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dynamic min max shape phase 2

This commit is contained in:
luoyang 2021-06-07 10:32:03 +08:00
parent 0ae751e0ba
commit dad289c679
5 changed files with 70 additions and 69 deletions
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12
mindspore/dataset/core/config.py
View File

@ -32,18 +32,6 @@ INT32_MAX = 2147483647
UINT32_MAX = 4294967295
_config = cde.GlobalContext.config_manager()
_dynamic_columns = dict()
def set_dynamic_columns(columns=None):
global _dynamic_columns
if not isinstance(columns, dict):
raise TypeError("Pass a dict to set dynamic shape, example: {\"data1\": [16, None, 256]}")
_dynamic_columns = columns
def get_dynamic_columns():
return _dynamic_columns
def _init_device_info():

46
mindspore/dataset/engine/datasets.py
View File

@ -60,7 +60,7 @@ from .validators import check_batch, check_shuffle, check_map, check_filter, che
check_bucket_batch_by_length, check_cluedataset, check_save, check_csvdataset, check_paddeddataset, \
check_tuple_iterator, check_dict_iterator, check_schema, check_to_device_send
from ..core.config import get_callback_timeout, _init_device_info, get_enable_shared_mem, get_num_parallel_workers, \
get_prefetch_size, get_dynamic_columns
get_prefetch_size
from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
from ..core.validator_helpers import replace_none
from ..core.py_util_helpers import ExceptionHandler
@ -209,15 +209,15 @@ class Dataset:
self._input_indexs = ()
self.saved_output_types = None
self.saved_output_shapes = None
self.dynamic_setting = [False, None]
self.saved_min_shapes = None
self.saved_max_shapes = None
self._col_names = None
self.dataset_size = None
self._batch_size = None
self._num_classes = None
self._repeat_count = None
self._class_indexing = None
self.min_shapes = None
self.max_shapes = None
self.dynamic_shapes = None
self._sync = False
def create_ir_tree(self):
@ -1531,8 +1531,9 @@ class Dataset:
if self.saved_output_shapes is None:
runtime_getter = self._init_tree_getters()
self.saved_output_shapes = runtime_getter[0].GetOutputShapes()
self.saved_output_types = runtime_getter[0].GetOutputTypes()
self.close_pool()
if self.dynamic_setting[0]:
self.saved_output_shapes, self.saved_min_shapes, self.saved_max_shapes = self._dynamic_output_shapes()
return self.saved_output_shapes
def output_types(self):
@ -1544,7 +1545,6 @@ class Dataset:
"""
if self.saved_output_types is None:
runtime_getter = self._init_tree_getters()
self.saved_output_shapes = runtime_getter[0].GetOutputShapes()
self.saved_output_types = runtime_getter[0].GetOutputTypes()
self.close_pool()
return self.saved_output_types
@ -1562,24 +1562,35 @@ class Dataset:
self.close_pool()
return self.dataset_size
def get_dynamic_min_max_shape(self):
def set_dynamic_columns(self, columns=None):
if not isinstance(columns, dict):
raise TypeError("Pass a dict to set dynamic shape, example: {\"data1\": [16, None, 256]}")
self.dynamic_setting[0] = True
self.dynamic_setting[1] = columns
def dynamic_min_max_shapes(self):
if self.saved_min_shapes is None or self.saved_max_shapes is None:
self.saved_output_shapes, self.saved_min_shapes, self.saved_max_shapes = self._dynamic_output_shapes()
return self.saved_min_shapes, self.saved_max_shapes
def _dynamic_output_shapes(self):
"""
Get dynamic information of source data.
Returns:
lists, min_shapes, max_shapes, dynamic_shapes of source data.
lists, dynamic_shapes, min_shapes, max_shapes of source data.
"""
# Assume data1 shape is dynamic, data2 shape is fix
# {"data1": [batch_size, None, feat_len], "data2": [batch_size, feat_len]}
dynamic_columns = get_dynamic_columns()
if not dynamic_columns:
if not self.dynamic_setting[1]:
raise RuntimeError("dynamic_columns is not set, call set_dynamic_columns() first.")
if self.min_shapes is not None and self.max_shapes is not None and self.dynamic_shapes is not None:
return self.min_shapes, self.max_shapes, self.dynamic_shapes
if self.saved_output_shapes is not None and self.saved_min_shapes is not None and \
self.saved_max_shapes is not None:
return self.saved_output_shapes, self.saved_min_shapes, self.saved_max_shapes
logger.warning("Calculating dynamic shape of input data, this will take a few minutes...")
# Assume data1 shape is dynamic, data2 shape is fix
# {"data1": [batch_size, None, feat_len], "data2": [batch_size, feat_len]}
dynamic_columns = self.dynamic_setting[1]
# ["data1", "data2"]
dataset_columns = self.get_col_names()
for column in dynamic_columns:
@ -1633,10 +1644,7 @@ class Dataset:
max_shapes.append(fix_shape)
min_shapes.append(fix_shape)
dynamic_shapes.append(fix_shape)
self.min_shapes = min_shapes
self.max_shapes = max_shapes
self.dynamic_shapes = dynamic_shapes
return self.min_shapes, self.max_shapes, self.dynamic_shapes
return dynamic_shapes, min_shapes, max_shapes
def num_classes(self):
"""

7
mindspore/train/dataset_helper.py
View File

@ -254,8 +254,9 @@ class DatasetHelper:
"""Get the types and shape of current batch."""
return self.iter.get_data_info()
def get_dynamic_min_max_shape(self):
return self.iter.get_dynamic_min_max_shape()
def dynamic_min_max_shapes(self):
"""Get shape range(min shape, max shape) of dynamic data."""
return self.iter.dynamic_min_max_shapes()
class _DatasetIter:
"""Base iter for dataset helper"""
@ -285,7 +286,7 @@ class _DatasetIter:
self.release = dataset.__transfer_dataset__.release
self.continue_send = dataset.__transfer_dataset__.continue_send
self.get_data_info = dataset.__transfer_dataset__.get_data_info
self.get_dynamic_min_max_shape = dataset.__transfer_dataset__.get_dynamic_min_max_shape
self.dynamic_min_max_shapes = dataset.__transfer_dataset__.dynamic_min_max_shapes
self.dataset_types, self.dataset_shapes = _get_types_and_shapes(dataset)
def __iter__(self):

2
tests/dataset_mock.py
View File

@ -73,7 +73,7 @@ class MindData:
def get_data_info(self):
pass
def get_dynamic_min_max_shape(self):
def dynamic_min_max_shapes(self):
pass
def __len__(self):

72
tests/ut/python/dataset/test_datasets_get_dynamic_shape.py
View File

@ -23,16 +23,17 @@ def generator0():
yield (np.ones((32, i)), np.zeros((16, i, i, 3)), np.ones((i)))
def test_get_dynamic_min_max_shape_0():
logger.info("Test get_dynamic_min_max_shape with dynamic shape columns")
def test_get_dynamic_min_max_shapes_0():
logger.info("Test dynamic_min_max_shapes with dynamic shape columns")
dataset = ds.GeneratorDataset(generator0, ["data1", "data2", "data3"])
# config dynamic shape
ds.config.set_dynamic_columns(columns={"data1": [32, None], "data2": [16, None, None, 3], "data3": [None]})
dataset.set_dynamic_columns(columns={"data1": [32, None], "data2": [16, None, None, 3], "data3": [None]})
# get dynamic information
min_shapes, max_shapes, dynamic_shapes = dataset.get_dynamic_min_max_shape()
min_shapes, max_shapes = dataset.dynamic_min_max_shapes()
dynamic_shapes = dataset.output_shapes()
# check result
np.testing.assert_array_equal(min_shapes, [[32, 1], [16, 1, 1, 3], [1]])
@ -45,16 +46,17 @@ def generator1():
yield (np.ones((16, i, 83)), np.array((i)))
def test_get_dynamic_min_max_shape_1():
logger.info("Test get_dynamic_min_max_shape with dynamic shape column and fix shape column")
def test_get_dynamic_min_max_shapes_1():
logger.info("Test dynamic_min_max_shapes with dynamic shape column and fix shape column")
dataset = ds.GeneratorDataset(generator1, ["data1", "data2"])
# config dynamic shape
ds.config.set_dynamic_columns(columns={"data1": [16, None, 83], "data2": []})
dataset.set_dynamic_columns(columns={"data1": [16, None, 83], "data2": []})
# get dynamic information
min_shapes, max_shapes, dynamic_shapes = dataset.get_dynamic_min_max_shape()
dynamic_shapes = dataset.output_shapes()
min_shapes, max_shapes = dataset.dynamic_min_max_shapes()
# check result
# raise a warning to tell user "data2" is not dynamic
@ -63,14 +65,15 @@ def test_get_dynamic_min_max_shape_1():
np.testing.assert_array_equal(dynamic_shapes, [[16, -1, 83], []])
def test_get_dynamic_min_max_shape_2():
logger.info("Test get_dynamic_min_max_shape with all dynamic config")
def test_get_dynamic_min_max_shapes_2():
logger.info("Test dynamic_min_max_shapes with all dynamic config")
dataset = ds.GeneratorDataset(generator1, ["data1", "data2"])
# config all dims have dynamic shape
ds.config.set_dynamic_columns(columns={"data1": [None, None, None]})
min_shapes, max_shapes, dynamic_shapes = dataset.get_dynamic_min_max_shape()
dataset.set_dynamic_columns(columns={"data1": [None, None, None]})
dynamic_shapes = dataset.output_shapes()
min_shapes, max_shapes = dataset.dynamic_min_max_shapes()
# check result
# Although shape[0] of data1 is fix in given data, user think it is dynamic, so shape[0] is dynamic
@ -84,16 +87,17 @@ def generator2():
yield (np.ones((16, i, 83)), np.ones((5, 5)))
def test_get_dynamic_min_max_shape_3():
logger.info("Test get_dynamic_min_max_shape with only config dynamic column")
def test_get_dynamic_min_max_shapes_3():
logger.info("Test dynamic_min_max_shapes with only config dynamic column")
dataset = ds.GeneratorDataset(generator2, ["data1", "data2"])
# only dynamic shape is required to config
ds.config.set_dynamic_columns(columns={"data1": [16, None, 83]})
dataset.set_dynamic_columns(columns={"data1": [16, None, 83]})
# get dynamic information
min_shapes, max_shapes, dynamic_shapes = dataset.get_dynamic_min_max_shape()
dynamic_shapes = dataset.output_shapes()
min_shapes, max_shapes = dataset.dynamic_min_max_shapes()
# check result
# column with fix shape will be also appended to shapes list
@ -102,51 +106,51 @@ def test_get_dynamic_min_max_shape_3():
np.testing.assert_array_equal(dynamic_shapes, [[16, -1, 83], [5, 5]])
def test_get_dynamic_min_max_shape_4():
logger.info("Test get_dynamic_min_max_shape with unexpected column setting")
def test_get_dynamic_min_max_shapes_4():
logger.info("Test dynamic_min_max_shapes with unexpected column setting")
dataset = ds.GeneratorDataset(generator1, ["data1", "data2"])
with pytest.raises(TypeError) as info:
# dynamic column is not in dict
ds.config.set_dynamic_columns(columns=list())
dataset.set_dynamic_columns(columns=list())
assert "Pass a dict to set dynamic shape" in str(info.value)
with pytest.raises(RuntimeError) as info:
# dynamic column is not set
ds.config.set_dynamic_columns(columns=dict())
dataset.get_dynamic_min_max_shape()
dataset.set_dynamic_columns(columns=dict())
dataset.dynamic_min_max_shapes()
assert "dynamic_columns is not set, call set_dynamic_columns() first" in str(info.value)
with pytest.raises(RuntimeError) as info:
# dynamic column is not set
ds.config.set_dynamic_columns(columns={"data2": []})
dataset.get_dynamic_min_max_shape()
dataset.set_dynamic_columns(columns={"data2": []})
dataset.dynamic_min_max_shapes()
assert "column [data1] has dynamic shape but not set by set_dynamic_columns()" in str(info.value)
with pytest.raises(RuntimeError) as info:
# column does not exist
ds.config.set_dynamic_columns(columns={"data3": [16, None, 83]})
dataset.get_dynamic_min_max_shape()
dataset.set_dynamic_columns(columns={"data3": [16, None, 83]})
dataset.dynamic_min_max_shapes()
assert "dynamic column [data3] does not match any column in dataset" in str(info.value)
with pytest.raises(RuntimeError) as info:
# unexpected column shape
ds.config.set_dynamic_columns(columns={"data1": [16, 83, None]})
dataset.get_dynamic_min_max_shape()
dataset.set_dynamic_columns(columns={"data1": [16, 83, None]})
dataset.dynamic_min_max_shapes()
assert "shape [16, 83, None] does not match dataset column [data1] with shape [16, 1, 83]" in str(info.value)
with pytest.raises(RuntimeError) as info:
# unexpected column shape
ds.config.set_dynamic_columns(columns={"data1": [16, None]})
dataset.get_dynamic_min_max_shape()
dataset.set_dynamic_columns(columns={"data1": [16, None]})
dataset.dynamic_min_max_shapes()
assert "shape [16, None] does not match dataset column [data1] with shape [16, 1, 83]" in str(info.value)
if __name__ == "__main__":
test_get_dynamic_min_max_shape_0()
test_get_dynamic_min_max_shape_1()
test_get_dynamic_min_max_shape_2()
test_get_dynamic_min_max_shape_3()
test_get_dynamic_min_max_shape_4()
test_get_dynamic_min_max_shapes_0()
test_get_dynamic_min_max_shapes_1()
test_get_dynamic_min_max_shapes_2()
test_get_dynamic_min_max_shapes_3()
test_get_dynamic_min_max_shapes_4()