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Integrating LogReg into test framework; allow for multiple root nodes

This commit is contained in:
Willi Richert 2016-04-04 18:28:10 +02:00
Родитель 24802a4b75
Коммит 1aefbad8b7
7 изменённых файлов: 1678 добавлений и 1055 удалений
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105
contrib/Python/cntk/context.py
Просмотреть файл

@ -9,7 +9,7 @@ import shutil as sh
from cntk.graph import ComputationNode
from cntk.ops.cntk1 import NewReshape
from cntk.utils import CNTK_EXECUTABLE_PATH, MODEL_INDENTATION
from .utils import cntk_to_numpy_shape
from .utils import cntk_to_numpy_shape, dedupe_readers
CNTK_TEMPLATE_DIR = os.path.join(os.path.dirname(__file__), "templates")
CNTK_TRAIN_TEMPLATE_PATH = os.path.join(
@ -58,7 +58,7 @@ class AbstractContext(object, metaclass=ABCMeta):
def __init__(self, name,
graph=None,
device_id=-1,
root_node=None,
root_nodes=None,
clean_up=True,
node_unit_test=False):
'''
@ -67,7 +67,7 @@ class AbstractContext(object, metaclass=ABCMeta):
:param name: context name
:param graph: the computational graph to be used for training, testing and prediction
:param device_id: whether to use CPU or a specific GPU. -1 for CPU larger values
:param root_node: the top node of the graph
:param root_nodes: list of top nodes of the graph or single node itself
:param clean_up: whether the temporary directory should be removed when the context is left
are the GPUs indices.
:param node_unit_test: set to True if you want to output the gradient of a node (backward pass)
@ -90,7 +90,10 @@ class AbstractContext(object, metaclass=ABCMeta):
self.device_id = device_id
self.clean_up = clean_up
self.input_nodes = set()
self.root_node = root_node
if root_nodes is None:
self.root_nodes = None
else:
self.root_nodes = root_nodes if isinstance(root_nodes, list) else [root_nodes]
self.node_unit_test= node_unit_test
def __enter__(self):
@ -104,6 +107,42 @@ class AbstractContext(object, metaclass=ABCMeta):
if self.clean_up:
sh.rmtree(self.directory)
def _generate_config(self, root_nodes=None):
'''
Helper function to create a configuration incorporating all root nodes
'''
has_inputs = False
desc = []
inputs = set()
readers = set()
unrolled_nodes = {}
node_counter = 0
dep_inputs = tuple()
reconciled_cache = {}
if root_nodes is None:
root_nodes = self.root_nodes
elif not isinstance(root_nodes, list):
root_nodes = [root_nodes]
for root_node in root_nodes:
var_name, node_counter, _desc, _has_inputs, _readers, _dep_inputs = \
root_node._to_config(desc,
unrolled_nodes,
inputs,
readers,
dep_inputs,
node_counter, reconciled_cache)
has_inputs |= _has_inputs
readers |= _readers
dep_inputs += _dep_inputs
description = "\n".join(desc)
return description, has_inputs, dedupe_readers(readers)
def _generate_train_config(self, optimizer, reader, override_existing):
'''
Generates the configuration file for the train action.
@ -124,7 +163,7 @@ class AbstractContext(object, metaclass=ABCMeta):
tmpl = open(CNTK_TRAIN_TEMPLATE_PATH, "r").read()
model_filename = os.path.join(model_dir, self.name)
description, has_inputs, readers = self.root_node.to_config()
description, has_inputs, readers = self._generate_config()
if reader:
readers.append(reader)
@ -148,7 +187,7 @@ class AbstractContext(object, metaclass=ABCMeta):
if reader:
reader_config = reader.generate_config()
else:
description, has_inputs, readers = self.root_node.to_config()
description, has_inputs, readers = self._generate_config()
reader_config = '\n'.join(r.generate_config() for r in readers)
tmpl_dict = {
@ -172,7 +211,7 @@ class AbstractContext(object, metaclass=ABCMeta):
if reader:
reader_config = reader.generate_config()
else:
description, has_inputs, readers = self.root_node.to_config()
description, has_inputs, readers = self._generate_config()
reader_config = '\n'.join(r.generate_config() for r in readers)
tmpl_dict = {
@ -183,17 +222,17 @@ class AbstractContext(object, metaclass=ABCMeta):
}
return tmpl % tmpl_dict
def _generate_eval_config(self, root_node, reader):
def _generate_eval_config(self, root_nodes, reader):
'''
Generates the configuration file for write action.
:param root_node: the node to evaluate.
:param root_nodes: the node to evaluate.
:param reader: the reader used to load the data, None if the network does not have input
'''
model_description, has_input, readers = root_node.to_config()
description, has_inputs, readers = self._generate_config(root_nodes)
if reader:
readers.append(reader)
if not has_input and not readers:
if not has_inputs and not readers:
# add dummy input to keep CNTK happy
# TODO relieve this requirement on CNTK side
data = [[1, 2], [3, 4]]
@ -203,7 +242,7 @@ class AbstractContext(object, metaclass=ABCMeta):
from .ops.cntk1 import Input
dummy_input_node = Input(2, var_name='dummy_node')
reader.add_input(dummy_input_node, 0, 2)
model_description += "\n" + " "*MODEL_INDENTATION + "dummy_node = Input(2, tag='output')"
description += "\n" + " "*MODEL_INDENTATION + "dummy_node = Input(2, tag='output')"
readers.append(reader)
tmpl = open(CNTK_EVAL_TEMPLATE_PATH, "r").read()
@ -212,7 +251,7 @@ class AbstractContext(object, metaclass=ABCMeta):
'DevideId': self.device_id,
'NodeUnitTest': self.node_unit_test,
'OutputFile': output_filename,
'ModelDescription': model_description,
'ModelDescription': description,
'Reader': '\n'.join(r.generate_config() for r in readers),
}
return tmpl % tmpl_dict
@ -317,7 +356,10 @@ class Context(AbstractContext):
can attach a reader directly to the input node.
'''
config_content = self._generate_test_config(reader)
return self._call_cntk(CNTK_TEST_CONFIG_FILENAME, config_content)
output = self._call_cntk(CNTK_TEST_CONFIG_FILENAME, config_content)
return Context._parse_test_result(output)
def predict(self, reader=None):
'''
@ -422,6 +464,41 @@ class Context(AbstractContext):
return list_of_tensors
TEST_RESULT_REGEX = re.compile('(?P<name>[^:]+): [^=]+ = (?P<number>[0-9.]+)')
@staticmethod
def _parse_test_result(output):
result = {}
PREAMPLE = 'Final Results: Minibatch[1-1]: '
for line in output.splitlines():
if not line.startswith(PREAMPLE):
continue
line = line[len(PREAMPLE):]
if not line.startswith('SamplesSeen = '):
raise ValueError('expected SamplesSeen but got "%s"'%line)
line = line[len('SamplesSeen = '):]
number_ends = line.index(' ')
result['SamplesSeen'] = int(line[:number_ends])
line = line[number_ends:]
perplexity_idx = line.index('Perplexity = ')
result['Perplexity'] = float(line[perplexity_idx+len('Perplexity = '):])
line = line[:perplexity_idx]
mo = Context.TEST_RESULT_REGEX.match(line)
while mo:
result[mo.group('name').strip()] = float(mo.group('number').strip())
line = line[mo.span()[1]:]
mo = Context.TEST_RESULT_REGEX.match(line)
return result
def _calc_expected_shape_and_size(self, node, data, shapes):
'''
Calculates the expected shape and size from the CNTK output and the

500
contrib/Python/cntk/examples/LogReg/Test-3Classes.txt Normal file
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@ -0,0 +1,500 @@
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5.351031 3.693640 1
2.637437 1.951445 0
-0.179258 0.349339 0
3.246295 1.013459 0
5.839643 4.556761 1
1.435225 0.937185 0
0.500440 0.348246 0
4.948782 4.994416 1
0.810541 0.456830 0
5.098827 4.142789 1
2.365307 0.729496 0
-0.117730 0.891913 0
0.485735 0.513485 0
0.680270 1.486851 0
1.143053 0.227480 0
6.615446 4.561501 1
1.016051 1.862106 0
0.668177 -0.212610 0
2.906047 2.415627 1
5.576097 5.068683 1
1.315063 -0.040980 0
5.375285 3.306877 1
4.549934 3.805014 1
1.189238 0.661279 0
4.156567 3.280736 1
2.061355 1.090958 0
4.499387 3.640263 1
3.503883 1.015591 0
0.390200 -1.037188 0
2.922873 4.696711 1
1.803928 3.846808 1
0.907921 -2.139287 0
1.640739 0.592793 0
5.108193 3.194757 1
4.297873 4.034234 1
4.832678 4.073469 1
4.391764 3.557895 1
2.006343 0.836557 0
0.351400 1.534742 0
4.933823 2.937944 1
3.926482 2.073712 1
5.382385 4.818642 1
4.739010 3.213326 1
0.026227 0.177150 0
5.001353 3.300961 1
5.022782 2.921902 1
4.225051 4.534986 1
3.745148 -0.169000 0
5.891838 2.817417 1

2000
contrib/Python/cntk/examples/LogReg/Train-3Classes.txt
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Разница между файлами не показана из-за своего большого размера Загрузить разницу

40
contrib/Python/cntk/examples/LogReg/logreg.py
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@ -4,27 +4,47 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', '..'))
from cntk import *
if (__name__ == "__main__"):
X = Input(2)
X.attach_uci_fast_reader("Train-3Classes.txt", 0)
cur_dir = os.path.dirname(__file__)
# Using data from https://github.com/Microsoft/CNTK/wiki/Tutorial
train_file = os.path.join(cur_dir, "Train-3Classes.txt")
test_file = os.path.join(cur_dir, "Test-3Classes.txt")
mapping_file = os.path.join(cur_dir, "SimpleMapping-3Classes.txt")
def train_eval_logreg(criterion_name, eval_name):
X = Input(2)
y = Input(3)
y.attach_uci_fast_reader(
"Train-3Classes.txt", 2, True, 1, "SimpleMapping-3Classes.txt")
W = LearnableParameter(3, 2)
b = LearnableParameter(3, 1)
out = Times(W, X) + b
out.tag = 'output'
ce = CrossEntropyWithSoftmax(y, out)
ce = CrossEntropyWithSoftmax(y, out, var_name=criterion_name)
ce.tag = 'criterion'
eval = SquareError(y, out, var_name=eval_name)
eval.tag = 'eval'
my_sgd = SGD(
epoch_size=0, minibatch_size=25, learning_ratesPerMB=0.1, max_epochs=3)
with Context('demo', root_node=ce, clean_up=False) as ctx:
ctx.train(my_sgd, None)
with Context('demo', root_nodes=[ce,eval], clean_up=False) as ctx:
X.attach_uci_fast_reader(train_file, 0)
y.attach_uci_fast_reader(train_file, 2, True, 1, mapping_file)
ctx.train(my_sgd)
X.attach_uci_fast_reader(test_file, 0)
y.attach_uci_fast_reader(test_file, 2, True, 1, mapping_file)
result = ctx.test()
result = ctx.eval(out)
print(result.argmax(axis=1))
return result
def test_logreg():
result = train_eval_logreg('crit_node', 'eval_node')
assert result['SamplesSeen'] == 500
assert result['Perplexity'] == 1.2216067
assert result['eval_node'] == 13.779223
assert result['crit_node'] == 0.20016696
if __name__ == "__main__":
print(train_eval_logreg())

62
contrib/Python/cntk/graph.py
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@ -9,7 +9,7 @@ class sparse(object):
return hasattr(obj, 'todense')
from .utils import MODEL_INDENTATION
from .utils import numpy_to_cntk_shape
from .utils import numpy_to_cntk_shape, dedupe_readers
def _tuple_to_cntk_shape(shape):
return ':'.join(str(v) for v in shape)
@ -153,9 +153,8 @@ class ComputationNode(object):
return is_loop_node and p_name == 'input' and isinstance(p_value, str)
def _to_config_recursively(self, desc, unrolled_nodes, inputs,
readers, dep_inputs=None, node_counter=0):
if dep_inputs is None:
dep_inputs = tuple()
readers, dep_inputs, node_counter,
reconciled_cache):
param_variable_names = []
# In case we have multiple unreconciled inputs, we will reconcile each
@ -186,7 +185,7 @@ class ComputationNode(object):
else:
child_var, node_counter, child_desc, child_dep_inputs = pv._to_config_recursively(
desc, unrolled_nodes, inputs, readers,
dep_inputs, node_counter)
dep_inputs, node_counter, reconciled_cache)
unrolled_nodes[pv] = child_var, dep_inputs
@ -200,10 +199,16 @@ class ComputationNode(object):
first_unreconciled_input = pv
else:
pv = ReconcileMBLayout(pv, first_unreconciled_input)
child_var, node_counter, child_desc, dep_inputs = pv._to_config_recursively(
desc, unrolled_nodes, inputs, readers,
dep_inputs, node_counter)
if (pv, first_unreconciled_input) in reconciled_cache:
child_var, dep_inputs = reconciled_cache[(pv, first_unreconciled_input)]
else:
unrec_pv = pv
pv = ReconcileMBLayout(unrec_pv, first_unreconciled_input)
child_var, node_counter, child_desc, dep_inputs = pv._to_config_recursively(
desc, unrolled_nodes, inputs, readers,
dep_inputs, node_counter,
reconciled_cache)
reconciled_cache[(unrec_pv, first_unreconciled_input)] = pv.var_name, dep_inputs
unrolled_nodes[pv] = child_var, dep_inputs
@ -246,41 +251,38 @@ class ComputationNode(object):
return self.var_name, node_counter, desc, dep_inputs
def _to_config(self):
def _to_config(self, description, unrolled_nodes, inputs, readers,
dep_inputs, node_counter, reconciled_cache):
'''
Helper method to generate the CNTK configuration for this node.
'''
unrolled_nodes = {}
inputs = set()
readers = set()
var_name, node_counter, desc, dep_inputs = self._to_config_recursively(
desc=[],
description,
unrolled_nodes=unrolled_nodes,
inputs=inputs,
readers=readers)
readers=readers,
dep_inputs=dep_inputs,
node_counter=node_counter,
reconciled_cache=reconciled_cache)
return var_name, node_counter, desc, len(inputs) > 0, readers
def _dedupe_readers(self, readers):
import copy
readers_map = {}
for r in readers:
filename = r['FileName']
if filename in readers_map:
readers_map[filename].inputs_def.extend(r.inputs_def)
else:
readers_map[filename] = copy.deepcopy(r)
return [r for r in readers_map.values()]
return var_name, node_counter, desc, len(inputs) > 0, readers, dep_inputs
def to_config(self):
'''
Generate CNTK configuration for this node including the configuration
for all dependent child nodes.
'''
var_name, node_counter, desc, has_inputs, readers = self._to_config()
var_name, node_counter, desc, has_inputs, readers, dep_inputs = \
self._to_config(description=[],
unrolled_nodes={},
inputs=set(),
readers=set(),
dep_inputs=tuple(),
node_counter=0,
reconciled_cache={})
return "\n".join(desc), has_inputs, self._dedupe_readers(readers)
return "\n".join(desc), has_inputs, dedupe_readers(readers)
class InputComputationNodeBase(ComputationNode, metaclass=ABCMeta):

13
contrib/Python/cntk/test/test_context.py
Просмотреть файл

@ -55,7 +55,7 @@ Validating --> v5 = Plus (v3, v4) : [3 {1} x *], [3 x 1 {1,3}] -> [3 x 1 {1,3} x
assert Context._parse_shapes_from_output(output) == expected
def test_parse_result_output_1():
def test_parse_eval_result_output_1():
output = '''\
0 |w.shape 1 1
0 |w 60.000000
@ -68,3 +68,14 @@ def test_parse_result_output_1():
for res, exp in zip(list_of_tensors, expected):
assert np.allclose(res, np.asarray(exp))
def test_parse_test_result_output():
output = '''\
Final Results: Minibatch[1-1]: SamplesSeen = 500 v8: SquareError/Sample = 13.779223 v7: CrossEntropyWithSoftmax/Sample = 0.20016696 Perplexity = 1.2216067 '''
result = Context._parse_test_result(output)
assert result['SamplesSeen'] == 500
assert result['Perplexity'] == 1.2216067
assert result['v8'] == 13.779223
assert result['v7'] == 0.20016696
assert len(result) == 4

13
contrib/Python/cntk/utils/__init__.py
Просмотреть файл

@ -40,3 +40,16 @@ def cntk_to_numpy_shape(shape):
shape = (1,)
return shape
def dedupe_readers(readers):
import copy
readers_map = {}
for r in readers:
filename = r['FileName']
if filename in readers_map:
readers_map[filename].inputs_def.extend(r.inputs_def)
else:
readers_map[filename] = copy.deepcopy(r)
return [r for r in readers_map.values()]