Merge pull request #13694 from panyx0718/cherry-pick-api

hide all left over kwargs

paddle/fluid/API.spec

@@ -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, False, 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, None, 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, False, 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, False, 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, True, 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, True, 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, False, 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, False, 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, True, 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, True, 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'], varargs=None, keywords=None, defaults=('ROC', 4095, 1))
 paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
@@ -313,11 +313,11 @@ 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, False))
+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, True))
 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, False))
+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', True))
 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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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()