Skip to content

P
Paddle

机器未来 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
未验证 提交 9bd44b94 编写于 作者: L lujun 提交者： GitHub
浏览文件
操作

Merge pull request #16561 from junjun315/move-api-to-root 

Move dygraph api to root
上级 95410652 14db0680
展开全部 隐藏空白更改
内联 并排
Showing with 731 addition and 295 deletion
paddle/fluid/API.spec
浏览文件 @ 9bd44b94
......@@ -13,6 +13,7 @@ paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, d
paddle.fluid.cuda_places (ArgSpec(args=['device_ids'], varargs=None, keywords=None, defaults=(None,)), ('document', '7d9a51fc9cf3c5245b5227080a8064c3'))
paddle.fluid.cpu_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', '4c0cd83f0b401fc2ff84c70974e5d210'))
paddle.fluid.cuda_pinned_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd0c3ebd813c39958c92b78e3eef7e912'))
paddle.fluid.in_dygraph_mode (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'f06314a1cb30c96b5808dde2219c2dae'))
paddle.fluid.Executor.__init__ (ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.Executor.close (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'f5369953dd0c443961cf79f7a00e1a03'))
paddle.fluid.Executor.infer_from_dataset (ArgSpec(args=['self', 'program', 'dataset', 'scope', 'thread', 'debug', 'fetch_list', 'fetch_info', 'print_period'], varargs=None, keywords=None, defaults=(None, None, None, 0, False, None, None, 100)), ('document', '9c7decb955b9c4f718114179c8985581'))
......
python/paddle/fluid/__init__.py
浏览文件 @ 9bd44b94
......@@ -66,6 +66,8 @@ from . import compiler
from .compiler import *
from paddle.fluid.layers.math_op_patch import monkey_patch_variable
from . import install_check
from .dygraph.nn import *
from .dygraph.layers import *
Tensor = LoDTensor
......
python/paddle/fluid/dygraph/base.py
浏览文件 @ 9bd44b94
......@@ -22,7 +22,7 @@ __all__ = ['enabled', 'guard', 'to_variable']
def enabled():
return framework._in_dygraph_mode()
return framework.in_dygraph_mode()
@signature_safe_contextmanager
......
python/paddle/fluid/dygraph/checkpoint.py
浏览文件 @ 9bd44b94
......@@ -97,20 +97,12 @@ def load_persistables(vardict, dirname, filename=None):
Examples:
.. code-block:: python
my_layer = layer(fluid.dygraph.Layer)
my_layer = layer(fluid.Layer)
param_path = "./my_paddle_model"
param_dict = fluid.dygraph.load_persistables(my_layer.parameters(), param_path)
param_1 = param_dict['PtbModel_0.w_1']
or:
my_layer = layer(fluid.dygraph.Layer)
param_path = "./my_paddle_model"
filename = "model.file"
param_dict = fluid.dygraph.load_persistables(my_layer.state_dict(), param_path,
filename=filename)
param_1 = param_dict['PtbModel_0.w_1']
"""
if isinstance(vardict, collections.OrderedDict):
return _load_var_from_file(vardict, dirname, filename)
......
python/paddle/fluid/dygraph/layer_object_helper.py
浏览文件 @ 9bd44b94
......@@ -16,7 +16,7 @@ from __future__ import print_function
import copy
import six
from ..framework import Parameter, _in_dygraph_mode
from ..framework import Parameter, in_dygraph_mode
from ..param_attr import ParamAttr
from .. import core
from six.moves import zip
......
python/paddle/fluid/dygraph/layers.py
浏览文件 @ 9bd44b94
......@@ -139,14 +139,14 @@ class Layer(core.Layer):
def clear_gradients(self):
for p in self.parameters():
p._clear_gradient()
p.clear_gradient()
def _build_once(self, *args):
def build_once(self, *args):
pass
def __call__(self, *inputs):
if not self._built:
self._build_once(*inputs)
self.build_once(*inputs)
outputs = self.forward(*inputs)
self._built = True
......
python/paddle/fluid/dygraph/nn.py
浏览文件 @ 9bd44b94
此差异已折叠。
python/paddle/fluid/framework.py
浏览文件 @ 9bd44b94
......@@ -67,6 +67,7 @@ __all__ = [
'cuda_places',
'cpu_places',
'cuda_pinned_places',
'in_dygraph_mode',
]
EMPTY_VAR_NAME = core.kEmptyVarName()
......@@ -79,7 +80,10 @@ _dygraph_tracer_ = None
_dygraph_current_expected_place_ = None
def _in_dygraph_mode():
def in_dygraph_mode():
'''
Returns(bool): True if the program is running in dynamic graph mode
'''
return _dygraph_tracer_ is not None
......@@ -396,7 +400,7 @@ class Variable(object):
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
if _in_dygraph_mode():
if in_dygraph_mode():
# record vars in tracer rather than blocks
self._ivar = kwargs.get("ivar", None)
if not self._ivar:
......@@ -482,21 +486,21 @@ class Variable(object):
self.block.vars[name] = self
self.op = None
self.stop_gradient = stop_gradient
self._stop_gradient = stop_gradient
self.is_data = is_data
def _numpy(self):
def numpy(self):
new_ivar = self._ivar._copy_to(core.CPUPlace(), True)
return np.array(new_ivar.value().get_tensor())
def _backward(self):
def backward(self):
self._ivar._run_backward()
def _gradient(self):
def gradient(self):
new_ivar = self._ivar._grad_ivar()._copy_to(core.CPUPlace(), True)
return np.array(new_ivar.value().get_tensor())
def _clear_gradient(self):
def clear_gradient(self):
self._ivar._clear_gradient()
def __str__(self):
......@@ -516,7 +520,7 @@ class Variable(object):
Returns:
str: The debug string.
"""
if _in_dygraph_mode():
if in_dygraph_mode():
# TODO(panyx0718): add more dygraph debug info.
return 'name %s, dtype: %s shape: %s' % (self.name, self.dtype,
self.shape)
......@@ -535,7 +539,7 @@ class Variable(object):
__repr__ = __str__
def _set_desc(self, input):
def set_desc(self, input):
"""
Set the variable description.
......@@ -548,43 +552,43 @@ class Variable(object):
self.desc = input
@property
def _stop_gradient(self):
if _in_dygraph_mode():
def stop_gradient(self):
if in_dygraph_mode():
return self._ivar.stop_gradient
else:
return self.stop_gradient
return self._stop_gradient
@_stop_gradient.setter
def _stop_gradient(self, s):
if _in_dygraph_mode():
@stop_gradient.setter
def stop_gradient(self, s):
if in_dygraph_mode():
self._ivar.stop_gradient = s
else:
self.stop_gradient = s
self._stop_gradient = s
@property
def persistable(self):
if _in_dygraph_mode():
if in_dygraph_mode():
return self._ivar.persistable
else:
return self.desc.persistable()
@persistable.setter
def persistable(self, p):
if _in_dygraph_mode():
if in_dygraph_mode():
return self._ivar.persistable
else:
self.desc.set_persistable(p)
@property
def name(self):
if _in_dygraph_mode():
if in_dygraph_mode():
return self._ivar.name
else:
return cpt.to_text(self.desc.name())
@name.setter
def name(self, new_name):
if _in_dygraph_mode():
if in_dygraph_mode():
self._ivar.name = new_name
else:
self.desc.set_name(new_name)
......@@ -592,14 +596,14 @@ class Variable(object):
@property
def shape(self):
# convert to tuple, make it as same as numpy API.
if _in_dygraph_mode():
if in_dygraph_mode():
return self._ivar.shape
else:
return tuple(self.desc.shape())
@property
def dtype(self):
if _in_dygraph_mode():
if in_dygraph_mode():
return self._ivar.dtype
else:
return self.desc.dtype()
......@@ -611,7 +615,7 @@ class Variable(object):
@property
def type(self):
if _in_dygraph_mode():
if in_dygraph_mode():
return self._ivar.dtype
else:
return self.desc.type()
......@@ -721,7 +725,7 @@ class Variable(object):
name=unique_name.generate(".".join(self.name)),
dtype=self.dtype,
persistable=self.persistable,
stop_gradient=self._stop_gradient, )
stop_gradient=self.stop_gradient, )
else:
return self
......@@ -930,7 +934,7 @@ class Operator(object):
inputs=None,
outputs=None,
attrs=None):
if _in_dygraph_mode():
if in_dygraph_mode():
if type is None:
raise ValueError(
"`type` to initialized an Operator can not be None.")
......@@ -1049,7 +1053,7 @@ class Operator(object):
for arg in out_args:
out_arg_names.append(cpt.to_text(arg.name))
# TODO(minqiyang): could we remove variable's op in static mode?
if not _in_dygraph_mode():
if not in_dygraph_mode():
arg.op = self
self.desc.set_output(out_proto.name, out_arg_names)
......@@ -1095,7 +1099,7 @@ class Operator(object):
@property
def type(self):
if _in_dygraph_mode():
if in_dygraph_mode():
return self.iop.type
else:
return self.desc.type()
......@@ -1638,7 +1642,7 @@ class Block(object):
Returns:
Operator: the append Operator.
"""
if _in_dygraph_mode():
if in_dygraph_mode():
op = Operator(
block=self,
desc=None,
......@@ -1710,7 +1714,7 @@ class Block(object):
return self.ops[start:end]
def _prepend_op(self, *args, **kwargs):
if _in_dygraph_mode():
if in_dygraph_mode():
op = Operator(
self,
None,
......
python/paddle/fluid/initializer.py
浏览文件 @ 9bd44b94
......@@ -165,7 +165,7 @@ class ConstantInitializer(Initializer):
'force_cpu': self._force_cpu or force_init_on_cpu()
},
stop_gradient=True)
if not framework._in_dygraph_mode():
if not framework.in_dygraph_mode():
var.op = op
return op
......@@ -245,7 +245,7 @@ class UniformInitializer(Initializer):
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
if not framework._in_dygraph_mode():
if not framework.in_dygraph_mode():
var.op = op
return op
......@@ -324,7 +324,7 @@ class NormalInitializer(Initializer):
outputs={"Out": var},
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
if not framework._in_dygraph_mode():
if not framework.in_dygraph_mode():
var.op = op
return op
......@@ -403,7 +403,7 @@ class TruncatedNormalInitializer(Initializer):
outputs={"Out": var},
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
if not framework._in_dygraph_mode():
if not framework.in_dygraph_mode():
var.op = op
return op
......@@ -509,7 +509,7 @@ class XavierInitializer(Initializer):
"seed": self._seed
},
stop_gradient=True)
if not framework._in_dygraph_mode():
if not framework.in_dygraph_mode():
var.op = op
return op
......@@ -610,7 +610,7 @@ class MSRAInitializer(Initializer):
"seed": self._seed
},
stop_gradient=True)
if not framework._in_dygraph_mode():
if not framework.in_dygraph_mode():
var.op = op
return op
......@@ -709,7 +709,7 @@ class BilinearInitializer(Initializer):
'shape': list(shape),
value_name: values
})
if not framework._in_dygraph_mode():
if not framework.in_dygraph_mode():
var.op = op
return op
......@@ -768,7 +768,7 @@ class NumpyArrayInitializer(Initializer):
value_name: values
},
stop_gradient=True)
if not framework._in_dygraph_mode():
if not framework.in_dygraph_mode():
var.op = op
return op
......
python/paddle/fluid/layer_helper.py
浏览文件 @ 9bd44b94
......@@ -17,7 +17,7 @@ from __future__ import print_function
import copy
import six
from .framework import Parameter, dtype_is_floating, _in_dygraph_mode
from .framework import Parameter, dtype_is_floating, in_dygraph_mode
from . import unique_name
from paddle.fluid.initializer import Constant, Xavier
from .param_attr import ParamAttr
......
python/paddle/fluid/layer_helper_base.py
浏览文件 @ 9bd44b94
......@@ -17,7 +17,7 @@ from __future__ import print_function
import copy
import numpy as np
from .framework import Variable, default_main_program, default_startup_program, _in_dygraph_mode, _current_expected_place
from .framework import Variable, default_main_program, default_startup_program, in_dygraph_mode, _current_expected_place
from . import unique_name
from .param_attr import ParamAttr, WeightNormParamAttr
from . import core
......@@ -54,7 +54,7 @@ class LayerHelperBase(object):
Return Variable construct from value
"""
if isinstance(value, np.ndarray):
assert _in_dygraph_mode(
assert in_dygraph_mode(
), "to_variable could only be called in dygraph mode"
if not block:
......@@ -302,7 +302,7 @@ class LayerHelperBase(object):
param = self._create_weight_normalize(attr, shape, dtype)
WeightNormParamAttr.params_with_weight_norm.append(param)
return param
if _in_dygraph_mode():
if in_dygraph_mode():
# In dygraph mode, we want the returned parameter to be
# initialized so that it can be used imperatively.
return self.main_program.global_block().create_parameter(
......@@ -370,7 +370,7 @@ class LayerHelperBase(object):
initializer: initializer to use
"""
assert isinstance(var, Variable)
if _in_dygraph_mode():
if in_dygraph_mode():
initializer(var, var.block)
else:
self.startup_program.global_block().create_var(
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 9bd44b94
......@@ -23,7 +23,7 @@ import os
import inspect
from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant, NumpyArrayInitializer
from ..framework import Variable, OpProtoHolder, _in_dygraph_mode
from ..framework import Variable, OpProtoHolder, in_dygraph_mode
from ..dygraph import base
from ..param_attr import ParamAttr
from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
......@@ -481,7 +481,7 @@ def dynamic_lstm(input,
forward, _ = fluid.layers.dynamic_lstm(
input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
"""
assert _in_dygraph_mode(
assert in_dygraph_mode(
) is not True, "please use lstm instead of dynamic_lstm in dygraph mode!"
assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
helper = LayerHelper('lstm', **locals())
......@@ -867,7 +867,7 @@ def dynamic_lstmp(input,
proj_activation="tanh")
"""
assert _in_dygraph_mode(
assert in_dygraph_mode(
) is not True, "please use lstm instead of dynamic_lstmp in dygraph mode!"
assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
......@@ -1041,7 +1041,7 @@ def dynamic_gru(input,
hidden = fluid.layers.dynamic_gru(input=x, size=hidden_dim)
"""
assert _in_dygraph_mode(
assert in_dygraph_mode(
) is not True, "please use gru instead of dynamic_gru in dygraph mode!"
helper = LayerHelper('gru', **locals())
......@@ -1760,7 +1760,7 @@ def sequence_conv(input,
Variable: output of sequence_conv
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_conv', **locals())
dtype = helper.input_dtype()
......@@ -1821,7 +1821,7 @@ def sequence_softmax(input, use_cudnn=False, name=None):
dtype='float32', lod_level=1)
x_sequence_softmax = fluid.layers.sequence_softmax(input=x)
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_softmax', **locals())
dtype = helper.input_dtype()
......@@ -2315,7 +2315,7 @@ def sequence_pool(input, pool_type, is_test=False):
last_x = fluid.layers.sequence_pool(input=x, pool_type='last')
first_x = fluid.layers.sequence_pool(input=x, pool_type='first')
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_pool', **locals())
dtype = helper.input_dtype()
......@@ -2356,7 +2356,7 @@ def sequence_concat(input, name=None):
out = fluid.layers.sequence_concat(input=[seq1, seq2, seq3])
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_concat', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
......@@ -2485,7 +2485,7 @@ def sequence_slice(input, offset, length, name=None):
subseqs = fluid.layers.sequence_slice(input=seqs, offset=offset,
length=length)
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper("sequence_slice", **locals())
dtype = helper.input_dtype()
......@@ -3307,7 +3307,7 @@ def layer_norm(input,
>>> dtype='float32')
>>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
"""
assert _in_dygraph_mode(
assert in_dygraph_mode(
) is not True, "please use FC instead of fc in dygraph mode!"
helper = LayerHelper('layer_norm', **locals())
dtype = helper.input_dtype()
......@@ -3946,7 +3946,7 @@ def sequence_expand(x, y, ref_level=-1, name=None):
dtype='float32', lod_level=1)
out = layers.sequence_expand(x=x, y=y, ref_level=0)
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_expand', input=x, **locals())
dtype = helper.input_dtype()
......@@ -4014,7 +4014,7 @@ def sequence_expand_as(x, y, name=None):
dtype='float32', lod_level=1)
out = layers.sequence_expand_as(x=x, y=y)
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_expand_as', input=x, **locals())
dtype = helper.input_dtype()
......@@ -4062,7 +4062,7 @@ def sequence_pad(x, pad_value, maxlen=None, name=None):
out = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_pad', input=x, **locals())
dtype = helper.input_dtype()
......@@ -4130,7 +4130,7 @@ def sequence_unpad(x, length, name=None):
out = fluid.layers.sequence_unpad(x=x, length=len)
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_unpad', input=x, **locals())
dtype = helper.input_dtype()
......@@ -5305,7 +5305,7 @@ def sequence_reshape(input, new_dim):
x = fluid.layers.data(shape=[5, 20], dtype='float32', lod_level=1)
x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=10)
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_reshape', **locals())
out = helper.create_variable_for_type_inference(helper.input_dtype())
......@@ -5841,7 +5841,7 @@ def im2sequence(input,
input=layer, stride=[1, 1], filter_size=[2, 2])
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
if isinstance(filter_size, int):
......@@ -6485,7 +6485,7 @@ def squeeze(input, axes, name=None):
x = layers.data(name='x', shape=[5, 1, 10])
y = layers.sequeeze(input=x, axes=[1])
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"squeeze layer is not supported in dygraph mode yet.")
helper = LayerHelper("squeeze", **locals())
out = helper.create_variable_for_type_inference(dtype=input.dtype)
......@@ -7631,7 +7631,7 @@ def sequence_scatter(input, index, updates, name=None):
output = fluid.layers.sequence_scatter(input, index, updates)
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_scatter', **locals())
dtype = helper.input_dtype()
......@@ -8721,7 +8721,7 @@ def sequence_enumerate(input, win_size, pad_value=0, name=None):
x = fluid.layers.data(shape[30, 1], dtype='int32', lod_level=1)
out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_enumerate', **locals())
out = helper.create_variable_for_type_inference(
......@@ -8762,7 +8762,7 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None):
Variable: The output sequence mask.
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_mask', **locals())
......@@ -9241,7 +9241,7 @@ def _elementwise_op(helper):
op_type = helper.layer_type
x = helper.kwargs.get('x', None)
y = helper.kwargs.get('y', None)
if _in_dygraph_mode():
if in_dygraph_mode():
x = base.to_variable(x)
y = base.to_variable(y)
......@@ -9814,7 +9814,7 @@ def sequence_reverse(x, name=None):
Returns:
out(${y_type}): ${y_comment}
"""
assert not _in_dygraph_mode(), (
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper("sequence_reverse", **locals())
if name is None:
......
python/paddle/fluid/optimizer.py
浏览文件 @ 9bd44b94
......@@ -55,7 +55,7 @@ class Optimizer(object):
"""
def __init__(self, learning_rate, regularization=None, name=None):
if framework._in_dygraph_mode():
if framework.in_dygraph_mode():
if not isinstance(learning_rate, float) and \
not isinstance(learning_rate, LearningRateDecay):
raise TypeError(
......@@ -205,7 +205,7 @@ class Optimizer(object):
name = self._name + "_" + name
if (name in self._accumulators and
param.name in self._accumulators[name]):
if framework._in_dygraph_mode():
if framework.in_dygraph_mode():
return self._accumulators[name][param.name]
raise Exception("Accumulator {} already exists for parameter {}".
format(name, param.name))
......@@ -363,7 +363,7 @@ class Optimizer(object):
See examples in `apply_gradients`.
"""
self._dtype = loss.dtype
if framework._in_dygraph_mode():
if framework.in_dygraph_mode():
if parameter_list is not None:
parameters = parameter_list
else:
......@@ -448,7 +448,7 @@ class Optimizer(object):
Returns:
list: A list of operators appended to the current program.
"""
if framework._in_dygraph_mode():
if framework.in_dygraph_mode():
with program_guard(framework.default_main_program(),
framework.default_startup_program()):
optimize_ops = self._create_optimization_pass(params_grads)
......
python/paddle/fluid/tests/unittests/test_base_layer.py
浏览文件 @ 9bd44b94
......@@ -18,7 +18,7 @@ import numpy as np
import paddle.fluid as fluid
class L1(fluid.dygraph.Layer):
class L1(fluid.Layer):
def __init__(self, prefix):
super(L1, self).__init__(prefix)
self._param_attr = fluid.ParamAttr(
......@@ -32,7 +32,7 @@ class L1(fluid.dygraph.Layer):
return self.w1 + self.w2
class L2(fluid.dygraph.Layer):
class L2(fluid.Layer):
def __init__(self, prefix):
super(L2, self).__init__(prefix)
self.layer1 = L1(self.full_name())
......@@ -42,7 +42,7 @@ class L2(fluid.dygraph.Layer):
return self.layer1() + self.layer2()
class L3(fluid.dygraph.Layer):
class L3(fluid.Layer):
def __init__(self, prefix):
super(L3, self).__init__(prefix)
self.layer1 = L2(self.full_name())
......@@ -59,7 +59,7 @@ class TestBaseLayer(unittest.TestCase):
ret = l()
self.assertEqual(l.w1.name, "test_one_level/L1_0.w_0")
self.assertEqual(l.w2.name, "test_one_level/L1_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.2 * np.ones([2, 2])))
self.assertTrue(np.allclose(ret.numpy(), 0.2 * np.ones([2, 2])))
def test_three_level(self):
with fluid.dygraph.guard():
......@@ -72,7 +72,7 @@ class TestBaseLayer(unittest.TestCase):
self.assertEqual(names[3], "test_three_level/L3_0/L2_0/L1_1.w_1")
self.assertEqual(names[4], "test_three_level/L3_0/L2_1/L1_0.w_0")
self.assertEqual(names[5], "test_three_level/L3_0/L2_1/L1_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.8 * np.ones([2, 2])))
self.assertTrue(np.allclose(ret.numpy(), 0.8 * np.ones([2, 2])))
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_imperative_basic.py
浏览文件 @ 9bd44b94
......@@ -18,11 +18,11 @@ import numpy as np
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.dygraph.nn import FC
from paddle.fluid import FC
from test_imperative_base import new_program_scope
class MyLayer(fluid.dygraph.Layer):
class MyLayer(fluid.Layer):
def __init__(self, name_scope):
super(MyLayer, self).__init__(name_scope)
......@@ -34,7 +34,7 @@ class MyLayer(fluid.dygraph.Layer):
return [x]
class MyPyLayer(fluid.dygraph.PyLayer):
class MyPyLayer(fluid.PyLayer):
def __init__(self):
super(MyPyLayer, self).__init__()
......@@ -48,7 +48,7 @@ class MyPyLayer(fluid.dygraph.PyLayer):
return np.array(dout) * (1 - np.square(np.array(out)))
class MLP(fluid.dygraph.Layer):
class MLP(fluid.Layer):
def __init__(self, name_scope):
super(MLP, self).__init__(name_scope)
self._fc1 = FC(self.full_name(),
......@@ -71,7 +71,7 @@ class MLP(fluid.dygraph.Layer):
return x
class SimpleRNNCell(fluid.dygraph.Layer):
class SimpleRNNCell(fluid.Layer):
def __init__(self, name_scope, step_input_size, hidden_size, output_size,
param_attr):
super(SimpleRNNCell, self).__init__(name_scope)
......@@ -81,7 +81,7 @@ class SimpleRNNCell(fluid.dygraph.Layer):
self._dtype = core.VarDesc.VarType.FP32
self.param_attr = param_attr
def _build_once(self, inputs, pre_hidden):
def build_once(self, inputs, pre_hidden):
i2h_param_shape = [self.step_input_size, self.hidden_size]
h2h_param_shape = [self.hidden_size, self.hidden_size]
h2o_param_shape = [self.output_size, self.hidden_size]
......@@ -159,7 +159,7 @@ class SimpleRNNCell(fluid.dygraph.Layer):
return reduce_out, hidden
class SimpleRNN(fluid.dygraph.Layer):
class SimpleRNN(fluid.Layer):
def __init__(self, name_scope):
super(SimpleRNN, self).__init__(name_scope)
self.seq_len = 4
......@@ -200,22 +200,22 @@ class TestImperative(unittest.TestCase):
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))
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")
l = fluid.Layer("l")
self.assertRaises(NotImplementedError, l.forward, [])
def test_pylayer_func_id(self):
with fluid.dygraph.guard():
class PyLayer1(fluid.dygraph.PyLayer):
class PyLayer1(fluid.PyLayer):
def __init__(self):
super(PyLayer1, self).__init__()
......@@ -227,7 +227,7 @@ class TestImperative(unittest.TestCase):
def backward(input):
return input
class PyLayer2(fluid.dygraph.PyLayer):
class PyLayer2(fluid.PyLayer):
def __init__(self):
super(PyLayer2, self).__init__()
......@@ -257,9 +257,9 @@ class TestImperative(unittest.TestCase):
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()
dy_out = np.sum(outs[0].numpy())
outs[0].backward()
dy_grad = var_inp.gradient()
with new_program_scope():
inp = fluid.layers.data(
......@@ -287,9 +287,9 @@ class TestImperative(unittest.TestCase):
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()
dy_out = x.numpy()
x.backward()
dy_grad = l._x_for_debug.gradient()
with new_program_scope():
inp = fluid.layers.data(
......@@ -314,9 +314,9 @@ class TestImperative(unittest.TestCase):
var_inp = fluid.dygraph.base.to_variable(np_inp)
mlp = MLP("mlp")
out = mlp(var_inp)
dy_out = out._numpy()
out._backward()
dy_grad = mlp._fc1._w._gradient()
dy_out = out.numpy()
out.backward()
dy_grad = mlp._fc1._w.gradient()
with new_program_scope():
inp = fluid.layers.data(
......@@ -358,7 +358,7 @@ class TestImperative(unittest.TestCase):
x = fluid.layers.elementwise_add(inp1, inp2)
else:
x = fluid.layers.elementwise_sub(inp1, inp2)
dygraph_result = x._numpy()
dygraph_result = x.numpy()
# static graph
with new_program_scope():
......@@ -407,11 +407,11 @@ class TestImperative(unittest.TestCase):
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()
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(
......
python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py
浏览文件 @ 9bd44b94
......@@ -18,11 +18,11 @@ import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
from paddle.fluid import Conv2D, Pool2D, FC
from paddle.fluid.dygraph.base import to_variable
class SimpleImgConvPool(fluid.dygraph.Layer):
class SimpleImgConvPool(fluid.Layer):
def __init__(self,
name_scope,
num_channels,
......@@ -71,7 +71,7 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
return x
class MNIST(fluid.dygraph.Layer):
class MNIST(fluid.Layer):
def __init__(self, name_scope):
super(MNIST, self).__init__(name_scope)
......@@ -125,21 +125,21 @@ class TestDygraphCheckpoint(unittest.TestCase):
img = to_variable(dy_x_data)
label = to_variable(y_data)
label._stop_gradient = True
label.stop_gradient = True
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss._numpy()
dy_out = avg_loss.numpy()
avg_loss._backward()
avg_loss.backward()
sgd.minimize(avg_loss)
fluid.dygraph.save_persistables(mnist, "save_dir")
mnist.clear_gradients()
for param in mnist.parameters():
dy_param_init_value[param.name] = param._numpy()
dy_param_init_value[param.name] = param.numpy()
mnist.load_dict(
fluid.dygraph.load_persistables(mnist, "save_dir"))
......
python/paddle/fluid/tests/unittests/test_imperative_deepcf.py
浏览文件 @ 9bd44b94
......@@ -32,11 +32,11 @@ NUM_BATCHES = int(os.environ.get('NUM_BATCHES', 5))
NUM_EPOCHES = int(os.environ.get('NUM_EPOCHES', 1))
class DMF(fluid.dygraph.Layer):
class DMF(fluid.Layer):
def __init__(self, name_scope):
super(DMF, self).__init__(name_scope)
self._user_latent = fluid.dygraph.FC(self.full_name(), 256)
self._item_latent = fluid.dygraph.FC(self.full_name(), 256)
self._user_latent = fluid.FC(self.full_name(), 256)
self._item_latent = fluid.FC(self.full_name(), 256)
self._user_layers = []
self._item_layers = []
......@@ -45,13 +45,11 @@ class DMF(fluid.dygraph.Layer):
self._user_layers.append(
self.add_sublayer(
'user_layer_%d' % i,
fluid.dygraph.FC(
self.full_name(), self._hid_sizes[i], act='relu')))
fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
self._item_layers.append(
self.add_sublayer(
'item_layer_%d' % i,
fluid.dygraph.FC(
self.full_name(), self._hid_sizes[i], act='relu')))
fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
def forward(self, users, items):
users = self._user_latent(users)
......@@ -63,19 +61,18 @@ class DMF(fluid.dygraph.Layer):
return fluid.layers.elementwise_mul(users, items)
class MLP(fluid.dygraph.Layer):
class MLP(fluid.Layer):
def __init__(self, name_scope):
super(MLP, self).__init__(name_scope)
self._user_latent = fluid.dygraph.FC(self.full_name(), 256)
self._item_latent = fluid.dygraph.FC(self.full_name(), 256)
self._user_latent = fluid.FC(self.full_name(), 256)
self._item_latent = fluid.FC(self.full_name(), 256)
self._match_layers = []
self._hid_sizes = [128, 64]
for i in range(len(self._hid_sizes)):
self._match_layers.append(
self.add_sublayer(
'match_layer_%d' % i,
fluid.dygraph.FC(
self.full_name(), self._hid_sizes[i], act='relu')))
fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
self._mat
def forward(self, users, items):
......@@ -88,7 +85,7 @@ class MLP(fluid.dygraph.Layer):
return match_vec
class DeepCF(fluid.dygraph.Layer):
class DeepCF(fluid.Layer):
def __init__(self, name_scope, num_users, num_items, matrix):
super(DeepCF, self).__init__(name_scope)
self._num_users = num_users
......@@ -99,11 +96,11 @@ class DeepCF(fluid.dygraph.Layer):
matrix.dtype,
is_bias=False,
default_initializer=fluid.initializer.NumpyArrayInitializer(matrix))
self._rating_matrix._stop_gradient = True
self._rating_matrix.stop_gradient = True
self._mlp = MLP(self.full_name())
self._dmf = DMF(self.full_name())
self._match_fc = fluid.dygraph.FC(self.full_name(), 1, act='sigmoid')
self._match_fc = fluid.FC(self.full_name(), 1, act='sigmoid')
def forward(self, users, items):
# users_emb = self._user_emb(users)
......@@ -255,10 +252,10 @@ class TestDygraphDeepCF(unittest.TestCase):
fluid.layers.log_loss(prediction,
to_variable(labels_np[
slice:slice + BATCH_SIZE])))
loss._backward()
loss.backward()
adam.minimize(loss)
deepcf.clear_gradients()
dy_loss = loss._numpy()
dy_loss = loss.numpy()
sys.stderr.write('dynamic loss: %s %s\n' % (slice, dy_loss))
self.assertEqual(static_loss, dy_loss)
......
python/paddle/fluid/tests/unittests/test_imperative_gan.py
浏览文件 @ 9bd44b94
......@@ -22,12 +22,12 @@ import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
from paddle.fluid import Conv2D, Pool2D, FC
from test_imperative_base import new_program_scope
from paddle.fluid.dygraph.base import to_variable
class Discriminator(fluid.dygraph.Layer):
class Discriminator(fluid.Layer):
def __init__(self, name_scope):
super(Discriminator, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), size=32, act='elu')
......@@ -38,7 +38,7 @@ class Discriminator(fluid.dygraph.Layer):
return self._fc2(x)
class Generator(fluid.dygraph.Layer):
class Generator(fluid.Layer):
def __init__(self, name_scope):
super(Generator, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), size=64, act='elu')
......@@ -150,7 +150,7 @@ class TestDygraphGAN(unittest.TestCase):
x=d_fake, label=to_variable(np.zeros([2, 1], np.float32))))
d_loss = d_loss_real + d_loss_fake
d_loss._backward()
d_loss.backward()
sgd.minimize(d_loss)
discriminator.clear_gradients()
generator.clear_gradients()
......@@ -160,15 +160,15 @@ class TestDygraphGAN(unittest.TestCase):
g_loss = fluid.layers.reduce_mean(
fluid.layers.sigmoid_cross_entropy_with_logits(
x=d_fake, label=to_variable(np.ones([2, 1], np.float32))))
g_loss._backward()
g_loss.backward()
sgd.minimize(g_loss)
for p in discriminator.parameters():
dy_params[p.name] = p._numpy()
dy_params[p.name] = p.numpy()
for p in generator.parameters():
dy_params[p.name] = p._numpy()
dy_params[p.name] = p.numpy()
dy_g_loss = g_loss._numpy()
dy_d_loss = d_loss._numpy()
dy_g_loss = g_loss.numpy()
dy_d_loss = d_loss.numpy()
self.assertEqual(dy_g_loss, static_g_loss)
self.assertEqual(dy_d_loss, static_d_loss)
......
python/paddle/fluid/tests/unittests/test_imperative_gnn.py
浏览文件 @ 9bd44b94
......@@ -15,14 +15,12 @@
import contextlib
import unittest
import numpy as np
import six
import sys
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.optimizer import AdamOptimizer
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
from test_imperative_base import new_program_scope
from paddle.fluid.dygraph.base import to_variable
......@@ -31,7 +29,7 @@ def gen_data():
pass
class GraphConv(fluid.dygraph.Layer):
class GraphConv(fluid.Layer):
def __init__(self, name_scope, in_features, out_features):
super(GraphConv, self).__init__(name_scope)
......@@ -50,7 +48,7 @@ class GraphConv(fluid.dygraph.Layer):
return fluid.layers.matmul(adj, support) + self.bias
class GCN(fluid.dygraph.Layer):
class GCN(fluid.Layer):
def __init__(self, name_scope, num_hidden):
super(GCN, self).__init__(name_scope)
self.gc = GraphConv(self.full_name(), num_hidden, 32)
......@@ -134,10 +132,9 @@ class TestDygraphGNN(unittest.TestCase):
loss = fluid.layers.reduce_sum(loss)
adam = AdamOptimizer(learning_rate=1e-3)
adam.minimize(loss)
self.assertEqual(static_loss, loss._numpy())
self.assertTrue(
np.allclose(static_weight, model.gc.weight._numpy()))
sys.stderr.write('%s %s\n' % (static_loss, loss._numpy()))
self.assertEqual(static_loss, loss.numpy())
self.assertTrue(np.allclose(static_weight, model.gc.weight.numpy()))
sys.stderr.write('%s %s\n' % (static_loss, loss.numpy()))
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
浏览文件 @ 9bd44b94
......@@ -128,25 +128,25 @@ class TestImperativeMnist(unittest.TestCase):
img = to_variable(dy_x_data)
label = to_variable(y_data)
label._stop_gradient = True
label.stop_gradient = True
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss._numpy()
dy_out = avg_loss.numpy()
if epoch == 0 and batch_id == 0:
for param in mnist.parameters():
dy_param_init_value[param.name] = param._numpy()
dy_param_init_value[param.name] = param.numpy()
avg_loss._backward()
avg_loss.backward()
sgd.minimize(avg_loss)
mnist.clear_gradients()
dy_param_value = {}
for param in mnist.parameters():
dy_param_value[param.name] = param._numpy()
dy_param_value[param.name] = param.numpy()
with new_program_scope():
fluid.default_startup_program().random_seed = seed
......
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
浏览文件 @ 9bd44b94
......@@ -28,7 +28,7 @@ from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope
class MLP(fluid.dygraph.Layer):
class MLP(fluid.Layer):
def __init__(self, name_scope, param_attr=None, bias_attr=None):
super(MLP, self).__init__(name_scope)
......@@ -75,18 +75,18 @@ class TestImperativeOptimizerBase(unittest.TestCase):
cost = mlp(img)
avg_loss = fluid.layers.reduce_mean(cost)
dy_out = avg_loss._numpy()
dy_out = avg_loss.numpy()
if batch_id == 0:
for param in mlp.parameters():
dy_param_init_value[param.name] = param._numpy()
dy_param_init_value[param.name] = param.numpy()
avg_loss._backward()
avg_loss.backward()
optimizer.minimize(avg_loss)
mlp.clear_gradients()
dy_param_value = {}
for param in mlp.parameters():
dy_param_value[param.name] = param._numpy()
dy_param_value[param.name] = param.numpy()
with new_program_scope():
fluid.default_startup_program().random_seed = seed
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ 9bd44b94
......@@ -24,10 +24,9 @@ from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope
import numpy as np
import six
from paddle.fluid.backward import append_backward
class SimpleLSTMRNN(fluid.dygraph.Layer):
class SimpleLSTMRNN(fluid.Layer):
def __init__(self,
name_scope,
hidden_size,
......@@ -45,7 +44,7 @@ class SimpleLSTMRNN(fluid.dygraph.Layer):
self.cell_array = []
self.hidden_array = []
def _build_once(self, input_embedding, init_hidden=None, init_cell=None):
def build_once(self, input_embedding, init_hidden=None, init_cell=None):
self.weight_1_arr = []
self.weight_2_arr = []
self.bias_arr = []
......@@ -132,7 +131,7 @@ class SimpleLSTMRNN(fluid.dygraph.Layer):
return real_res, last_hidden, last_cell
class PtbModel(fluid.dygraph.Layer):
class PtbModel(fluid.Layer):
def __init__(self,
name_scope,
hidden_size,
......@@ -177,7 +176,7 @@ class PtbModel(fluid.dygraph.Layer):
default_initializer=fluid.initializer.UniformInitializer(
low=-self.init_scale, high=self.init_scale))
def _build_once(self, input, label, init_hidden, init_cell):
def build_once(self, input, label, init_hidden, init_cell):
pass
def forward(self, input, label, init_hidden, init_cell):
......@@ -260,13 +259,13 @@ class TestDygraphPtbRnn(unittest.TestCase):
init_cell)
if i == 0:
for param in ptb_model.parameters():
dy_param_init[param.name] = param._numpy()
dy_loss._backward()
dy_param_init[param.name] = param.numpy()
dy_loss.backward()
sgd.minimize(dy_loss)
ptb_model.clear_gradients()
if i == batch_num - 1:
for param in ptb_model.parameters():
dy_param_updated[param.name] = param._numpy()
dy_param_updated[param.name] = param.numpy()
with new_program_scope():
fluid.default_startup_program().random_seed = seed
......@@ -334,11 +333,11 @@ class TestDygraphPtbRnn(unittest.TestCase):
static_param_updated[static_param_name_list[k -
3]] = out[k]
self.assertTrue(np.array_equal(static_loss_value, dy_loss._numpy()))
self.assertTrue(np.array_equal(static_loss_value, dy_loss.numpy()))
self.assertTrue(
np.array_equal(static_last_cell_value, last_cell._numpy()))
np.array_equal(static_last_cell_value, last_cell.numpy()))
self.assertTrue(
np.array_equal(static_last_hidden_value, last_hidden._numpy()))
np.array_equal(static_last_hidden_value, last_hidden.numpy()))
for key, value in six.iteritems(static_param_init):
self.assertTrue(np.array_equal(value, dy_param_init[key]))
for key, value in six.iteritems(static_param_updated):
......
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @ 9bd44b94
......@@ -21,7 +21,7 @@ import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, FC
from paddle.fluid import Conv2D, Pool2D, BatchNorm, FC
from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope
......@@ -68,7 +68,7 @@ def optimizer_setting(params):
return optimizer
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(fluid.Layer):
def __init__(self,
name_scope,
num_channels,
......@@ -99,7 +99,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class BottleneckBlock(fluid.dygraph.Layer):
class BottleneckBlock(fluid.Layer):
def __init__(self,
name_scope,
num_channels,
......@@ -156,7 +156,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
return layer_helper.append_activation(y)
class ResNet(fluid.dygraph.Layer):
class ResNet(fluid.Layer):
def __init__(self, name_scope, layers=50, class_dim=102):
super(ResNet, self).__init__(name_scope)
......@@ -247,7 +247,7 @@ class TestDygraphResnet(unittest.TestCase):
dy_param_init_value = {}
for param in resnet.parameters():
dy_param_init_value[param.name] = param._numpy()
dy_param_init_value[param.name] = param.numpy()
for batch_id, data in enumerate(train_reader()):
if batch_id >= batch_num:
......@@ -260,20 +260,20 @@ class TestDygraphResnet(unittest.TestCase):
img = to_variable(dy_x_data)
label = to_variable(y_data)
label._stop_gradient = True
label.stop_gradient = True
out = resnet(img)
loss = fluid.layers.cross_entropy(input=out, label=label)
avg_loss = fluid.layers.mean(x=loss)
dy_out = avg_loss._numpy()
dy_out = avg_loss.numpy()
if batch_id == 0:
for param in resnet.parameters():
if param.name not in dy_param_init_value:
dy_param_init_value[param.name] = param._numpy()
dy_param_init_value[param.name] = param.numpy()
avg_loss._backward()
avg_loss.backward()
dy_grad_value = {}
for param in resnet.parameters():
......@@ -288,7 +288,7 @@ class TestDygraphResnet(unittest.TestCase):
dy_param_value = {}
for param in resnet.parameters():
dy_param_value[param.name] = param._numpy()
dy_param_value[param.name] = param.numpy()
with new_program_scope():
fluid.default_startup_program().random_seed = seed
......
python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py
浏览文件 @ 9bd44b94
......@@ -333,7 +333,7 @@ class TestImperativeResneXt(unittest.TestCase):
dy_param_init_value = {}
for param in se_resnext.parameters():
dy_param_init_value[param.name] = param._numpy()
dy_param_init_value[param.name] = param.numpy()
for epoch_id in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
......@@ -349,19 +349,19 @@ class TestImperativeResneXt(unittest.TestCase):
img = to_variable(dy_x_data)
label = to_variable(y_data)
label._stop_gradient = True
label.stop_gradient = True
out = se_resnext(img)
loss = fluid.layers.cross_entropy(input=out, label=label)
avg_loss = fluid.layers.mean(x=loss)
dy_out = avg_loss._numpy()
dy_out = avg_loss.numpy()
if batch_id == 0:
for param in se_resnext.parameters():
if param.name not in dy_param_init_value:
dy_param_init_value[param.name] = param._numpy()
avg_loss._backward()
dy_param_init_value[param.name] = param.numpy()
avg_loss.backward()
#dy_grad_value = {}
#for param in se_resnext.parameters():
......@@ -375,7 +375,7 @@ class TestImperativeResneXt(unittest.TestCase):
dy_param_value = {}
for param in se_resnext.parameters():
dy_param_value[param.name] = param._numpy()
dy_param_value[param.name] = param.numpy()
with new_program_scope():
fluid.default_startup_program().random_seed = seed
......
python/paddle/fluid/tests/unittests/test_imperative_transformer.py
浏览文件 @ 9bd44b94
......@@ -16,7 +16,8 @@ from __future__ import print_function
import unittest
import paddle.fluid as fluid
from paddle.fluid.dygraph import Embedding, LayerNorm, FC, to_variable, Layer, guard
from paddle.fluid import Embedding, LayerNorm, FC, Layer
from paddle.fluid.dygraph import to_variable, guard
from test_imperative_base import new_program_scope
from paddle.fluid import core
import numpy as np
......@@ -992,14 +993,14 @@ class TestDygraphTransformer(unittest.TestCase):
enc_inputs, dec_inputs, label, weights)
if i == 0:
for param in transformer.parameters():
dy_param_init[param.name] = param._numpy()
dy_param_init[param.name] = param.numpy()
dy_avg_cost._backward()
dy_avg_cost.backward()
optimizer.minimize(dy_avg_cost)
transformer.clear_gradients()
if i == batch_num - 1:
for param in transformer.parameters():
dy_param_updated[param.name] = param._numpy()
dy_param_updated[param.name] = param.numpy()
with new_program_scope():
fluid.default_startup_program().random_seed = seed
......@@ -1076,13 +1077,14 @@ class TestDygraphTransformer(unittest.TestCase):
4]] = out[k]
self.assertTrue(
np.array_equal(static_avg_cost_value, dy_avg_cost._numpy()))
np.array_equal(static_avg_cost_value, dy_avg_cost.numpy()))
self.assertTrue(
np.array_equal(static_sum_cost_value, dy_sum_cost._numpy()))
np.array_equal(static_sum_cost_value, dy_sum_cost.numpy()))
self.assertTrue(
np.array_equal(static_predict_value, dy_predict._numpy()))
np.array_equal(static_predict_value, dy_predict.numpy()))
self.assertTrue(
np.array_equal(static_token_num_value, dy_token_num._numpy()))
np.array_equal(static_token_num_value, dy_token_num.numpy()))
for key, value in six.iteritems(static_param_init):
self.assertTrue(np.array_equal(value, dy_param_init[key]))
for key, value in six.iteritems(static_param_updated):
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 9bd44b94
......@@ -114,7 +114,7 @@ class TestLayer(LayerTest):
dy_ret = fc2(ret)
self.assertTrue(np.array_equal(static_ret, static_ret2))
self.assertTrue(np.array_equal(static_ret, dy_ret._numpy()))
self.assertTrue(np.array_equal(static_ret, dy_ret.numpy()))
def test_layer_norm(self):
inp = np.ones([3, 32, 32], dtype='float32')
......@@ -142,7 +142,7 @@ class TestLayer(LayerTest):
dy_ret = lm(base.to_variable(inp))
self.assertTrue(np.allclose(static_ret, static_ret2))
self.assertTrue(np.allclose(dy_ret._numpy(), static_ret2))
self.assertTrue(np.allclose(dy_ret.numpy(), static_ret2))
def test_relu(self):
with self.static_graph():
......@@ -156,7 +156,7 @@ class TestLayer(LayerTest):
t = np.ones([3, 3], dtype='float32')
dy_ret = layers.relu(base.to_variable(t))
self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
def test_matmul(self):
with self.static_graph():
......@@ -177,7 +177,7 @@ class TestLayer(LayerTest):
t2 = np.ones([3, 3], dtype='float32')
dy_ret = layers.matmul(base.to_variable(t), base.to_variable(t2))
self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
def test_conv2d(self):
with self.static_graph():
......@@ -204,7 +204,7 @@ class TestLayer(LayerTest):
'conv2d', num_channels=3, num_filters=3, filter_size=[2, 2])
dy_ret = conv2d(base.to_variable(images))
self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
self.assertTrue(np.allclose(static_ret, static_ret2))
def test_gru_unit(self):
......@@ -246,7 +246,7 @@ class TestLayer(LayerTest):
for i in range(len(static_ret)):
self.assertTrue(np.allclose(static_ret[i], static_ret2[i]))
self.assertTrue(np.allclose(static_ret[i], dy_ret[i]._numpy()))
self.assertTrue(np.allclose(static_ret[i], dy_ret[i].numpy()))
def test_elementwise_math(self):
n = np.ones([3, 3], dtype='float32')
......@@ -288,8 +288,8 @@ class TestLayer(LayerTest):
ret = layers.elementwise_sub(ret, n5)
dy_ret = layers.elementwise_mul(ret, n6)
self.assertTrue(
np.allclose(static_ret, dy_ret._numpy()),
'%s vs %s' % (static_ret, dy_ret._numpy()))
np.allclose(static_ret, dy_ret.numpy()),
'%s vs %s' % (static_ret, dy_ret.numpy()))
def test_elementwise_minmax(self):
n = np.ones([3, 3], dtype='float32')
......@@ -299,8 +299,8 @@ class TestLayer(LayerTest):
min_ret = layers.elementwise_min(n, n2)
max_ret = layers.elementwise_max(n, n2)
self.assertTrue(np.allclose(n, min_ret._numpy()))
self.assertTrue(np.allclose(n2, max_ret._numpy()))
self.assertTrue(np.allclose(n, min_ret.numpy()))
self.assertTrue(np.allclose(n2, max_ret.numpy()))
def test_sequence_conv(self):
inp_np = np.arange(12).reshape([3, 4]).astype('float32')
......@@ -367,7 +367,7 @@ class TestLayer(LayerTest):
'conv2d_transpose', num_filters=10, output_size=28)
dy_rlt = conv2d_transpose(base.to_variable(inp_np))
self.assertTrue(np.allclose(static_rlt2, static_rlt))
self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
self.assertTrue(np.allclose(dy_rlt.numpy(), static_rlt))
def test_bilinear_tensor_product(self):
inp_np_x = np.array([[1, 2, 3]]).astype('float32')
......@@ -410,7 +410,7 @@ class TestLayer(LayerTest):
dy_rlt = btp(base.to_variable(inp_np_x), base.to_variable(inp_np_y))
self.assertTrue(np.allclose(static_rlt2, static_rlt))
self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
self.assertTrue(np.allclose(dy_rlt.numpy(), static_rlt))
def test_prelu(self):
inp_np = np.ones([5, 200, 100, 100]).astype('float32')
......@@ -451,7 +451,7 @@ class TestLayer(LayerTest):
dy_rlt = prelu(base.to_variable(inp_np))
self.assertTrue(np.allclose(static_rlt2, static_rlt))
self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
self.assertTrue(np.allclose(dy_rlt.numpy(), static_rlt))
def test_embeding(self):
inp_word = np.array([[[1]]]).astype('int64')
......@@ -484,7 +484,7 @@ class TestLayer(LayerTest):
static_rlt3 = emb2(base.to_variable(inp_word))
self.assertTrue(np.allclose(static_rlt2, static_rlt))
self.assertTrue(np.allclose(static_rlt3._numpy(), static_rlt))
self.assertTrue(np.allclose(static_rlt3.numpy(), static_rlt))
def test_nce(self):
window_size = 5
......@@ -598,7 +598,7 @@ class TestLayer(LayerTest):
nce_loss3 = nce(embs3, words[label_word])
self.assertTrue(np.allclose(static_rlt2, static_rlt))
self.assertTrue(np.allclose(nce_loss3._numpy(), static_rlt))
self.assertTrue(np.allclose(nce_loss3.numpy(), static_rlt))
def test_conv3d(self):
with self.static_graph():
......@@ -625,7 +625,7 @@ class TestLayer(LayerTest):
conv3d = nn.Conv3D('conv3d', num_filters=3, filter_size=2)
dy_ret = conv3d(base.to_variable(images))
self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
self.assertTrue(np.allclose(static_ret, static_ret2))
def test_row_conv(self):
......@@ -719,7 +719,7 @@ class TestLayer(LayerTest):
groupNorm = nn.GroupNorm('GroupNorm', groups=2)
dy_ret = groupNorm(base.to_variable(input))
self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
self.assertTrue(np.allclose(static_ret, static_ret2))
def test_spectral_norm(self):
......@@ -769,7 +769,7 @@ class TestLayer(LayerTest):
spectralNorm = nn.SpectralNorm('SpectralNorm', dim=1, power_iters=2)
dy_ret = spectralNorm(base.to_variable(input))
self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
self.assertTrue(np.allclose(static_ret, static_ret2))
def test_tree_conv(self):
......@@ -842,7 +842,7 @@ class TestLayer(LayerTest):
dy_ret = treeConv(base.to_variable(vectors), base.to_variable(adj))
self.assertTrue(np.allclose(static_ret, static_ret2))
self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
self.assertTrue(np.allclose(static_ret, dy_ret.numpy()))
def test_conv3d_transpose(self):
input_array = np.arange(0, 48).reshape(
......@@ -872,7 +872,7 @@ class TestLayer(LayerTest):
use_cudnn=False)
dy_rlt = conv3d_transpose(base.to_variable(input_array))
self.assertTrue(np.allclose(static_rlt2, static_rlt))
self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
self.assertTrue(np.allclose(dy_rlt.numpy(), static_rlt))
class TestBook(LayerTest):
......@@ -907,7 +907,7 @@ class TestBook(LayerTest):
if isinstance(dy_result, tuple):
dy_result = dy_result[0]
self.assertTrue(np.array_equal(static_result[0], dy_result._numpy()))
self.assertTrue(np.array_equal(static_result[0], dy_result.numpy()))
def _get_np_data(self, shape, dtype, append_batch_size=True):
np.random.seed(self.seed)
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或