From 1f1be6c97a4d9f93a39bf126ed1c12d9cac15517 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Thu, 19 Oct 2017 12:02:02 -0700 Subject: [PATCH] Test recognize_digits_conv (#4926) * Init * unify layer names * Update * Add pool2d layer * Test recognize_digits_conv * Clean up --- python/paddle/v2/framework/framework.py | 4 +- python/paddle/v2/framework/layers.py | 52 +++++++++++++++++-- python/paddle/v2/framework/nets.py | 24 +++++++++ .../paddle/v2/framework/tests/test_layers.py | 51 ++++++++++++++---- 4 files changed, 116 insertions(+), 15 deletions(-) create mode 100644 python/paddle/v2/framework/nets.py diff --git a/python/paddle/v2/framework/framework.py b/python/paddle/v2/framework/framework.py index a68f2afcfa..622e09fdde 100644 --- a/python/paddle/v2/framework/framework.py +++ b/python/paddle/v2/framework/framework.py @@ -432,11 +432,13 @@ class Program(object): def current_block(self): return self.blocks[self.current_block_idx] - def append_backward(self, target, no_grad_set): + def append_backward(self, target, no_grad_set=None): """ return map(param_name -> (grad_name, block_index, op_index)) """ assert isinstance(target, Variable) + if no_grad_set is None: + no_grad_set = set() param_to_grad_info = self.desc.append_backward(target.desc, no_grad_set) self.sync_with_cpp() return param_to_grad_info diff --git a/python/paddle/v2/framework/layers.py b/python/paddle/v2/framework/layers.py index 329a6830b6..236427efce 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', 'data', 'cross_entropy', 'conv2d'] +__all__ = ['fc', 'data', 'cross_entropy', 'conv2d', 'pool2d'] def fc(input, @@ -35,7 +35,10 @@ def fc(input, "Y": w, }, outputs={"Out": tmp}, - attrs={'x_num_col_dims': num_flatten_dims}) + attrs={ + 'x_num_col_dims': num_flatten_dims, + 'y_num_col_dims': len(input_shape) - num_flatten_dims + }) mul_results.append(tmp) # sum @@ -115,7 +118,6 @@ def _create_op_func_(op_type): _create_op_func_('mean') _create_op_func_('mul') -_create_op_func_('pool2d') def cross_entropy(input, label, **kwargs): @@ -170,6 +172,13 @@ def conv2d(input, raise ValueError("num_channels must be divisible by groups.") num_filter_channels = num_channels / groups + if isinstance(filter_size, int): + filter_size = [filter_size, filter_size] + if isinstance(stride, int): + stride = [stride, stride] + if isinstance(padding, int): + padding = [padding, padding] + input_shape = input.shape filter_shape = [num_filters, num_filter_channels] + filter_size filter = helper.create_parameter( @@ -190,3 +199,40 @@ def conv2d(input, pre_act = helper.append_bias_op(pre_bias) return helper.append_activation(pre_act) + + +def pool2d(input, + pool_size, + pool_type, + pool_stride=[1, 1], + pool_padding=[0, 0], + global_pooling=False, + program=None): + if pool_type not in ["max", "avg"]: + raise ValueError( + "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.", + str(pool_type)) + if isinstance(pool_size, int): + pool_size = [pool_size, pool_size] + if isinstance(pool_stride, int): + pool_stride = [pool_stride, pool_stride] + if isinstance(pool_padding, int): + pool_padding = [pool_padding, pool_padding] + + helper = LayerHelper('conv2d', **locals()) + dtype = helper.input_dtype() + pool_out = helper.create_tmp_variable(dtype) + + helper.append_op( + type="pool2d", + inputs={"X": input}, + outputs={"Out": pool_out}, + attrs={ + "pooling_type": pool_type, + "ksize": pool_size, + "global_pooling": global_pooling, + "strides": pool_stride, + "paddings": pool_padding + }) + + return pool_out diff --git a/python/paddle/v2/framework/nets.py b/python/paddle/v2/framework/nets.py new file mode 100644 index 0000000000..381da55da3 --- /dev/null +++ b/python/paddle/v2/framework/nets.py @@ -0,0 +1,24 @@ +import paddle.v2.framework.layers as layers + + +def simple_img_conv_pool(input, + filter_size, + num_filters, + pool_size, + pool_stride, + act, + program=None): + conv_out = layers.conv2d( + input=input, + num_filters=num_filters, + filter_size=filter_size, + act=act, + program=program) + + pool_out = layers.pool2d( + input=conv_out, + pool_size=pool_size, + pool_type='max', + pool_stride=pool_stride, + program=program) + return pool_out diff --git a/python/paddle/v2/framework/tests/test_layers.py b/python/paddle/v2/framework/tests/test_layers.py index dbbb653538..4ecc02b12d 100644 --- a/python/paddle/v2/framework/tests/test_layers.py +++ b/python/paddle/v2/framework/tests/test_layers.py @@ -1,4 +1,5 @@ import paddle.v2.framework.layers as layers +import paddle.v2.framework.nets as nets from paddle.v2.framework.framework import Program, g_program import paddle.v2.framework.core as core import unittest @@ -18,7 +19,7 @@ class TestBook(unittest.TestCase): avg_cost = layers.mean(x=cost, program=program) self.assertIsNotNone(avg_cost) - program.append_backward(avg_cost, set()) + program.append_backward(avg_cost) print str(program) def test_recognize_digits_mlp(self): @@ -38,24 +39,52 @@ class TestBook(unittest.TestCase): cost = layers.cross_entropy(input=predict, label=label, program=program) avg_cost = layers.mean(x=cost, program=program) self.assertIsNotNone(avg_cost) - # print str(program) + print str(program) def test_simple_conv2d(self): - pd = core.ProgramDesc.__create_program_desc__() - program = Program(desc=pd) - images = data_layer( + program = Program() + images = layers.data( name='pixel', shape=[3, 48, 48], data_type='int32', program=program) - conv2d_layer( + layers.conv2d( input=images, num_filters=3, filter_size=[4, 4], program=program) - # print str(program) + print str(program) - def test_simple_conv2d(self): + def test_recognize_digits_conv(self): program = Program() + images = layers.data( - name='pixel', shape=[3, 48, 48], data_type='int32', program=program) - layers.conv2d( - input=images, num_filters=3, filter_size=[4, 4], program=program) + name='pixel', + shape=[1, 28, 28], + data_type='float32', + program=program) + label = layers.data( + name='label', shape=[1], data_type='int32', program=program) + conv_pool_1 = nets.simple_img_conv_pool( + input=images, + filter_size=5, + num_filters=2, + pool_size=2, + pool_stride=2, + act="relu", + program=program) + conv_pool_2 = nets.simple_img_conv_pool( + input=conv_pool_1, + filter_size=5, + num_filters=4, + pool_size=2, + pool_stride=2, + act="relu", + program=program) + + predict = layers.fc(input=conv_pool_2, + size=10, + act="softmax", + program=program) + cost = layers.cross_entropy(input=predict, label=label, program=program) + avg_cost = layers.mean(x=cost, program=program) + + program.append_backward(avg_cost) print str(program) -- GitLab