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PaddleDetection
未验证 提交 96e9b658 编写于 作者: X Xin Pan 提交者： GitHub
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Merge pull request #13941 from panyx0718/fix2 

handle var type inference
上级 5d6783f8 c1383744
隐藏空白更改
内联 并排
Showing with 288 addition and 251 deletion
paddle/fluid/framework/op_desc.cc
浏览文件 @ 96e9b658
......@@ -515,20 +515,14 @@ void OpDesc::InferShape(const BlockDesc &block) const {
}
void OpDesc::InferVarType(BlockDesc *block) const {
// There are a few places that var type can be set.
// When VarDesc is created, default set to LOD_TENSOR.
// When output variable is created, default is defaut set to LOD_TENSOR.
// We limit here to be the only place that operator defines its customized
// var type inference. Hence, we don't do any "default" setting here.
auto &info = OpInfoMap::Instance().Get(this->Type());
if (info.infer_var_type_) {
info.infer_var_type_(*this, block);
} else {
// all output type is LoDTensor by default
VLOG(10) << this->Type()
<< " has not registered InferVarType. Set output variables to "
"LOD_TENSOR";
for (auto &out_pair : this->outputs_) {
for (auto &out_var_name : out_pair.second) {
block->FindRecursiveOrCreateVar(out_var_name)
.SetType(proto::VarType::LOD_TENSOR);
}
}
}
}
......
python/paddle/fluid/layer_helper.py
浏览文件 @ 96e9b658
......@@ -324,10 +324,19 @@ class LayerHelper(object):
raise ValueError("no Parameter name %s found" % name)
return param
def create_tmp_variable(self, dtype, stop_gradient=False):
def create_variable_for_type_inference(self, dtype, stop_gradient=False):
"""Create a temporary variable that should be type inferred layer.
Note:
The default type will be set to LOD_TENSOR. However, when
the var is used as operator output, its type will be updated
based on operator's `VarTypeInference` implementation in
infer_var_type.
"""
return self.main_program.current_block().create_var(
name=unique_name.generate(".".join([self.name, 'tmp'])),
dtype=dtype,
type=core.VarDesc.VarType.LOD_TENSOR,
persistable=False,
stop_gradient=stop_gradient)
......@@ -388,7 +397,7 @@ class LayerHelper(object):
b = self.create_parameter(
attr=bias_attr, shape=size, dtype=input_var.dtype, is_bias=True)
tmp = self.create_tmp_variable(dtype=input_var.dtype)
tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
self.append_op(
type='elementwise_add',
inputs={'X': [input_var],
......@@ -414,7 +423,7 @@ class LayerHelper(object):
tmp = input_var
# NOTE(dzhwinter): some activation support inplace compution.
if not core.IsInplace(act_type):
tmp = self.create_tmp_variable(dtype=input_var.dtype)
tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
self.append_op(
type=act_type,
inputs={"X": [input_var]},
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 96e9b658
......@@ -80,8 +80,8 @@ def split_lod_tensor(input, mask, level=0):
"""
helper = LayerHelper('split_lod_tensor', **locals())
out_true = helper.create_tmp_variable(dtype=input.dtype)
out_false = helper.create_tmp_variable(dtype=input.dtype)
out_true = helper.create_variable_for_type_inference(dtype=input.dtype)
out_false = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='split_lod_tensor',
inputs={
......@@ -131,7 +131,7 @@ def merge_lod_tensor(in_true, in_false, x, mask, level=0):
in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
"""
helper = LayerHelper('merge_lod_tensor', **locals())
out = helper.create_tmp_variable(dtype=in_true.dtype)
out = helper.create_variable_for_type_inference(dtype=in_true.dtype)
helper.append_op(
type='merge_lod_tensor',
inputs={'X': x,
......@@ -524,7 +524,7 @@ class StaticRNN(object):
if not isinstance(o, Variable):
raise TypeError("step output takes a Variable")
tmp_o = self.helper.create_tmp_variable(dtype=o.dtype)
tmp_o = self.helper.create_variable_for_type_inference(dtype=o.dtype)
self.helper.append_op(
type='rnn_memory_helper',
inputs={'X': [o]},
......@@ -606,7 +606,8 @@ class StaticRNN(object):
pre_memories.append(mem.pre_mem.name)
mem_var = rnn_block.var(mem.mem.name)
assert isinstance(mem_var, Variable)
new_mem = self.helper.create_tmp_variable(dtype=mem_var.dtype)
new_mem = self.helper.create_variable_for_type_inference(
dtype=mem_var.dtype)
rnn_block.append_op(
type='rnn_memory_helper',
......@@ -813,7 +814,7 @@ def max_sequence_len(rank_table):
${out_comment}.
"""
helper = LayerHelper("max_seqence_len", **locals())
res = helper.create_tmp_variable(dtype="int64")
res = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="max_sequence_len",
inputs={"RankTable": rank_table},
......@@ -884,7 +885,7 @@ def array_to_lod_tensor(x, table):
lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
"""
helper = LayerHelper("array_to_lod_tensor", **locals())
tmp = helper.create_tmp_variable(dtype=x.dtype)
tmp = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="array_to_lod_tensor",
inputs={'X': x,
......@@ -915,7 +916,7 @@ def increment(x, value=1.0, in_place=True):
"""
helper = LayerHelper("increment", **locals())
if not in_place:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = x
helper.append_op(
......@@ -1012,7 +1013,7 @@ def less_than(x, y, force_cpu=None, cond=None, **ignored):
"""
helper = LayerHelper("less_than", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
attrs = dict()
......@@ -1051,7 +1052,7 @@ def equal(x, y, cond=None, **ignored):
"""
helper = LayerHelper("equal", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
helper.append_op(
......@@ -1098,7 +1099,7 @@ def array_read(array, i):
array,
Variable) or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY:
raise TypeError("array should be tensor array vairable")
out = helper.create_tmp_variable(dtype=array.dtype)
out = helper.create_variable_for_type_inference(dtype=array.dtype)
helper.append_op(
type='read_from_array',
inputs={'X': [array],
......@@ -1133,7 +1134,7 @@ def shrink_memory(x, i, table):
usage.
"""
helper = LayerHelper('shrink_memory', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='shrink_rnn_memory',
inputs={'X': [x],
......@@ -1170,7 +1171,7 @@ def array_length(array):
"""
helper = LayerHelper('array_length', **locals())
tmp = helper.create_tmp_variable(dtype='int64')
tmp = helper.create_variable_for_type_inference(dtype='int64')
tmp.stop_gradient = True
helper.append_op(
type='lod_array_length', inputs={'X': [array]}, outputs={'Out': [tmp]})
......@@ -1590,7 +1591,7 @@ class DynamicRNN(object):
self.mem_dict = dict()
self.output_array = []
self.outputs = []
self.cond = self.helper.create_tmp_variable(dtype='bool')
self.cond = self.helper.create_variable_for_type_inference(dtype='bool')
self.cond.stop_gradient = False
self.while_op = While(self.cond)
self.input_array = []
......@@ -1924,7 +1925,7 @@ def reorder_lod_tensor_by_rank(x, rank_table):
helper.is_instance('x', Variable)
helper.is_instance('rank_table', Variable)
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reorder_lod_tensor_by_rank',
inputs={'X': [x],
......@@ -1958,7 +1959,7 @@ def is_empty(x, cond=None, **ignored):
"""
helper = LayerHelper("is_empty", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
elif not isinstance(cond, Variable):
raise TypeError("cond takes a variable")
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 96e9b658
......@@ -147,10 +147,11 @@ def rpn_target_assign(bbox_pred,
helper = LayerHelper('rpn_target_assign', **locals())
# Assign target label to anchors
loc_index = helper.create_tmp_variable(dtype='int32')
score_index = helper.create_tmp_variable(dtype='int32')
target_label = helper.create_tmp_variable(dtype='int32')
target_bbox = helper.create_tmp_variable(dtype=anchor_box.dtype)
loc_index = helper.create_variable_for_type_inference(dtype='int32')
score_index = helper.create_variable_for_type_inference(dtype='int32')
target_label = helper.create_variable_for_type_inference(dtype='int32')
target_bbox = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype)
helper.append_op(
type="rpn_target_assign",
inputs={
......@@ -282,7 +283,8 @@ def detection_output(loc,
scores = nn.reshape(x=scores, shape=compile_shape, actual_shape=run_shape)
scores = nn.transpose(scores, perm=[0, 2, 1])
scores.stop_gradient = True
nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
nmsed_outs = helper.create_variable_for_type_inference(
dtype=decoded_box.dtype)
helper.append_op(
type="multiclass_nms",
inputs={'Scores': scores,
......@@ -314,7 +316,7 @@ def iou_similarity(x, y, name=None):
"""
helper = LayerHelper("iou_similarity", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -351,7 +353,8 @@ def box_coder(prior_box,
helper = LayerHelper("box_coder", **locals())
if name is None:
output_box = helper.create_tmp_variable(dtype=prior_box.dtype)
output_box = helper.create_variable_for_type_inference(
dtype=prior_box.dtype)
else:
output_box = helper.create_variable(
name=name, dtype=prior_box.dtype, persistable=False)
......@@ -382,7 +385,7 @@ def polygon_box_transform(input, name=None):
"""
helper = LayerHelper("polygon_box_transform", **locals())
if name is None:
output = helper.create_tmp_variable(dtype=input.dtype)
output = helper.create_variable_for_type_inference(dtype=input.dtype)
else:
output = helper.create_variable(
name=name, dtype=prior_box.input, persistable=False)
......@@ -450,7 +453,7 @@ def detection_map(detect_res,
helper = LayerHelper("detection_map", **locals())
def __create_var(type):
return helper.create_tmp_variable(dtype=type)
return helper.create_variable_for_type_inference(dtype=type)
map_out = __create_var('float32')
accum_pos_count_out = out_states[0] if out_states else __create_var('int32')
......@@ -557,8 +560,9 @@ def bipartite_match(dist_matrix,
>>> matched_indices, matched_dist = fluid.layers.bipartite_match(iou)
"""
helper = LayerHelper('bipartite_match', **locals())
match_indices = helper.create_tmp_variable(dtype='int32')
match_distance = helper.create_tmp_variable(dtype=dist_matrix.dtype)
match_indices = helper.create_variable_for_type_inference(dtype='int32')
match_distance = helper.create_variable_for_type_inference(
dtype=dist_matrix.dtype)
helper.append_op(
type='bipartite_match',
inputs={'DistMat': dist_matrix},
......@@ -644,8 +648,8 @@ def target_assign(input,
gt, matched_indices, mismatch_value=0)
"""
helper = LayerHelper('target_assign', **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
out_weight = helper.create_tmp_variable(dtype='float32')
out = helper.create_variable_for_type_inference(dtype=input.dtype)
out_weight = helper.create_variable_for_type_inference(dtype='float32')
helper.append_op(
type='target_assign',
inputs={
......@@ -816,9 +820,10 @@ def ssd_loss(location,
conf_loss = nn.reshape(
x=conf_loss, shape=(num, num_prior), actual_shape=actual_shape)
conf_loss.stop_gradient = True
neg_indices = helper.create_tmp_variable(dtype='int32')
neg_indices = helper.create_variable_for_type_inference(dtype='int32')
dtype = matched_indices.dtype
updated_matched_indices = helper.create_tmp_variable(dtype=dtype)
updated_matched_indices = helper.create_variable_for_type_inference(
dtype=dtype)
helper.append_op(
type='mine_hard_examples',
inputs={
......@@ -998,8 +1003,8 @@ def prior_box(input,
max_sizes = [max_sizes]
attrs['max_sizes'] = max_sizes
box = helper.create_tmp_variable(dtype)
var = helper.create_tmp_variable(dtype)
box = helper.create_variable_for_type_inference(dtype)
var = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="prior_box",
inputs={"Input": input,
......@@ -1337,8 +1342,8 @@ def anchor_generator(input,
'offset': offset
}
anchor = helper.create_tmp_variable(dtype)
var = helper.create_tmp_variable(dtype)
anchor = helper.create_variable_for_type_inference(dtype)
var = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="anchor_generator",
inputs={"Input": input},
......@@ -1384,7 +1389,7 @@ def roi_perspective_transform(input,
"""
helper = LayerHelper('roi_perspective_transform', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="roi_perspective_transform",
inputs={"X": input,
......@@ -1418,11 +1423,15 @@ def generate_proposal_labels(rpn_rois,
helper = LayerHelper('generate_proposal_labels', **locals())
rois = helper.create_tmp_variable(dtype=rpn_rois.dtype)
labels_int32 = helper.create_tmp_variable(dtype=gt_classes.dtype)
bbox_targets = helper.create_tmp_variable(dtype=rpn_rois.dtype)
bbox_inside_weights = helper.create_tmp_variable(dtype=rpn_rois.dtype)
bbox_outside_weights = helper.create_tmp_variable(dtype=rpn_rois.dtype)
rois = helper.create_variable_for_type_inference(dtype=rpn_rois.dtype)
labels_int32 = helper.create_variable_for_type_inference(
dtype=gt_classes.dtype)
bbox_targets = helper.create_variable_for_type_inference(
dtype=rpn_rois.dtype)
bbox_inside_weights = helper.create_variable_for_type_inference(
dtype=rpn_rois.dtype)
bbox_outside_weights = helper.create_variable_for_type_inference(
dtype=rpn_rois.dtype)
helper.append_op(
type="generate_proposal_labels",
......@@ -1504,8 +1513,10 @@ def generate_proposals(scores,
"""
helper = LayerHelper('generate_proposals', **locals())
rpn_rois = helper.create_tmp_variable(dtype=bbox_deltas.dtype)
rpn_roi_probs = helper.create_tmp_variable(dtype=scores.dtype)
rpn_rois = helper.create_variable_for_type_inference(
dtype=bbox_deltas.dtype)
rpn_roi_probs = helper.create_variable_for_type_inference(
dtype=scores.dtype)
helper.append_op(
type="generate_proposals",
inputs={
......
python/paddle/fluid/layers/io.py
浏览文件 @ 96e9b658
......@@ -954,7 +954,7 @@ def read_file(reader):
"""
helper = LayerHelper('read_file')
out = [
helper.create_tmp_variable(
helper.create_variable_for_type_inference(
stop_gradient=True, dtype='float32')
for _ in range(len(reader.desc.shapes()))
]
......
python/paddle/fluid/layers/layer_function_generator.py
浏览文件 @ 96e9b658
......@@ -202,10 +202,12 @@ def generate_layer_fn(op_type):
out_var = out[0] if (isinstance(out, list) or
isinstance(out, tuple)) else out
else:
out_var = helper.create_tmp_variable(dtype=dtype)
out_var = helper.create_variable_for_type_inference(dtype=dtype)
outputs[o_name] = [out_var]
for name in intermediate_output_names:
outputs[name] = [helper.create_tmp_variable(dtype=dtype)]
outputs[name] = [
helper.create_variable_for_type_inference(dtype=dtype)
]
helper.append_op(
type=op_type, inputs=inputs, outputs=outputs, attrs=kwargs)
return helper.append_activation(out_var)
......@@ -229,7 +231,7 @@ def generate_layer_fn_noattr(op_type):
def func(x, name=None):
helper = LayerHelper(op_type, **locals())
output = helper.create_tmp_variable(dtype=x.dtype)
output = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type=op_type, inputs={"X": x}, outputs={"Out": output})
return output
......
python/paddle/fluid/layers/metric_op.py
浏览文件 @ 96e9b658
......@@ -58,11 +58,11 @@ def accuracy(input, label, k=1, correct=None, total=None):
"""
helper = LayerHelper("accuracy", **locals())
topk_out, topk_indices = nn.topk(input, k=k)
acc_out = helper.create_tmp_variable(dtype="float32")
acc_out = helper.create_variable_for_type_inference(dtype="float32")
if correct is None:
correct = helper.create_tmp_variable(dtype="int64")
correct = helper.create_variable_for_type_inference(dtype="int64")
if total is None:
total = helper.create_tmp_variable(dtype="int64")
total = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="accuracy",
inputs={
......@@ -124,8 +124,8 @@ def auc(input,
auc_out=fluid.layers.auc(input=prediction, label=label)
"""
helper = LayerHelper("auc", **locals())
auc_out = helper.create_tmp_variable(dtype="float64")
batch_auc_out = helper.create_tmp_variable(dtype="float64")
auc_out = helper.create_variable_for_type_inference(dtype="float64")
batch_auc_out = helper.create_variable_for_type_inference(dtype="float64")
# make tp, tn, fp, fn persistable, so that can accumulate all batches.
# for batch auc
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 96e9b658
......@@ -242,7 +242,7 @@ def fc(input,
w = helper.create_parameter(
attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
tmp = helper.create_tmp_variable(dtype)
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="mul",
inputs={"X": input_var,
......@@ -255,7 +255,7 @@ def fc(input,
if len(mul_results) == 1:
pre_bias = mul_results[0]
else:
pre_bias = helper.create_tmp_variable(dtype)
pre_bias = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="sum",
inputs={"X": mul_results},
......@@ -314,7 +314,7 @@ def embedding(input,
helper = LayerHelper('embedding', **locals())
w = helper.create_parameter(
attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False)
tmp = helper.create_tmp_variable(dtype)
tmp = helper.create_variable_for_type_inference(dtype)
padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
size[0] + padding_idx)
helper.append_op(
......@@ -418,10 +418,10 @@ def dynamic_lstm(input,
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
hidden = helper.create_tmp_variable(dtype)
cell = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_cell_pre_act = helper.create_tmp_variable(dtype)
hidden = helper.create_variable_for_type_inference(dtype)
cell = helper.create_variable_for_type_inference(dtype)
batch_gate = helper.create_variable_for_type_inference(dtype)
batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
batch_size = input.shape[0]
if h_0:
......@@ -621,12 +621,12 @@ def dynamic_lstmp(input,
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
projection = helper.create_tmp_variable(dtype)
cell = helper.create_tmp_variable(dtype)
ordered_proj0 = helper.create_tmp_variable(dtype)
batch_hidden = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_cell_pre_act = helper.create_tmp_variable(dtype)
projection = helper.create_variable_for_type_inference(dtype)
cell = helper.create_variable_for_type_inference(dtype)
ordered_proj0 = helper.create_variable_for_type_inference(dtype)
batch_hidden = helper.create_variable_for_type_inference(dtype)
batch_gate = helper.create_variable_for_type_inference(dtype)
batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='lstmp',
......@@ -751,10 +751,10 @@ def dynamic_gru(input,
), 'The shape of h0 should be(batch_size, %d)' % size
inputs['H0'] = h_0
hidden = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_reset_hidden_prev = helper.create_tmp_variable(dtype)
batch_hidden = helper.create_tmp_variable(dtype)
hidden = helper.create_variable_for_type_inference(dtype)
batch_gate = helper.create_variable_for_type_inference(dtype)
batch_reset_hidden_prev = helper.create_variable_for_type_inference(dtype)
batch_hidden = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='gru',
......@@ -844,9 +844,9 @@ def gru_unit(input,
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
gate = helper.create_tmp_variable(dtype)
reset_hidden_pre = helper.create_tmp_variable(dtype)
updated_hidden = helper.create_tmp_variable(dtype)
gate = helper.create_variable_for_type_inference(dtype)
reset_hidden_pre = helper.create_variable_for_type_inference(dtype)
updated_hidden = helper.create_variable_for_type_inference(dtype)
inputs = {'Input': input, 'HiddenPrev': hidden, 'Weight': weight}
# create bias
if helper.bias_attr:
......@@ -896,10 +896,14 @@ def linear_chain_crf(input, label, param_attr=None):
attr=helper.param_attr,
shape=[size + 2, size],
dtype=helper.input_dtype())
alpha = helper.create_tmp_variable(dtype=helper.input_dtype())
emission_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
transition_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
log_likelihood = helper.create_tmp_variable(dtype=helper.input_dtype())
alpha = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
emission_exps = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
transition_exps = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
log_likelihood = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
helper.append_op(
type='linear_chain_crf',
inputs={"Emission": [input],
......@@ -938,7 +942,8 @@ def crf_decoding(input, param_attr, label=None):
"""
helper = LayerHelper('crf_decoding', **locals())
transition = helper.get_parameter(param_attr.name)
viterbi_path = helper.create_tmp_variable(dtype=helper.input_dtype())
viterbi_path = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
helper.append_op(
type='crf_decoding',
inputs={"Emission": [input],
......@@ -962,9 +967,9 @@ def cos_sim(X, Y):
Variable: the output of cosine(X, Y).
"""
helper = LayerHelper('cos_sim', **locals())
out = helper.create_tmp_variable(dtype=X.dtype)
xnorm = helper.create_tmp_variable(dtype=X.dtype)
ynorm = helper.create_tmp_variable(dtype=X.dtype)
out = helper.create_variable_for_type_inference(dtype=X.dtype)
xnorm = helper.create_variable_for_type_inference(dtype=X.dtype)
ynorm = helper.create_variable_for_type_inference(dtype=X.dtype)
helper.append_op(
type='cos_sim',
inputs={'X': [X],
......@@ -1008,8 +1013,9 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
"""
helper = LayerHelper('dropout', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
mask = helper.create_variable_for_type_inference(
dtype=x.dtype, stop_gradient=True)
if (seed is None or seed == 0) and helper.main_program.random_seed != 0:
seed = helper.main_program.random_seed
......@@ -1094,7 +1100,7 @@ def cross_entropy(input, label, soft_label=False, ignore_index=-100):
cost = fluid.layers.cross_entropy(input=predict, label=label)
"""
helper = LayerHelper('cross_entropy', **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
out = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='cross_entropy',
inputs={'X': [input],
......@@ -1141,14 +1147,14 @@ def square_error_cost(input, label):
"""
helper = LayerHelper('square_error_cost', **locals())
minus_out = helper.create_tmp_variable(dtype=input.dtype)
minus_out = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='elementwise_sub',
inputs={'X': [input],
'Y': [label]},
outputs={'Out': [minus_out]})
square_out = helper.create_tmp_variable(dtype=input.dtype)
square_out = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='square', inputs={'X': [minus_out]},
outputs={'Out': [square_out]})
......@@ -1254,12 +1260,13 @@ def chunk_eval(input,
helper = LayerHelper("chunk_eval", **locals())
# prepare output
precision = helper.create_tmp_variable(dtype="float32")
recall = helper.create_tmp_variable(dtype="float32")
f1_score = helper.create_tmp_variable(dtype="float32")
num_infer_chunks = helper.create_tmp_variable(dtype="int64")
num_label_chunks = helper.create_tmp_variable(dtype="int64")
num_correct_chunks = helper.create_tmp_variable(dtype="int64")
precision = helper.create_variable_for_type_inference(dtype="float32")
recall = helper.create_variable_for_type_inference(dtype="float32")
f1_score = helper.create_variable_for_type_inference(dtype="float32")
num_infer_chunks = helper.create_variable_for_type_inference(dtype="int64")
num_label_chunks = helper.create_variable_for_type_inference(dtype="int64")
num_correct_chunks = helper.create_variable_for_type_inference(
dtype="int64")
helper.append_op(
type="chunk_eval",
......@@ -1326,7 +1333,7 @@ def sequence_conv(input,
filter_shape = [filter_size * input.shape[1], num_filters]
filter_param = helper.create_parameter(
attr=helper.param_attr, shape=filter_shape, dtype=dtype)
pre_bias = helper.create_tmp_variable(dtype)
pre_bias = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='sequence_conv',
......@@ -1382,7 +1389,7 @@ def sequence_softmax(input, use_cudnn=False, name=None):
"""
helper = LayerHelper('sequence_softmax', **locals())
dtype = helper.input_dtype()
softmax_out = helper.create_tmp_variable(dtype)
softmax_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="sequence_softmax",
inputs={"X": input},
......@@ -1436,7 +1443,7 @@ def softmax(input, use_cudnn=True, name=None):
"""
helper = LayerHelper('softmax', **locals())
dtype = helper.input_dtype()
softmax_out = helper.create_tmp_variable(dtype)
softmax_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="softmax",
inputs={"X": input},
......@@ -1599,7 +1606,7 @@ def conv2d(input,
dtype=dtype,
default_initializer=_get_default_param_initializer())
pre_bias = helper.create_tmp_variable(dtype)
pre_bias = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type=l_type,
......@@ -1770,7 +1777,7 @@ def conv3d(input,
dtype=dtype,
default_initializer=_get_default_param_initializer())
pre_bias = helper.create_tmp_variable(dtype)
pre_bias = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type=l_type,
......@@ -1849,8 +1856,8 @@ def sequence_pool(input, pool_type):
"""
helper = LayerHelper('sequence_pool', **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
max_index = helper.create_tmp_variable(dtype)
pool_out = helper.create_variable_for_type_inference(dtype)
max_index = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="sequence_pool",
......@@ -1886,7 +1893,7 @@ def sequence_concat(input, name=None):
out = fluid.layers.sequence_concat(input=[seq1, seq2, seq3])
"""
helper = LayerHelper('sequence_concat', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='sequence_concat', inputs={'X': input}, outputs={'Out': [out]})
return out
......@@ -2013,7 +2020,7 @@ def sequence_slice(input, offset, length, name=None):
"""
helper = LayerHelper("sequence_slice", **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
offset.stop_gradient = True
length.stop_gradient = True
......@@ -2099,7 +2106,7 @@ def pool2d(input,
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
pool_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type=l_type,
......@@ -2167,7 +2174,7 @@ def pool3d(input,
l_type = "pool3d"
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
pool_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type=l_type,
......@@ -2310,10 +2317,13 @@ def batch_norm(input,
mean_out = mean
# variance and variance out share the same memory
variance_out = variance
saved_mean = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
saved_variance = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
saved_mean = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
saved_variance = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
batch_norm_out = input if in_place else helper.create_tmp_variable(dtype)
batch_norm_out = input if in_place else helper.create_variable_for_type_inference(
dtype)
helper.append_op(
type="batch_norm",
......@@ -2430,9 +2440,11 @@ def layer_norm(input,
inputs['Bias'] = bias
# create output
mean_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
variance_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
layer_norm_out = helper.create_tmp_variable(dtype)
mean_out = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
variance_out = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
layer_norm_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="layer_norm",
......@@ -2619,7 +2631,7 @@ def conv2d_transpose(input,
img_filter = helper.create_parameter(
dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
pre_bias = helper.create_tmp_variable(dtype=input.dtype)
pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type=op_type,
inputs={'Input': [input],
......@@ -2797,7 +2809,7 @@ def conv3d_transpose(input,
img_filter = helper.create_parameter(
dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
pre_bias = helper.create_tmp_variable(dtype=input.dtype)
pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type=l_type,
inputs={'Input': [input],
......@@ -2876,7 +2888,7 @@ def sequence_expand(x, y, ref_level=-1, name=None):
"""
helper = LayerHelper('sequence_expand', input=x, **locals())
dtype = helper.input_dtype()
tmp = helper.create_tmp_variable(dtype)
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='sequence_expand',
inputs={'X': x,
......@@ -2942,7 +2954,7 @@ def sequence_expand_as(x, y, name=None):
"""
helper = LayerHelper('sequence_expand_as', input=x, **locals())
dtype = helper.input_dtype()
tmp = helper.create_tmp_variable(dtype)
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='sequence_expand_as',
inputs={'X': x,
......@@ -2987,8 +2999,8 @@ def sequence_pad(x, pad_value, maxlen=None, name=None):
helper = LayerHelper('sequence_pad', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
length = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
length = helper.create_variable_for_type_inference(dtype)
pad_value.stop_gradient = True
length.stop_gradient = True
......@@ -3053,7 +3065,7 @@ def sequence_unpad(x, length, name=None):
helper = LayerHelper('sequence_unpad', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
length.stop_gradient = True
......@@ -3152,8 +3164,9 @@ def beam_search(pre_ids,
score_type = scores.dtype
id_type = ids.dtype
selected_scores = helper.create_tmp_variable(dtype=score_type)
selected_ids = helper.create_tmp_variable(dtype=id_type)
selected_scores = helper.create_variable_for_type_inference(
dtype=score_type)
selected_ids = helper.create_variable_for_type_inference(dtype=id_type)
helper.append_op(
type='beam_search',
......@@ -3210,8 +3223,8 @@ def beam_search_decode(ids, scores, beam_size, end_id, name=None):
ids, scores, beam_size=5, end_id=0)
"""
helper = LayerHelper('beam_search_decode', **locals())
sentence_ids = helper.create_tmp_variable(dtype=ids.dtype)
sentence_scores = helper.create_tmp_variable(dtype=ids.dtype)
sentence_ids = helper.create_variable_for_type_inference(dtype=ids.dtype)
sentence_scores = helper.create_variable_for_type_inference(dtype=ids.dtype)
helper.append_op(
type="beam_search_decode",
......@@ -3341,8 +3354,8 @@ def lstm_unit(x_t,
param_attr=param_attr,
bias_attr=bias_attr)
dtype = x_t.dtype
c = helper.create_tmp_variable(dtype)
h = helper.create_tmp_variable(dtype)
c = helper.create_variable_for_type_inference(dtype)
h = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='lstm_unit',
......@@ -3396,7 +3409,7 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
"""
helper = LayerHelper('reduce_sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
helper.append_op(
......@@ -3453,7 +3466,7 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_mean(x, dim=[0, 1]) # [4.0, 5.0]
"""
helper = LayerHelper('reduce_mean', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
helper.append_op(
......@@ -3508,7 +3521,7 @@ def reduce_max(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_max(x, dim=[0, 1]) # [7.0, 8.0]
"""
helper = LayerHelper('reduce_max', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
helper.append_op(
......@@ -3563,7 +3576,7 @@ def reduce_min(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_min(x, dim=[0, 1]) # [1.0, 2.0]
"""
helper = LayerHelper('reduce_min', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
helper.append_op(
......@@ -3619,7 +3632,7 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_prod(x, dim=[0, 1]) # [105.0, 384.0]
"""
helper = LayerHelper('reduce_prod', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
helper.append_op(
......@@ -3679,7 +3692,7 @@ def split(input, num_or_sections, dim=-1, name=None):
dim], 'len(num_or_sections) must not be more than input.shape[dim].'
num = len(num_or_sections)
outs = [
helper.create_tmp_variable(dtype=helper.input_dtype())
helper.create_variable_for_type_inference(dtype=helper.input_dtype())
for i in range(num)
]
helper.append_op(
......@@ -3736,8 +3749,8 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
axis = 0
helper = LayerHelper("l2_normalize", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
norm = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
norm = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="norm",
inputs={"X": x},
......@@ -3846,7 +3859,7 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
__check_input(x, y)
helper = LayerHelper('matmul', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='matmul',
inputs={'X': x,
......@@ -3917,8 +3930,8 @@ def topk(input, k, name=None):
top5_values, top5_indices = layers.topk(input, k=5)
"""
helper = LayerHelper("top_k", **locals())
values = helper.create_tmp_variable(dtype=input.dtype)
indices = helper.create_tmp_variable(dtype="int64")
values = helper.create_variable_for_type_inference(dtype=input.dtype)
indices = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="top_k",
inputs={"X": [input]},
......@@ -3976,8 +3989,8 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None):
# remove some tokens from input and labels
if ignored_tokens is not None and len(ignored_tokens) > 0:
erased_input = helper.create_tmp_variable(dtype="int64")
erased_label = helper.create_tmp_variable(dtype="int64")
erased_input = helper.create_variable_for_type_inference(dtype="int64")
erased_label = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="sequence_erase",
......@@ -3994,8 +4007,8 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None):
label = erased_label
# edit distance op
edit_distance_out = helper.create_tmp_variable(dtype="int64")
sequence_num = helper.create_tmp_variable(dtype="int64")
edit_distance_out = helper.create_variable_for_type_inference(dtype="int64")
sequence_num = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="edit_distance",
inputs={"Hyps": [input],
......@@ -4070,7 +4083,7 @@ def ctc_greedy_decoder(input, blank, name=None):
_, topk_indices = topk(input, k=1)
# ctc align op
ctc_out = helper.create_tmp_variable(dtype="int64")
ctc_out = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="ctc_align",
inputs={"Input": [topk_indices]},
......@@ -4120,8 +4133,8 @@ def warpctc(input, label, blank=0, norm_by_times=False):
"""
helper = LayerHelper('warpctc', **locals())
loss_out = helper.create_tmp_variable(dtype=input.dtype)
grad_out = helper.create_tmp_variable(dtype=input.dtype)
loss_out = helper.create_variable_for_type_inference(dtype=input.dtype)
grad_out = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='warpctc',
inputs={'Logits': [input],
......@@ -4182,7 +4195,7 @@ def sequence_reshape(input, new_dim):
x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=10)
"""
helper = LayerHelper('sequence_reshape', **locals())
out = helper.create_tmp_variable(helper.input_dtype())
out = helper.create_variable_for_type_inference(helper.input_dtype())
helper.append_op(
type='sequence_reshape',
inputs={'X': [input]},
......@@ -4279,9 +4292,9 @@ def nce(input,
is_bias=True,
dtype=input.dtype)
inputs['Bias'] = b
cost = helper.create_tmp_variable(dtype=input.dtype)
sample_logits = helper.create_tmp_variable(dtype=input.dtype)
sample_labels = helper.create_tmp_variable(dtype=label.dtype)
cost = helper.create_variable_for_type_inference(dtype=input.dtype)
sample_logits = helper.create_variable_for_type_inference(dtype=input.dtype)
sample_labels = helper.create_variable_for_type_inference(dtype=label.dtype)
if num_neg_samples is None:
num_neg_samples = 10
......@@ -4357,8 +4370,8 @@ def hsigmoid(input,
helper = LayerHelper('hierarchical_sigmoid', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
pre_out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
pre_out = helper.create_variable_for_type_inference(dtype)
dim = input.shape[1]
if num_classes < 2:
raise ValueError("num_classes must not be less than 2.")
......@@ -4418,8 +4431,8 @@ def transpose(x, perm, name=None):
(idx, perm[idx], len(x.shape)))
helper = LayerHelper('transpose', **locals())
out = helper.create_tmp_variable(x.dtype)
x_shape = helper.create_tmp_variable(x.dtype)
out = helper.create_variable_for_type_inference(x.dtype)
x_shape = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='transpose2',
inputs={'X': [x]},
......@@ -4561,7 +4574,7 @@ def im2sequence(input,
inputs["Y"] = input_image_size
attrs["out_stride"] = out_stride
helper = LayerHelper('im2sequence', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='im2sequence', inputs=inputs, outputs={'Out': out}, attrs=attrs)
return out
......@@ -4594,7 +4607,7 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
filter_shape = [future_context_size + 1, input.shape[1]]
filter_param = helper.create_parameter(
attr=helper.param_attr, shape=filter_shape, dtype=dtype)
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='row_conv',
inputs={'X': [input],
......@@ -4627,7 +4640,7 @@ def multiplex(inputs, index):
raise ValueError("inputs should be a list object and contains at least "
"2 elements.")
out = helper.create_tmp_variable(inputs[0].dtype)
out = helper.create_variable_for_type_inference(inputs[0].dtype)
helper.append_op(
type='multiplex',
inputs={'X': inputs,
......@@ -4698,8 +4711,8 @@ def softmax_with_cross_entropy(logits,
logits=fc, label=label)
"""
helper = LayerHelper('softmax_with_cross_entropy', **locals())
softmax = helper.create_tmp_variable(dtype=logits.dtype)
loss = helper.create_tmp_variable(dtype=logits.dtype)
softmax = helper.create_variable_for_type_inference(dtype=logits.dtype)
loss = helper.create_variable_for_type_inference(dtype=logits.dtype)
helper.append_op(
type='softmax_with_cross_entropy',
inputs={'Logits': logits,
......@@ -4749,8 +4762,8 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
"""
helper = LayerHelper('smooth_l1_loss', **locals())
diff = helper.create_tmp_variable(dtype=x.dtype)
loss = helper.create_tmp_variable(dtype=x.dtype)
diff = helper.create_variable_for_type_inference(dtype=x.dtype)
loss = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='smooth_l1_loss',
inputs={
......@@ -4783,7 +4796,7 @@ def one_hot(input, depth):
one_hot_label = layers.one_hot(input=label, depth=10)
"""
helper = LayerHelper("one_hot", **locals())
one_hot_out = helper.create_tmp_variable(dtype='float32')
one_hot_out = helper.create_variable_for_type_inference(dtype='float32')
helper.append_op(
type="one_hot",
inputs={'X': input},
......@@ -4925,8 +4938,8 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
"except one unknown dimension.")
helper = LayerHelper("reshape2", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
x_shape = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="reshape2",
inputs=inputs,
......@@ -4975,8 +4988,8 @@ def squeeze(input, axes, name=None):
y = layers.sequeeze(input=x, axes=[1])
"""
helper = LayerHelper("squeeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
x_shape = helper.create_tmp_variable(dtype=input.dtype)
out = helper.create_variable_for_type_inference(dtype=input.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type="squeeze2",
inputs={"X": input},
......@@ -5012,8 +5025,8 @@ def unsqueeze(input, axes, name=None):
y = layers.unsequeeze(input=x, axes=[1])
"""
helper = LayerHelper("unsqueeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
x_shape = helper.create_tmp_variable(dtype=input.dtype)
out = helper.create_variable_for_type_inference(dtype=input.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type="unsqueeze2",
inputs={"X": input},
......@@ -5103,7 +5116,7 @@ def lod_reset(x, y=None, target_lod=None):
out = layers.lod_reset(x=x, y=y)
"""
helper = LayerHelper("lod_reset", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
if y is not None:
helper.append_op(
type="lod_reset", inputs={'X': x,
......@@ -5172,8 +5185,9 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
"dims of input must be 4(not %d), and it's order must be NCHW" %
(dims))
mid_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
lrn_out = helper.create_tmp_variable(dtype)
mid_out = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
lrn_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="lrn",
inputs={"X": input},
......@@ -5238,7 +5252,7 @@ def pad(x, paddings, pad_value=0., name=None):
"""
helper = LayerHelper('pad', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='pad',
inputs={'X': x},
......@@ -5318,7 +5332,7 @@ def pad_constant_like(x, y, pad_value=0., name=None):
"""
helper = LayerHelper('pad_constant_like', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='pad_constant_like',
inputs={'X': x,
......@@ -5383,7 +5397,7 @@ def label_smooth(label,
raise ValueError("The value of epsilon must be between 0 and 1.")
helper = LayerHelper("label_smooth", **locals())
label.stop_gradient = True
smooth_label = helper.create_tmp_variable(dtype)
smooth_label = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="label_smooth",
inputs={"X": label,
......@@ -5415,8 +5429,8 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
"""
helper = LayerHelper('roi_pool', **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
argmaxes = helper.create_tmp_variable(dtype='int32')
pool_out = helper.create_variable_for_type_inference(dtype)
argmaxes = helper.create_variable_for_type_inference(dtype='int32')
helper.append_op(
type="roi_pool",
inputs={"X": input,
......@@ -5464,7 +5478,7 @@ def roi_align(input,
"""
helper = LayerHelper('roi_align', **locals())
dtype = helper.input_dtype()
align_out = helper.create_tmp_variable(dtype)
align_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="roi_align",
inputs={"X": input,
......@@ -5589,7 +5603,7 @@ def image_resize(input,
out_h = int(input.shape[2] * scale)
out_w = int(input.shape[3] * scale)
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type=resample_methods[resample],
inputs=inputs,
......@@ -5698,7 +5712,7 @@ def gather(input, index):
"""
helper = LayerHelper('gather', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="gather",
inputs={"X": input,
......@@ -5738,7 +5752,7 @@ def scatter(input, index, updates, name=None):
"""
helper = LayerHelper('scatter', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="scatter",
inputs={"X": input,
......@@ -5798,7 +5812,7 @@ def sequence_scatter(input, index, updates, name=None):
"""
helper = LayerHelper('sequence_scatter', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="sequence_scatter",
inputs={"X": input,
......@@ -5828,7 +5842,7 @@ def random_crop(x, shape, seed=None):
"""
helper = LayerHelper("random_crop", **locals())
dtype = x.dtype
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
if seed is None:
seed = np.random.randint(-65536, 65536)
op_attrs = {"shape": shape}
......@@ -5874,7 +5888,7 @@ def log(x, name=None):
"""
helper = LayerHelper('log', **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(type="log", inputs={"X": x}, outputs={"Out": out})
return out
......@@ -5905,7 +5919,7 @@ def relu(x, name=None):
"""
helper = LayerHelper('relu', **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(type="relu", inputs={"X": x}, outputs={"Out": out})
return out
......@@ -5944,9 +5958,9 @@ def mean_iou(input, label, num_classes):
"""
helper = LayerHelper('mean_iou', **locals())
dtype = helper.input_dtype()
out_mean_iou = helper.create_tmp_variable(dtype='float32')
out_wrong = helper.create_tmp_variable(dtype='int32')
out_correct = helper.create_tmp_variable(dtype='int32')
out_mean_iou = helper.create_variable_for_type_inference(dtype='float32')
out_wrong = helper.create_variable_for_type_inference(dtype='int32')
out_correct = helper.create_variable_for_type_inference(dtype='int32')
helper.append_op(
type="mean_iou",
inputs={"Predictions": input,
......@@ -6038,7 +6052,7 @@ def crop(x, shape=None, offsets=None, name=None):
if offsets is None:
offsets = [0] * len(x.shape)
out = helper.create_tmp_variable(x.dtype)
out = helper.create_variable_for_type_inference(x.dtype)
ipts = {'X': x}
attrs = {}
if isinstance(shape, Variable):
......@@ -6118,7 +6132,7 @@ def rank_loss(label, left, right, name=None):
if not (isinstance(right, Variable)):
raise ValueError("The right should be a Variable")
out = helper.create_tmp_variable("float32")
out = helper.create_variable_for_type_inference("float32")
helper.append_op(
type='rank_loss',
......@@ -6164,8 +6178,8 @@ def margin_rank_loss(label, left, right, margin=0.1, name=None):
raise ValueError("The left should be a Variable.")
if not isinstance(right, Variable):
raise ValueError("The right should be a Variable.")
out = helper.create_tmp_variable(left.dtype)
act = helper.create_tmp_variable(left.dtype)
out = helper.create_variable_for_type_inference(left.dtype)
act = helper.create_variable_for_type_inference(left.dtype)
helper.append_op(
type='margin_rank_loss',
inputs={"Label": label,
......@@ -6250,7 +6264,7 @@ def pad2d(input,
helper = LayerHelper('pad2d', **locals())
dtype = helper.input_dtype(input_param_name='input')
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='pad2d',
inputs={'X': input},
......@@ -6279,7 +6293,7 @@ def elu(x, alpha=1.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('elu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='elu',
inputs={'X': x},
......@@ -6302,7 +6316,7 @@ def relu6(x, threshold=6.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('relu6', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='relu6',
inputs={'X': x},
......@@ -6325,7 +6339,7 @@ def pow(x, factor=1.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('pow', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='pow',
inputs={'X': x},
......@@ -6349,7 +6363,7 @@ def stanh(x, scale_a=2.0 / 3.0, scale_b=1.7159, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('stanh', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='stanh',
inputs={'X': x},
......@@ -6374,7 +6388,7 @@ def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('hard_sigmoid', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='hard_sigmoid',
inputs={'X': x},
......@@ -6398,7 +6412,7 @@ def swish(x, beta=1.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('swish', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='swish',
inputs={'X': x},
......@@ -6450,7 +6464,7 @@ def prelu(x, mode, param_attr=None, name=None):
dtype='float32',
is_bias=False,
default_initializer=Constant(1.0))
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="prelu",
inputs={"X": x,
......@@ -6474,7 +6488,7 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('brelu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='brelu',
inputs={'X': x},
......@@ -6497,7 +6511,7 @@ def leaky_relu(x, alpha=0.02, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('leaky_relu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='leaky_relu',
inputs={'X': x},
......@@ -6519,7 +6533,7 @@ def soft_relu(x, threshold=40.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('soft_relu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='soft_relu',
inputs={'X': x},
......@@ -6586,8 +6600,8 @@ def flatten(x, axis=1, name=None):
if not (isinstance(axis, int)) or axis > len(x.shape) or axis < 0:
raise ValueError("The axis should be a int, and in range [0, rank(x)]")
out = helper.create_tmp_variable(x.dtype)
x_shape = helper.create_tmp_variable(x.dtype)
out = helper.create_variable_for_type_inference(x.dtype)
x_shape = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='flatten2',
inputs={"X": x},
......@@ -6633,7 +6647,8 @@ def sequence_enumerate(input, win_size, pad_value=0, name=None):
out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
"""
helper = LayerHelper('sequence_enumerate', **locals())
out = helper.create_tmp_variable(helper.input_dtype(), stop_gradient=True)
out = helper.create_variable_for_type_inference(
helper.input_dtype(), stop_gradient=True)
helper.append_op(
type='sequence_enumerate',
inputs={'X': input},
......@@ -6673,9 +6688,9 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None):
helper = LayerHelper('sequence_mask', **locals())
if name is None:
out = helper.create_tmp_variable(dtype=dtype)
out = helper.create_variable_for_type_inference(dtype=dtype)
else:
out = helper.create_tmp_variable(dtype=dtype, name=name)
out = helper.create_variable_for_type_inference(dtype=dtype, name=name)
helper.append_op(
type='sequence_mask',
......@@ -6718,7 +6733,7 @@ def stack(x, axis=0):
if not isinstance(x, list) and not isinstance(x, tuple):
x = [x]
out = helper.create_tmp_variable(x[0].dtype)
out = helper.create_variable_for_type_inference(x[0].dtype)
helper.append_op(
type='stack', inputs={'X': x}, outputs={'Y': out},
attrs={'axis': axis})
......@@ -6756,7 +6771,7 @@ def unstack(x, axis=0, num=None):
outs = []
for _ in num:
outs.append(helper.create_tmp_variable(x.dtype))
outs.append(helper.create_variable_for_type_inference(x.dtype))
helper.append_op(
type='unstack',
......@@ -6808,7 +6823,7 @@ def expand(x, expand_times, name=None):
"""
helper = LayerHelper('expand', input=x, **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='expand',
inputs={'X': x},
......@@ -6847,7 +6862,7 @@ def uniform_random_batch_size_like(input,
"""
helper = LayerHelper('uniform_random_batch_size_like', **locals())
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='uniform_random_batch_size_like',
......@@ -6884,7 +6899,7 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
"""
helper = LayerHelper('gaussian_random', **locals())
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='gaussian_random',
......@@ -6919,7 +6934,7 @@ def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'):
"""
helper = LayerHelper('sampling_id', **locals())
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='sampling_id',
inputs={'X': x},
......@@ -6958,7 +6973,7 @@ def gaussian_random_batch_size_like(input,
"""
helper = LayerHelper('gaussian_random_batch_size_like', **locals())
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='gaussian_random_batch_size_like',
......@@ -6990,7 +7005,8 @@ def sum(x):
"""
helper = LayerHelper('sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('x'))
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype('x'))
helper.append_op(
type='sum',
inputs={'X': x},
......@@ -7017,7 +7033,8 @@ def slice(input, axes, starts, ends):
"""
helper = LayerHelper('slice', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype('input'))
helper.append_op(
type='slice',
inputs={'Input': input},
......@@ -7043,7 +7060,8 @@ def shape(input):
"""
helper = LayerHelper('shape', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype('input'))
helper.append_op(
type='shape', inputs={'Input': input}, outputs={'Out': out})
......@@ -7060,7 +7078,7 @@ def _elementwise_op(helper):
use_mkldnn = helper.kwargs.get('use_mkldnn', False)
name = helper.kwargs.get('name', None)
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7094,7 +7112,7 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
helper = LayerHelper('scale', **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7160,7 +7178,7 @@ def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
if out is None:
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7268,7 +7286,7 @@ def clip(x, min, max, name=None):
helper = LayerHelper("clip", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7300,7 +7318,7 @@ def clip_by_norm(x, max_norm, name=None):
helper = LayerHelper("clip_by_norm", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7330,7 +7348,7 @@ def mean(x, name=None):
helper = LayerHelper("mean", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7360,7 +7378,7 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
helper = LayerHelper("mul", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7394,7 +7412,7 @@ def sigmoid_cross_entropy_with_logits(x, label, name=None):
helper = LayerHelper("sigmoid_cross_entropy_with_logits", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7424,7 +7442,7 @@ def maxout(x, groups, name=None):
helper = LayerHelper("maxout", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7463,7 +7481,7 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
helper = LayerHelper("affine_channel", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 96e9b658
......@@ -152,7 +152,7 @@ def cast(x, dtype):
result = fluid.layers.cast(x=data, dtype='float64')
"""
helper = LayerHelper('cast', **locals())
out = helper.create_tmp_variable(dtype=dtype)
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='cast',
inputs={'X': [x]},
......@@ -184,7 +184,7 @@ def concat(input, axis=0, name=None):
out = fluid.layers.concat(input=[Efirst, Esecond, Ethird, Efourth])
"""
helper = LayerHelper('concat', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='concat',
inputs={'X': input},
......@@ -221,7 +221,8 @@ def sums(input, out=None):
"""
helper = LayerHelper('sum', **locals())
if out is None:
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
helper.append_op(
type='sum',
inputs={'X': input},
......@@ -252,7 +253,7 @@ def assign(input, output=None):
"""
helper = LayerHelper('assign', **locals())
if output is None:
output = helper.create_tmp_variable(dtype=input.dtype)
output = helper.create_variable_for_type_inference(dtype=input.dtype)
if isinstance(input, Variable):
helper.append_op(
type='assign', inputs={'X': [input]}, outputs={'Out': [output]})
......@@ -311,7 +312,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
helper = LayerHelper("fill_constant", **locals())
if out is None:
out = helper.create_tmp_variable(dtype=dtype)
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='fill_constant',
inputs={},
......@@ -358,7 +359,7 @@ def fill_constant_batch_size_like(input,
${out_comment}.
"""
helper = LayerHelper("fill_constant_batch_size_like", **locals())
out = helper.create_tmp_variable(dtype=dtype)
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='fill_constant_batch_size_like',
inputs={'Input': input},
......@@ -396,7 +397,7 @@ def argmin(x, axis=0):
out = fluid.layers.argmin(x=in, axis=-1)
"""
helper = LayerHelper("arg_min", **locals())
out = helper.create_tmp_variable(VarDesc.VarType.INT64)
out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
helper.append_op(
type='arg_min',
inputs={'X': x},
......@@ -427,7 +428,7 @@ def argmax(x, axis=0):
out = fluid.layers.argmax(x=in, axis=-1)
"""
helper = LayerHelper("arg_max", **locals())
out = helper.create_tmp_variable(VarDesc.VarType.INT64)
out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
helper.append_op(
type='arg_max',
inputs={'X': x},
......@@ -477,8 +478,10 @@ def argsort(input, axis=-1, name=None):
out, indices = fluid.layers.argsort(input, axis=0)
"""
helper = LayerHelper("argsort", **locals())
out = helper.create_tmp_variable(dtype=input.dtype, stop_gradient=True)
ids = helper.create_tmp_variable(VarDesc.VarType.INT64, stop_gradient=True)
out = helper.create_variable_for_type_inference(
dtype=input.dtype, stop_gradient=True)
ids = helper.create_variable_for_type_inference(
VarDesc.VarType.INT64, stop_gradient=True)
helper.append_op(
type='argsort',
inputs={'X': input},
......@@ -562,7 +565,7 @@ def reverse(x, axis):
if isinstance(axis, int):
axis = [axis]
helper = LayerHelper("reverse", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reverse',
inputs={'Input': x},
......@@ -654,7 +657,7 @@ def has_inf(x):
Variable: The tensor variable storing the output, only a bool value.
"""
helper = LayerHelper("isinf", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isinf", inputs={"X": x}, outputs={"Out": out})
return out
......@@ -670,7 +673,7 @@ def has_nan(x):
Variable: The tensor variable storing the output, only a bool value.
"""
helper = LayerHelper("isnan", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isnan", inputs={"X": x}, outputs={"Out": out})
return out
......@@ -687,6 +690,6 @@ def isfinite(x):
Variable: The tensor variable storing the output, contains a bool value.
"""
helper = LayerHelper("isfinite", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isfinite", inputs={"X": x}, outputs={"Out": out})
return out
python/paddle/fluid/regularizer.py
浏览文件 @ 96e9b658
......@@ -151,7 +151,7 @@ class L2DecayRegularizer(WeightDecayRegularizer):
decay = block.create_var(
dtype="float32",
shape=param.shape,
type=core.VarDesc.VarType.SELECTED_ROWS)
type=core.VarDesc.VarType.LOD_TENSOR)
block.append_op(
type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
block.append_op(
......@@ -228,7 +228,7 @@ class L1DecayRegularizer(WeightDecayRegularizer):
decay = block.create_var(
dtype="float32",
shape=param.shape,
type=core.VarDesc.VarType.SELECTED_ROWS)
type=core.VarDesc.VarType.LOD_TENSOR)
block.append_op(
type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
block.append_op(
......
python/paddle/fluid/tests/unittests/test_slice_var.py
浏览文件 @ 96e9b658
......@@ -30,7 +30,6 @@ class TestSliceVar(unittest.TestCase):
var = program.global_block().create_var(
name=str(random.randint(10000, 99999)),
persistable=True,
# dtype=core.VarDesc.VarType.LOD_TENSOR,
shape=shape)
var_list.append(var)
blocks = slice_variable(var_list, 10, min_size)
......
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