From 6b7bb6b54a7e19d530bd12fcbfadf5f9d33eb0e0 Mon Sep 17 00:00:00 2001 From: Tao Luo Date: Thu, 6 Feb 2020 11:50:29 +0800 Subject: [PATCH] change check_type_and_dtype to check_variable_and_dtype (#22465) --- python/paddle/fluid/data_feeder.py | 13 ++- python/paddle/fluid/input.py | 4 +- python/paddle/fluid/layers/control_flow.py | 8 +- .../fluid/layers/layer_function_generator.py | 6 +- python/paddle/fluid/layers/loss.py | 15 ++-- python/paddle/fluid/layers/metric_op.py | 6 +- python/paddle/fluid/layers/nn.py | 84 ++++++++----------- python/paddle/fluid/layers/tensor.py | 10 +-- 8 files changed, 67 insertions(+), 79 deletions(-) diff --git a/python/paddle/fluid/data_feeder.py b/python/paddle/fluid/data_feeder.py index b9016bb7d3..6b49f7a8b4 100644 --- a/python/paddle/fluid/data_feeder.py +++ b/python/paddle/fluid/data_feeder.py @@ -71,13 +71,12 @@ def convert_dtype(dtype): "int32, int64, uint8]") -def check_type_and_dtype(input, - input_name, - expected_type, - expected_dtype, - op_name, - extra_message=''): - check_type(input, input_name, expected_type, op_name, extra_message) +def check_variable_and_dtype(input, + input_name, + expected_dtype, + op_name, + extra_message=''): + check_type(input, input_name, Variable, op_name, extra_message) check_dtype(input.dtype, input_name, expected_dtype, op_name, extra_message) diff --git a/python/paddle/fluid/input.py b/python/paddle/fluid/input.py index 1506b9d4f2..458f386919 100644 --- a/python/paddle/fluid/input.py +++ b/python/paddle/fluid/input.py @@ -16,7 +16,7 @@ from __future__ import print_function import warnings from .framework import Variable, in_dygraph_mode from .layer_helper import LayerHelper -from .data_feeder import check_type_and_dtype, check_dtype +from .data_feeder import check_variable_and_dtype, check_dtype __all__ = ['one_hot', 'embedding'] @@ -233,7 +233,7 @@ def embedding(input, """ helper = LayerHelper('embedding', **locals()) - check_type_and_dtype(input, 'input', Variable, ['int64'], 'fluid.embedding') + check_variable_and_dtype(input, 'input', ['int64'], 'fluid.embedding') check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'], 'fluid.embedding') remote_prefetch = is_sparse and (not is_distributed) diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py index 8afd161101..1842088ad4 100755 --- a/python/paddle/fluid/layers/control_flow.py +++ b/python/paddle/fluid/layers/control_flow.py @@ -26,7 +26,7 @@ import numpy import warnings import six from functools import reduce, partial -from ..data_feeder import convert_dtype, check_type_and_dtype +from ..data_feeder import convert_dtype, check_variable_and_dtype from ... import compat as cpt from ..backward import _infer_var_data_type_shape_ @@ -257,9 +257,9 @@ def Print(input, data: 3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3, ''' - check_type_and_dtype(input, 'input', Variable, - ['float32', 'float64', 'int32', 'int64', 'bool'], - 'fluid.layers.Print') + check_variable_and_dtype(input, 'input', + ['float32', 'float64', 'int32', 'int64', 'bool'], + 'fluid.layers.Print') helper = LayerHelper('print' + "_" + input.name, **locals()) output = helper.create_variable_for_type_inference(input.dtype) diff --git a/python/paddle/fluid/layers/layer_function_generator.py b/python/paddle/fluid/layers/layer_function_generator.py index 9328b8000e..d113868fbc 100755 --- a/python/paddle/fluid/layers/layer_function_generator.py +++ b/python/paddle/fluid/layers/layer_function_generator.py @@ -22,7 +22,7 @@ from six.moves import cStringIO from ..proto import framework_pb2 from ..framework import OpProtoHolder, Variable, core, convert_np_dtype_to_dtype_, in_dygraph_mode from ..layer_helper import LayerHelper -from ..data_feeder import check_type_and_dtype +from ..data_feeder import check_variable_and_dtype __all__ = [ 'deprecated', 'generate_layer_fn', 'generate_activation_fn', 'autodoc', @@ -258,8 +258,8 @@ def generate_activation_fn(op_type): outs = op(inputs) return outs['Out'][0] - check_type_and_dtype(x, 'x', Variable, - ['float16', 'float32', 'float64'], op_type) + check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], + op_type) helper = LayerHelper(op_type, **locals()) output = helper.create_variable_for_type_inference(dtype=x.dtype) diff --git a/python/paddle/fluid/layers/loss.py b/python/paddle/fluid/layers/loss.py index a2d84301a7..5b9a5f26b3 100644 --- a/python/paddle/fluid/layers/loss.py +++ b/python/paddle/fluid/layers/loss.py @@ -21,7 +21,7 @@ from .layer_function_generator import templatedoc from ..layer_helper import LayerHelper from ..framework import Variable, in_dygraph_mode from .. import core -from ..data_feeder import check_type_and_dtype +from ..data_feeder import check_variable_and_dtype from ..param_attr import ParamAttr from ..initializer import NumpyArrayInitializer, Constant from .. import core @@ -245,8 +245,8 @@ def cross_entropy(input, label, soft_label=False, ignore_index=kIgnoreIndex): outs = core.ops.cross_entropy(inputs, attrs) return outs['Y'][0] - check_type_and_dtype(input, 'input', Variable, - ['float16', 'float32', 'float64'], 'cross_entropy') + check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'], + 'cross_entropy') helper = LayerHelper('cross_entropy', **locals()) out = helper.create_variable_for_type_inference(dtype=input.dtype) helper.append_op( @@ -262,8 +262,8 @@ def cross_entropy2(input, label, ignore_index=kIgnoreIndex): outs = core.ops.cross_entropy2(inputs, attrs) return outs['Y'][0] - check_type_and_dtype(input, 'input', Variable, - ['float16', 'float32', 'float64'], 'cross_entropy2') + check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'], + 'cross_entropy2') helper = LayerHelper('cross_entropy2', **locals()) out = helper.create_variable_for_type_inference(dtype=input.dtype) xshape = helper.create_variable_for_type_inference(dtype=input.dtype) @@ -717,9 +717,8 @@ def nce(input, custom_dist=dist) """ helper = LayerHelper('nce', **locals()) - check_type_and_dtype(input, 'input', Variable, ['float32', 'float64'], - 'nce') - check_type_and_dtype(label, 'label', Variable, ['int64'], 'nce') + check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'nce') + check_variable_and_dtype(label, 'label', ['int64'], 'nce') dim = input.shape[1] num_true_class = label.shape[1] diff --git a/python/paddle/fluid/layers/metric_op.py b/python/paddle/fluid/layers/metric_op.py index 60cfb816bc..de92f3c3cf 100644 --- a/python/paddle/fluid/layers/metric_op.py +++ b/python/paddle/fluid/layers/metric_op.py @@ -24,7 +24,7 @@ from ..framework import Variable, in_dygraph_mode, _varbase_creator from .. import core from ..param_attr import ParamAttr from . import nn -from ..data_feeder import check_type_and_dtype +from ..data_feeder import check_variable_and_dtype __all__ = ['accuracy', 'auc'] @@ -94,8 +94,8 @@ def accuracy(input, label, k=1, correct=None, total=None): return outs['Accuracy'][0] helper = LayerHelper("accuracy", **locals()) - check_type_and_dtype(input, 'input', Variable, - ['float16', 'float32', 'float64'], 'accuracy') + check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'], + 'accuracy') topk_out, topk_indices = nn.topk(input, k=k) acc_out = helper.create_variable_for_type_inference(dtype="float32") if correct is None: diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 9c83f83d1c..1f024fc109 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -33,7 +33,7 @@ from . import utils from .. import unique_name from functools import reduce from .. import core -from ..data_feeder import convert_dtype, check_type_and_dtype, check_type, check_dtype +from ..data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype __all__ = [ 'fc', @@ -472,8 +472,8 @@ def embedding(input, """ helper = LayerHelper('embedding', **locals()) - check_type_and_dtype(input, 'input', Variable, ['int64'], - 'fluid.layers.embedding') + check_variable_and_dtype(input, 'input', ['int64'], + 'fluid.layers.embedding') check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'], 'fluid.layers.embedding') remote_prefetch = is_sparse and (not is_distributed) @@ -840,8 +840,8 @@ def dropout(x, return outs['Out'][0] helper = LayerHelper('dropout', **locals()) - check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64'], - 'dropout') + check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], + 'dropout') out = helper.create_variable_for_type_inference(dtype=x.dtype) mask = helper.create_variable_for_type_inference( @@ -1124,8 +1124,8 @@ def softmax(input, use_cudnn=False, name=None, axis=-1): return outs['Out'][0] helper = LayerHelper('softmax', **locals()) - check_type_and_dtype(input, 'input', Variable, - ['float16', 'float32', 'float64'], 'softmax') + check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'], + 'softmax') dtype = helper.input_dtype() softmax_out = helper.create_variable_for_type_inference(dtype) @@ -1280,8 +1280,8 @@ def conv2d(input, conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu") """ - check_type_and_dtype(input, 'input', Variable, - ['float16', 'float32', 'float64'], 'conv2d') + check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'], + 'conv2d') num_channels = input.shape[1] if not isinstance(use_cudnn, bool): raise ValueError("Attr(use_cudnn) should be True or False. Received " @@ -2555,8 +2555,8 @@ def batch_norm(input, assert bias_attr is not False, "bias_attr should not be False in batch_norm." helper = LayerHelper('batch_norm', **locals()) - check_type_and_dtype(input, 'input', Variable, - ['float16', 'float32', 'float64'], 'batch_norm') + check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'], + 'batch_norm') dtype = helper.input_dtype() has_reserve_space = False @@ -3896,8 +3896,8 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None): outs = core.ops.reduce_sum(inputs, attrs) return outs['Out'][0] - check_type_and_dtype(input, 'input', Variable, - ['float32', 'float64', 'int32', 'int64'], 'reduce_sum') + check_variable_and_dtype( + input, 'input', ['float32', 'float64', 'int32', 'int64'], 'reduce_sum') helper = LayerHelper('reduce_sum', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) helper.append_op( @@ -3971,9 +3971,8 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None): outs = core.ops.reduce_mean(inputs, attrs) return outs['Out'][0] - check_type_and_dtype(input, 'input', Variable, - ['float32', 'float64', 'int32', 'int64'], - 'reduce_mean') + check_variable_and_dtype( + input, 'input', ['float32', 'float64', 'int32', 'int64'], 'reduce_mean') helper = LayerHelper('reduce_mean', **locals()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) helper.append_op( @@ -4601,8 +4600,8 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None): def __check_input(x, y): var_names = {'x': x, 'y': y} for name, val in var_names.items(): - check_type_and_dtype(val, name, Variable, - ['float16', 'float32', 'float64'], 'matmul') + check_variable_and_dtype( + val, name, ['float16', 'float32', 'float64'], 'matmul') x_shape = list(x.shape) y_shape = list(y.shape) if len(x_shape) == 1: @@ -4962,9 +4961,9 @@ def transpose(x, perm, name=None): outs = core.ops.transpose2(inputs, attrs) return outs['Out'][0] - check_type_and_dtype(x, 'x', Variable, - ['float16', 'float32', 'float64', 'int32', 'int64'], - 'transpose') + check_variable_and_dtype( + x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], + 'transpose') check_type(perm, 'perm', list, 'transpose') if len(perm) != len(x.shape): @@ -5589,9 +5588,8 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None): out = outs['Out'][0] return dygraph_utils._append_activation_in_dygraph(out, act) - check_type_and_dtype(x, 'x', Variable, - ['float16', 'float32', 'float64', 'int32', 'int64'], - 'reshape') + check_variable_and_dtype( + x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'reshape') check_type(shape, 'shape', (list, tuple, Variable), 'reshape') check_type(actual_shape, 'actual_shape', (Variable, type(None)), 'reshape') @@ -5719,9 +5717,9 @@ def squeeze(input, axes, name=None): """ helper = LayerHelper("squeeze", **locals()) - check_type_and_dtype(input, 'input', Variable, - ['float32', 'float64', 'int8', 'int32', 'int64'], - 'squeeze') + check_variable_and_dtype(input, 'input', + ['float32', 'float64', 'int8', 'int32', 'int64'], + 'squeeze') check_type(axes, 'axes', list, 'squeeze') out = helper.create_variable_for_type_inference(dtype=input.dtype) x_shape = helper.create_variable_for_type_inference(dtype=input.dtype) @@ -8228,9 +8226,8 @@ def crop_tensor(x, shape=None, offsets=None, name=None): """ helper = LayerHelper('crop_tensor', **locals()) - check_type_and_dtype(x, 'x', Variable, - ['float32', 'float64', 'int32', 'int64'], - 'crop_tensor') + check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'], + 'crop_tensor') check_type(shape, 'shape', (list, tuple, Variable), 'crop_tensor') check_type(offsets, 'offsets', (list, tuple, Variable, type(None)), 'crop_tensor') @@ -8523,8 +8520,7 @@ def elu(x, alpha=1.0, name=None): # [ 1. 15.6 ]] """ helper = LayerHelper('elu', **locals()) - check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64'], - 'elu') + check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'elu') out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='elu', @@ -9342,9 +9338,8 @@ def expand(x, expand_times, name=None): outs = core.ops.expand(inputs, attrs) return outs['Out'][0] - check_type_and_dtype(x, 'x', Variable, - ['bool', 'float32', 'float64', 'int32', 'int64'], - 'expand') + check_variable_and_dtype( + x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'expand') check_type(expand_times, 'expand_times', (list, tuple, Variable), 'expand') if convert_dtype(x.dtype) == 'bool' and x.stop_gradient == True: raise ValueError( @@ -10277,12 +10272,10 @@ def _elementwise_op(helper): assert x is not None, 'x cannot be None in {}'.format(op_type) assert y is not None, 'y cannot be None in {}'.format(op_type) - check_type_and_dtype(x, 'x', Variable, - ['float16', 'float32', 'float64', 'int32', 'int64'], - op_type) - check_type_and_dtype(y, 'y', Variable, - ['float16', 'float32', 'float64', 'int32', 'int64'], - op_type) + check_variable_and_dtype( + x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], op_type) + check_variable_and_dtype( + y, 'y', ['float16', 'float32', 'float64', 'int32', 'int64'], op_type) axis = helper.kwargs.get('axis', -1) use_mkldnn = helper.kwargs.get('use_mkldnn', False) @@ -11338,8 +11331,7 @@ def mean(x, name=None): return outs['Out'][0] helper = LayerHelper("mean", **locals()) - check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64'], - 'mean') + check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'mean') if name is None: out = helper.create_variable_for_type_inference(dtype=x.dtype) else: @@ -11425,10 +11417,8 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None): return outs['Out'][0] helper = LayerHelper("mul", **locals()) - check_type_and_dtype(x, 'x', Variable, ['float16', 'float32', 'float64'], - 'mul') - check_type_and_dtype(y, 'y', Variable, ['float16', 'float32', 'float64'], - 'mul') + check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'mul') + check_variable_and_dtype(y, 'y', ['float16', 'float32', 'float64'], 'mul') if name is None: out = helper.create_variable_for_type_inference(dtype=x.dtype) else: diff --git a/python/paddle/fluid/layers/tensor.py b/python/paddle/fluid/layers/tensor.py index 429fa6f569..5f2579b103 100644 --- a/python/paddle/fluid/layers/tensor.py +++ b/python/paddle/fluid/layers/tensor.py @@ -23,7 +23,7 @@ from ..core import VarDesc from .. import core from .layer_function_generator import templatedoc from . import utils -from ..data_feeder import check_type_and_dtype, check_type, check_dtype, convert_dtype +from ..data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype import numpy import warnings @@ -193,8 +193,8 @@ def cast(x, dtype): # [ 0 4]] int32 """ helper = LayerHelper('cast', **locals()) - check_type_and_dtype( - x, 'x', Variable, + check_variable_and_dtype( + x, 'x', ['bool', 'float16', 'float32', 'float64', 'int32', 'int64', 'uint8'], 'cast') out = helper.create_variable_for_type_inference(dtype=dtype) @@ -269,8 +269,8 @@ def concat(input, axis=0, name=None): (type(input))) input = [input] for id, x in enumerate(input): - check_type_and_dtype( - x, 'input[' + str(id) + ']', Variable, + check_variable_and_dtype( + x, 'input[' + str(id) + ']', ['float16', 'float32', 'float64', 'int32', 'int64'], 'concat') check_type(axis, 'axis', (int, Variable), 'concat') inputs = {'X': input} -- GitLab