->numel(), src_tensor->data<float>(),
// dst_tensor->mutable_data<float>(place));
// boost::apply_visitor(func, place);
detail::TensorAddToFunctor<float> func(
src_tensor->numel(), src_tensor->data<float>(),
dst_tensor->mutable_data<float>(place));
boost::apply_visitor(func, place);
}
class Autograd {
......@@ -134,7 +122,7 @@ class Autograd {
std::map<std::string, std::vector<VarBase*>> input_grads =
ready_op->ApplyGrad();
for (auto it : input_grads) {
for (auto it = input_grads.rbegin(); it != input_grads.rend(); ++it) {
const std::vector<VarBase*>& ingrads = it.second;
for (int64_t i = ingrads.size() - 1; i >= 0; --i) {
if (!ingrads[i]) continue;
......
python/paddle/fluid/framework.py
浏览文件 @ cce766d7
......@@ -104,14 +104,14 @@ def cuda_places(device_ids=None):
:code:`FLAGS_selected_gpus=0,1,2`, the returned list would
be [fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)].
If :code:`FLAGS_selected_gpus` is not set, all visible
gpu places would be returned.
gpu places would be returned.
If :code:`device_ids` is not None, it should be the device
ids of gpus. For example, if :code:`device_ids=[0,1,2]`,
the returned list would be
ids of gpus. For example, if :code:`device_ids=[0,1,2]`,
the returned list would be
[fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)].
Args:
Args:
device_ids (None|list(int)|tuple(int)): gpu device id list.
Returns:
......@@ -133,11 +133,11 @@ def cuda_places(device_ids=None):
def cpu_places(device_count=None):
'''
Create a list of :code:`fluid.CPUPlace` objects.
If :code:`device_count` is None, the device count would
be determined by environment variable :code:`CPU_NUM`.
be determined by environment variable :code:`CPU_NUM`.
If :code:`CPU_NUM` is not set, the device count would
be determined by :code:`multiprocessing.cpu_count()`.
be determined by :code:`multiprocessing.cpu_count()`.
Args:
device_count (None|int): device number.
......@@ -155,9 +155,9 @@ def cuda_pinned_places(device_count=None):
Create a list of :code:`fluid.CUDAPinnedPlace` objects.
If :code:`device_count` is None, the device count would
be determined by environment variable :code:`CPU_NUM`.
be determined by environment variable :code:`CPU_NUM`.
If :code:`CPU_NUM` is not set, the device count would
be determined by :code:`multiprocessing.cpu_count()`.
be determined by :code:`multiprocessing.cpu_count()`.
Args:
device_count (None|int): device number.
......@@ -2716,6 +2716,11 @@ class Program(object):
# whether the program is optimized by memory_optimize_transpiler
self.__is_mem_optimized = False
# if this program has been optimized by distributed optimizer
# fleet_opt will be given a value
self._fleet_opt = None
self._program_config = None
@property
def _is_mem_optimized(self):
# if the program is optimized, operator input/outputs
......
python/paddle/fluid/tests/unittests/test_imperative_basic.py
浏览文件 @ cce766d7
......@@ -51,22 +51,23 @@ class MyPyLayer(fluid.dygraph.PyLayer):
class MLP(fluid.dygraph.Layer):
def __init__(self, name_scope):
super(MLP, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), 3)
# self._fc2 = FC(self.full_name(),
# 4)
# self._fc3 = FC(self.full_name(),
# 4)
self._fc_list = []
for i in range(100):
fc3 = FC(self.full_name(), 4)
self._fc_list.append(fc3)
self._fc1 = FC(self.full_name(),
3,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
self._fc2 = FC(self.full_name(),
4,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
def forward(self, inputs):
x = self._fc1(inputs)
y1 = self._fc2(x)
y2 = self._fc3(x)
z = fluid.layers.concat([y1, y2])
x = fluid.layers.reduce_sum(z)
x = self._fc2(x)
x = fluid.layers.reduce_sum(x)
return x
......@@ -191,215 +192,196 @@ class SimpleRNN(fluid.dygraph.Layer):
class TestImperative(unittest.TestCase):
# def test_sum_op(self):
# x = np.ones([2, 2], np.float32)
# with fluid.dygraph.guard():
# inputs = []
# for _ in range(10):
# inputs.append(fluid.dygraph.base.to_variable(x))
# ret = fluid.layers.sums(inputs)
# loss = fluid.layers.reduce_sum(ret)
# loss._backward()
# self.assertTrue(np.allclose(ret._numpy(), x * 10))
# self.assertTrue(np.allclose(inputs[0]._gradient(), x))
# def test_layer(self):
# with fluid.dygraph.guard():
# cl = core.Layer()
# cl.forward([])
# l = fluid.dygraph.Layer("l")
# self.assertRaises(NotImplementedError, l.forward, [])
# def test_pylayer_func_id(self):
# with fluid.dygraph.guard():
# class PyLayer1(fluid.dygraph.PyLayer):
# def __init__(self):
# super(PyLayer1, self).__init__()
# @staticmethod
# def forward(input):
# return input
# @staticmethod
# def backward(input):
# return input
# class PyLayer2(fluid.dygraph.PyLayer):
# def __init__(self):
# super(PyLayer2, self).__init__()
# @staticmethod
# def forward(input):
# return input
# @staticmethod
# def backward(input):
# return input
# py_layer_1 = PyLayer1()
# py_layer_2 = PyLayer2()
# py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2])))
# py_layer_2(fluid.dygraph.base.to_variable(np.ones([2, 2])))
# id = py_layer_1.forward_id
# self.assertGreater(id, 0)
# self.assertEqual(py_layer_1.backward_id, id + 1)
# self.assertEqual(py_layer_2.forward_id, id + 2)
# self.assertEqual(py_layer_2.backward_id, id + 3)
# py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2])))
# self.assertEqual(py_layer_1.forward_id, id)
# def test_pylayer(self):
# np_inp = np.ones([2, 2], np.float32)
# with fluid.dygraph.guard():
# my_py_layer = MyPyLayer()
# var_inp = fluid.dygraph.base.to_variable(np_inp)
# outs = my_py_layer(var_inp)
# dy_out = np.sum(outs[0]._numpy())
# outs[0]._backward()
# dy_grad = var_inp._gradient()
# with new_program_scope():
# inp = fluid.layers.data(
# name="inp", shape=[2, 2], append_batch_size=False)
# # TODO(panyx0718): Paddle doesn't diff against data `inp`.
# x1 = inp * 1
# # TODO(panyx0718): If reduce_sum is skipped, the result is wrong.
# x = fluid.layers.reduce_sum(fluid.layers.tanh(x1))
# param_grads = fluid.backward.append_backward(
# x, parameter_list=[x1.name])[0]
# exe = fluid.Executor(fluid.CPUPlace(
# ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
# 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_layer_in_out(self):
# np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
# with fluid.dygraph.guard():
# var_inp = fluid.dygraph.base.to_variable(np_inp)
# l = MyLayer("my_layer")
# x = l(var_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("my_layer")
# x = l(inp)[0]
# param_grads = fluid.backward.append_backward(
# x, parameter_list=[l._x_for_debug.name])[0]
# exe = fluid.Executor(fluid.CPUPlace(
# ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
# 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_sum_op(self):
x = np.ones([2, 2], np.float32)
with fluid.dygraph.guard():
inputs = []
for _ in range(10):
inputs.append(fluid.dygraph.base.to_variable(x))
ret = fluid.layers.sums(inputs)
loss = fluid.layers.reduce_sum(ret)
loss._backward()
self.assertTrue(np.allclose(ret._numpy(), x * 10))
self.assertTrue(np.allclose(inputs[0]._gradient(), x))
def test_layer(self):
with fluid.dygraph.guard():
cl = core.Layer()
cl.forward([])
l = fluid.dygraph.Layer("l")
self.assertRaises(NotImplementedError, l.forward, [])
def test_pylayer_func_id(self):
with fluid.dygraph.guard():
class PyLayer1(fluid.dygraph.PyLayer):
def __init__(self):
super(PyLayer1, self).__init__()
@staticmethod
def forward(input):
return input
@staticmethod
def backward(input):
return input
class PyLayer2(fluid.dygraph.PyLayer):
def __init__(self):
super(PyLayer2, self).__init__()
@staticmethod
def forward(input):
return input
@staticmethod
def backward(input):
return input
py_layer_1 = PyLayer1()
py_layer_2 = PyLayer2()
py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2])))
py_layer_2(fluid.dygraph.base.to_variable(np.ones([2, 2])))
id = py_layer_1.forward_id
self.assertGreater(id, 0)
self.assertEqual(py_layer_1.backward_id, id + 1)
self.assertEqual(py_layer_2.forward_id, id + 2)
self.assertEqual(py_layer_2.backward_id, id + 3)
py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2])))
self.assertEqual(py_layer_1.forward_id, id)
def test_pylayer(self):
np_inp = np.ones([2, 2], np.float32)
with fluid.dygraph.guard():
my_py_layer = MyPyLayer()
var_inp = fluid.dygraph.base.to_variable(np_inp)
outs = my_py_layer(var_inp)
dy_out = np.sum(outs[0]._numpy())
outs[0]._backward()
dy_grad = var_inp._gradient()
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[2, 2], append_batch_size=False)
# TODO(panyx0718): Paddle doesn't diff against data `inp`.
x1 = inp * 1
# TODO(panyx0718): If reduce_sum is skipped, the result is wrong.
x = fluid.layers.reduce_sum(fluid.layers.tanh(x1))
param_grads = fluid.backward.append_backward(
x, parameter_list=[x1.name])[0]
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
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_layer_in_out(self):
np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
with fluid.dygraph.guard():
var_inp = fluid.dygraph.base.to_variable(np_inp)
l = MyLayer("my_layer")
x = l(var_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("my_layer")
x = l(inp)[0]
param_grads = fluid.backward.append_backward(
x, parameter_list=[l._x_for_debug.name])[0]
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
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_mlp(self):
seed = 90
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
with fluid.dygraph.guard(place=fluid.CPUPlace()):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
with fluid.dygraph.guard():
var_inp = fluid.dygraph.base.to_variable(np_inp)
mlp = MLP("mlp")
opt = fluid.optimizer.SGDOptimizer(learning_rate=0.001)
for i in range(100):
out = mlp(var_inp)
dy_out = out._numpy()
out._backward()
opt.minimize(out)
dy_grad = mlp._fc1._w._gradient()
dy_fc0_w0 = mlp._fc1._w._numpy()
mlp.clear_gradients()
out = mlp(var_inp)
dy_out = out._numpy()
out._backward()
dy_grad = mlp._fc1._w._gradient()
with new_program_scope():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
inp = fluid.layers.data(
name="inp", shape=[2, 2], append_batch_size=False)
mlp = MLP("mlp")
out = mlp(inp)
opt = fluid.optimizer.SGDOptimizer(learning_rate=0.001)
opt.minimize(out)
# param_grads = fluid.backward.append_backward(
# out, parameter_list=[mlp._fc1._w.name])[0]
exe = fluid.Executor(fluid.CPUPlace())
param_grads = fluid.backward.append_backward(
out, parameter_list=[mlp._fc1._w.name])[0]
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
exe.run(fluid.default_startup_program())
for i in range(100):
static_out, static_grad, static_fc0_w0 = exe.run(
feed={inp.name: np_inp},
fetch_list=[
out.name, "mlp/MLP_0/FC_0.w_0@GRAD",
"mlp/MLP_0/FC_0.w_0"
])
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))
params = mlp.parameters(True)
self.assertEqual("mlp/MLP_0/FC_0.w_0", params[0].name)
self.assertEqual("mlp/MLP_0/FC_0.b_0", params[1].name)
self.assertEqual("mlp/MLP_0/FC_1.w_0", params[2].name)
self.assertEqual("mlp/MLP_0/FC_1.b_0", params[3].name)
self.assertEqual(len(params), 4)
sublayers = mlp.sublayers(True)
self.assertEqual(mlp._fc1, sublayers[0])
self.assertEqual(mlp._fc2, sublayers[1])
self.assertEqual(len(sublayers), 2)
def test_rnn(self):
np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
[10.0, 11.0, 12.0]])
np_inp = np_inp.reshape((1, 4, 3))
np_inp = np_inp.astype(np.float32)
with fluid.dygraph.guard():
var_inp = fluid.dygraph.base.to_variable(np_inp)
var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
simple_rnn = SimpleRNN("simple_rnn")
outs, pre_hiddens = simple_rnn.forward(var_inp)
dy_out = outs[3]._numpy()
outs[3]._backward()
dy_grad_h2o = simple_rnn._cell._h2o_w._gradient()
dy_grad_h2h = simple_rnn._cell._h2h_w._gradient()
dy_grad_i2h = simple_rnn._cell._i2h_w._gradient()
print(dy_out, static_out)
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[1, 4, 3], append_batch_size=False)
simple_rnn = SimpleRNN("simple_rnn")
outs, pre_hiddens = simple_rnn(inp)
param_grads = fluid.backward.append_backward(outs[3])
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
static_out, static_grad_h2o, static_grad_h2h, static_grad_i2h = exe.run(
feed={inp.name: np_inp},
fetch_list=[
outs[3].name, param_grads[0][1].name,
param_grads[1][1].name, param_grads[2][1].name
])
self.assertTrue(np.allclose(dy_out, static_out))
self.assertTrue(np.array_equal(dy_grad, static_grad))
print(dy_fc0_w0, static_fc0_w0)
#params = mlp.parameters(True)
#self.assertEqual("mlp/MLP_0/FC_0.w_0", params[0].name)
#self.assertEqual("mlp/MLP_0/FC_0.b_0", params[1].name)
#self.assertEqual("mlp/MLP_0/FC_1.w_0", params[2].name)
#self.assertEqual("mlp/MLP_0/FC_1.b_0", params[3].name)
#self.assertEqual(len(params), 4)
#sublayers = mlp.sublayers(True)
#self.assertEqual(mlp._fc1, sublayers[0])
#self.assertEqual(mlp._fc2, sublayers[1])
#self.assertEqual(len(sublayers), 2)
# def test_rnn(self):
# np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
# [10.0, 11.0, 12.0]])
# np_inp = np_inp.reshape((1, 4, 3))
# np_inp = np_inp.astype(np.float32)
# with fluid.dygraph.guard():
# var_inp = fluid.dygraph.base.to_variable(np_inp)
# var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
# simple_rnn = SimpleRNN("simple_rnn")
# outs, pre_hiddens = simple_rnn.forward(var_inp)
# dy_out = outs[3]._numpy()
# outs[3]._backward()
# dy_grad_h2o = simple_rnn._cell._h2o_w._gradient()
# dy_grad_h2h = simple_rnn._cell._h2h_w._gradient()
# dy_grad_i2h = simple_rnn._cell._i2h_w._gradient()
# with new_program_scope():
# inp = fluid.layers.data(
# name="inp", shape=[1, 4, 3], append_batch_size=False)
# simple_rnn = SimpleRNN("simple_rnn")
# outs, pre_hiddens = simple_rnn(inp)
# param_grads = fluid.backward.append_backward(outs[3])
# exe = fluid.Executor(fluid.CPUPlace())
# exe.run(fluid.default_startup_program())
# static_out, static_grad_h2o, static_grad_h2h, static_grad_i2h = exe.run(
# feed={inp.name: np_inp},
# fetch_list=[
# outs[3].name, param_grads[0][1].name,
# param_grads[1][1].name, param_grads[2][1].name
# ])
# self.assertTrue(np.allclose(dy_out, static_out))
# self.assertTrue(np.allclose(dy_grad_h2o, static_grad_h2o))
# self.assertTrue(np.allclose(dy_grad_h2h, static_grad_h2h))
# self.assertTrue(np.allclose(dy_grad_i2h, static_grad_i2h))
self.assertTrue(np.allclose(dy_grad_h2o, static_grad_h2o))
self.assertTrue(np.allclose(dy_grad_h2h, static_grad_h2h))
self.assertTrue(np.allclose(dy_grad_i2h, static_grad_i2h))
if __name__ == '__main__':
......
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