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Paddle
未验证 提交 86e990d4 编写于 作者: Z zqw_1997 提交者： GitHub
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Remove paddle.fluid.layers.utils.* (#51033) 

* move fluid.utils to paddle.utils.layers_utils

* fix error

* delete original fluid layers utils

* remove import and old utils

* remove more old utils import

* change import path of fill_constant in the layers_utils.py

* fix mistake

* fix error

* expose in __init__.py

* for comment

* when change the ref of func is_sequence, it should change to the root of is_sequence instead

* for codecheck
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Showing with 433 addition and 385 deletion
python/paddle/common_ops_import.py
浏览文件 @ 86e990d4
......@@ -33,7 +33,7 @@ from paddle.fluid.framework import ( # noqa: F401
in_dygraph_mode,
)
from paddle.fluid.layer_helper import LayerHelper # noqa: F401
from paddle.fluid.layers import fill_constant, utils # noqa: F401
from paddle.fluid.layers import fill_constant # noqa: F401
from paddle.fluid.layers.layer_function_generator import ( # noqa: F401
templatedoc,
)
......
python/paddle/device/cuda/graphs.py
浏览文件 @ 86e990d4
......@@ -22,7 +22,6 @@ from paddle.fluid.core import (
is_compiled_with_cuda,
is_compiled_with_rocm,
)
from paddle.fluid.layers.utils import _hash_with_id
if is_compiled_with_cuda() and not is_compiled_with_rocm():
from paddle.fluid.core import CUDAGraph as CoreCUDAGraph
......@@ -395,7 +394,7 @@ def replace_cuda_graph_section(
stop_gradient=True,
)
program_id = _hash_with_id(section_program, ins_and_outs)
program_id = paddle.utils._hash_with_id(section_program, ins_and_outs)
# insert the run_program_op into the block
origin_block._insert_op(
......
python/paddle/distributed/auto_parallel/engine.py
浏览文件 @ 86e990d4
......@@ -27,7 +27,6 @@ import paddle.utils as utils
from paddle import static
from paddle.distributed import fleet
from paddle.fluid.executor import _to_name_str
from paddle.fluid.layers.utils import flatten
from paddle.framework import IrGraph
from paddle.framework import _current_expected_place as _get_device
from paddle.framework import core, in_dygraph_mode
......@@ -602,7 +601,7 @@ class Engine:
feed_vars = {"inputs": self._inputs, "labels": self._labels}
fetch_vars = {
"outputs": flatten(outputs),
"outputs": paddle.utils.flatten(outputs),
"loss": self._losses,
"metrics": metrics,
}
......
python/paddle/distributed/auto_parallel/reshard.py
浏览文件 @ 86e990d4
......@@ -15,7 +15,6 @@
from functools import reduce
import paddle
import paddle.fluid.layers.utils as utils
from paddle.distributed.fleet.meta_optimizers.common import OpRole
from paddle.framework import LayerHelper, OpProtoHolder, Program, core
from paddle.utils import unique_name
......@@ -589,7 +588,7 @@ class Inserter:
attrs['value'] = int("1")
attrs['dtype'] = out.dtype
attrs['op_role'] = op_role
utils.get_shape_tensor_inputs(
paddle.utils.get_shape_tensor_inputs(
inputs=inputs, attrs=attrs, shape=[0], op_type='fill_constant'
)
fillconstant_op = block._insert_op(
......
python/paddle/fluid/contrib/layers/nn.py
浏览文件 @ 86e990d4
......@@ -22,7 +22,6 @@ import inspect
import numpy as np
import paddle
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers import utils
from ... import unique_name
from paddle.fluid.data_feeder import (
check_variable_and_dtype,
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 86e990d4
......@@ -25,12 +25,11 @@ from ..framework import (
in_dygraph_mode,
)
from ..layer_helper import LayerHelper, unique_name
from .utils import (
from ...utils import (
assert_same_structure,
map_structure,
hold_mutable_vars,
copy_mutable_vars,
padding_to_same_structure,
is_sequence,
pack_sequence_as,
flatten,
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 86e990d4
......@@ -42,7 +42,6 @@ from .layer_function_generator import (
_generate_doc_string_,
)
from .tensor import fill_constant, zeros
from . import utils
from .. import unique_name
from .. import core
from ...utils import deprecated
......@@ -735,8 +734,10 @@ def unsqueeze(input, axes, name=None):
axes.stop_gradient = True
inputs["AxesTensor"] = axes
elif isinstance(axes, (list, tuple)):
if utils._contain_var(axes):
inputs["AxesTensorList"] = utils._convert_to_tensor_list(axes)
if paddle.utils._contain_var(axes):
inputs["AxesTensorList"] = paddle.utils._convert_to_tensor_list(
axes
)
else:
attrs["axes"] = axes
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 86e990d4
......@@ -27,7 +27,6 @@ from ..framework import Variable
from ..core import VarDesc
from .. import core
from .layer_function_generator import templatedoc
from . import utils
from ..data_feeder import (
check_variable_and_dtype,
check_type,
......@@ -36,7 +35,6 @@ from ..data_feeder import (
)
from paddle.utils import deprecated
from .utils import check_shape
from paddle import _C_ops, _legacy_C_ops
__all__ = [
......@@ -99,7 +97,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
if force_cpu:
place = core.CPUPlace()
if isinstance(shape, (list, tuple)):
shape = utils.convert_shape_to_list(shape)
shape = paddle.utils.convert_shape_to_list(shape)
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
......@@ -132,7 +130,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
value = paddle.cast(value, dtype)
inputs['ValueTensor'] = value
check_shape(shape)
paddle.utils.check_shape(shape)
check_dtype(
dtype,
'dtype',
......@@ -159,7 +157,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
)
helper = LayerHelper("fill_constant", **locals())
utils.get_shape_tensor_inputs(
paddle.utils.get_shape_tensor_inputs(
inputs=inputs, attrs=attrs, shape=shape, op_type='fill_constant'
)
......
python/paddle/fluid/tests/unittests/autograd/test_jvp_and_transpose.py
浏览文件 @ 86e990d4
......@@ -16,7 +16,6 @@ import unittest
import paddle
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers.utils import flatten
from paddle.incubate.autograd.primrules import _jvp, _transpose
paddle.enable_static()
......@@ -79,14 +78,14 @@ class TestAddPJVPAndTranspose(unittest.TestCase):
)
jvp_out = _jvp(op, *self.jvp_args)
jvp_out = flatten(jvp_out)
jvp_out = paddle.utils.flatten(jvp_out)
for k, v in self.jvp_out_shape_map.items():
self.assertEqual(jvp_out[k].shape, v.shape)
# Some prim ops dont have transpose rule
if hasattr(self, 'transpose_args'):
transpose_out = _transpose(op, *self.transpose_args)
transpose_out = flatten(transpose_out)
transpose_out = paddle.utils.flatten(transpose_out)
for k, v in self.transpose_out_shape_map.items():
self.assertEqual(transpose_out[k].shape, v.shape)
......
python/paddle/fluid/tests/unittests/autograd/test_orig2prim.py
浏览文件 @ 86e990d4
......@@ -16,7 +16,6 @@ import unittest
import paddle
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers.utils import flatten
from paddle.incubate.autograd.primrules import _orig2prim
paddle.enable_static()
......@@ -67,7 +66,7 @@ class TestElementWiseAddOrig2Prim(unittest.TestCase):
all_ops = [op.type for op in self.main_program.block(0).ops]
self.assertEqual(sorted(all_ops), sorted(self.all_ops))
prim_out = flatten(prim_out)
prim_out = paddle.utils.flatten(prim_out)
for k, v in self.out_map.items():
self.assertEqual(prim_out[k].shape, v.shape)
......
python/paddle/fluid/tests/unittests/autograd/test_prim2orig.py
浏览文件 @ 86e990d4
......@@ -16,7 +16,6 @@ import unittest
import paddle
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers.utils import flatten
from paddle.incubate.autograd.primrules import _prim2orig
paddle.enable_static()
......@@ -66,7 +65,7 @@ class TestAddPPrim2Orig(unittest.TestCase):
orig_out = _prim2orig(op, *self.prim2orig_args)
all_ops = [op.type for op in self.main_program.block(0).ops]
self.assertEqual(sorted(all_ops), sorted(self.all_ops))
orig_out = flatten(orig_out)
orig_out = paddle.utils.flatten(orig_out)
for k, v in self.out_map.items():
self.assertEqual(k.shape, orig_out[v].shape)
......
python/paddle/fluid/tests/unittests/autograd/test_transform.py
浏览文件 @ 86e990d4
......@@ -15,7 +15,6 @@
import unittest
import paddle
from paddle.fluid.layers.utils import flatten
from paddle.incubate.autograd.primx import Transform, orig2prim, prim2orig
paddle.enable_static()
......@@ -157,10 +156,10 @@ class TestAutoGradTransformForAdd(unittest.TestCase):
xs_dot, ys_dot = ad.linearize(self.orig_xs, self.orig_ys)
linearize_ops = [op.type for op in self.main_program.block(0).ops]
self.assertEqual(sorted(linearize_ops), sorted(self.linearize_ops))
flatten_xs_dot = flatten(xs_dot)
flatten_xs_dot = paddle.utils.flatten(xs_dot)
for k, v in self.xs_shape_map.items():
self.assertEqual(flatten_xs_dot[k].shape, v)
flatten_ys_dot = flatten(ys_dot)
flatten_ys_dot = paddle.utils.flatten(ys_dot)
for k, v in self.ys_shape_map.items():
self.assertEqual(flatten_ys_dot[k].shape, v)
......@@ -168,12 +167,12 @@ class TestAutoGradTransformForAdd(unittest.TestCase):
ys_bar, xs_bar = ad.transpose(ys_dot, xs_dot, retain_fwd=False)
transpose_ops = [op.type for op in self.main_program.block(0).ops]
self.assertEqual(sorted(transpose_ops), sorted(self.transpose_ops))
flatten_xs_bar = flatten(xs_bar)
flatten_xs_bar = paddle.utils.flatten(xs_bar)
for k, v in self.xs_shape_map.items():
# There may be None in the result of transpose like gather op
if flatten_xs_bar[k] is not None:
self.assertEqual(flatten_xs_bar[k].shape, v)
flatten_ys_bar = flatten(ys_bar)
flatten_ys_bar = paddle.utils.flatten(ys_bar)
for k, v in self.ys_shape_map.items():
self.assertEqual(flatten_ys_bar[k].shape, v)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_list.py
浏览文件 @ 86e990d4
......@@ -19,7 +19,6 @@ import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.layers.utils import map_structure
SEED = 2020
np.random.seed(SEED)
......@@ -231,7 +230,7 @@ class TestListWithoutControlFlow(unittest.TestCase):
def varbase_to_numpy(self, res):
if isinstance(res, (list, tuple)):
res = map_structure(lambda x: x.numpy(), res)
res = paddle.utils.map_structure(lambda x: x.numpy(), res)
else:
res = [res.numpy()]
return res
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_partial_program.py
浏览文件 @ 86e990d4
......@@ -19,7 +19,6 @@ from test_fetch_feed import Linear
import paddle
import paddle.fluid as fluid
from paddle.fluid.layers.utils import flatten
from paddle.jit.api import to_static
SEED = 2020
......@@ -109,10 +108,10 @@ class TestWithNestedOutput(unittest.TestCase):
def test_nest(self):
dygraph_res = self._run(to_static=False)
dygraph_res = flatten(dygraph_res)
dygraph_res = paddle.utils.flatten(dygraph_res)
static_res = self._run(to_static=True)
static_res = flatten(static_res)
static_res = paddle.utils.flatten(static_res)
self.assertTrue(len(dygraph_res) == len(static_res))
......
python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py
浏览文件 @ 86e990d4
......@@ -19,7 +19,6 @@ import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.nn.functional as F
from paddle.fluid.dygraph import Layer, to_variable
from paddle.fluid.layers.utils import map_structure
from paddle.jit.api import dygraph_to_static_func
from paddle.nn import Linear
......@@ -857,13 +856,13 @@ class Transformer(Layer):
trg_pos = layers.fill_constant(
shape=trg_word.shape, dtype="int64", value=i
)
caches = map_structure(
caches = paddle.utils.map_structure(
merge_batch_beams, caches
) # TODO: modified for dygraph2static
logits = self.decoder(
trg_word, trg_pos, None, trg_src_attn_bias, enc_output, caches
)
caches = map_structure(split_batch_beams, caches)
caches = paddle.utils.map_structure(split_batch_beams, caches)
step_log_probs = split_batch_beams(
paddle.log(paddle.nn.functional.softmax(logits))
)
......@@ -883,7 +882,7 @@ class Transformer(Layer):
token_indices = paddle.remainder(topk_indices, vocab_size_tensor)
# update states
caches = map_structure(
caches = paddle.utils.map_structure(
lambda x: gather(x, beam_indices, batch_pos), caches
)
log_probs = gather(log_probs, topk_indices, batch_pos)
......
python/paddle/fluid/tests/unittests/eager_op_test.py
浏览文件 @ 86e990d4
......@@ -2310,7 +2310,9 @@ class OpTest(unittest.TestCase):
if in_dygraph_mode():
core.eager.run_backward(
fluid.layers.utils.flatten(outputs), grad_outputs, False
paddle.utils.flatten(outputs),
grad_outputs,
False,
)
grad_inputs = []
for inputs_list in inputs.values():
......@@ -2319,8 +2321,8 @@ class OpTest(unittest.TestCase):
return grad_inputs
else:
grad_inputs = paddle.grad(
outputs=fluid.layers.utils.flatten(outputs),
inputs=fluid.layers.utils.flatten(inputs),
outputs=paddle.utils.flatten(outputs),
inputs=paddle.utils.flatten(inputs),
grad_outputs=grad_outputs,
)
return [grad.numpy() for grad in grad_inputs]
......
python/paddle/fluid/tests/unittests/npu/test_run_program_op_npu.py
浏览文件 @ 86e990d4
......@@ -24,7 +24,6 @@ import paddle
from paddle import _C_ops, _legacy_C_ops
import paddle.fluid as fluid
from paddle.fluid import core, framework, executor
from paddle.fluid.layers.utils import _hash_with_id
from paddle.fluid.framework import global_var
paddle.enable_static()
......@@ -103,7 +102,7 @@ class RunProgramNPUOpTest(unittest.TestCase):
'end_op_index',
self.fwd_op_num,
'program_id',
_hash_with_id(self.program_desc, self),
paddle.utils._hash_with_id(self.program_desc, self),
)
def get_param_grad_names(self):
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 86e990d4
......@@ -2618,7 +2618,9 @@ class OpTest(unittest.TestCase):
if in_dygraph_mode():
core.eager.run_backward(
fluid.layers.utils.flatten(outputs), grad_outputs, False
paddle.utils.flatten(outputs),
grad_outputs,
False,
)
grad_inputs = []
for inputs_list in inputs.values():
......@@ -2627,8 +2629,8 @@ class OpTest(unittest.TestCase):
return grad_inputs
else:
grad_inputs = paddle.grad(
outputs=fluid.layers.utils.flatten(outputs),
inputs=fluid.layers.utils.flatten(inputs),
outputs=paddle.utils.flatten(outputs),
inputs=paddle.utils.flatten(inputs),
grad_outputs=grad_outputs,
)
return [grad.numpy() for grad in grad_inputs]
......
python/paddle/fluid/tests/unittests/prim_op_test.py
浏览文件 @ 86e990d4
......@@ -22,7 +22,6 @@ import numpy as np
import paddle
import paddle.fluid.core as core
from paddle.fluid.framework import _dygraph_tracer, in_dygraph_mode
from paddle.fluid.layers.utils import map_structure
from paddle.jit.dy2static.utils import parse_arg_and_kwargs
......@@ -430,9 +429,11 @@ class PrimForwardChecker:
args, len(inputs_sig)
)
ret = flatten(_as_list(self.python_api(*args)))
ret = map_structure(lambda x: x.numpy(), ret)
ret = paddle.utils.map_structure(lambda x: x.numpy(), ret)
if OpTestUtils.is_bfloat16_type(self.dtype):
ret = map_structure(lambda x: convert_uint16_to_float(x), ret)
ret = paddle.utils.map_structure(
lambda x: convert_uint16_to_float(x), ret
)
return ret
def get_eager_input_attr_and_inputdict(self, stop_gradient):
......@@ -577,7 +578,9 @@ class PrimForwardChecker:
exe.run(startup_program)
ret = exe.run(main_program, feed=feed, fetch_list=ret)
if OpTestUtils.is_bfloat16_type(self.dtype):
ret = map_structure(lambda x: convert_uint16_to_float(x), ret)
ret = paddle.utils.map_structure(
lambda x: convert_uint16_to_float(x), ret
)
# check static forward
if len(ret) != len(self.eager_desire):
msg = (
......@@ -640,9 +643,11 @@ class PrimForwardChecker:
net = PrimNet(self.python_api)
net = apply_to_static(net, False)
ret = flatten(_as_list(net(args)))
ret = map_structure(lambda x: x.numpy(), ret)
ret = paddle.utils.map_structure(lambda x: x.numpy(), ret)
if OpTestUtils.is_bfloat16_type(self.dtype):
ret = map_structure(lambda x: convert_uint16_to_float(x), ret)
ret = paddle.utils.map_structure(
lambda x: convert_uint16_to_float(x), ret
)
# check jit comp forward
if len(ret) != len(self.eager_desire):
msg = (
......@@ -719,9 +724,11 @@ class PrimForwardChecker:
net, core.is_compiled_with_cinn() and self.enable_cinn
)
ret = flatten(_as_list(net(args)))
ret = map_structure(lambda x: x.numpy(), ret)
ret = paddle.utils.map_structure(lambda x: x.numpy(), ret)
if OpTestUtils.is_bfloat16_type(self.dtype):
ret = map_structure(lambda x: convert_uint16_to_float(x), ret)
ret = paddle.utils.map_structure(
lambda x: convert_uint16_to_float(x), ret
)
# check jit comp forward
if len(ret) != len(self.eager_desire):
msg = (
......@@ -895,9 +902,11 @@ class PrimGradChecker(PrimForwardChecker):
ret = paddle.grad(
ys, xs, vs, allow_unused=True, no_grad_vars=no_grad_vars
)
ret = map_structure(lambda x: x.numpy(), ret)
ret = paddle.utils.map_structure(lambda x: x.numpy(), ret)
if OpTestUtils.is_bfloat16_type(self.dtype):
ret = map_structure(lambda x: convert_uint16_to_float(x), ret)
ret = paddle.utils.map_structure(
lambda x: convert_uint16_to_float(x), ret
)
return ret
def check_eager_comp(self):
......@@ -1005,7 +1014,7 @@ class PrimGradChecker(PrimForwardChecker):
exe.run(startup_program)
actual_ret = exe.run(main_program, feed=feed, fetch_list=ret)
if OpTestUtils.is_bfloat16_type(self.dtype):
actual_ret = map_structure(
actual_ret = paddle.utils.map_structure(
lambda x: convert_uint16_to_float(x), actual_ret
)
# check static grad out
......@@ -1104,9 +1113,11 @@ class PrimGradChecker(PrimForwardChecker):
ret = paddle.grad(
ys, xs, vs, allow_unused=True, no_grad_vars=no_grad_vars
)
ret = map_structure(lambda x: x.numpy(), ret)
ret = paddle.utils.map_structure(lambda x: x.numpy(), ret)
if OpTestUtils.is_bfloat16_type(self.dtype):
ret = map_structure(lambda x: convert_uint16_to_float(x), ret)
ret = paddle.utils.map_structure(
lambda x: convert_uint16_to_float(x), ret
)
# check jit comp grad out
if len(ret) != len(self.eager_desire):
msg = (
......@@ -1216,9 +1227,11 @@ class PrimGradChecker(PrimForwardChecker):
ret = paddle.grad(
ys, xs, vs, allow_unused=True, no_grad_vars=no_grad_vars
)
ret = map_structure(lambda x: x.numpy(), ret)
ret = paddle.utils.map_structure(lambda x: x.numpy(), ret)
if OpTestUtils.is_bfloat16_type(self.dtype):
ret = map_structure(lambda x: convert_uint16_to_float(x), ret)
ret = paddle.utils.map_structure(
lambda x: convert_uint16_to_float(x), ret
)
# check jit comp grad out
if len(ret) != len(self.eager_desire):
msg = (
......
python/paddle/fluid/tests/unittests/test_conv2d_layer.py
浏览文件 @ 86e990d4
......@@ -53,7 +53,7 @@ class Conv2DTestCase(unittest.TestCase):
self.padding = padding
if padding_mode in {'reflect', 'replicate', 'circular'}:
_paired_padding = fluid.layers.utils.convert_to_list(
_paired_padding = paddle.utils.convert_to_list(
padding, 2, 'padding'
)
self._reversed_padding_repeated_twice = _reverse_repeat_list(
......
python/paddle/fluid/tests/unittests/test_eager_run_program.py
浏览文件 @ 86e990d4
......@@ -25,7 +25,6 @@ from paddle.fluid.executor import (
_is_enable_standalone_executor,
)
from paddle.fluid.framework import Variable
from paddle.fluid.layers.utils import _hash_with_id
def _append_backward_desc(main_program, outs):
......@@ -134,7 +133,7 @@ class TestRunProgram(unittest.TestCase):
'is_test',
False,
'program_id',
_hash_with_id(program),
paddle.utils._hash_with_id(program),
'param_grad_names',
['Fake_var@GRAD'],
'out_grad_names',
......
python/paddle/fluid/tests/unittests/test_get_inputs_outputs_in_block.py
浏览文件 @ 86e990d4
......@@ -17,7 +17,6 @@ import unittest
import numpy as np
import paddle
from paddle.fluid.layers import utils
paddle.enable_static()
......@@ -49,9 +48,10 @@ class TestGetInputsOutputsInBlock(unittest.TestCase):
i = paddle.static.nn.while_loop(while_cond, while_body, [i])
sub_block = main_program.block(1)
inner_inputs, inner_outputs = utils.get_inputs_outputs_in_block(
sub_block
)
(
inner_inputs,
inner_outputs,
) = paddle.utils.get_inputs_outputs_in_block(sub_block)
# 'assign_0.tmp_0', 'assign_1.tmp_0' are name of i and ten in program
self.assertTrue(inner_inputs == {'assign_0.tmp_0', 'assign_1.tmp_0'})
# 'tmp_0', 'assign_0.tmp_0' are name of i < ten and i in program
......@@ -67,9 +67,10 @@ class TestGetInputsOutputsInBlock(unittest.TestCase):
out = paddle.static.nn.cond(a < b, lambda: a + c, lambda: b * b)
sub_block = main_program.block(1)
inner_inputs, inner_outputs = utils.get_inputs_outputs_in_block(
sub_block
)
(
inner_inputs,
inner_outputs,
) = paddle.utils.get_inputs_outputs_in_block(sub_block)
# 'fill_constant_1.tmp_0', 'tmp_3' are names of a, c
self.assertTrue(inner_inputs == {'fill_constant_1.tmp_0', 'tmp_3'})
# '_generated_var_1', is name of a + c
......
python/paddle/fluid/tests/unittests/test_jit_save_load.py
浏览文件 @ 86e990d4
......@@ -24,7 +24,6 @@ import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid import unique_name
from paddle.fluid.layers.utils import flatten
from paddle.jit.api import to_static
from paddle.jit.translated_layer import INFER_PARAMS_INFO_SUFFIX
from paddle.nn import Linear
......@@ -455,14 +454,14 @@ class TestSaveLoadWithNestOut(unittest.TestCase):
)
net = LinearNetWithNestOut(8, 8)
dy_outs = flatten(net(x))
dy_outs = paddle.utils.flatten(net(x))
net = to_static(net, input_spec=[InputSpec([None, 8], name='x')])
model_path = os.path.join(self.temp_dir.name, "net_with_nest_out/model")
paddle.jit.save(net, model_path)
load_net = paddle.jit.load(model_path)
load_outs = flatten(load_net(x))
load_outs = paddle.utils.flatten(load_net(x))
self.assertTrue(len(dy_outs) == 4)
for dy_out, load_out in zip(dy_outs, load_outs):
......
python/paddle/fluid/tests/unittests/test_rnn_cell_api.py
浏览文件 @ 86e990d4
......@@ -22,7 +22,6 @@ from rnn.rnn_numpy import rnn as numpy_rnn
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.layers.utils as utils
from paddle.fluid import framework
from paddle.fluid.executor import Executor
from paddle.fluid.framework import Program, program_guard
......@@ -226,11 +225,11 @@ class TestRnnUtil(unittest.TestCase):
def test_case(self):
inputs = {"key1": 1, "key2": 2}
func = lambda x: x + 1
outputs = utils.map_structure(func, inputs)
utils.assert_same_structure(inputs, outputs)
outputs = paddle.utils.map_structure(func, inputs)
paddle.utils.assert_same_structure(inputs, outputs)
try:
inputs["key3"] = 3
utils.assert_same_structure(inputs, outputs)
paddle.utils.assert_same_structure(inputs, outputs)
except ValueError as identifier:
pass
......
python/paddle/fluid/tests/unittests/test_rnn_decode_api.py
浏览文件 @ 86e990d4
......@@ -24,7 +24,6 @@ import paddle.fluid.layers as layers
import paddle.nn as nn
from paddle import Model, set_device
from paddle.fluid.data_feeder import convert_dtype
from paddle.fluid.layers.utils import map_structure
from paddle.nn import (
RNN,
BeamSearchDecoder,
......@@ -510,7 +509,7 @@ class TrainingHelper:
self.inputs = inputs
self.sequence_length = sequence_length
self.time_major = time_major
self.inputs_ = map_structure(
self.inputs_ = paddle.utils.map_structure(
lambda x: paddle.nn.functional.pad(
x,
pad=([0, 1] + [0, 0] * (len(x.shape) - 1))
......@@ -527,7 +526,7 @@ class TrainingHelper:
shape=[1], dtype=self.sequence_length.dtype, fill_value=0
),
)
init_inputs = map_structure(
init_inputs = paddle.utils.map_structure(
lambda x: x[0] if self.time_major else x[:, 0], self.inputs
)
return init_inputs, init_finished
......@@ -556,7 +555,7 @@ class TrainingHelper:
axis=axes,
)
next_inputs = map_structure(_slice, self.inputs_)
next_inputs = paddle.utils.map_structure(_slice, self.inputs_)
return finished, next_inputs, states
......
python/paddle/fluid/tests/unittests/test_run_program_op.py
浏览文件 @ 86e990d4
......@@ -27,7 +27,6 @@ from paddle.fluid.executor import (
_is_enable_standalone_executor,
)
from paddle.fluid.framework import global_var
from paddle.fluid.layers.utils import _hash_with_id
paddle.enable_static()
......@@ -145,7 +144,7 @@ class RunProgramOpTest(unittest.TestCase):
'end_op_index',
self.fwd_op_num,
'program_id',
_hash_with_id(self.program_desc, self),
paddle.utils._hash_with_id(self.program_desc, self),
]
def get_param_grad_names(self):
......
python/paddle/fluid/tests/unittests/test_static_shape_inferrence_for_shape_tensor.py
浏览文件 @ 86e990d4
......@@ -15,7 +15,6 @@
import unittest
import paddle
from paddle.fluid.layers.utils import try_set_static_shape_tensor
class StaticShapeInferrenceTest(unittest.TestCase):
......@@ -24,7 +23,7 @@ class StaticShapeInferrenceTest(unittest.TestCase):
data = paddle.static.data(name="x", shape=[-1, 2], dtype='float32')
shape = paddle.shape(data) # shape should be [-1, 2]
x = paddle.uniform(shape)
try_set_static_shape_tensor(x, shape)
paddle.utils.try_set_static_shape_tensor(x, shape)
self.assertEqual(x.shape, data.shape)
paddle.disable_static()
......
python/paddle/fluid/variable_index.py
浏览文件 @ 86e990d4
......@@ -297,10 +297,9 @@ def is_bool_tensor(ele):
def deal_attrs(attrs, attr, attr_name, tensor_attr_name, inputs, infer_flags):
from .framework import Variable
from .layers import utils
if utils._contain_var(attr):
inputs[tensor_attr_name] = utils._convert_to_tensor_list(
if paddle.utils._contain_var(attr):
inputs[tensor_attr_name] = paddle.utils._convert_to_tensor_list(
attr, dtype="int64"
)
for i, dim in enumerate(attr):
......@@ -763,16 +762,16 @@ def _setitem_impl_(var, item, value):
'none_axes': none_axes,
}
from .layers import utils
if utils._contain_var(starts):
inputs['StartsTensorList'] = utils._convert_to_tensor_list(starts)
if paddle.utils._contain_var(starts):
inputs['StartsTensorList'] = paddle.utils._convert_to_tensor_list(
starts
)
del attrs['starts']
if utils._contain_var(ends):
inputs['EndsTensorList'] = utils._convert_to_tensor_list(ends)
if paddle.utils._contain_var(ends):
inputs['EndsTensorList'] = paddle.utils._convert_to_tensor_list(ends)
del attrs['ends']
if utils._contain_var(steps):
inputs['StepsTensorList'] = utils._convert_to_tensor_list(steps)
if paddle.utils._contain_var(steps):
inputs['StepsTensorList'] = paddle.utils._convert_to_tensor_list(steps)
del attrs['steps']
# 2. Parse value
......
python/paddle/hapi/model.py
浏览文件 @ 86e990d4
......@@ -35,7 +35,6 @@ from paddle.fluid.framework import Variable
from paddle.fluid.framework import _current_expected_place as _get_device
from paddle.fluid.framework import _get_paddle_place, _non_static_mode
from paddle.fluid.layers import collective
from paddle.fluid.layers.utils import flatten
from paddle.framework.io_utils import is_belong_to_optimizer
from paddle.io import DataLoader, Dataset, DistributedBatchSampler
from paddle.jit.translated_layer import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
......@@ -2293,7 +2292,7 @@ class Model:
# 4. custumed iterator yield separated inputs and labels:
# ([input1, input2, ...], [label1, lable2, ...])
# To handle all of these, flatten (nested) list to list.
data = flatten(data)
data = paddle.utils.flatten(data)
# LoDTensor.shape is callable, where LoDTensor comes from
# DataLoader in static graph
......
python/paddle/incubate/operators/resnet_unit.py
浏览文件 @ 86e990d4
......@@ -14,9 +14,9 @@
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers import utils
from paddle.fluid.param_attr import ParamAttr
from paddle.nn import Layer
from paddle.nn import initializer as I
......@@ -184,7 +184,9 @@ class ResNetUnit(Layer):
self._stride = stride
self._stride_z = stride_z
self._dilation = 1
self._kernel_size = utils.convert_to_list(filter_size, 2, 'kernel_size')
self._kernel_size = paddle.utils.convert_to_list(
filter_size, 2, 'kernel_size'
)
self._padding = (filter_size - 1) // 2
self._groups = 1
self._momentum = momentum
......
python/paddle/incubate/xpu/resnet_block.py
浏览文件 @ 86e990d4
......@@ -14,10 +14,10 @@
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle import _legacy_C_ops
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers import utils
from paddle.fluid.param_attr import ParamAttr
from paddle.nn import Layer
from paddle.nn import initializer as I
......@@ -478,10 +478,10 @@ class ResNetBasicBlock(Layer):
super().__init__()
self._stride1 = stride1
self._stride2 = stride2
self._kernel1_size = utils.convert_to_list(
self._kernel1_size = paddle.utils.convert_to_list(
filter1_size, 2, 'filter1_size'
)
self._kernel2_size = utils.convert_to_list(
self._kernel2_size = paddle.utils.convert_to_list(
filter2_size, 2, 'filter2_size'
)
self._dilation1 = dilation1
......@@ -500,7 +500,7 @@ class ResNetBasicBlock(Layer):
self._find_conv_max = find_conv_max
if has_shortcut:
self._kernel3_size = utils.convert_to_list(
self._kernel3_size = paddle.utils.convert_to_list(
filter3_size, 2, 'filter3_size'
)
self._padding3 = padding3
......
python/paddle/jit/api.py
浏览文件 @ 86e990d4
......@@ -34,7 +34,6 @@ from paddle.fluid.compiler import (
ExecutionStrategy,
)
from paddle.fluid.data_feeder import check_type
from paddle.fluid.layers.utils import flatten, pack_sequence_as
from paddle.fluid.dygraph.base import (
program_desc_tracing_guard,
switch_to_static_graph,
......@@ -509,7 +508,9 @@ def _get_input_var_names(inputs, input_spec):
)
result_list = []
input_var_names = [
var.name for var in flatten(inputs) if isinstance(var, Variable)
var.name
for var in paddle.utils.flatten(inputs)
if isinstance(var, Variable)
]
if input_spec is None:
# no prune
......@@ -562,7 +563,7 @@ def _get_output_vars(outputs, output_spec, with_hook=False):
)
result_list = []
output_vars_dict = OrderedDict()
for var in flatten(outputs):
for var in paddle.utils.flatten(outputs):
if isinstance(var, Variable):
output_vars_dict[var.name] = var
if output_spec is None:
......@@ -970,7 +971,7 @@ def save(layer, path, input_spec=None, **configs):
% type(input_spec)
)
inner_input_spec = []
for var in flatten(input_spec):
for var in paddle.utils.flatten(input_spec):
if isinstance(var, paddle.static.InputSpec):
inner_input_spec.append(var)
elif isinstance(var, (core.VarBase, core.eager.Tensor, Variable)):
......@@ -1034,7 +1035,7 @@ def save(layer, path, input_spec=None, **configs):
# inner_input_spec is list[InputSpec], it should be packed with same structure
# as original input_spec here.
if inner_input_spec:
inner_input_spec = pack_sequence_as(
inner_input_spec = paddle.utils.pack_sequence_as(
input_spec, inner_input_spec
)
static_forward = to_static(
......@@ -1067,7 +1068,7 @@ def save(layer, path, input_spec=None, **configs):
)
else:
if inner_input_spec:
inner_input_spec = pack_sequence_as(
inner_input_spec = paddle.utils.pack_sequence_as(
input_spec, inner_input_spec
)
static_function = to_static(
......
python/paddle/jit/dy2static/convert_operators.py
浏览文件 @ 86e990d4
......@@ -20,7 +20,6 @@ from paddle.fluid.dygraph.base import _convert_into_variable
from paddle.fluid.framework import Variable, core
from paddle.fluid.layers import Print, control_flow, fill_constant
from paddle.fluid.layers.control_flow import while_loop
from paddle.fluid.layers.utils import copy_mutable_vars
from paddle.jit.dy2static.utils import (
Dygraph2StaticException,
GetterSetterHelper,
......@@ -371,7 +370,10 @@ def _run_paddle_cond(
def new_true_fn():
# init args may contain mutable python container like [var, 2], we copy then like in while_loop
helper.set(return_name_ids, copy_mutable_vars(init_args))
helper.set(
return_name_ids,
paddle.utils.copy_mutable_vars(init_args),
)
ret = true_fn()
# IfExpr will return a non-None return value, so we just return ret.
# We assume normal return has no return value.
......@@ -382,7 +384,10 @@ def _run_paddle_cond(
def new_false_fn():
# init args may contain mutable python container like [var, 2], we copy then like in while_loop
helper.set(return_name_ids, copy_mutable_vars(init_args))
helper.set(
return_name_ids,
paddle.utils.copy_mutable_vars(init_args),
)
ret = false_fn()
if ret is None:
return helper.get(return_name_ids)
......
python/paddle/jit/dy2static/function_spec.py
浏览文件 @ 86e990d4
......@@ -21,7 +21,6 @@ import paddle
from paddle.fluid import core
from paddle.fluid.dygraph import layers
from paddle.fluid.dygraph.base import switch_to_static_graph
from paddle.fluid.layers.utils import flatten, pack_sequence_as
from paddle.jit.translated_layer import TranslatedLayer
from . import logging_utils
......@@ -47,7 +46,7 @@ class FunctionSpec:
self._flat_input_spec = None
else:
self._input_spec = self._verify_input_spec(input_spec)
self._flat_input_spec = flatten(self._input_spec)
self._flat_input_spec = paddle.utils.flatten(self._input_spec)
# parse full argument names list.
self._arg_names, self._default_kwargs = parse_arg_and_kwargs(function)
......@@ -171,7 +170,7 @@ class FunctionSpec:
input_with_spec(tuple): input arguments by replacing argument with InputSpec.
main_program(Program): main program for inserting feed layer.
"""
flat_input_spec = flatten(input_with_spec)
flat_input_spec = paddle.utils.flatten(input_with_spec)
inputs = []
block = main_program.global_block()
......@@ -191,7 +190,7 @@ class FunctionSpec:
feed_layer = var_spec
inputs.append(feed_layer)
return pack_sequence_as(input_with_spec, inputs)
return paddle.utils.pack_sequence_as(input_with_spec, inputs)
def _verify_input_spec(self, input_spec):
"""
......@@ -283,7 +282,7 @@ def get_buffers(layer_instance, include_sublayer=True):
def _replace_value_with_input_spec(args):
args_with_spec = []
for idx, input_var in enumerate(flatten(args)):
for idx, input_var in enumerate(paddle.utils.flatten(args)):
if isinstance(input_var, np.ndarray):
input_var = paddle.static.InputSpec.from_numpy(input_var)
input_var.stop_gradient = True
......@@ -299,7 +298,7 @@ def _replace_value_with_input_spec(args):
input_var.stop_gradient = stop_gradient
args_with_spec.append(input_var)
args_with_spec = pack_sequence_as(args, args_with_spec)
args_with_spec = paddle.utils.pack_sequence_as(args, args_with_spec)
return args_with_spec
......@@ -450,12 +449,12 @@ def _hash_spec_names(args_specs, kwargs_specs):
"""
spec_names = [
spec.name
for spec in flatten(args_specs)
for spec in paddle.utils.flatten(args_specs)
if isinstance(spec, paddle.static.InputSpec)
]
spec_names += [
spec.name
for spec in flatten(kwargs_specs)
for spec in paddle.utils.flatten(kwargs_specs)
if isinstance(spec, paddle.static.InputSpec)
]
i, name_ids = 0, {}
......
python/paddle/jit/dy2static/partial_program.py
浏览文件 @ 86e990d4
......@@ -23,7 +23,6 @@ from paddle.fluid.compiler import BuildStrategy
from paddle.fluid.dygraph import layers
from paddle.fluid.dygraph.base import switch_to_static_graph
from paddle.fluid.framework import _apply_pass
from paddle.fluid.layers.utils import _hash_with_id, flatten, pack_sequence_as
from . import logging_utils
from .return_transformer import RETURN_NO_VALUE_MAGIC_NUM
......@@ -48,14 +47,14 @@ class NestSequence:
"""
Flattens the nested sequences into single list.
"""
return flatten(self.__raw_input)
return paddle.utils.flatten(self.__raw_input)
def restore(self, value_list):
"""
Restores the nested sequence from value list.
"""
assert len(self.__input_list) == len(value_list)
return pack_sequence_as(self.__raw_input, value_list)
return paddle.utils.pack_sequence_as(self.__raw_input, value_list)
def _get_var_ids(self):
var_ids = []
......@@ -374,7 +373,7 @@ class PartialProgramLayer:
@LazyInitialized
def _train_program_id(self):
program_id = _hash_with_id(self._train_program, self)
program_id = paddle.utils._hash_with_id(self._train_program, self)
core._set_cached_executor_build_strategy(
program_id, self._build_strategy
)
......@@ -382,11 +381,11 @@ class PartialProgramLayer:
@LazyInitialized
def _infer_program_id(self):
return _hash_with_id(self._infer_program, self)
return paddle.utils._hash_with_id(self._infer_program, self)
@LazyInitialized
def _train_amp_program_id(self):
program_id = _hash_with_id(self._train_amp_program, self)
program_id = paddle.utils._hash_with_id(self._train_amp_program, self)
core._set_cached_executor_build_strategy(
program_id, self._build_strategy
)
......@@ -394,11 +393,13 @@ class PartialProgramLayer:
@LazyInitialized
def _infer_amp_program_id(self):
return _hash_with_id(self._infer_amp_program, self)
return paddle.utils._hash_with_id(self._infer_amp_program, self)
@LazyInitialized
def _train_pure_fp16_program_id(self):
program_id = _hash_with_id(self._train_pure_fp16_program, self)
program_id = paddle.utils._hash_with_id(
self._train_pure_fp16_program, self
)
core._set_cached_executor_build_strategy(
program_id, self._build_strategy
)
......@@ -406,7 +407,7 @@ class PartialProgramLayer:
@LazyInitialized
def _infer_pure_fp16_program_id(self):
return _hash_with_id(self._infer_pure_fp16_program, self)
return paddle.utils._hash_with_id(self._infer_pure_fp16_program, self)
@LazyInitialized
def _param_grad_names(self):
......@@ -854,7 +855,7 @@ class PartialProgramLayer:
"""
assert isinstance(inputs, (tuple, list))
# Flatten inputs with nested structure into single list.
flatten_inputs = flatten(inputs)
flatten_inputs = paddle.utils.flatten(inputs)
# Convert variable into VarBase and feed in training data.
input_vars = []
expected_place = framework._current_expected_place()
......
python/paddle/jit/dy2static/program_translator.py
浏览文件 @ 86e990d4
......@@ -24,8 +24,7 @@ from paddle.fluid import _non_static_mode, core, framework
from paddle.fluid.data_feeder import check_type
from paddle.fluid.dygraph import layers
from paddle.fluid.dygraph.base import param_guard, switch_to_static_graph
from paddle.fluid.layers.utils import flatten
from paddle.utils import gast
from paddle.utils import flatten, gast
from . import error, logging_utils
from .ast_transformer import DygraphToStaticAst
......
python/paddle/jit/dy2static/variable_trans_func.py
浏览文件 @ 86e990d4
......@@ -14,8 +14,7 @@
import paddle
from paddle.fluid.framework import Variable
from paddle.fluid.layers.utils import is_sequence, map_structure
from paddle.utils import gast
from paddle.utils import gast, is_sequence, map_structure
from .utils import UndefinedVar, create_undefined_variable
......
python/paddle/jit/translated_layer.py
浏览文件 @ 86e990d4
......@@ -27,7 +27,6 @@ from paddle.fluid.executor import (
_is_enable_standalone_executor,
)
from paddle.fluid.framework import OpProtoHolder, _non_static_mode
from paddle.fluid.layers.utils import _hash_with_id
from paddle.jit.dy2static.partial_program import (
LazyInitialized,
add_build_strategy_for,
......@@ -1025,7 +1024,7 @@ def _run_dygraph(instance, input, program_holder):
'is_test',
instance._is_test,
'program_id',
_hash_with_id(trace_program, instance),
paddle.utils._hash_with_id(trace_program, instance),
]
if not instance._is_test:
attrs.extend(
......
python/paddle/nn/decode.py
浏览文件 @ 86e990d4
......@@ -23,7 +23,6 @@ from paddle.common_ops_import import default_main_program
from paddle.framework import _non_static_mode
from ..fluid.data_feeder import convert_dtype
from ..fluid.layers.utils import flatten, map_structure
__all__ = []
......@@ -434,7 +433,7 @@ class BeamSearchDecoder(Decoder):
`finished` is a `bool` tensor filled by False with shape `[batch_size, beam_size]`.
"""
self.kinf = 1e9
state = flatten(initial_cell_states)[0]
state = paddle.utils.flatten(initial_cell_states)[0]
self.batch_size = paddle.shape(state)[0]
self.start_token_tensor = paddle.full(
......@@ -444,7 +443,7 @@ class BeamSearchDecoder(Decoder):
shape=[1], dtype="int64", fill_value=self.end_token
)
init_cell_states = map_structure(
init_cell_states = paddle.utils.map_structure(
self._expand_to_beam_size, initial_cell_states
)
init_inputs = paddle.full(
......@@ -542,7 +541,7 @@ class BeamSearchDecoder(Decoder):
topk_indices,
self.batch_size,
)
next_cell_states = map_structure(
next_cell_states = paddle.utils.map_structure(
lambda x: self._gather(x, beam_indices, self.batch_size),
next_cell_states,
)
......@@ -596,13 +595,17 @@ class BeamSearchDecoder(Decoder):
`[batch_size, beam_size]` with data type `float32, int64, int64`. \
`finished` is a `bool` tensor with shape `[batch_size, beam_size]`.
"""
inputs = map_structure(self._merge_batch_beams, inputs)
cell_states = map_structure(self._merge_batch_beams, states.cell_states)
inputs = paddle.utils.map_structure(self._merge_batch_beams, inputs)
cell_states = paddle.utils.map_structure(
self._merge_batch_beams, states.cell_states
)
cell_outputs, next_cell_states = self.cell(
inputs, cell_states, **kwargs
)
cell_outputs = map_structure(self._split_batch_beams, cell_outputs)
next_cell_states = map_structure(
cell_outputs = paddle.utils.map_structure(
self._split_batch_beams, cell_outputs
)
next_cell_states = paddle.utils.map_structure(
self._split_batch_beams, next_cell_states
)
......@@ -729,7 +732,7 @@ def _dynamic_decode_imperative(
),
)
if impute_finished: # rectify the states for the finished.
next_states = map_structure(
next_states = paddle.utils.map_structure(
lambda x, y: _maybe_copy(x, y, finished),
states,
next_states,
......@@ -743,9 +746,9 @@ def _dynamic_decode_imperative(
)
outputs = (
map_structure(lambda x: ArrayWrapper(x), step_outputs)
paddle.utils.map_structure(lambda x: ArrayWrapper(x), step_outputs)
if step_idx == 0
else map_structure(
else paddle.utils.map_structure(
lambda x, x_array: x_array.append(x), step_outputs, outputs
)
)
......@@ -763,7 +766,7 @@ def _dynamic_decode_imperative(
if max_step_num is not None and step_idx > max_step_num:
break
final_outputs = map_structure(
final_outputs = paddle.utils.map_structure(
lambda x: paddle.stack(x.array, axis=0), outputs
)
final_states = states
......@@ -776,7 +779,7 @@ def _dynamic_decode_imperative(
pass
if not output_time_major:
final_outputs = map_structure(
final_outputs = paddle.utils.map_structure(
lambda x: paddle.transpose(
x, [1, 0] + list(range(2, len(x.shape)))
),
......@@ -822,15 +825,15 @@ def _dynamic_decode_declarative(
if is_test:
# for test, reuse inputs and states variables to save memory
inputs = map_structure(lambda x: x, initial_inputs)
states = map_structure(lambda x: x, initial_states)
inputs = paddle.utils.map_structure(lambda x: x, initial_inputs)
states = paddle.utils.map_structure(lambda x: x, initial_states)
else:
# inputs and states of all steps must be saved for backward and training
inputs_arrays = map_structure(
inputs_arrays = paddle.utils.map_structure(
lambda x: paddle.tensor.array.array_write(x, step_idx),
initial_inputs,
)
states_arrays = map_structure(
states_arrays = paddle.utils.map_structure(
lambda x: paddle.tensor.array.array_write(x, step_idx),
initial_states,
)
......@@ -869,11 +872,11 @@ def _dynamic_decode_declarative(
# While
with while_op.block():
if not is_test:
inputs = map_structure(
inputs = paddle.utils.map_structure(
lambda array: paddle.tensor.array.array_read(array, step_idx),
inputs_arrays,
)
states = map_structure(
states = paddle.utils.map_structure(
lambda array: paddle.tensor.array.array_read(array, step_idx),
states_arrays,
)
......@@ -894,7 +897,7 @@ def _dynamic_decode_declarative(
),
)
if impute_finished: # rectify the states for the finished.
next_states = map_structure(
next_states = paddle.utils.map_structure(
lambda x, y: _maybe_copy(x, y, global_finished),
states,
next_states,
......@@ -908,11 +911,11 @@ def _dynamic_decode_declarative(
)
# create tensor array in global block after dtype[s] of outputs can be got
outputs_arrays = map_structure(
outputs_arrays = paddle.utils.map_structure(
lambda x: _create_array_out_of_while(x.dtype), outputs
)
map_structure(
paddle.utils.map_structure(
lambda x, x_array: paddle.tensor.array.array_write(
x, i=step_idx, array=x_array
),
......@@ -925,17 +928,21 @@ def _dynamic_decode_declarative(
paddle.assign(next_finished, global_finished)
paddle.assign(next_sequence_lengths, sequence_lengths)
if is_test:
map_structure(paddle.assign, next_inputs, global_inputs)
map_structure(paddle.assign, next_states, global_states)
paddle.utils.map_structure(
paddle.assign, next_inputs, global_inputs
)
paddle.utils.map_structure(
paddle.assign, next_states, global_states
)
else:
map_structure(
paddle.utils.map_structure(
lambda x, x_array: paddle.tensor.array.array_write(
x, i=step_idx, array=x_array
),
next_inputs,
inputs_arrays,
)
map_structure(
paddle.utils.map_structure(
lambda x, x_array: paddle.tensor.array.array_write(
x, i=step_idx, array=x_array
),
......@@ -951,7 +958,7 @@ def _dynamic_decode_declarative(
else:
paddle.logical_not(paddle.all(global_finished), cond)
final_outputs = map_structure(
final_outputs = paddle.utils.map_structure(
lambda array: paddle.tensor.manipulation.tensor_array_to_tensor(
array, axis=0, use_stack=True
)[0],
......@@ -960,7 +967,7 @@ def _dynamic_decode_declarative(
if is_test:
final_states = global_states
else:
final_states = map_structure(
final_states = paddle.utils.map_structure(
lambda array: paddle.tensor.array.array_read(array, step_idx),
states_arrays,
)
......@@ -973,7 +980,9 @@ def _dynamic_decode_declarative(
pass
if not output_time_major:
final_outputs = map_structure(_transpose_batch_time, final_outputs)
final_outputs = paddle.utils.map_structure(
_transpose_batch_time, final_outputs
)
return (
(final_outputs, final_states, sequence_lengths)
......
python/paddle/nn/functional/conv.py
浏览文件 @ 86e990d4
......@@ -26,14 +26,14 @@ from ...common_ops_import import Variable
from ...device import get_cudnn_version
from ...fluid.data_feeder import check_dtype, check_variable_and_dtype
from ...fluid.layer_helper import LayerHelper
from ...fluid.layers.utils import (
from ...framework import no_grad
from ...tensor.manipulation import squeeze, unsqueeze
from ...utils import (
_contain_var,
_convert_to_tensor_list,
_is_symmetric_padding,
convert_to_list,
)
from ...framework import no_grad
from ...tensor.manipulation import squeeze, unsqueeze
__all__ = []
......
python/paddle/nn/functional/pooling.py
浏览文件 @ 86e990d4
......@@ -16,10 +16,16 @@ from paddle import _C_ops, _legacy_C_ops, in_dynamic_mode
from paddle.fluid.framework import Variable, in_dygraph_mode
from ...fluid.data_feeder import check_type, check_variable_and_dtype
from ...fluid.layers import LayerHelper
from ...tensor.manipulation import squeeze, unsqueeze
# TODO: define pooling functions
from ...fluid.layers import LayerHelper, utils
from ...tensor.manipulation import squeeze, unsqueeze
from ...utils import (
_contain_var,
_convert_to_tensor_list,
_is_symmetric_padding,
convert_to_list,
)
__all__ = []
......@@ -135,24 +141,24 @@ def _update_padding_nd(padding, num_dims, channel_last=False, ceil_mode=False):
padding = _exclude_padding_in_batch_and_channel(
padding, channel_last
)
if utils._is_symmetric_padding(padding, num_dims):
if _is_symmetric_padding(padding, num_dims):
padding = padding[0::2]
# for padding like [pad_before, pad_after, pad_before, pad_after, ...]
elif len(padding) == 2 * num_dims and isinstance(padding[0], int):
padding_algorithm = "EXPLICIT"
padding = utils.convert_to_list(padding, 2 * num_dims, 'padding')
if utils._is_symmetric_padding(padding, num_dims):
padding = convert_to_list(padding, 2 * num_dims, 'padding')
if _is_symmetric_padding(padding, num_dims):
padding = padding[0::2]
# for padding like [pad_d1, pad_d2, ...]
elif len(padding) == num_dims and isinstance(padding[0], int):
padding_algorithm = "EXPLICIT"
padding = utils.convert_to_list(padding, num_dims, 'padding')
padding = convert_to_list(padding, num_dims, 'padding')
else:
raise ValueError("Invalid padding: {}".format(padding))
# for integer padding
else:
padding_algorithm = "EXPLICIT"
padding = utils.convert_to_list(padding, num_dims, 'padding')
padding = convert_to_list(padding, num_dims, 'padding')
return padding, padding_algorithm
......@@ -228,12 +234,12 @@ def avg_pool1d(
)
_check_input(x, 3)
x = unsqueeze(x, [2])
kernel_size = utils.convert_to_list(kernel_size, 1, 'kernel_size')
kernel_size = convert_to_list(kernel_size, 1, 'kernel_size')
kernel_size = [1] + kernel_size
if stride is None:
stride = kernel_size
else:
stride = utils.convert_to_list(stride, 1, 'pool_stride')
stride = convert_to_list(stride, 1, 'pool_stride')
stride = [1] + stride
_check_value_limitation(kernel_size, "kernel_size", min_limit=1e-3)
......@@ -353,11 +359,11 @@ def avg_pool2d(
stride=2, padding=0)
# out.shape [1, 3, 16, 16]
"""
kernel_size = utils.convert_to_list(kernel_size, 2, 'pool_size')
kernel_size = convert_to_list(kernel_size, 2, 'pool_size')
if stride is None:
stride = kernel_size
else:
stride = utils.convert_to_list(stride, 2, 'pool_stride')
stride = convert_to_list(stride, 2, 'pool_stride')
_check_value_limitation(kernel_size, "kernel_size", min_limit=1e-3)
_check_value_limitation(stride, "stride", min_limit=1e-3)
......@@ -482,11 +488,11 @@ def avg_pool3d(
padding=0)
# out.shape: [1, 3, 16, 16, 16]
"""
kernel_size = utils.convert_to_list(kernel_size, 3, 'pool_size')
kernel_size = convert_to_list(kernel_size, 3, 'pool_size')
if stride is None:
stride = kernel_size
else:
stride = utils.convert_to_list(stride, 3, 'pool_stride')
stride = convert_to_list(stride, 3, 'pool_stride')
channel_last = _channel_last(data_format, 3)
padding, padding_algorithm = _update_padding_nd(
......@@ -603,11 +609,11 @@ def max_pool1d(
data_format = "NCHW"
_check_input(x, 3)
x = unsqueeze(x, [2])
kernel_size = [1] + utils.convert_to_list(kernel_size, 1, 'pool_size')
kernel_size = [1] + convert_to_list(kernel_size, 1, 'pool_size')
if stride is None:
stride = kernel_size
else:
stride = [1] + utils.convert_to_list(stride, 1, 'pool_stride')
stride = [1] + convert_to_list(stride, 1, 'pool_stride')
padding, padding_algorithm = _update_padding_nd(
padding, 1, ceil_mode=ceil_mode
......@@ -693,10 +699,10 @@ def _unpool_output_size(x, kernel_size, stride, padding, output_size):
has_static_var = False
if output_size is None:
return default_size
elif utils._contain_var(output_size):
elif _contain_var(output_size):
if not in_dygraph_mode():
has_static_var = True
output_size = utils._convert_to_tensor_list(output_size)
output_size = _convert_to_tensor_list(output_size)
else:
for i, var in enumerate(output_size):
if isinstance(var, Variable):
......@@ -799,11 +805,11 @@ def max_unpool1d(
data_format = "NCHW"
x = unsqueeze(x, [2])
indices = unsqueeze(indices, [2])
kernel_size = [1] + utils.convert_to_list(kernel_size, 1, 'pool_size')
kernel_size = [1] + convert_to_list(kernel_size, 1, 'pool_size')
if stride is None:
stride = kernel_size
else:
stride = [1] + utils.convert_to_list(stride, 1, 'pool_stride')
stride = [1] + convert_to_list(stride, 1, 'pool_stride')
padding, padding_algorithm = _update_padding_nd(padding, 1)
# use 2d to implenment 1d should expand padding in advance.
padding = _expand_low_nd_padding(padding)
......@@ -940,12 +946,12 @@ def max_unpool2d(
f'The indices should have [N, C, H, W] format, but received {indices.shape}.'
)
kernel_size = utils.convert_to_list(kernel_size, 2, 'pool_size')
kernel_size = convert_to_list(kernel_size, 2, 'pool_size')
if stride is None:
stride = kernel_size
else:
stride = utils.convert_to_list(stride, 2, 'pool_stride')
padding = utils.convert_to_list(padding, 2, 'padding')
stride = convert_to_list(stride, 2, 'pool_stride')
padding = convert_to_list(padding, 2, 'padding')
if data_format not in ["NCHW"]:
raise ValueError(
......@@ -1083,12 +1089,12 @@ def max_unpool3d(
f'The indices should have [N, C, D, H, W] format, but received {indices.shape}.'
)
kernel_size = utils.convert_to_list(kernel_size, 3, 'pool_size')
kernel_size = convert_to_list(kernel_size, 3, 'pool_size')
if stride is None:
stride = kernel_size
else:
stride = utils.convert_to_list(stride, 3, 'pool_stride')
padding = utils.convert_to_list(padding, 3, 'padding')
stride = convert_to_list(stride, 3, 'pool_stride')
padding = convert_to_list(padding, 3, 'padding')
if data_format not in ["NCDHW"]:
raise ValueError(
......@@ -1203,11 +1209,11 @@ def max_pool2d(
# out.shape [1, 3, 16, 16], max_indices.shape [1, 3, 16, 16],
"""
kernel_size = utils.convert_to_list(kernel_size, 2, 'pool_size')
kernel_size = convert_to_list(kernel_size, 2, 'pool_size')
if stride is None:
stride = kernel_size
else:
stride = utils.convert_to_list(stride, 2, 'pool_stride')
stride = convert_to_list(stride, 2, 'pool_stride')
if data_format not in ["NCHW", "NHWC"]:
raise ValueError(
......@@ -1370,11 +1376,11 @@ def max_pool3d(
# output.shape [1, 3, 16, 16, 16], max_indices.shape [1, 3, 16, 16, 16]
"""
kernel_size = utils.convert_to_list(kernel_size, 3, 'pool_size')
kernel_size = convert_to_list(kernel_size, 3, 'pool_size')
if stride is None:
stride = kernel_size
else:
stride = utils.convert_to_list(stride, 3, 'pool_stride')
stride = convert_to_list(stride, 3, 'pool_stride')
channel_last = _channel_last(data_format, 3)
......@@ -1478,7 +1484,7 @@ def adaptive_avg_pool1d(x, output_size, name=None):
"""
pool_type = 'avg'
_check_input(x, 3)
pool_size = [1] + utils.convert_to_list(output_size, 1, 'pool_size')
pool_size = [1] + convert_to_list(output_size, 1, 'pool_size')
x = unsqueeze(x, [2])
if in_dygraph_mode():
......@@ -1593,7 +1599,7 @@ def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None):
in_h, in_w = x.shape[1:3]
if isinstance(output_size, int):
output_size = utils.convert_to_list(output_size, 2, 'output_size')
output_size = convert_to_list(output_size, 2, 'output_size')
else:
output_size = list(output_size)
if output_size[0] is None:
......@@ -1607,8 +1613,8 @@ def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None):
for item in output_size
]
# output_size support Variable in static graph mode
elif utils._contain_var(output_size):
output_size = utils._convert_to_tensor_list(output_size)
elif _contain_var(output_size):
output_size = _convert_to_tensor_list(output_size)
if in_dygraph_mode():
x = x._use_gpudnn(False)
......@@ -1728,7 +1734,7 @@ def adaptive_avg_pool3d(x, output_size, data_format='NCDHW', name=None):
in_l, in_h, in_w = x.shape[1:4]
if isinstance(output_size, int):
output_size = utils.convert_to_list(output_size, 3, 'output_size')
output_size = convert_to_list(output_size, 3, 'output_size')
else:
output_size = list(output_size)
if output_size[0] is None:
......@@ -1827,7 +1833,7 @@ def adaptive_max_pool1d(x, output_size, return_mask=False, name=None):
"""
_check_input(x, 3)
pool_size = [1] + utils.convert_to_list(output_size, 1, 'pool_size')
pool_size = [1] + convert_to_list(output_size, 1, 'pool_size')
x = unsqueeze(x, [2])
if in_dygraph_mode():
......@@ -1917,7 +1923,7 @@ def adaptive_max_pool2d(x, output_size, return_mask=False, name=None):
in_h, in_w = x.shape[2:4]
if isinstance(output_size, int):
output_size = utils.convert_to_list(output_size, 2, 'output_size')
output_size = convert_to_list(output_size, 2, 'output_size')
else:
output_size = list(output_size)
if output_size[0] is None:
......@@ -2006,7 +2012,7 @@ def adaptive_max_pool3d(x, output_size, return_mask=False, name=None):
in_l, in_h, in_w = x.shape[2:5]
if isinstance(output_size, int):
output_size = utils.convert_to_list(output_size, 3, 'output_size')
output_size = convert_to_list(output_size, 3, 'output_size')
else:
output_size = list(output_size)
if output_size[0] is None:
......
python/paddle/nn/layer/conv.py
浏览文件 @ 86e990d4
......@@ -23,7 +23,7 @@ from ...device import (
is_compiled_with_cuda,
is_compiled_with_rocm,
)
from ...fluid.layers import utils
from ...utils import convert_to_list
from .. import Layer
from .. import functional as F
from ..functional.conv import _update_padding_nd
......@@ -110,11 +110,9 @@ class _ConvNd(Layer):
else:
self._channel_dim = 1
self._stride = utils.convert_to_list(stride, dims, 'stride')
self._dilation = utils.convert_to_list(dilation, dims, 'dilation')
self._kernel_size = utils.convert_to_list(
kernel_size, dims, 'kernel_size'
)
self._stride = convert_to_list(stride, dims, 'stride')
self._dilation = convert_to_list(dilation, dims, 'dilation')
self._kernel_size = convert_to_list(kernel_size, dims, 'kernel_size')
self._padding = padding
self._padding_mode = padding_mode
self.output_padding = output_padding
......@@ -133,9 +131,7 @@ class _ConvNd(Layer):
raise ValueError("in_channels must be divisible by groups.")
if padding_mode in {'reflect', 'replicate', 'circular'}:
_paired_padding = utils.convert_to_list(
padding, dims, 'padding'
)
_paired_padding = convert_to_list(padding, dims, 'padding')
self._reversed_padding_repeated_twice = _reverse_repeat_list(
_paired_padding, 2
)
......
python/paddle/nn/layer/rnn.py
浏览文件 @ 86e990d4
......@@ -28,8 +28,7 @@ from paddle.fluid.framework import (
in_dygraph_mode,
program_guard,
)
from paddle.fluid.layers import control_flow, utils
from paddle.fluid.layers.utils import flatten, map_structure
from paddle.fluid.layers import control_flow
from paddle.framework import core
from paddle.nn import Layer
from paddle.nn import functional as F
......@@ -159,7 +158,7 @@ def _rnn_dynamic_graph(
**kwargs
):
time_step_index = 0 if time_major else 1
flat_inputs = flatten(inputs)
flat_inputs = paddle.utils.flatten(inputs)
time_steps = flat_inputs[0].shape[time_step_index]
if initial_states is None:
......@@ -168,7 +167,7 @@ def _rnn_dynamic_graph(
)
if not time_major:
inputs = map_structure(_transpose_batch_time, inputs)
inputs = paddle.utils.map_structure(_transpose_batch_time, inputs)
if sequence_length is not None:
mask = paddle.static.nn.sequence_lod.sequence_mask(
......@@ -177,7 +176,9 @@ def _rnn_dynamic_graph(
mask = paddle.transpose(mask, [1, 0])
if is_reverse:
inputs = map_structure(lambda x: paddle.reverse(x, axis=[0]), inputs)
inputs = paddle.utils.map_structure(
lambda x: paddle.reverse(x, axis=[0]), inputs
)
mask = (
paddle.reverse(mask, axis=[0])
if sequence_length is not None
......@@ -187,27 +188,27 @@ def _rnn_dynamic_graph(
states = initial_states
outputs = []
for i in range(time_steps):
step_inputs = map_structure(lambda x: x[i], inputs)
step_inputs = paddle.utils.map_structure(lambda x: x[i], inputs)
step_outputs, new_states = cell(step_inputs, states, **kwargs)
if sequence_length is not None:
new_states = map_structure(
new_states = paddle.utils.map_structure(
partial(_maybe_copy, step_mask=mask[i]), states, new_states
)
states = new_states
outputs = (
map_structure(lambda x: ArrayWrapper(x), step_outputs)
paddle.utils.map_structure(lambda x: ArrayWrapper(x), step_outputs)
if i == 0
else map_structure(
else paddle.utils.map_structure(
lambda x, x_array: x_array.append(x), step_outputs, outputs
)
)
final_outputs = map_structure(
final_outputs = paddle.utils.map_structure(
lambda x: paddle.stack(x.array, axis=time_step_index), outputs
)
if is_reverse:
final_outputs = map_structure(
final_outputs = paddle.utils.map_structure(
lambda x: paddle.reverse(x, axis=time_step_index), final_outputs
)
......@@ -249,21 +250,23 @@ def _rnn_static_graph(
initial_states = cell.get_initial_states(
batch_ref=inputs, batch_dim_idx=1 if time_major else 0
)
initial_states = map_structure(_switch_grad, initial_states)
initial_states = paddle.utils.map_structure(_switch_grad, initial_states)
if not time_major:
inputs = map_structure(_transpose_batch_time, inputs)
inputs = paddle.utils.map_structure(_transpose_batch_time, inputs)
max_seq_len = paddle.shape(flatten(inputs)[0])[0]
max_seq_len = paddle.shape(paddle.utils.flatten(inputs)[0])[0]
if sequence_length:
mask = paddle.static.nn.sequence_lod.sequence_mask(
sequence_length,
maxlen=max_seq_len,
dtype=flatten(initial_states)[0].dtype,
dtype=paddle.utils.flatten(initial_states)[0].dtype,
)
mask = paddle.transpose(mask, [1, 0])
if is_reverse:
inputs = map_structure(lambda x: paddle.reverse(x, axis=[0]), inputs)
inputs = paddle.utils.map_structure(
lambda x: paddle.reverse(x, axis=[0]), inputs
)
mask = paddle.reverse(mask, axis=[0]) if sequence_length else None
with paddle.fluid.framework.device_guard("cpu"):
......@@ -274,13 +277,15 @@ def _rnn_static_graph(
cond = start_i < end
while_op = control_flow.While(cond)
out_array = paddle.tensor.create_array(dtype=flatten(inputs)[0].dtype)
out_array = paddle.tensor.create_array(
dtype=paddle.utils.flatten(inputs)[0].dtype
)
init_array = map_structure(
init_array = paddle.utils.map_structure(
lambda x: paddle.tensor.create_array(dtype=x.dtype), initial_states
)
map_structure(
paddle.utils.map_structure(
lambda x, y: paddle.tensor.array_write(x, start_i, y),
initial_states,
init_array,
......@@ -290,13 +295,13 @@ def _rnn_static_graph(
step_in = inputs[start_i]
# step_in = paddle.fluid.layers.Print( step_in, message="step in")
pre_state = map_structure(
pre_state = paddle.utils.map_structure(
lambda x: paddle.tensor.array_read(x, start_i), init_array
)
# pre_state = paddle.fluid.layers.Print( pre_state, message="pre")
outputs, new_states = cell(step_in, pre_state, **kwargs)
assert isinstance(outputs, paddle.fluid.framework.Variable)
utils.assert_same_structure(new_states, pre_state)
paddle.utils.assert_same_structure(new_states, pre_state)
if sequence_length:
step_mask = paddle.unsqueeze(mask[start_i], 1)
# paddle.fluid.layers.Print( step_mask, message="mask")
......@@ -304,7 +309,7 @@ def _rnn_static_graph(
# partial(_maybe_copy, step_mask=step_mask),
# pre_state, new_states
# )
new_states = map_structure(
new_states = paddle.utils.map_structure(
lambda x, y: (x * step_mask + y * (1.0 - step_mask)),
new_states,
pre_state,
......@@ -315,7 +320,7 @@ def _rnn_static_graph(
with paddle.fluid.framework.device_guard("cpu"):
start_i = paddle.tensor.increment(x=start_i, value=1)
map_structure(
paddle.utils.map_structure(
lambda x, y: paddle.tensor.array_write(x, start_i, y),
new_states,
init_array,
......@@ -327,20 +332,22 @@ def _rnn_static_graph(
out, _ = tensor_array_to_tensor(out_array, axis=0, use_stack=True)
all_state = map_structure(
all_state = paddle.utils.map_structure(
lambda x: tensor_array_to_tensor(x, axis=0, use_stack=True)[0],
init_array,
)
final_outputs = out
final_states = map_structure(lambda x: x[-1], all_state)
final_states = paddle.utils.map_structure(lambda x: x[-1], all_state)
if is_reverse:
final_outputs = map_structure(
final_outputs = paddle.utils.map_structure(
lambda x: paddle.reverse(x, axis=[0]), final_outputs
)
if not time_major:
final_outputs = map_structure(_transpose_batch_time, final_outputs)
final_outputs = paddle.utils.map_structure(
_transpose_batch_time, final_outputs
)
return (final_outputs, final_states)
......@@ -438,7 +445,7 @@ def birnn(
**kwargs
)
outputs = map_structure(
outputs = paddle.utils.map_structure(
lambda x, y: paddle.concat([x, y], -1), outputs_fw, outputs_bw
)
......@@ -532,9 +539,9 @@ def concat_states(states, bidirectional=False, state_components=1):
"""
if state_components == 1:
return paddle.stack(flatten(states))
return paddle.stack(paddle.utils.flatten(states))
else:
states = flatten(states)
states = paddle.utils.flatten(states)
componnets = []
for i in range(state_components):
componnets.append(states[i::state_components])
......@@ -582,7 +589,7 @@ class RNNCellBase(Layer):
packed in the same structure as `shape` and `type` does.
"""
# TODO: use inputs and batch_size
batch_ref = flatten(batch_ref)[0]
batch_ref = paddle.utils.flatten(batch_ref)[0]
def _is_shape_sequence(seq):
"""For shape, list/tuple of integer is the finest-grained objection"""
......@@ -602,21 +609,25 @@ class RNNCellBase(Layer):
# nested structure of shapes
states_shapes = self.state_shape if shape is None else shape
is_sequence_ori = utils.is_sequence
utils.is_sequence = _is_shape_sequence
states_shapes = map_structure(lambda shape: Shape(shape), states_shapes)
utils.is_sequence = is_sequence_ori
is_sequence_ori = paddle.utils.layers_utils.is_sequence
paddle.utils.layers_utils.is_sequence = _is_shape_sequence
states_shapes = paddle.utils.map_structure(
lambda shape: Shape(shape), states_shapes
)
paddle.utils.layers_utils.is_sequence = is_sequence_ori
# nested structure of dtypes
try:
states_dtypes = self.state_dtype if dtype is None else dtype
except NotImplementedError:
states_dtypes = framework.get_default_dtype()
if len(flatten(states_dtypes)) == 1:
dtype = flatten(states_dtypes)[0]
states_dtypes = map_structure(lambda shape: dtype, states_shapes)
if len(paddle.utils.flatten(states_dtypes)) == 1:
dtype = paddle.utils.flatten(states_dtypes)[0]
states_dtypes = paddle.utils.map_structure(
lambda shape: dtype, states_shapes
)
init_states = map_structure(
init_states = paddle.utils.map_structure(
lambda shape, dtype: paddle.fluid.layers.fill_constant_batch_size_like(
input=batch_ref,
shape=shape.shape,
......
python/paddle/sparse/nn/functional/conv.py
浏览文件 @ 86e990d4
......@@ -16,8 +16,8 @@ __all__ = []
from paddle import _C_ops, in_dynamic_mode
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers.utils import convert_to_list
from paddle.nn.functional.conv import _update_padding_nd
from paddle.utils import convert_to_list
from ...binary import add
......
python/paddle/sparse/nn/functional/pooling.py
浏览文件 @ 86e990d4
......@@ -13,8 +13,8 @@
# limitations under the License.
from paddle import _C_ops, in_dynamic_mode
from paddle.fluid.layers import utils
from paddle.nn.functional.pooling import _update_padding_nd
from paddle.utils import convert_to_list
__all__ = []
......@@ -82,11 +82,11 @@ def max_pool3d(
data_format == 'NDHWC'
), "Currently, sparse.max_pool3d only support data format of 'NDHWC'"
kernel_size = utils.convert_to_list(kernel_size, 3, 'pool_size')
kernel_size = convert_to_list(kernel_size, 3, 'pool_size')
if stride is None:
stride = kernel_size
else:
stride = utils.convert_to_list(stride, 3, 'pool_stride')
stride = convert_to_list(stride, 3, 'pool_stride')
channel_last = True
......
python/paddle/sparse/nn/layer/conv.py
浏览文件 @ 86e990d4
......@@ -14,10 +14,10 @@
import numpy as np
from paddle.fluid.layers import utils
from paddle.nn import Layer
from paddle.nn.functional.conv import _update_padding_nd
from paddle.nn.initializer import Normal
from paddle.utils import convert_to_list
from .. import functional as F
......@@ -70,11 +70,9 @@ class _Conv3D(Layer):
channel_last = data_format == "NDHWC"
dims = 3
self._stride = utils.convert_to_list(stride, dims, 'stride')
self._dilation = utils.convert_to_list(dilation, dims, 'dilation')
self._kernel_size = utils.convert_to_list(
kernel_size, dims, 'kernel_size'
)
self._stride = convert_to_list(stride, dims, 'stride')
self._dilation = convert_to_list(dilation, dims, 'dilation')
self._kernel_size = convert_to_list(kernel_size, dims, 'kernel_size')
self._padding = padding
self._padding_mode = padding_mode
self._updated_padding, self._padding_algorithm = _update_padding_nd(
......
python/paddle/static/nn/common.py
浏览文件 @ 86e990d4
......@@ -23,7 +23,6 @@ from paddle.common_ops_import import (
LayerHelper,
check_type,
check_variable_and_dtype,
utils,
)
from paddle.fluid import core
from paddle.fluid.data_feeder import check_dtype
......@@ -953,9 +952,9 @@ def conv2d(
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
stride = utils.convert_to_list(stride, 2, 'stride')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
filter_size = paddle.utils.convert_to_list(filter_size, 2, 'filter_size')
stride = paddle.utils.convert_to_list(stride, 2, 'stride')
dilation = paddle.utils.convert_to_list(dilation, 2, 'dilation')
# padding
def _update_padding(padding, data_format):
......@@ -981,12 +980,12 @@ def conv2d(
)
padding = padding[1:3]
padding = [ele for a_list in padding for ele in a_list]
padding = utils.convert_to_list(padding, 4, 'padding')
if utils._is_symmetric_padding(padding, 2):
padding = paddle.utils.convert_to_list(padding, 4, 'padding')
if paddle.utils._is_symmetric_padding(padding, 2):
padding = [padding[0], padding[2]]
else:
padding = utils.convert_to_list(padding, 2, 'padding')
padding = paddle.utils.convert_to_list(padding, 2, 'padding')
return padding
......@@ -1250,9 +1249,9 @@ def conv3d(
)
num_filter_channels = num_channels // groups
filter_size = utils.convert_to_list(filter_size, 3, 'filter_size')
stride = utils.convert_to_list(stride, 3, 'stride')
dilation = utils.convert_to_list(dilation, 3, 'dilation')
filter_size = paddle.utils.convert_to_list(filter_size, 3, 'filter_size')
stride = paddle.utils.convert_to_list(stride, 3, 'stride')
dilation = paddle.utils.convert_to_list(dilation, 3, 'dilation')
def _update_padding(padding, data_format):
def is_list_or_tuple(ele):
......@@ -1277,15 +1276,15 @@ def conv3d(
)
padding = padding[1:4]
padding = [ele for a_list in padding for ele in a_list]
padding = utils.convert_to_list(padding, 6, 'padding')
if utils._is_symmetric_padding(padding, 3):
padding = paddle.utils.convert_to_list(padding, 6, 'padding')
if paddle.utils._is_symmetric_padding(padding, 3):
padding = [padding[0], padding[2], padding[4]]
elif is_list_or_tuple(padding) and len(padding) == 6:
padding = utils.convert_to_list(padding, 6, 'padding')
if utils._is_symmetric_padding(padding, 3):
padding = paddle.utils.convert_to_list(padding, 6, 'padding')
if paddle.utils._is_symmetric_padding(padding, 3):
padding = [padding[0], padding[2], padding[4]]
else:
padding = utils.convert_to_list(padding, 3, 'padding')
padding = paddle.utils.convert_to_list(padding, 3, 'padding')
return padding
......@@ -1571,8 +1570,8 @@ def conv2d_transpose(
if not isinstance(input, Variable):
raise TypeError("Input of conv2d_transpose must be Tensor")
stride = utils.convert_to_list(stride, 2, 'stride')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
stride = paddle.utils.convert_to_list(stride, 2, 'stride')
dilation = paddle.utils.convert_to_list(dilation, 2, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
......@@ -1600,9 +1599,9 @@ def conv2d_transpose(
)
padding = padding[1:3]
padding = [ele for a_list in padding for ele in a_list]
padding = utils.convert_to_list(padding, 4, 'padding')
padding = paddle.utils.convert_to_list(padding, 4, 'padding')
else:
padding = utils.convert_to_list(padding, 2, 'padding')
padding = paddle.utils.convert_to_list(padding, 2, 'padding')
padding = [padding[0], padding[0], padding[1], padding[1]]
return padding
......@@ -1626,12 +1625,16 @@ def conv2d_transpose(
if output_size is None:
output_size = []
elif isinstance(output_size, (list, tuple)):
if utils._contain_var(output_size):
output_size = utils._convert_to_tensor_list(output_size)
if paddle.utils._contain_var(output_size):
output_size = paddle.utils._convert_to_tensor_list(output_size)
else:
output_size = utils.convert_to_list(output_size, 2, 'output_size')
output_size = paddle.utils.convert_to_list(
output_size, 2, 'output_size'
)
elif isinstance(output_size, int):
output_size = utils.convert_to_list(output_size, 2, 'output_size')
output_size = paddle.utils.convert_to_list(
output_size, 2, 'output_size'
)
elif isinstance(output_size, Variable):
check_dtype(
output_size.dtype,
......@@ -1655,16 +1658,16 @@ def conv2d_transpose(
if output_size is []:
raise ValueError("output_size must be set when filter_size is None")
if not _non_static_mode():
if isinstance(output_size, Variable) or utils._contain_var(
if isinstance(output_size, Variable) or paddle.utils._contain_var(
output_size
):
raise ValueError(
"filter_size should not be None when output_size is Tensor or contain Tensor in static graph mode."
)
else:
output_size = utils.convert_shape_to_list(output_size)
output_size = paddle.utils.convert_shape_to_list(output_size)
if len(output_size) == 1:
output_size = utils.convert_to_list(
output_size = paddle.utils.convert_to_list(
output_size[0], 2, 'output_size'
)
......@@ -1687,11 +1690,11 @@ def conv2d_transpose(
) // dilation[1] + 1
filter_size = [filter_size_h, filter_size_w]
else:
filter_size = utils.convert_to_list(
filter_size = paddle.utils.convert_to_list(
filter_size, 2, 'conv2d_transpose.filter_size'
)
if len(padding) == 4 and utils._is_symmetric_padding(padding, 2):
if len(padding) == 4 and paddle.utils._is_symmetric_padding(padding, 2):
padding = [padding[0], padding[2]]
if groups is None:
......@@ -1938,8 +1941,8 @@ def conv3d_transpose(
input.shape[1] if data_format == 'NCDHW' else input.shape[-1]
)
stride = utils.convert_to_list(stride, 3, 'stride')
dilation = utils.convert_to_list(dilation, 3, 'dilation')
stride = paddle.utils.convert_to_list(stride, 3, 'stride')
dilation = paddle.utils.convert_to_list(dilation, 3, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
......@@ -1967,13 +1970,13 @@ def conv3d_transpose(
)
padding = padding[1:4]
padding = [ele for a_list in padding for ele in a_list]
padding = utils.convert_to_list(padding, 6, 'padding')
padding = paddle.utils.convert_to_list(padding, 6, 'padding')
elif is_list_or_tuple(padding) and len(padding) == 6:
padding = utils.convert_to_list(padding, 6, 'padding')
padding = paddle.utils.convert_to_list(padding, 6, 'padding')
else:
padding = utils.convert_to_list(padding, 3, 'padding')
padding = paddle.utils.convert_to_list(padding, 3, 'padding')
padding = [
padding[0],
padding[0],
......@@ -2034,17 +2037,19 @@ def conv3d_transpose(
) // dilation[2] + 1
filter_size = [filter_size_d, filter_size_h, filter_size_w]
else:
filter_size = utils.convert_to_list(
filter_size = paddle.utils.convert_to_list(
filter_size, 3, 'conv3d_transpose.filter_size'
)
if len(padding) == 6 and utils._is_symmetric_padding(padding, 3):
if len(padding) == 6 and paddle.utils._is_symmetric_padding(padding, 3):
padding = [padding[0], padding[2], padding[4]]
if output_size is None:
output_size = []
elif isinstance(output_size, (list, tuple, int)):
output_size = utils.convert_to_list(output_size, 3, 'output_size')
output_size = paddle.utils.convert_to_list(
output_size, 3, 'output_size'
)
else:
raise ValueError("output_size should be int, list[int] or tuple[int]")
......@@ -2275,10 +2280,10 @@ def deformable_conv(
raise ValueError("num_channels must be divisible by groups.")
num_filter_channels = num_channels // groups
filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
stride = utils.convert_to_list(stride, 2, 'stride')
padding = utils.convert_to_list(padding, 2, 'padding')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
filter_size = paddle.utils.convert_to_list(filter_size, 2, 'filter_size')
stride = paddle.utils.convert_to_list(stride, 2, 'stride')
padding = paddle.utils.convert_to_list(padding, 2, 'padding')
dilation = paddle.utils.convert_to_list(dilation, 2, 'dilation')
input_shape = input.shape
filter_shape = [num_filters, int(num_filter_channels)] + filter_size
......
python/paddle/static/nn/control_flow.py
浏览文件 @ 86e990d4
......@@ -28,7 +28,7 @@ from paddle.fluid.framework import Operator, Program, Variable
# Temporary solution, it will be deleted later
from paddle.fluid.layers.control_flow import ConditionalBlock, select_input
from paddle.fluid.layers.utils import (
from paddle.utils import (
assert_same_structure,
copy_mutable_vars,
flatten,
......
python/paddle/tensor/creation.py
浏览文件 @ 86e990d4
......@@ -35,7 +35,6 @@ from ..fluid.framework import (
_in_eager_without_dygraph_check,
device_guard,
)
from ..fluid.layers import utils
from ..fluid.param_attr import ParamAttr
from ..framework import (
LayerHelper,
......@@ -1775,7 +1774,7 @@ def empty(shape, dtype=None, name=None):
dtype = convert_dtype(dtype)
if in_dygraph_mode():
shape = utils.convert_shape_to_list(shape)
shape = paddle.utils.convert_shape_to_list(shape)
out = _C_ops.empty(
shape, convert_np_dtype_to_dtype_(dtype), _current_expected_place()
)
......@@ -1797,7 +1796,7 @@ def empty(shape, dtype=None, name=None):
check_dtype(shape.dtype, 'shape', ['int32', 'int64'], 'empty')
attrs = {}
utils.get_shape_tensor_inputs(
paddle.utils.get_shape_tensor_inputs(
inputs=inputs, attrs=attrs, shape=shape, op_type='empty'
)
......@@ -1874,7 +1873,7 @@ def empty_like(x, dtype=None, name=None):
attrs = {}
attrs['dtype'] = convert_np_dtype_to_dtype_(dtype)
shape = paddle.shape(x)
utils.get_shape_tensor_inputs(
paddle.utils.get_shape_tensor_inputs(
inputs=inputs, attrs=attrs, shape=shape, op_type='empty_like'
)
......
python/paddle/tensor/manipulation.py
浏览文件 @ 86e990d4
......@@ -27,7 +27,6 @@ from ..fluid.data_feeder import (
check_variable_and_dtype,
convert_dtype,
)
from ..fluid.layers import utils
from ..framework import (
LayerHelper,
convert_np_dtype_to_dtype_,
......@@ -376,10 +375,10 @@ def slice(input, axes, starts, ends):
infer_flags = list(-1 for i in range(len(axes)))
elif isinstance(starts, (list, tuple)):
attrs['starts'] = []
if utils._contain_var(starts):
inputs['StartsTensorList'] = utils._convert_to_tensor_list(
starts
)
if paddle.utils._contain_var(starts):
inputs[
'StartsTensorList'
] = paddle.utils._convert_to_tensor_list(starts)
for i, dim in enumerate(starts):
if isinstance(dim, Variable):
attrs['starts'].append(-1)
......@@ -396,8 +395,10 @@ def slice(input, axes, starts, ends):
infer_flags = list(-1 for i in range(len(axes)))
elif isinstance(ends, (list, tuple)):
attrs['ends'] = []
if utils._contain_var(ends):
inputs['EndsTensorList'] = utils._convert_to_tensor_list(ends)
if paddle.utils._contain_var(ends):
inputs['EndsTensorList'] = paddle.utils._convert_to_tensor_list(
ends
)
for i, dim in enumerate(ends):
if isinstance(dim, Variable):
attrs['ends'].append(-1)
......@@ -793,7 +794,7 @@ def crop(x, shape=None, offsets=None, name=None):
offsets.stop_gradient = True
ipts['Offsets'] = offsets
attrs['offsets'] = [-1] * len(x.shape)
elif utils._contain_var(offsets):
elif paddle.utils._contain_var(offsets):
new_offsets_tensor = []
offsets_attr = []
for dim in offsets:
......@@ -817,7 +818,7 @@ def crop(x, shape=None, offsets=None, name=None):
if isinstance(shape, Variable):
shape.stop_gradient = True
ipts['Shape'] = shape
elif utils._contain_var(shape):
elif paddle.utils._contain_var(shape):
new_shape_tensor = []
shape_attr = []
for dim_size in shape:
......@@ -1942,7 +1943,7 @@ def split(x, num_or_sections, axis=0, name=None):
dim = (len(input.shape) + dim) if dim < 0 else dim
if isinstance(num_or_sections, (list, tuple)):
if utils._contain_var(num_or_sections):
if paddle.utils._contain_var(num_or_sections):
for index, item in enumerate(num_or_sections):
if isinstance(item, Variable):
num_or_sections[index] = num_or_sections[index].numpy()[
......@@ -2043,7 +2044,7 @@ def split(x, num_or_sections, axis=0, name=None):
num_or_sections,
)
)
if utils._contain_var(num_or_sections):
if paddle.utils._contain_var(num_or_sections):
inputs['SectionsTensorList'] = _get_SectionsTensorList(
num_or_sections
)
......@@ -2213,8 +2214,8 @@ def squeeze(x, axis=None, name=None):
axes.stop_gradient = True
attrs["axes"] = axes
elif isinstance(axes, (list, tuple)):
if utils._contain_var(axes):
attrs["axes"] = utils._convert_to_tensor_list(axes)
if paddle.utils._contain_var(axes):
attrs["axes"] = paddle.utils._convert_to_tensor_list(axes)
else:
attrs["axes"] = axes
......@@ -2613,8 +2614,10 @@ def unsqueeze(x, axis, name=None):
axes.stop_gradient = True
inputs["AxesTensor"] = axes
elif isinstance(axes, (list, tuple)):
if utils._contain_var(axes):
inputs["AxesTensorList"] = utils._convert_to_tensor_list(axes)
if paddle.utils._contain_var(axes):
inputs["AxesTensorList"] = paddle.utils._convert_to_tensor_list(
axes
)
else:
attrs["axes"] = axes
......@@ -3197,10 +3200,10 @@ def tile(x, repeat_times, name=None):
attrs['repeat_times'] = [-1]
elif isinstance(repeat_times, (list, tuple)):
attrs['repeat_times'] = get_attr_repeat_times(repeat_times)
if utils._contain_var(repeat_times):
inputs['repeat_times_tensor'] = utils._convert_to_tensor_list(
repeat_times
)
if paddle.utils._contain_var(repeat_times):
inputs[
'repeat_times_tensor'
] = paddle.utils._convert_to_tensor_list(repeat_times)
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_variable_for_type_inference(dtype)
......@@ -3351,10 +3354,10 @@ def broadcast_to(x, shape, name=None):
inputs['Shape'] = shape
elif isinstance(shape, (list, tuple)):
attrs['shape'] = get_attr_expand_shape(shape)
if utils._contain_var(shape):
inputs['expand_shapes_tensor'] = utils._convert_to_tensor_list(
shape
)
if paddle.utils._contain_var(shape):
inputs[
'expand_shapes_tensor'
] = paddle.utils._convert_to_tensor_list(shape)
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_variable_for_type_inference(dtype)
......@@ -3445,10 +3448,10 @@ def expand(x, shape, name=None):
inputs['Shape'] = shape
elif isinstance(shape, (list, tuple)):
attrs['shape'] = get_attr_expand_shape(shape)
if utils._contain_var(shape):
inputs['expand_shapes_tensor'] = utils._convert_to_tensor_list(
shape
)
if paddle.utils._contain_var(shape):
inputs[
'expand_shapes_tensor'
] = paddle.utils._convert_to_tensor_list(shape)
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_variable_for_type_inference(dtype)
......@@ -3602,8 +3605,10 @@ def reshape(x, shape, name=None):
inputs["Shape"] = shape
elif isinstance(shape, (list, tuple)):
attrs["shape"] = get_attr_shape(shape)
if utils._contain_var(shape):
inputs['ShapeTensor'] = utils._convert_to_tensor_list(shape)
if paddle.utils._contain_var(shape):
inputs['ShapeTensor'] = paddle.utils._convert_to_tensor_list(
shape
)
helper = LayerHelper("reshape2", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -3894,7 +3899,7 @@ def strided_slice(x, axes, starts, ends, strides, name=None):
inputs['StartsTensor'] = starts
elif isinstance(starts, (list, tuple)):
attrs['starts'] = []
if utils._contain_var(starts):
if paddle.utils._contain_var(starts):
inputs['StartsTensorList'] = get_new_list_tensor(starts)
for i, dim in enumerate(starts):
if isinstance(dim, Variable):
......@@ -3911,7 +3916,7 @@ def strided_slice(x, axes, starts, ends, strides, name=None):
inputs['EndsTensor'] = ends
elif isinstance(ends, (list, tuple)):
attrs['ends'] = []
if utils._contain_var(ends):
if paddle.utils._contain_var(ends):
inputs['EndsTensorList'] = get_new_list_tensor(ends)
for i, dim in enumerate(ends):
if isinstance(dim, Variable):
......@@ -3928,7 +3933,7 @@ def strided_slice(x, axes, starts, ends, strides, name=None):
inputs['StridesTensor'] = strides
elif isinstance(strides, (list, tuple)):
attrs['strides'] = []
if utils._contain_var(strides):
if paddle.utils._contain_var(strides):
inputs['StridesTensorList'] = get_new_list_tensor(strides)
for i, dim in enumerate(strides):
if isinstance(dim, Variable):
......
python/paddle/tensor/math.py
浏览文件 @ 86e990d4
......@@ -32,7 +32,6 @@ from ..fluid.data_feeder import (
check_variable_and_dtype,
convert_dtype,
)
from ..fluid.layers import utils
from ..framework import (
LayerHelper,
convert_np_dtype_to_dtype_,
......@@ -121,8 +120,8 @@ def _get_reduce_axis_with_tensor(axis, x):
reduce_all = False
else:
reduce_all, axis = _get_reduce_axis(axis, x)
if utils._contain_var(axis):
axis = utils._convert_to_tensor_list(axis)
if paddle.utils._contain_var(axis):
axis = paddle.utils._convert_to_tensor_list(axis)
return reduce_all, axis
......@@ -2319,8 +2318,8 @@ def max(x, axis=None, keepdim=False, name=None):
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'max'
)
if not isinstance(axis, Variable) and utils._contain_var(axis):
axis = utils._convert_to_tensor_list(axis)
if not isinstance(axis, Variable) and paddle.utils._contain_var(axis):
axis = paddle.utils._convert_to_tensor_list(axis)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
......
python/paddle/tensor/random.py
浏览文件 @ 86e990d4
......@@ -25,7 +25,6 @@ from ..fluid.data_feeder import (
check_type,
check_variable_and_dtype,
)
from ..fluid.layers import utils
from ..framework import (
LayerHelper,
convert_np_dtype_to_dtype_,
......@@ -336,7 +335,7 @@ def gaussian(shape, mean=0.0, std=1.0, seed=0, dtype=None, name=None):
dtype = convert_np_dtype_to_dtype_(dtype)
if in_dygraph_mode():
shape = utils.convert_shape_to_list(shape)
shape = paddle.utils.convert_shape_to_list(shape)
place = _current_expected_place()
return _C_ops.gaussian(
shape, float(mean), float(std), seed, dtype, place
......@@ -353,7 +352,7 @@ def gaussian(shape, mean=0.0, std=1.0, seed=0, dtype=None, name=None):
'dtype': dtype,
'use_mkldnn': False,
}
utils.get_shape_tensor_inputs(
paddle.utils.get_shape_tensor_inputs(
inputs=inputs, attrs=attrs, shape=shape, op_type=op_type_for_check
)
......@@ -644,7 +643,7 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
dtype = convert_np_dtype_to_dtype_(dtype)
if in_dygraph_mode():
shape = utils.convert_shape_to_list(shape)
shape = paddle.utils.convert_shape_to_list(shape)
return _C_ops.uniform(
shape,
dtype,
......@@ -661,7 +660,7 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
inputs = dict()
attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}
utils.get_shape_tensor_inputs(
paddle.utils.get_shape_tensor_inputs(
inputs=inputs, attrs=attrs, shape=shape, op_type='uniform/rand'
)
......@@ -794,7 +793,7 @@ def randint(low=0, high=None, shape=[1], dtype=None, name=None):
dtype = convert_np_dtype_to_dtype_(dtype)
if in_dygraph_mode():
shape = utils.convert_shape_to_list(shape)
shape = paddle.utils.convert_shape_to_list(shape)
place = _current_expected_place()
return _C_ops.randint(low, high, shape, dtype, place)
else:
......@@ -808,7 +807,7 @@ def randint(low=0, high=None, shape=[1], dtype=None, name=None):
inputs = dict()
attrs = {'low': low, 'high': high, 'seed': 0, 'dtype': dtype}
utils.get_shape_tensor_inputs(
paddle.utils.get_shape_tensor_inputs(
inputs=inputs, attrs=attrs, shape=shape, op_type='randint'
)
......@@ -967,7 +966,7 @@ def randint_like(x, low=0, high=None, dtype=None, name=None):
)
if in_dygraph_mode():
shape = utils.convert_shape_to_list(shape)
shape = paddle.utils.convert_shape_to_list(shape)
out = _legacy_C_ops.randint(
'shape',
shape,
......
python/paddle/utils/__init__.py
浏览文件 @ 86e990d4
......@@ -27,5 +27,33 @@ from . import download # noqa: F401
from . import image_util # noqa: F401
from . import cpp_extension # noqa: F401
from . import dlpack
from . import layers_utils # noqa: F401
from .layers_utils import convert_to_list # noqa: F401
from .layers_utils import is_sequence # noqa: F401
from .layers_utils import to_sequence # noqa: F401
from .layers_utils import flatten # noqa: F401
from .layers_utils import pack_sequence_as # noqa: F401
from .layers_utils import map_structure # noqa: F401
from .layers_utils import hold_mutable_vars # noqa: F401
from .layers_utils import copy_mutable_vars # noqa: F401
from .layers_utils import padding_to_same_structure # noqa: F401
from .layers_utils import assert_same_structure # noqa: F401
from .layers_utils import get_shape_tensor_inputs # noqa: F401
from .layers_utils import convert_shape_to_list # noqa: F401
from .layers_utils import check_shape # noqa: F401
from .layers_utils import try_set_static_shape_tensor # noqa: F401
from .layers_utils import try_get_constant_shape_from_tensor # noqa: F401
from .layers_utils import get_inputs_outputs_in_block # noqa: F401
from .layers_utils import _hash_with_id # noqa: F401
from .layers_utils import _sorted # noqa: F401
from .layers_utils import _yield_value # noqa: F401
from .layers_utils import _yield_flat_nest # noqa: F401
from .layers_utils import _sequence_like # noqa: F401
from .layers_utils import _packed_nest_with_indices # noqa: F401
from .layers_utils import _recursive_assert_same_structure # noqa: F401
from .layers_utils import _is_symmetric_padding # noqa: F401
from .layers_utils import _contain_var # noqa: F401
from .layers_utils import _convert_to_tensor_list # noqa: F401
__all__ = ['deprecated', 'run_check', 'require_version', 'try_import'] # noqa
python/paddle/utils/cpp_extension/extension_utils.py
浏览文件 @ 86e990d4
......@@ -289,7 +289,7 @@ class VersionManager:
self.version = self.hasher(version_field)
def hasher(self, version_field):
from paddle.fluid.layers.utils import flatten
from paddle.utils import flatten
md5 = hashlib.md5()
for field in version_field._fields:
......
python/paddle/fluid/layers/utils.py → python/paddle/utils/layers_utils.py
浏览文件 @ 86e990d4
......@@ -12,24 +12,16 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import collections
import copy
import numpy as np
from ..framework import Block, Variable, _non_static_mode
from ..data_feeder import (
convert_dtype,
check_variable_and_dtype,
check_type,
check_dtype,
)
from ..layer_helper import LayerHelper
from sys import version_info
from collections.abc import Sequence
from weakref import WeakKeyDictionary
from collections import defaultdict
from collections.abc import Sequence
from uuid import uuid4
from weakref import WeakKeyDictionary
import paddle
from ..fluid.data_feeder import check_dtype, convert_dtype
from ..fluid.framework import Block, Variable, _non_static_mode
def convert_to_list(value, n, name, dtype=int):
......@@ -388,7 +380,7 @@ def _contain_var(list_or_tuple):
def get_shape_tensor_inputs(inputs, attrs, shape, op_type):
from .tensor import fill_constant
from ..fluid.layers.tensor import fill_constant
def _get_attr_shape(list_shape):
attr_shape = []
......@@ -443,7 +435,7 @@ def _convert_to_tensor_list(old_list, dtype="int32"):
"""
Converts all elements of a list to Variable.
"""
from .tensor import fill_constant
from ..fluid.layers.tensor import fill_constant
new_list_tensor = []
for ele in old_list:
......
python/paddle/vision/ops.py
浏览文件 @ 86e990d4
......@@ -16,11 +16,11 @@ import numpy as np
from paddle import _C_ops, _legacy_C_ops
from paddle.tensor.math import _add_with_axis
from paddle.utils import convert_to_list
from ..fluid.data_feeder import check_type, check_variable_and_dtype
from ..fluid.framework import Variable, in_dygraph_mode
from ..fluid.layer_helper import LayerHelper
from ..fluid.layers import utils
from ..framework import _current_expected_place
from ..nn import BatchNorm2D, Conv2D, Layer, ReLU, Sequential
from ..nn.initializer import Normal
......@@ -863,9 +863,9 @@ def deform_conv2d(
# returns
[8, 16, 26, 26]
"""
stride = utils.convert_to_list(stride, 2, 'stride')
padding = utils.convert_to_list(padding, 2, 'padding')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
stride = convert_to_list(stride, 2, 'stride')
padding = convert_to_list(padding, 2, 'padding')
dilation = convert_to_list(dilation, 2, 'dilation')
use_deform_conv2d_v1 = True if mask is None else False
......@@ -899,9 +899,9 @@ def deform_conv2d(
helper = LayerHelper('deformable_conv', **locals())
dtype = helper.input_dtype()
stride = utils.convert_to_list(stride, 2, 'stride')
padding = utils.convert_to_list(padding, 2, 'padding')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
stride = convert_to_list(stride, 2, 'stride')
padding = convert_to_list(padding, 2, 'padding')
dilation = convert_to_list(dilation, 2, 'dilation')
pre_bias = helper.create_variable_for_type_inference(dtype)
......@@ -1106,14 +1106,14 @@ class DeformConv2D(Layer):
self._out_channels = out_channels
self._channel_dim = 1
self._stride = utils.convert_to_list(stride, 2, 'stride')
self._dilation = utils.convert_to_list(dilation, 2, 'dilation')
self._kernel_size = utils.convert_to_list(kernel_size, 2, 'kernel_size')
self._stride = convert_to_list(stride, 2, 'stride')
self._dilation = convert_to_list(dilation, 2, 'dilation')
self._kernel_size = convert_to_list(kernel_size, 2, 'kernel_size')
if in_channels % groups != 0:
raise ValueError("in_channels must be divisible by groups.")
self._padding = utils.convert_to_list(padding, 2, 'padding')
self._padding = convert_to_list(padding, 2, 'padding')
filter_shape = [out_channels, in_channels // groups] + self._kernel_size
......
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