From f715c740bf2bfedb779ba4876f4d6b16e770e61d Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 21 Aug 2017 23:07:51 +0800 Subject: [PATCH] Add_config_parser_for_Conv3D_DeConv3D --- proto/ModelConfig.proto | 1 + python/paddle/trainer/config_parser.py | 266 ++++++++++++++- python/paddle/trainer/recurrent_units.py | 0 .../paddle/trainer_config_helpers/layers.py | 316 ++++++++++++------ .../paddle/trainer_config_helpers/networks.py | 4 +- .../configs/conv3d_deconv3d_test_config.py | 98 ++++++ .../tests/layers_test.py | 4 +- 7 files changed, 581 insertions(+), 108 deletions(-) mode change 100755 => 100644 python/paddle/trainer/recurrent_units.py mode change 100755 => 100644 python/paddle/trainer_config_helpers/layers.py mode change 100755 => 100644 python/paddle/trainer_config_helpers/networks.py create mode 100644 python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 8c6eb5b7e17..21049ba0a02 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -489,6 +489,7 @@ message LayerConfig { // to indicate rectangle image data optional uint64 height = 50; optional uint64 width = 51; + optional uint64 depth = 57 [ default = 1 ]; // blank label used in ctc loss optional uint32 blank = 52 [ default = 0 ]; diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index b7b696ef0c1..49b3c430e70 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -881,6 +881,42 @@ class Conv(Cfg): config_assert(output_x <= 0) +# please refer to the comments in proto/ModelConfig.proto +@config_class +class Conv3D(Cfg): + def __init__(self, + filter_size, + channels, + padding=None, + stride=None, + groups=None, + filter_channels=None, + output_x=None, + img_size=None, + caffe_mode=True, + filter_size_y=None, + padding_y=None, + stride_y=None, + filter_size_z=None, + padding_z=None, + stride_z=None): + self.add_keys(locals()) + if filter_size_y is None: + self.filter_size_y = filter_size + if padding_y is None: + self.padding_y = padding + if stride_y is None: + self.stride_y = stride + if output_x is not None: + config_assert(output_x <= 0) + if filter_size_z is None: + self.filter_size_z = filter_size + if padding_z is None: + self.padding_z = padding + if stride_z is None: + self.stride_z = stride + + @config_class class BilinearInterp(Cfg): def __init__(self, out_size_x=None, out_size_y=None, channels=None): @@ -1167,6 +1203,20 @@ def get_img_size(input_layer_name, channels): return img_size, img_size_y +def get_img3d_size(input_layer_name, channels): + input = g_layer_map[input_layer_name] + img_pixels = input.size / channels + img_size = input.width if input.width > 0 else int(img_pixels**0.5) + img_size_y = input.height if input.height > 0 else int(img_pixels / + img_size) + img_size_z = input.depth if input.depth > 1 else 1 + config_assert( + img_size * img_size_y * img_size_z == img_pixels, + "Input layer %s: Incorrect input image size %d * %d * %d for input image pixels %d" + % (input_layer_name, img_size, img_size_y, img_size_z, img_pixels)) + return img_size, img_size_y, img_size_z + + def parse_bilinear(bilinear, input_layer_name, bilinear_conf): parse_image(bilinear, input_layer_name, bilinear_conf.image_conf) bilinear_conf.out_size_x = bilinear.out_size_x @@ -1277,6 +1327,50 @@ def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False): conv_conf.stride_y, conv_conf.caffe_mode) +#caffe_mode: compute the output size using floor instead of ceil, +# which is consistent of caffe and CuDNN's convention. +def parse_conv3d(conv, input_layer_name, conv_conf, num_filters, trans=False): + conv_conf.filter_size = conv.filter_size + conv_conf.filter_size_y = conv.filter_size_y + conv_conf.filter_size_z = conv.filter_size_z + conv_conf.channels = conv.channels + conv_conf.padding = conv.padding + conv_conf.padding_y = conv.padding_y + conv_conf.padding_z = conv.padding_z + conv_conf.stride = conv.stride + conv_conf.stride_y = conv.stride_y + conv_conf.stride_z = conv.stride_z + conv_conf.groups = conv.groups + conv_conf.caffe_mode = conv.caffe_mode + + if not trans: + conv_conf.filter_channels = conv.channels / conv.groups + conv_conf.img_size, conv_conf.img_size_y, conv_conf.img_size_z = \ + get_img3d_size(input_layer_name, conv.channels) + conv_conf.output_x = cnn_output_size( + conv_conf.img_size, conv_conf.filter_size, conv_conf.padding, + conv_conf.stride, conv_conf.caffe_mode) + conv_conf.output_y = cnn_output_size( + conv_conf.img_size_y, conv_conf.filter_size_y, conv_conf.padding_y, + conv_conf.stride_y, conv_conf.caffe_mode) + conv_conf.output_z = cnn_output_size( + conv_conf.img_size_z, conv_conf.filter_size_z, conv_conf.padding_z, + conv_conf.stride_z, conv_conf.caffe_mode) + else: + conv_conf.filter_channels = num_filters / conv.groups + conv_conf.output_x, conv_conf.output_y, conv_conf.output_z = \ + get_img3d_size(input_layer_name, conv.channels) + conv_conf.img_size = cnn_image_size( + conv_conf.output_x, conv_conf.filter_size, conv_conf.padding, + conv_conf.stride, conv_conf.caffe_mode) + conv_conf.img_size_y = cnn_image_size( + conv_conf.output_y, conv_conf.filter_size_y, conv_conf.padding_y, + conv_conf.stride_y, conv_conf.caffe_mode) + conv_conf.img_size_z = cnn_image_size( + conv_conf.output_z, conv_conf.filter_size_z, conv_conf.padding_z, + conv_conf.stride_z, conv_conf.caffe_mode) + + def parse_block_expand(block_expand, input_layer_name, block_expand_conf): block_expand_conf.channels = block_expand.channels block_expand_conf.stride_x = block_expand.stride_x @@ -1580,6 +1674,9 @@ class LayerBase(object): self.config.height = height self.config.width = width + def set_layer_depth(self, depth): + self.config.depth = depth + def set_cnn_layer(self, input_layer_name, height, @@ -1763,11 +1860,19 @@ class DetectionOutputLayer(LayerBase): @config_layer('data') class DataLayer(LayerBase): - def __init__(self, name, size, height=None, width=None, device=None): + def __init__(self, + name, + size, + height=None, + width=None, + depth=None, + device=None): super(DataLayer, self).__init__( name, 'data', size, inputs=[], device=device) if height and width: self.set_layer_height_width(height, width) + if depth: + self.set_layer_depth(depth) ''' @@ -1882,7 +1987,7 @@ class ConvLayerBase(LayerBase): def calc_parameter_size(self, conv_conf): return self.config.num_filters * conv_conf.filter_channels \ - * (conv_conf.filter_size * conv_conf.filter_size_y) + * (conv_conf.filter_size * conv_conf.filter_size_y) @config_layer('exconv') @@ -1895,6 +2000,163 @@ class ConvLayer(ConvLayerBase): layer_type = 'cudnn_conv' +@config_layer('conv_3d') +class Conv3DLayerBase(LayerBase): + def __init__(self, + name, + inputs=[], + bias=True, + num_filters=None, + shared_biases=False, + **xargs): + super(Conv3DLayerBase, self).__init__( + name, self.layer_type, 0, inputs=inputs, **xargs) + + if num_filters is not None: + self.config.num_filters = num_filters + + use_gpu = int(g_command_config_args.get("use_gpu", 0)) + parallel_nn = int(g_command_config_args.get("parallel_nn", 0)) + + # Automatically select cudnn_type for GPU and exconv for CPU + # if set type=conv, but still reserve the way user specify + # exconv or cudnn_conv manually. + if self.layer_type == "cudnn_conv3d": + config_assert(use_gpu, "cudnn_conv3d only support GPU") + + # need to specify layer in config + self.config.type = self.layer_type + + if shared_biases is not None: + self.config.shared_biases = shared_biases + + for input_index in xrange(len(self.inputs)): + input_layer = self.get_input_layer(input_index) + conv_conf = self.config.inputs[input_index].conv_conf + parse_conv3d( + self.inputs[input_index].conv, input_layer.name, conv_conf, + num_filters + ) # for z-axis pad:0, strid:1, filter_size:1, img_size:1 + psize = self.calc_parameter_size(conv_conf) + self.create_input_parameter(input_index, psize) + self.set_cnn_layer(name, conv_conf.output_z, conv_conf.output_y, + conv_conf.output_x, self.config.num_filters) + + psize = self.config.size + if shared_biases: + psize = self.config.num_filters + self.create_bias_parameter(bias, psize, [psize, 1]) + + def calc_parameter_size(self, conv_conf): + return self.config.num_filters * conv_conf.filter_channels \ + * (conv_conf.filter_size * conv_conf.filter_size_y \ + * conv_conf.filter_size_z) + + def set_layer_height_width(self, depth, height, width): + self.config.depth = depth + self.config.height = height + self.config.width = width + + def set_cnn_layer(self, + input_layer_name, + depth, + height, + width, + channels, + is_print=True): + size = depth * height * width * channels + self.set_layer_size(size) + self.set_layer_height_width(depth, height, width) + if is_print: + print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" % + (input_layer_name, channels, depth, height, width, size)) + + +@config_layer('conv3d') +class Conv3DLayer(Conv3DLayerBase): + layer_type = 'conv3d' + + +@config_layer('convt_3d') +class Conv3DTransLayerBase(LayerBase): + def __init__(self, + name, + inputs=[], + bias=True, + num_filters=None, + shared_biases=False, + **xargs): + super(Conv3DTransLayerBase, self).__init__( + name, self.layer_type, 0, inputs=inputs, **xargs) + + if num_filters is not None: + self.config.num_filters = num_filters + + use_gpu = int(g_command_config_args.get("use_gpu", 0)) + parallel_nn = int(g_command_config_args.get("parallel_nn", 0)) + + # Automatically select cudnn_type for GPU and exconv for CPU + # if set type=conv, but still reserve the way user specify + # exconv or cudnn_conv manually. + if self.layer_type == "cudnn_deconv3d": + config_assert(use_gpu, "cudnn_conv3d only support GPU") + + # need to specify layer in config + self.config.type = self.layer_type + + if shared_biases is not None: + self.config.shared_biases = shared_biases + + for input_index in xrange(len(self.inputs)): + input_layer = self.get_input_layer(input_index) + conv_conf = self.config.inputs[input_index].conv_conf + parse_conv3d( + self.inputs[input_index].conv, + input_layer.name, + conv_conf, + num_filters, + trans=True + ) # for z-axis pad:0, strid:1, filter_size:1, img_size:1 + psize = self.calc_parameter_size(conv_conf) + self.create_input_parameter(input_index, psize) + self.set_cnn_layer(name, conv_conf.img_size_z, conv_conf.img_size_y, + conv_conf.img_size, self.config.num_filters) + + psize = self.config.size + if shared_biases: + psize = self.config.num_filters + self.create_bias_parameter(bias, psize, [psize, 1]) + + def calc_parameter_size(self, conv_conf): + return self.config.num_filters * conv_conf.filter_channels \ + * (conv_conf.filter_size * conv_conf.filter_size_y \ + * conv_conf.filter_size_z) + + def set_layer_height_width(self, depth, height, width): + self.config.depth = depth + self.config.height = height + self.config.width = width + + def set_cnn_layer(self, + input_layer_name, + depth, + height, + width, + channels, + is_print=True): + size = depth * height * width * channels + self.set_layer_size(size) + self.set_layer_height_width(depth, height, width) + if is_print: + print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" % + (input_layer_name, channels, depth, height, width, size)) + + +@config_layer('deconv3d') +class DeConv3DLayer(Conv3DTransLayerBase): + layer_type = 'deconv3d' + + @config_layer('convt') class ConvTransLayerBase(LayerBase): layer_type = 'convt' diff --git a/python/paddle/trainer/recurrent_units.py b/python/paddle/trainer/recurrent_units.py old mode 100755 new mode 100644 diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py old mode 100755 new mode 100644 index 1bc55c86960..6953f134c5d --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -31,108 +31,34 @@ except ImportError: import copy __all__ = [ - 'full_matrix_projection', - 'AggregateLevel', - 'ExpandLevel', - 'identity_projection', - 'dotmul_projection', - 'dotmul_operator', - 'repeat_layer', - 'seq_reshape_layer', - 'table_projection', - 'mixed_layer', - 'data_layer', - 'embedding_layer', - 'fc_layer', - 'grumemory', - 'pooling_layer', - 'lstmemory', - 'last_seq', - 'first_seq', - 'cos_sim', - 'hsigmoid', - 'conv_projection', - 'mse_cost', - 'regression_cost', - 'classification_cost', - 'LayerOutput', - 'img_conv_layer', - 'img_pool_layer', - 'batch_norm_layer', - 'img_cmrnorm_layer', - 'addto_layer', - 'concat_layer', - 'seq_concat_layer', - 'lstm_step_layer', - 'recurrent_group', - 'memory', - 'StaticInput', - 'expand_layer', - 'scaling_layer', - 'scaling_projection', - 'power_layer', - 'interpolation_layer', - 'bilinear_interp_layer', - 'trans_layer', - 'rotate_layer', - 'sum_to_one_norm_layer', - 'row_l2_norm_layer', - 'get_output_layer', - 'LayerType', - 'context_projection', - 'beam_search', - 'maxid_layer', - 'GeneratedInput', - 'SubsequenceInput', - 'gru_step_layer', - 'gru_step_naive_layer', - 'recurrent_layer', - 'BaseGeneratedInput', - 'conv_operator', - 'conv_shift_layer', - 'tensor_layer', - 'selective_fc_layer', - 'sampling_id_layer', - 'slope_intercept_layer', - 'trans_full_matrix_projection', - 'linear_comb_layer', - 'convex_comb_layer', - 'ctc_layer', - 'warp_ctc_layer', - 'crf_layer', - 'crf_decoding_layer', - 'nce_layer', - 'cross_entropy_with_selfnorm', - 'cross_entropy', - 'multi_binary_label_cross_entropy', - 'sum_cost', - 'rank_cost', - 'lambda_cost', - 'huber_cost', - 'block_expand_layer', - 'maxout_layer', - 'out_prod_layer', - 'printer_layer', - 'print_layer', - 'priorbox_layer', - 'cross_channel_norm_layer', - 'multibox_loss_layer', - 'detection_output_layer', - 'spp_layer', - 'pad_layer', - 'eos_layer', - 'smooth_l1_cost', - 'layer_support', - 'multiplex_layer', - 'row_conv_layer', - 'dropout_layer', - 'prelu_layer', - 'gated_unit_layer', - 'crop_layer', - 'sub_nested_seq_layer', - 'clip_layer', - 'slice_projection', - 'kmax_sequence_score_layer', + 'full_matrix_projection', 'AggregateLevel', 'ExpandLevel', + 'identity_projection', 'dotmul_projection', 'dotmul_operator', + 'repeat_layer', 'seq_reshape_layer', 'table_projection', 'mixed_layer', + 'data_layer', 'embedding_layer', 'fc_layer', 'grumemory', 'pooling_layer', + 'lstmemory', 'last_seq', 'first_seq', 'cos_sim', 'hsigmoid', + 'conv_projection', 'mse_cost', 'regression_cost', 'classification_cost', + 'LayerOutput', 'img_conv_layer', 'img_pool_layer', 'batch_norm_layer', + 'img_cmrnorm_layer', 'addto_layer', 'concat_layer', 'seq_concat_layer', + 'lstm_step_layer', 'recurrent_group', 'memory', 'StaticInput', + 'expand_layer', 'scaling_layer', 'scaling_projection', 'power_layer', + 'interpolation_layer', 'bilinear_interp_layer', 'trans_layer', + 'rotate_layer', 'sum_to_one_norm_layer', 'row_l2_norm_layer', + 'get_output_layer', 'LayerType', 'context_projection', 'beam_search', + 'maxid_layer', 'GeneratedInput', 'SubsequenceInput', 'gru_step_layer', + 'gru_step_naive_layer', 'recurrent_layer', 'BaseGeneratedInput', + 'conv_operator', 'conv_shift_layer', 'tensor_layer', 'selective_fc_layer', + 'sampling_id_layer', 'slope_intercept_layer', + 'trans_full_matrix_projection', 'linear_comb_layer', 'convex_comb_layer', + 'ctc_layer', 'warp_ctc_layer', 'crf_layer', 'crf_decoding_layer', + 'nce_layer', 'cross_entropy_with_selfnorm', 'cross_entropy', + 'multi_binary_label_cross_entropy', 'sum_cost', 'rank_cost', 'lambda_cost', + 'huber_cost', 'block_expand_layer', 'maxout_layer', 'out_prod_layer', + 'printer_layer', 'print_layer', 'priorbox_layer', + 'cross_channel_norm_layer', 'multibox_loss_layer', 'detection_output_layer', + 'spp_layer', 'pad_layer', 'eos_layer', 'smooth_l1_cost', 'layer_support', + 'multiplex_layer', 'row_conv_layer', 'dropout_layer', 'prelu_layer', + 'gated_unit_layer', 'crop_layer', 'sub_nested_seq_layer', 'clip_layer', + 'slice_projection', 'kmax_sequence_score_layer', 'img_conv3d_layer' ] @@ -214,6 +140,9 @@ class LayerType(object): CRF_DECODING_LAYER = 'crf_decoding' NCE_LAYER = 'nce' + CONV3D_LAYER = 'conv3d' + DECONV3D_LAYER = 'deconv3d' + RANK_COST = 'rank-cost' LAMBDA_COST = 'lambda_cost' HUBER = 'huber' @@ -878,7 +807,8 @@ def mixed_layer(size=0, @layer_support() -def data_layer(name, size, height=None, width=None, layer_attr=None): +def data_layer(name, size, height=None, width=None, depth=None, + layer_attr=None): """ Define DataLayer For NeuralNetwork. @@ -907,6 +837,7 @@ def data_layer(name, size, height=None, width=None, layer_attr=None): size=size, height=height, width=width, + depth=depth, **ExtraLayerAttribute.to_kwargs(layer_attr)) return LayerOutput(name, LayerType.DATA, size=size) @@ -6210,3 +6141,182 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1): return LayerOutput( name, LayerType.KMAX_SEQ_SCORE, parents=[input], size=input.size) + + +@wrap_name_default("conv3d") +@wrap_param_attr_default() +@wrap_bias_attr_default() +@wrap_act_default(act=ReluActivation()) +@layer_support(DROPOUT) +def img_conv3d_layer(input, + filter_size, + num_filters, + name=None, + num_channels=None, + act=None, + groups=1, + stride=1, + padding=0, + bias_attr=None, + param_attr=None, + shared_biases=True, + layer_attr=None, + filter_size_y=None, + stride_y=None, + padding_y=None, + filter_size_z=None, + stride_z=None, + padding_z=None, + trans=False, + layer_type=None): + """ + + The example usage is: + + .. code-block:: python + + conv = img_conv3d_layer(input=data, filter_size=1, filter_size_y=1, + num_channels=8, + num_filters=16, stride=1, + bias_attr=False, + act=ReluActivation()) + + :param name: Layer name. + :type name: basestring + :param input: Layer Input. + :type input: LayerOutput + :param filter_size: The x dimension of a filter kernel. Or input a tuple for + two image dimension. + :type filter_size: int|tuple|list + :param filter_size_y: The y dimension of a filter kernel. Since PaddlePaddle + currently supports rectangular filters, the filter's + shape will be (filter_size, filter_size_y). + :type filter_size_y: int|None + :param num_filters: Each filter group's number of filter + :param act: Activation type. Default is tanh + :type act: BaseActivation + :param groups: Group size of filters. + :type groups: int + :param stride: The x dimension of the stride. Or input a tuple for two image + dimension. + :type stride: int|tuple|list + :param stride_y: The y dimension of the stride. + :type stride_y: int + :param padding: The x dimension of the padding. Or input a tuple for two + image dimension + :type padding: int|tuple|list + :param padding_y: The y dimension of the padding. + :type padding_y: int + :param bias_attr: Convolution bias attribute. None means default bias. + False means no bias. + :type bias_attr: ParameterAttribute|False + :param num_channels: number of input channels. If None will be set + automatically from previous output. + :type num_channels: int + :param param_attr: Convolution param attribute. None means default attribute + :type param_attr: ParameterAttribute + :param shared_biases: Is biases will be shared between filters or not. + :type shared_biases: bool + :param layer_attr: Layer Extra Attribute. + :type layer_attr: ExtraLayerAttribute + :param trans: true if it is a convTransLayer, false if it is a convLayer + :type trans: bool + :param layer_type: specify the layer_type, default is None. If trans=True, + layer_type has to be "exconvt" or "cudnn_convt", + otherwise layer_type has to be either "exconv" or + "cudnn_conv" + :type layer_type: String + :return: LayerOutput object. + :rtype: LayerOutput + """ + if num_channels is None: + assert input.num_filters is not None + num_channels = input.num_filters + + if filter_size_y is None: + if isinstance(filter_size, collections.Sequence): + assert len(filter_size) == 2 + filter_size, filter_size_y = filter_size + else: + filter_size_y = filter_size + + if filter_size_z is None: + if isinstance(filter_size, collections.Sequence): + assert len(filter_size) == 2 + filter_size, filter_size_z = filter_size + else: + filter_size_z = filter_size + + if stride_y is None: + if isinstance(stride, collections.Sequence): + assert len(stride) == 2 + stride, stride_y = stride + else: + stride_y = stride + + if stride_z is None: + if isinstance(stride, collections.Sequence): + assert len(stride) == 2 + stride, stride_z = stride + else: + stride_z = stride + + if padding_y is None: + if isinstance(padding, collections.Sequence): + assert len(padding) == 2 + padding, padding_y = padding + else: + padding_y = padding + + if padding_z is None: + if isinstance(padding, collections.Sequence): + assert len(padding) == 2 + padding, padding_z = padding + else: + padding_z = padding + + if param_attr.attr.get('initial_smart'): + # special initial for conv layers. + init_w = (2.0 / (filter_size**2 * num_channels))**0.5 + param_attr.attr["initial_mean"] = 0.0 + param_attr.attr["initial_std"] = init_w + param_attr.attr["initial_strategy"] = 0 + param_attr.attr["initial_smart"] = False + + if layer_type: + if trans: + assert layer_type in ["deconv3d"] + lt = layer_type + else: + lt = LayerType.DECONV3D_LAYER if trans else LayerType.CONV3D_LAYER + + l = Layer( + name=name, + inputs=Input( + input.name, + conv=Conv3D( + filter_size=filter_size, + padding=padding, + stride=stride, + channels=num_channels, + groups=groups, + filter_size_y=filter_size_y, + padding_y=padding_y, + stride_y=stride_y, + filter_size_z=filter_size_z, + padding_z=padding_z, + stride_z=stride_z), + **param_attr.attr), + active_type=act.name, + num_filters=num_filters, + bias=ParamAttr.to_bias(bias_attr), + shared_biases=shared_biases, + type=lt, + **ExtraLayerAttribute.to_kwargs(layer_attr)) + return LayerOutput( + name, + lt, + parents=[input], + activation=act, + num_filters=num_filters, + size=l.config.size) diff --git a/python/paddle/trainer_config_helpers/networks.py b/python/paddle/trainer_config_helpers/networks.py old mode 100755 new mode 100644 index 34be203ee25..28a71cf788f --- a/python/paddle/trainer_config_helpers/networks.py +++ b/python/paddle/trainer_config_helpers/networks.py @@ -1406,7 +1406,7 @@ def inputs(layers, *args): if len(args) != 0: layers.extend(args) - Inputs(*[l.name for l in layers]) + Inputs(* [l.name for l in layers]) def outputs(layers, *args): @@ -1456,7 +1456,7 @@ def outputs(layers, *args): assert len(layers) > 0 if HasInputsSet(): # input already set - Outputs(*[l.name for l in layers]) + Outputs(* [l.name for l in layers]) return # just return outputs. if len(layers) != 1: diff --git a/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py b/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py new file mode 100644 index 00000000000..da0d23d057d --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py @@ -0,0 +1,98 @@ +from paddle.trainer_config_helpers import * + +settings(batch_size=1000, learning_rate=1e-5) + +num_channels = 3 +filter_size = 3 +filter_size_y = 3 +filter_size_z = 3 +stride = 2 +stride_y = 2 +stride_z = 2 +padding = 1 +padding_y = 1 +padding_z = 1 +groups = 1 + +data = data_layer( + name='data1', size=12096 * num_channels, height=48, width=42, depth=6) + +conv3d = img_conv3d_layer( + input=data, + name='conv3d_1', + num_filters=16, + num_channels=num_channels, + filter_size=filter_size, + filter_size_y=filter_size, + filter_size_z=filter_size, + stride=stride, + stride_y=stride_y, + stride_z=stride_z, + padding=padding, + padding_y=padding_y, + padding_z=padding_z, + groups=groups, + bias_attr=True, + shared_biases=True, + trans=False, + layer_type="conv3d", + act=LinearActivation()) + +deconv3d = img_conv3d_layer( + input=data, + name='deconv3d_1', + num_filters=16, + num_channels=num_channels, + filter_size=filter_size, + filter_size_y=filter_size, + filter_size_z=filter_size, + stride=stride, + stride_y=stride_y, + stride_z=stride_z, + padding=padding, + padding_y=padding_y, + padding_z=padding_z, + groups=groups, + bias_attr=True, + shared_biases=True, + trans=True, + layer_type="deconv3d", + act=LinearActivation()) + +data = data_layer(name="input", size=8 * 16 * 16) +conv1 = img_conv_layer( + input=data, + filter_size=1, + filter_size_y=1, + num_channels=8, + num_filters=16, + stride=1, + bias_attr=False, + act=ReluActivation(), + layer_type="exconv") +conv2 = img_conv_layer( + input=data, + filter_size=1, + filter_size_y=1, + num_channels=8, + num_filters=16, + stride=1, + bias_attr=False, + act=ReluActivation(), + layer_type="exconv") + +concat = concat_layer(input=[conv1, conv2]) + +conv = img_conv_layer( + input=data, + filter_size=1, + filter_size_y=1, + num_channels=8, + num_filters=16, + stride=1, + bias_attr=True, + act=LinearActivation(), + groups=2, + layer_type="exconv") + +outputs(concat, conv) diff --git a/python/paddle/trainer_config_helpers/tests/layers_test.py b/python/paddle/trainer_config_helpers/tests/layers_test.py index 05902ea293d..44d1c1c9b28 100644 --- a/python/paddle/trainer_config_helpers/tests/layers_test.py +++ b/python/paddle/trainer_config_helpers/tests/layers_test.py @@ -16,4 +16,6 @@ from paddle.trainer.config_parser import parse_config_and_serialize if __name__ == '__main__': parse_config_and_serialize( - 'trainer_config_helpers/tests/layers_test_config.py', '') + 'trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py', + '') +# layers_test_config.py -- GitLab