Feature/py executor test (#4922)
* Implement FC layer with helper * Update LayerHelper * Add debug string for Python ProtoBuf and Rename `Sync` to `Flush` * Add check of ProtoBuf initialization * Layer wrapper for FC * Fix unittest * Fix CI * Add code generator * AttributeChecker Better error log and speicalize bool Since lots of types can be cast to bool * Complete mlp, fit_a_line * Expose get global scope * Make global scope not thread-safe 1. It is no need to make global scope thread-safe, since it will be invoked in Python main thread. 2. Do not free the global scope when C++ exit. Let the OS free memories, otherwise, we need to handle the destroy dependencies. See https://google.github.io/styleguide/cppguide.html#Static_and_Global_Variables * Fix * Implementation of simple conv_2d layer * Stash * Remove private data members in OpRegister * Fix bugs * Stash * Expose FeedFetchList as VarType * Change ProgramDesc not a global variable * Polish code style * Stash * Correct implement BlockDesc destructor * Correct implement BlockDesc destructor * Unify program as parameter name * Fix bugs * Add unittest * Fix unit test error * Remove unused functions * Add clone for Python Program * Working on executor * Stash * Add glog as dependencies of ops * Use VLOG to logging some information is helpful when we debug Paddle * Expose VarDesc::persistable to Python * Test executor * Complete unittest * Polish code * Fix merge error * Follow comment * Polish Python Coderevert-4814-Add_sequence_project_op
Yu Yang
8 years ago
committed by
GitHub
parent
63ffe5250a
commit
3db5278301
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import paddle.v2.framework.core as core
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from paddle.v2.framework.framework import Block, Program
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class Executor(object):
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def __init__(self, places):
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if not isinstance(places, list) and not isinstance(places, tuple):
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places = [places]
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act_places = []
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for each in places:
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p = core.Place()
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p.set_place(each)
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act_places.append(p)
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self.executor = core.Executor(act_places)
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def run(self,
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program,
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feed,
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fetch_list,
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feed_var_name='feed',
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fetch_var_name='fetch'):
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if not isinstance(program, Program):
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raise TypeError()
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program = program.clone()
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global_block = program.global_block()
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feed_var = global_block.create_var(
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name=feed_var_name,
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type=core.VarDesc.VarType.FEED_MINIBATCH,
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persistable=True)
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for i, name in enumerate(feed):
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out = global_block.var(name)
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global_block.prepend_op(
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'feed',
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inputs={'X': [feed_var]},
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outputs={'Out': [out]},
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attrs={'col': i})
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# FIXME
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core.set_feed_variable_float(feed[name], feed_var.name, i)
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fetch_var = global_block.create_var(
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name=fetch_var_name,
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type=core.VarDesc.VarType.FETCH_LIST,
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persistable=True)
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for i, var in enumerate(fetch_list):
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global_block.append_op(
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type='fetch',
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inputs={'X': [var]},
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outputs={'Out': [fetch_var]},
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attrs={'col': i})
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self.executor.run(program.desc, 0)
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return [
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core.get_fetch_variable(fetch_var_name, i)
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for i in xrange(len(fetch_list))
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]
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@ -0,0 +1,36 @@
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import unittest
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from paddle.v2.framework.layers import mul, data
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import paddle.v2.framework.core as core
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from paddle.v2.framework.executor import Executor
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from paddle.v2.framework.framework import g_program
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import numpy
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class TestExecutor(unittest.TestCase):
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def test_mul(self):
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a = data(name='a', shape=[784], data_type='float32')
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b = data(
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name='b',
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shape=[784, 100],
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data_type='float32',
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append_batch_size=False)
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out = mul(x=a, y=b)
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place = core.CPUPlace()
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a_np = numpy.random.random((100, 784)).astype('float32')
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tensor_a = core.LoDTensor()
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tensor_a.set(a_np, place)
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b_np = numpy.random.random((784, 100)).astype('float32')
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tensor_b = core.LoDTensor()
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tensor_b.set(b_np, place)
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exe = Executor(place)
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outs = exe.run(g_program,
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feed={'a': tensor_a,
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'b': tensor_b},
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fetch_list=[out])
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out = numpy.array(outs[0])
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self.assertEqual((100, 100), out.shape)
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self.assertTrue(numpy.allclose(out, numpy.dot(a_np, b_np)))
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if __name__ == '__main__':
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unittest.main()
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