From 23a29be49d363a5c911432c2a66a8286e81e0344 Mon Sep 17 00:00:00 2001 From: Xin Pan Date: Fri, 28 Sep 2018 21:45:39 +0800 Subject: [PATCH] hide all left over kwargs test=develop --- python/paddle/fluid/layers/detection.py | 103 ++++++- python/paddle/fluid/layers/nn.py | 268 ++++++++++++++++-- python/paddle/fluid/layers/ops.py | 7 +- .../fluid/tests/unittests/test_layers.py | 9 + 4 files changed, 348 insertions(+), 39 deletions(-) diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py index 9772c65738a..1cfcbbb9c16 100644 --- a/python/paddle/fluid/layers/detection.py +++ b/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, diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index c41ed052478..f22fb9e6fb7 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/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', ] @@ -6886,3 +6982,125 @@ 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 diff --git a/python/paddle/fluid/layers/ops.py b/python/paddle/fluid/layers/ops.py index 824c5be0ff4..9a8300524d8 100644 --- a/python/paddle/fluid/layers/ops.py +++ b/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) diff --git a/python/paddle/fluid/tests/unittests/test_layers.py b/python/paddle/fluid/tests/unittests/test_layers.py index b8dc9e8ad7c..1d8d0b55f0c 100644 --- a/python/paddle/fluid/tests/unittests/test_layers.py +++ b/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() -- GitLab