diff --git a/python/paddle/v2/framework/framework.py b/python/paddle/v2/framework/framework.py index 3fb6efe42a2c69e37274dbb655fa47d78eabdf26..e16bc72447f7f038f34723fe5f385328cf546404 100644 --- a/python/paddle/v2/framework/framework.py +++ b/python/paddle/v2/framework/framework.py @@ -232,7 +232,7 @@ class Operator(object): if attrs is not None: for attr in proto.attrs: attr_name = attr.name - if not attr_name in attrs: + if (not attr_name in attrs) or (attrs[attr_name] is None): continue if not isinstance(attrs[attr_name], Block): self.desc.set_attr(attr_name, attrs[attr_name]) diff --git a/python/paddle/v2/framework/layer_helper.py b/python/paddle/v2/framework/layer_helper.py index 26d3e04310b6b0415af31d1630575b32dce186d5..6615bdcd3b1afa493c9ad05c789664818e64d2f2 100644 --- a/python/paddle/v2/framework/layer_helper.py +++ b/python/paddle/v2/framework/layer_helper.py @@ -66,15 +66,15 @@ class LayerHelper(object): actual = self.kwargs.get('param_attr', None) return actual if actual is not None else default - def bias_attr(self, size, dtype): - bias_attr = self.kwargs.get('bias_attr', False) - if bias_attr is None or bias_attr: + def bias_attr(self, shape, dtype): + bias_attr = self.kwargs.get('bias_attr', None) + if bias_attr is True: bias_attr = { 'name': None, 'init_attr': { 'type': 'fill_constant', 'value': 0.0, - 'shape': [size], + 'shape': shape, 'dataType': dtype } } @@ -127,15 +127,13 @@ class LayerHelper(object): return self.program.global_block().create_var(*args, **kwargs) def append_bias_op(self, input_var): - bias_attr = self.bias_attr( - self.kwargs['size'], dtype=input_var.data_type) + size = list(input_var.shape[1:]) + bias_attr = self.bias_attr(size, dtype=input_var.data_type) if not bias_attr: return input_var + b = self.create_parameter( - attr=bias_attr, - shape=[self.kwargs['size']], - dtype=input_var.data_type, - suffix='b') + attr=bias_attr, shape=size, dtype=input_var.data_type, suffix='b') tmp = self.create_tmp_variable(dtype=input_var.data_type) self.append_op( type='elementwise_add', diff --git a/python/paddle/v2/framework/layers.py b/python/paddle/v2/framework/layers.py index 44b587b116e2ebfc5329348027492a4ee27b04e5..1821da197ece4f2bfdf9474fb6be02b1281eac53 100644 --- a/python/paddle/v2/framework/layers.py +++ b/python/paddle/v2/framework/layers.py @@ -3,7 +3,7 @@ import paddle.v2.framework.core as core from paddle.v2.framework.framework import OpProtoHolder, Variable import re -__all__ = ['fc_layer', 'data_layer', 'cross_entropy'] +__all__ = ['fc_layer', 'data_layer', 'cross_entropy', 'conv2d_layer'] def fc_layer(input, @@ -24,6 +24,7 @@ def fc_layer(input, for input_var, param_attr in helper.iter_inputs_and_params(): input_shape = input_var.shape param_shape = list(input_shape[num_flatten_dims:]) + [size] + w = helper.create_parameter( attr=param_attr, shape=param_shape, dtype=dtype) tmp = helper.create_tmp_variable(dtype) @@ -111,6 +112,7 @@ def _create_op_func_(op_type): _create_op_func_('mean') +_create_op_func_('pool2d') def cross_entropy(input, label, **kwargs): @@ -141,3 +143,47 @@ def square_error_cost(input, label, **kwargs): outputs={'Y': [square_out]}, attrs={'factor': 2.0}) return square_out + + +def conv2d_layer(input, + num_filters, + name=None, + filter_size=[1, 1], + act=None, + groups=None, + stride=[1, 1], + padding=None, + bias_attr=None, + param_attr=None, + program=None): + helper = LayerHelper('conv2d', **locals()) + dtype = helper.input_dtype() + + num_channels = input.shape[1] + if groups is None: + num_filter_channels = num_channels + else: + if num_channels % groups is not 0: + raise ValueError("num_channels must be divisible by groups.") + num_filter_channels = num_channels / groups + + input_shape = input.shape + filter_shape = [num_filters, num_filter_channels] + filter_size + filter = helper.create_parameter( + attr=helper.param_attr, shape=filter_shape, dtype=dtype) + pre_bias = helper.create_tmp_variable(dtype) + + helper.append_op( + type='conv2d', + inputs={ + 'Input': input, + 'Filter': filter, + }, + outputs={"Output": pre_bias}, + attrs={'strides': stride, + 'paddings': padding, + 'groups': groups}) + + pre_act = helper.append_bias_op(pre_bias) + + return helper.append_activation(pre_act) diff --git a/python/paddle/v2/framework/tests/test_layers.py b/python/paddle/v2/framework/tests/test_layers.py index 1ef2591cca066788deed8a1c0f6443850251fb80..ce20371cfba56464dc0fcd388643c1331d9a7f94 100644 --- a/python/paddle/v2/framework/tests/test_layers.py +++ b/python/paddle/v2/framework/tests/test_layers.py @@ -1,4 +1,4 @@ -from paddle.v2.framework.layers import fc_layer, data_layer, cross_entropy, mean, square_error_cost +from paddle.v2.framework.layers import fc_layer, data_layer, cross_entropy, mean, square_error_cost, conv2d_layer from paddle.v2.framework.framework import Program, g_program import paddle.v2.framework.core as core import unittest @@ -38,6 +38,16 @@ class TestBook(unittest.TestCase): self.assertIsNotNone(avg_cost) print str(program) + def test_simple_conv2d(self): + pd = core.ProgramDesc.__create_program_desc__() + program = Program(desc=pd) + images = data_layer( + name='pixel', shape=[3, 48, 48], data_type='int32', program=program) + conv2d_layer( + input=images, num_filters=3, filter_size=[4, 4], program=program) + + print str(program) + if __name__ == '__main__': unittest.main()