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体验新版 GitCode,发现更多精彩内容 >>
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提交
2f5a7cc4
编写于
9月 29, 2018
作者:
X
Xin Pan
提交者:
GitHub
9月 29, 2018
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差异文件
Merge pull request #13673 from panyx0718/infer
hide all left over kwargs
上级
b35239df
88ab1ea1
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
396 addition
and
117 deletion
+396
-117
paddle/fluid/API.spec
paddle/fluid/API.spec
+24
-24
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+95
-8
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+261
-63
python/paddle/fluid/layers/ops.py
python/paddle/fluid/layers/ops.py
+1
-6
python/paddle/fluid/nets.py
python/paddle/fluid/nets.py
+6
-16
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+9
-0
未找到文件。
paddle/fluid/API.spec
浏览文件 @
2f5a7cc4
...
...
@@ -49,7 +49,7 @@ paddle.fluid.initializer.BilinearInitializer.__init__ ArgSpec(args=['self'], var
paddle.fluid.initializer.MSRAInitializer.__init__ ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0))
paddle.fluid.initializer.force_init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.initializer.init_on_cpu ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', '
use_mkldnn', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, Fals
e, None, False, None))
paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', '
act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, Non
e, None, False, None))
paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
paddle.fluid.layers.dynamic_lstm ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None))
paddle.fluid.layers.dynamic_lstmp ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None))
...
...
@@ -62,14 +62,14 @@ paddle.fluid.layers.cross_entropy ArgSpec(args=['input', 'label', 'soft_label',
paddle.fluid.layers.square_error_cost ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.chunk_eval ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_conv ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None))
paddle.fluid.layers.conv2d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', '
use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, Fals
e, None, None))
paddle.fluid.layers.conv3d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', '
use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, Fals
e, None, None))
paddle.fluid.layers.conv2d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', '
act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, Tru
e, None, None))
paddle.fluid.layers.conv3d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', '
act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, Tru
e, None, None))
paddle.fluid.layers.sequence_pool ArgSpec(args=['input', 'pool_type'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'param_attr', 'bias_attr', 'use_cudnn'], varargs=None, keywords=None, defaults=(None, None, False))
paddle.fluid.layers.softmax ArgSpec(args=['input', 'param_attr', 'bias_attr', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(None, None, True, None))
paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', '
use_mkldnn', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, Fals
e, False, None))
paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', '
use_mkldnn', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, Fals
e, False, None))
paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', '
use_mkldnn', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False
, False, None, None, None, False, False))
paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', '
name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, Tru
e, False, None))
paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', '
name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, Tru
e, False, None))
paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', '
name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW'
, False, None, None, None, False, False))
paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
paddle.fluid.layers.conv3d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
...
...
@@ -146,18 +146,18 @@ paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_v
paddle.fluid.layers.expand ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_concat ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.scale ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None))
paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'axis', '
use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False
, None, None))
paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'axis', '
use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False
, None, None))
paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'axis', '
use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False
, None, None))
paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'axis', '
use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False
, None, None))
paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'axis', '
use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False
, None, None))
paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'axis', '
use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False
, None, None))
paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'axis', '
use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False
, None, None))
paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'axis', '
act', 'name'], varargs=None, keywords=None, defaults=(-1
, None, None))
paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'axis', '
act', 'name'], varargs=None, keywords=None, defaults=(-1
, None, None))
paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'axis', '
act', 'name'], varargs=None, keywords=None, defaults=(-1
, None, None))
paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'axis', '
act', 'name'], varargs=None, keywords=None, defaults=(-1
, None, None))
paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'axis', '
act', 'name'], varargs=None, keywords=None, defaults=(-1
, None, None))
paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'axis', '
act', 'name'], varargs=None, keywords=None, defaults=(-1
, None, None))
paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'axis', '
act', 'name'], varargs=None, keywords=None, defaults=(-1
, None, None))
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0))
paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'
, 'use_mkldnn'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32', False
))
paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'
], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'
))
paddle.fluid.layers.sampling_id ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32'))
paddle.fluid.layers.sum ArgSpec(args=['x'
, 'use_mkldnn'], varargs=None, keywords=None, defaults=(False,)
)
paddle.fluid.layers.sum ArgSpec(args=['x'
], varargs=None, keywords=None, defaults=None
)
paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.logical_and ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
...
...
@@ -166,6 +166,10 @@ paddle.fluid.layers.logical_xor ArgSpec(args=['x', 'y', 'out', 'name'], varargs=
paddle.fluid.layers.logical_not ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
...
...
@@ -228,10 +232,6 @@ paddle.fluid.layers.StaticRNN.update_memory ArgSpec(args=['self', 'mem', 'var'],
paddle.fluid.layers.reorder_lod_tensor_by_rank ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.Print ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both'))
paddle.fluid.layers.is_empty ArgSpec(args=['x', 'cond'], varargs=None, keywords='ignored', defaults=(None,))
paddle.fluid.layers.mean ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.mul ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.maxout ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.exp ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
...
...
@@ -265,9 +265,9 @@ paddle.fluid.layers.anchor_generator ArgSpec(args=['input', 'anchor_sizes', 'asp
paddle.fluid.layers.roi_perspective_transform ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,))
paddle.fluid.layers.generate_proposal_labels ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True))
paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None))
paddle.fluid.layers.iou_similarity ArgSpec(args=[
], varargs='args', keywords='kwargs', defaults=None
)
paddle.fluid.layers.box_coder ArgSpec(args=[
], varargs='args', keywords='kwargs', defaults=None
)
paddle.fluid.layers.polygon_box_transform ArgSpec(args=[
], varargs='args', keywords='kwargs', defaults=None
)
paddle.fluid.layers.iou_similarity ArgSpec(args=[
'x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)
)
paddle.fluid.layers.box_coder ArgSpec(args=[
'prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name'], varargs=None, keywords=None, defaults=('encode_center_size', True, None)
)
paddle.fluid.layers.polygon_box_transform ArgSpec(args=[
'input', 'name'], varargs=None, keywords=None, defaults=(None,)
)
paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
...
...
@@ -318,11 +318,11 @@ paddle.fluid.transpiler.RoundRobin.__init__ ArgSpec(args=['self', 'pserver_endpo
paddle.fluid.transpiler.RoundRobin.dispatch ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.RoundRobin.reset ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspilerConfig.__init__
paddle.fluid.nets.simple_img_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'
, 'use_mkldnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True, Fals
e))
paddle.fluid.nets.simple_img_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'
], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, Tru
e))
paddle.fluid.nets.sequence_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max'))
paddle.fluid.nets.glu ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,))
paddle.fluid.nets.scaled_dot_product_attention ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0))
paddle.fluid.nets.img_conv_group ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'
, 'use_mkldnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True, Fals
e))
paddle.fluid.nets.img_conv_group ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'
], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', Tru
e))
paddle.fluid.optimizer.SGDOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.optimizer.SGDOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None))
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
2f5a7cc4
...
...
@@ -42,19 +42,11 @@ __all__ = [
'roi_perspective_transform'
,
'generate_proposal_labels'
,
'generate_proposals'
,
]
__auto__
=
[
'iou_similarity'
,
'box_coder'
,
'polygon_box_transform'
,
]
__all__
+=
__auto__
for
_OP
in
set
(
__auto__
):
globals
()[
_OP
]
=
generate_layer_fn
(
_OP
)
def
rpn_target_assign
(
bbox_pred
,
cls_logits
,
...
...
@@ -308,6 +300,101 @@ def detection_output(loc,
return
nmsed_outs
@
templatedoc
()
def
iou_similarity
(
x
,
y
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
y(${y_type}): ${y_comment}
Returns:
out(${out_type}): ${out_comment}
"""
helper
=
LayerHelper
(
"iou_similarity"
,
**
locals
())
if
name
is
None
:
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
else
:
out
=
helper
.
create_variable
(
name
=
name
,
dtype
=
x
.
dtype
,
persistable
=
False
)
helper
.
append_op
(
type
=
"iou_similarity"
,
inputs
=
{
"X"
:
x
,
"Y"
:
y
},
attrs
=
{},
outputs
=
{
"Out"
:
out
})
return
out
@
templatedoc
()
def
box_coder
(
prior_box
,
prior_box_var
,
target_box
,
code_type
=
"encode_center_size"
,
box_normalized
=
True
,
name
=
None
):
"""
${comment}
Args:
prior_box(${prior_box_type}): ${prior_box_comment}
prior_box_var(${prior_box_var_type}): ${prior_box_var_comment}
target_box(${target_box_type}): ${target_box_comment}
code_type(${code_type_type}): ${code_type_comment}
box_normalized(${box_normalized_type}): ${box_normalized_comment}
Returns:
output_box(${output_box_type}): ${output_box_comment}
"""
helper
=
LayerHelper
(
"box_coder"
,
**
locals
())
if
name
is
None
:
output_box
=
helper
.
create_tmp_variable
(
dtype
=
prior_box
.
dtype
)
else
:
output_box
=
helper
.
create_variable
(
name
=
name
,
dtype
=
prior_box
.
dtype
,
persistable
=
False
)
helper
.
append_op
(
type
=
"box_coder"
,
inputs
=
{
"PriorBox"
:
prior_box
,
"PriorBoxVar"
:
prior_box_var
,
"TargetBox"
:
target_box
},
attrs
=
{
"code_type"
:
code_type
,
"box_normalized"
:
box_normalized
},
outputs
=
{
"OutputBox"
:
output_box
})
return
output_box
@
templatedoc
()
def
polygon_box_transform
(
input
,
name
=
None
):
"""
${comment}
Args:
input(${input_type}): ${input_comment}
Returns:
output(${output_type}): ${output_comment}
"""
helper
=
LayerHelper
(
"polygon_box_transform"
,
**
locals
())
if
name
is
None
:
output
=
helper
.
create_tmp_variable
(
dtype
=
input
.
dtype
)
else
:
output
=
helper
.
create_variable
(
name
=
name
,
dtype
=
prior_box
.
input
,
persistable
=
False
)
helper
.
append_op
(
type
=
"polygon_box_transform"
,
inputs
=
{
"Input"
:
input
},
attrs
=
{},
outputs
=
{
"Output"
:
output
})
return
output
@
templatedoc
()
def
detection_map
(
detect_res
,
label
,
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
2f5a7cc4
...
...
@@ -29,31 +29,127 @@ from .. import unique_name
from
functools
import
reduce
__all__
=
[
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'dynamic_lstmp'
,
'dynamic_gru'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'square_error_cost'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'conv3d'
,
'sequence_pool'
,
'sequence_softmax'
,
'softmax'
,
'pool2d'
,
'pool3d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'conv3d_transpose'
,
'sequence_expand'
,
'sequence_expand_as'
,
'sequence_pad'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'reduce_prod'
,
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
,
'split'
,
'ctc_greedy_decoder'
,
'edit_distance'
,
'l2_normalize'
,
'matmul'
,
'topk'
,
'warpctc'
,
'sequence_reshape'
,
'transpose'
,
'im2sequence'
,
'nce'
,
'hsigmoid'
,
'beam_search'
,
'row_conv'
,
'multiplex'
,
'layer_norm'
,
'softmax_with_cross_entropy'
,
'smooth_l1'
,
'one_hot'
,
'autoincreased_step_counter'
,
'reshape'
,
'squeeze'
,
'unsqueeze'
,
'lod_reset'
,
'lrn'
,
'pad'
,
'pad_constant_like'
,
'label_smooth'
,
'roi_pool'
,
'dice_loss'
,
'image_resize'
,
'image_resize_short'
,
'resize_bilinear'
,
'gather'
,
'scatter'
,
'sequence_scatter'
,
'random_crop'
,
'mean_iou'
,
'relu'
,
'log'
,
'crop'
,
'rank_loss'
,
'elu'
,
'relu6'
,
'pow'
,
'stanh'
,
'hard_sigmoid'
,
'swish'
,
'prelu'
,
'brelu'
,
'leaky_relu'
,
'soft_relu'
,
'flatten'
,
'sequence_mask'
,
'stack'
,
'pad2d'
,
'unstack'
,
'sequence_enumerate'
,
'expand'
,
'sequence_concat'
,
'scale'
,
'elementwise_add'
,
'elementwise_div'
,
'elementwise_sub'
,
'elementwise_mul'
,
'elementwise_max'
,
'elementwise_min'
,
'elementwise_pow'
,
'uniform_random_batch_size_like'
,
'gaussian_random'
,
'sampling_id'
,
'gaussian_random_batch_size_like'
,
'sum'
,
'slice'
,
'shape'
,
'logical_and'
,
'logical_or'
,
'logical_xor'
,
'logical_not'
,
'clip'
,
'clip_by_norm'
'fc'
,
'embedding'
,
'dynamic_lstm'
,
'dynamic_lstmp'
,
'dynamic_gru'
,
'gru_unit'
,
'linear_chain_crf'
,
'crf_decoding'
,
'cos_sim'
,
'cross_entropy'
,
'square_error_cost'
,
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'conv3d'
,
'sequence_pool'
,
'sequence_softmax'
,
'softmax'
,
'pool2d'
,
'pool3d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'conv3d_transpose'
,
'sequence_expand'
,
'sequence_expand_as'
,
'sequence_pad'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'reduce_prod'
,
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
,
'split'
,
'ctc_greedy_decoder'
,
'edit_distance'
,
'l2_normalize'
,
'matmul'
,
'topk'
,
'warpctc'
,
'sequence_reshape'
,
'transpose'
,
'im2sequence'
,
'nce'
,
'hsigmoid'
,
'beam_search'
,
'row_conv'
,
'multiplex'
,
'layer_norm'
,
'softmax_with_cross_entropy'
,
'smooth_l1'
,
'one_hot'
,
'autoincreased_step_counter'
,
'reshape'
,
'squeeze'
,
'unsqueeze'
,
'lod_reset'
,
'lrn'
,
'pad'
,
'pad_constant_like'
,
'label_smooth'
,
'roi_pool'
,
'dice_loss'
,
'image_resize'
,
'image_resize_short'
,
'resize_bilinear'
,
'gather'
,
'scatter'
,
'sequence_scatter'
,
'random_crop'
,
'mean_iou'
,
'relu'
,
'log'
,
'crop'
,
'rank_loss'
,
'elu'
,
'relu6'
,
'pow'
,
'stanh'
,
'hard_sigmoid'
,
'swish'
,
'prelu'
,
'brelu'
,
'leaky_relu'
,
'soft_relu'
,
'flatten'
,
'sequence_mask'
,
'stack'
,
'pad2d'
,
'unstack'
,
'sequence_enumerate'
,
'expand'
,
'sequence_concat'
,
'scale'
,
'elementwise_add'
,
'elementwise_div'
,
'elementwise_sub'
,
'elementwise_mul'
,
'elementwise_max'
,
'elementwise_min'
,
'elementwise_pow'
,
'uniform_random_batch_size_like'
,
'gaussian_random'
,
'sampling_id'
,
'gaussian_random_batch_size_like'
,
'sum'
,
'slice'
,
'shape'
,
'logical_and'
,
'logical_or'
,
'logical_xor'
,
'logical_not'
,
'clip'
,
'clip_by_norm'
,
'mean'
,
'mul'
,
'sigmoid_cross_entropy_with_logits'
,
'maxout'
,
]
...
...
@@ -62,7 +158,6 @@ def fc(input,
num_flatten_dims
=
1
,
param_attr
=
None
,
bias_attr
=
None
,
use_mkldnn
=
False
,
act
=
None
,
is_test
=
False
,
name
=
None
):
...
...
@@ -114,8 +209,6 @@ def fc(input,
If it is set to None, the bias is initialized zero. Default: None.
act (str, default None): Activation to be applied to the output of this layer.
is_test(bool): A flag indicating whether execution is in test phase.
use_mkldnn(bool): Use mkldnn kernel or not, it is valid only when the mkldnn
library is installed. Default: False
name (str, default None): The name of this layer.
Returns:
...
...
@@ -162,7 +255,7 @@ def fc(input,
type
=
"sum"
,
inputs
=
{
"X"
:
mul_results
},
outputs
=
{
"Out"
:
pre_bias
},
attrs
=
{
"use_mkldnn"
:
use_mkldnn
})
attrs
=
{
"use_mkldnn"
:
False
})
# add bias
pre_activation
=
helper
.
append_bias_op
(
pre_bias
,
dim_start
=
num_flatten_dims
)
# add activation
...
...
@@ -1326,7 +1419,6 @@ def conv2d(input,
param_attr
=
None
,
bias_attr
=
None
,
use_cudnn
=
True
,
use_mkldnn
=
False
,
act
=
None
,
name
=
None
):
"""
...
...
@@ -1404,8 +1496,6 @@ def conv2d(input,
bias_attr (ParamAttr): Bias parameter for the Conv2d layer. Default: None
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
with mkldnn library. Default: False
act (str): Activation type. Default: None
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
...
...
@@ -1478,7 +1568,7 @@ def conv2d(input,
'dilations'
:
dilation
,
'groups'
:
groups
,
'use_cudnn'
:
use_cudnn
,
'use_mkldnn'
:
use_mkldnn
'use_mkldnn'
:
False
})
pre_act
=
helper
.
append_bias_op
(
pre_bias
,
dim_start
=
1
,
dim_end
=
2
)
...
...
@@ -1496,7 +1586,6 @@ def conv3d(input,
param_attr
=
None
,
bias_attr
=
None
,
use_cudnn
=
True
,
use_mkldnn
=
False
,
act
=
None
,
name
=
None
):
"""
...
...
@@ -1570,7 +1659,6 @@ def conv3d(input,
bias_attr (ParamAttr): Bias parameter for the Conv3d layer. Default: None
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
use_mkldnn (bool): Use mkldnn kernels or not.
act (str): Activation type. Default: None
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
...
...
@@ -1640,7 +1728,7 @@ def conv3d(input,
'dilations'
:
dilation
,
'groups'
:
groups
,
'use_cudnn'
:
use_cudnn
,
'use_mkldnn'
:
use_mkldnn
'use_mkldnn'
:
False
})
pre_act
=
helper
.
append_bias_op
(
pre_bias
,
dim_start
=
1
,
dim_end
=
2
)
...
...
@@ -1822,7 +1910,6 @@ def pool2d(input,
global_pooling
=
False
,
use_cudnn
=
True
,
ceil_mode
=
False
,
use_mkldnn
=
False
,
name
=
None
):
"""
${comment}
...
...
@@ -1840,7 +1927,6 @@ def pool2d(input,
global_pooling: ${global_pooling_comment}
use_cudnn: ${use_cudnn_comment}
ceil_mode: ${ceil_mode_comment}
use_mkldnn: ${use_mkldnn_comment}
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
...
...
@@ -1900,7 +1986,7 @@ def pool2d(input,
"paddings"
:
pool_padding
,
"use_cudnn"
:
use_cudnn
,
"ceil_mode"
:
ceil_mode
,
"use_mkldnn"
:
use_mkldnn
"use_mkldnn"
:
False
})
return
pool_out
...
...
@@ -1914,7 +2000,6 @@ def pool3d(input,
global_pooling
=
False
,
use_cudnn
=
True
,
ceil_mode
=
False
,
use_mkldnn
=
False
,
name
=
None
):
"""
This function adds the operator for pooling in 3-dimensions, using the
...
...
@@ -1929,7 +2014,6 @@ def pool3d(input,
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
use_mkldnn (bool): ${use_mkldnn_comment}
name (str): A name for this layer(optional). If set None, the layer
will be named automatically.
...
...
@@ -1970,7 +2054,7 @@ def pool3d(input,
"paddings"
:
pool_padding
,
"use_cudnn"
:
use_cudnn
,
"ceil_mode"
:
ceil_mode
,
"use_mkldnn"
:
use_mkldnn
"use_mkldnn"
:
False
})
return
pool_out
...
...
@@ -1985,7 +2069,6 @@ def batch_norm(input,
bias_attr
=
None
,
data_layout
=
'NCHW'
,
in_place
=
False
,
use_mkldnn
=
False
,
name
=
None
,
moving_mean_name
=
None
,
moving_variance_name
=
None
,
...
...
@@ -2027,7 +2110,6 @@ def batch_norm(input,
bias_attr(ParamAttr): The parameter attribute for Parameter `bias`.
data_layout(string, default NCHW): NCHW|NHWC
in_place(bool, Default False): Make the input and output of batch norm reuse memory.
use_mkldnn(bool, Default false): ${use_mkldnn_comment}
name(string, Default None): A name for this layer(optional). If set None, the layer
will be named automatically.
moving_mean_name(string, Default None): The name of moving_mean which store the global Mean.
...
...
@@ -2119,7 +2201,7 @@ def batch_norm(input,
"momentum"
:
momentum
,
"epsilon"
:
epsilon
,
"is_test"
:
is_test
,
"use_mkldnn"
:
use_mkldnn
,
"use_mkldnn"
:
False
,
"fuse_with_relu"
:
fuse_with_relu
})
...
...
@@ -6434,12 +6516,7 @@ def uniform_random_batch_size_like(input,
@
templatedoc
()
def
gaussian_random
(
shape
,
mean
=
0.0
,
std
=
1.0
,
seed
=
0
,
dtype
=
'float32'
,
use_mkldnn
=
False
):
def
gaussian_random
(
shape
,
mean
=
0.0
,
std
=
1.0
,
seed
=
0
,
dtype
=
'float32'
):
"""
${comment}
...
...
@@ -6449,7 +6526,6 @@ def gaussian_random(shape,
std (Float): ${std_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): Output data type.
use_mkldnn (Bool): Only used in mkldnn kernel.
Returns:
out (Variable): ${out_comment}
...
...
@@ -6468,7 +6544,7 @@ def gaussian_random(shape,
'std'
:
std
,
'seed'
:
seed
,
'dtype'
:
c_dtype
,
'use_mkldnn'
:
use_mkldnn
'use_mkldnn'
:
False
})
return
out
...
...
@@ -6551,13 +6627,12 @@ def gaussian_random_batch_size_like(input,
@
templatedoc
()
def
sum
(
x
,
use_mkldnn
=
False
):
def
sum
(
x
):
"""
${comment}
Args:
x (Variable): ${x_comment}
use_mkldnn (Bool): ${use_mkldnn_comment}
Returns:
out (Variable): ${out_comment}
...
...
@@ -6569,7 +6644,7 @@ def sum(x, use_mkldnn=False):
type
=
'sum'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'use_mkldnn'
:
use_mkldnn
})
attrs
=
{
'use_mkldnn'
:
False
})
return
out
...
...
@@ -6685,31 +6760,31 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
return
helper
.
append_activation
(
out
)
def
elementwise_add
(
x
,
y
,
axis
=-
1
,
use_mkldnn
=
False
,
act
=
None
,
name
=
None
):
def
elementwise_add
(
x
,
y
,
axis
=-
1
,
act
=
None
,
name
=
None
):
return
_elementwise_op
(
LayerHelper
(
'elementwise_add'
,
**
locals
()))
def
elementwise_div
(
x
,
y
,
axis
=-
1
,
use_mkldnn
=
False
,
act
=
None
,
name
=
None
):
def
elementwise_div
(
x
,
y
,
axis
=-
1
,
act
=
None
,
name
=
None
):
return
_elementwise_op
(
LayerHelper
(
'elementwise_div'
,
**
locals
()))
def
elementwise_sub
(
x
,
y
,
axis
=-
1
,
use_mkldnn
=
False
,
act
=
None
,
name
=
None
):
def
elementwise_sub
(
x
,
y
,
axis
=-
1
,
act
=
None
,
name
=
None
):
return
_elementwise_op
(
LayerHelper
(
'elementwise_sub'
,
**
locals
()))
def
elementwise_mul
(
x
,
y
,
axis
=-
1
,
use_mkldnn
=
False
,
act
=
None
,
name
=
None
):
def
elementwise_mul
(
x
,
y
,
axis
=-
1
,
act
=
None
,
name
=
None
):
return
_elementwise_op
(
LayerHelper
(
'elementwise_mul'
,
**
locals
()))
def
elementwise_max
(
x
,
y
,
axis
=-
1
,
use_mkldnn
=
False
,
act
=
None
,
name
=
None
):
def
elementwise_max
(
x
,
y
,
axis
=-
1
,
act
=
None
,
name
=
None
):
return
_elementwise_op
(
LayerHelper
(
'elementwise_max'
,
**
locals
()))
def
elementwise_min
(
x
,
y
,
axis
=-
1
,
use_mkldnn
=
False
,
act
=
None
,
name
=
None
):
def
elementwise_min
(
x
,
y
,
axis
=-
1
,
act
=
None
,
name
=
None
):
return
_elementwise_op
(
LayerHelper
(
'elementwise_min'
,
**
locals
()))
def
elementwise_pow
(
x
,
y
,
axis
=-
1
,
use_mkldnn
=
False
,
act
=
None
,
name
=
None
):
def
elementwise_pow
(
x
,
y
,
axis
=-
1
,
act
=
None
,
name
=
None
):
return
_elementwise_op
(
LayerHelper
(
'elementwise_pow'
,
**
locals
()))
...
...
@@ -6886,3 +6961,126 @@ def clip_by_norm(x, max_norm, name=None):
outputs
=
{
"Out"
:
out
})
return
out
@
templatedoc
()
def
mean
(
x
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper
=
LayerHelper
(
"mean"
,
**
locals
())
if
name
is
None
:
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
else
:
out
=
helper
.
create_variable
(
name
=
name
,
dtype
=
x
.
dtype
,
persistable
=
False
)
helper
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
x
},
attrs
=
{},
outputs
=
{
"Out"
:
out
})
return
out
@
templatedoc
()
def
mul
(
x
,
y
,
x_num_col_dims
=
1
,
y_num_col_dims
=
1
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
y(${y_type}): ${y_comment}
x_num_col_dims(${x_num_col_dims_type}): ${x_num_col_dims_comment}
y_num_col_dims(${y_num_col_dims_type}): ${y_num_col_dims_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper
=
LayerHelper
(
"mul"
,
**
locals
())
if
name
is
None
:
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
else
:
out
=
helper
.
create_variable
(
name
=
name
,
dtype
=
x
.
dtype
,
persistable
=
False
)
helper
.
append_op
(
type
=
"mul"
,
inputs
=
{
"X"
:
x
,
"Y"
:
y
},
attrs
=
{
"x_num_col_dims"
:
x_num_col_dims
,
"y_num_col_dims"
:
y_num_col_dims
},
outputs
=
{
"Out"
:
out
})
return
out
@
templatedoc
()
def
sigmoid_cross_entropy_with_logits
(
x
,
label
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
label(${label_type}): ${label_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper
=
LayerHelper
(
"sigmoid_cross_entropy_with_logits"
,
**
locals
())
if
name
is
None
:
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
else
:
out
=
helper
.
create_variable
(
name
=
name
,
dtype
=
x
.
dtype
,
persistable
=
False
)
helper
.
append_op
(
type
=
"sigmoid_cross_entropy_with_logits"
,
inputs
=
{
"X"
:
x
,
"Label"
:
label
},
attrs
=
{},
outputs
=
{
"Out"
:
out
})
return
out
@
templatedoc
()
def
maxout
(
x
,
groups
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
groups(${groups_type}): ${groups_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper
=
LayerHelper
(
"maxout"
,
**
locals
())
if
name
is
None
:
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
else
:
out
=
helper
.
create_variable
(
name
=
name
,
dtype
=
x
.
dtype
,
persistable
=
False
)
helper
.
append_op
(
type
=
"maxout"
,
inputs
=
{
"X"
:
x
},
attrs
=
{
"groups"
:
groups
},
outputs
=
{
"Out"
:
out
})
return
out
python/paddle/fluid/layers/ops.py
浏览文件 @
2f5a7cc4
...
...
@@ -35,12 +35,7 @@ __activations_noattr__ = [
'softsign'
,
]
__all__
=
[
'mean'
,
'mul'
,
'sigmoid_cross_entropy_with_logits'
,
'maxout'
,
]
__all__
=
[]
for
_OP
in
set
(
__all__
):
globals
()[
_OP
]
=
generate_layer_fn
(
_OP
)
...
...
python/paddle/fluid/nets.py
浏览文件 @
2f5a7cc4
...
...
@@ -40,8 +40,7 @@ def simple_img_conv_pool(input,
param_attr
=
None
,
bias_attr
=
None
,
act
=
None
,
use_cudnn
=
True
,
use_mkldnn
=
False
):
use_cudnn
=
True
):
"""
The simple_img_conv_pool is composed with one Convolution2d and one Pool2d.
...
...
@@ -84,8 +83,6 @@ def simple_img_conv_pool(input,
act (str): Activation type for Conv2d. Default: None
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
with mkldnn library. Default: False
Return:
Variable: The result of input after Convolution2d and Pool2d.
...
...
@@ -112,8 +109,7 @@ def simple_img_conv_pool(input,
param_attr
=
param_attr
,
bias_attr
=
bias_attr
,
act
=
act
,
use_cudnn
=
use_cudnn
,
use_mkldnn
=
use_mkldnn
)
use_cudnn
=
use_cudnn
)
pool_out
=
layers
.
pool2d
(
input
=
conv_out
,
...
...
@@ -122,8 +118,7 @@ def simple_img_conv_pool(input,
pool_stride
=
pool_stride
,
pool_padding
=
pool_padding
,
global_pooling
=
global_pooling
,
use_cudnn
=
use_cudnn
,
use_mkldnn
=
use_mkldnn
)
use_cudnn
=
use_cudnn
)
return
pool_out
...
...
@@ -138,8 +133,7 @@ def img_conv_group(input,
conv_batchnorm_drop_rate
=
0.0
,
pool_stride
=
1
,
pool_type
=
"max"
,
use_cudnn
=
True
,
use_mkldnn
=
False
):
use_cudnn
=
True
):
"""
The Image Convolution Group is composed of Convolution2d, BatchNorm, DropOut,
and Pool2d. According to the input arguments, img_conv_group will do serials of
...
...
@@ -177,8 +171,6 @@ def img_conv_group(input,
average-pooling. Default :math:`max`.
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
with mkldnn library. Default: False
Return:
Variable: The final result after serial computation using Convolution2d,
...
...
@@ -226,8 +218,7 @@ def img_conv_group(input,
padding
=
conv_padding
[
i
],
param_attr
=
param_attr
[
i
],
act
=
local_conv_act
,
use_cudnn
=
use_cudnn
,
use_mkldnn
=
use_mkldnn
)
use_cudnn
=
use_cudnn
)
if
conv_with_batchnorm
[
i
]:
tmp
=
layers
.
batch_norm
(
input
=
tmp
,
act
=
conv_act
,
in_place
=
True
)
...
...
@@ -240,8 +231,7 @@ def img_conv_group(input,
pool_size
=
pool_size
,
pool_type
=
pool_type
,
pool_stride
=
pool_stride
,
use_cudnn
=
use_cudnn
,
use_mkldnn
=
use_mkldnn
)
use_cudnn
=
use_cudnn
)
return
pool_out
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
2f5a7cc4
...
...
@@ -825,6 +825,15 @@ class TestBook(unittest.TestCase):
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
iou_similarity
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
"x"
,
shape
=
[
16
],
dtype
=
"float32"
)
y
=
layers
.
data
(
name
=
"y"
,
shape
=
[
16
],
dtype
=
"float32"
)
out
=
layers
.
iou_similarity
(
x
,
y
,
name
=
'iou_similarity'
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
if
__name__
==
'__main__'
:
unittest
.
main
()
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