dynamic min max shape phase 2
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@ -32,18 +32,6 @@ INT32_MAX = 2147483647
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UINT32_MAX = 4294967295
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_config = cde.GlobalContext.config_manager()
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_dynamic_columns = dict()
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def set_dynamic_columns(columns=None):
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global _dynamic_columns
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if not isinstance(columns, dict):
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raise TypeError("Pass a dict to set dynamic shape, example: {\"data1\": [16, None, 256]}")
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_dynamic_columns = columns
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def get_dynamic_columns():
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return _dynamic_columns
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def _init_device_info():
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@ -60,7 +60,7 @@ from .validators import check_batch, check_shuffle, check_map, check_filter, che
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check_bucket_batch_by_length, check_cluedataset, check_save, check_csvdataset, check_paddeddataset, \
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check_tuple_iterator, check_dict_iterator, check_schema, check_to_device_send
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from ..core.config import get_callback_timeout, _init_device_info, get_enable_shared_mem, get_num_parallel_workers, \
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get_prefetch_size, get_dynamic_columns
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get_prefetch_size
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from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
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from ..core.validator_helpers import replace_none
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from ..core.py_util_helpers import ExceptionHandler
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@ -209,15 +209,15 @@ class Dataset:
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self._input_indexs = ()
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self.saved_output_types = None
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self.saved_output_shapes = None
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self.dynamic_setting = [False, None]
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self.saved_min_shapes = None
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self.saved_max_shapes = None
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self._col_names = None
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self.dataset_size = None
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self._batch_size = None
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self._num_classes = None
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self._repeat_count = None
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self._class_indexing = None
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self.min_shapes = None
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self.max_shapes = None
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self.dynamic_shapes = None
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self._sync = False
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def create_ir_tree(self):
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@ -1531,8 +1531,9 @@ class Dataset:
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if self.saved_output_shapes is None:
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runtime_getter = self._init_tree_getters()
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self.saved_output_shapes = runtime_getter[0].GetOutputShapes()
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self.saved_output_types = runtime_getter[0].GetOutputTypes()
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self.close_pool()
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if self.dynamic_setting[0]:
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self.saved_output_shapes, self.saved_min_shapes, self.saved_max_shapes = self._dynamic_output_shapes()
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return self.saved_output_shapes
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def output_types(self):
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@ -1544,7 +1545,6 @@ class Dataset:
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"""
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if self.saved_output_types is None:
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runtime_getter = self._init_tree_getters()
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self.saved_output_shapes = runtime_getter[0].GetOutputShapes()
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self.saved_output_types = runtime_getter[0].GetOutputTypes()
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self.close_pool()
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return self.saved_output_types
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@ -1562,24 +1562,35 @@ class Dataset:
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self.close_pool()
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return self.dataset_size
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def get_dynamic_min_max_shape(self):
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def set_dynamic_columns(self, columns=None):
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if not isinstance(columns, dict):
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raise TypeError("Pass a dict to set dynamic shape, example: {\"data1\": [16, None, 256]}")
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self.dynamic_setting[0] = True
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self.dynamic_setting[1] = columns
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def dynamic_min_max_shapes(self):
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if self.saved_min_shapes is None or self.saved_max_shapes is None:
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self.saved_output_shapes, self.saved_min_shapes, self.saved_max_shapes = self._dynamic_output_shapes()
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return self.saved_min_shapes, self.saved_max_shapes
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def _dynamic_output_shapes(self):
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"""
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Get dynamic information of source data.
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Returns:
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lists, min_shapes, max_shapes, dynamic_shapes of source data.
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lists, dynamic_shapes, min_shapes, max_shapes of source data.
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"""
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# Assume data1 shape is dynamic, data2 shape is fix
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# {"data1": [batch_size, None, feat_len], "data2": [batch_size, feat_len]}
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dynamic_columns = get_dynamic_columns()
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if not dynamic_columns:
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if not self.dynamic_setting[1]:
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raise RuntimeError("dynamic_columns is not set, call set_dynamic_columns() first.")
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if self.min_shapes is not None and self.max_shapes is not None and self.dynamic_shapes is not None:
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return self.min_shapes, self.max_shapes, self.dynamic_shapes
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if self.saved_output_shapes is not None and self.saved_min_shapes is not None and \
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self.saved_max_shapes is not None:
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return self.saved_output_shapes, self.saved_min_shapes, self.saved_max_shapes
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logger.warning("Calculating dynamic shape of input data, this will take a few minutes...")
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# Assume data1 shape is dynamic, data2 shape is fix
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# {"data1": [batch_size, None, feat_len], "data2": [batch_size, feat_len]}
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dynamic_columns = self.dynamic_setting[1]
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# ["data1", "data2"]
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dataset_columns = self.get_col_names()
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for column in dynamic_columns:
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@ -1633,10 +1644,7 @@ class Dataset:
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max_shapes.append(fix_shape)
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min_shapes.append(fix_shape)
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dynamic_shapes.append(fix_shape)
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self.min_shapes = min_shapes
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self.max_shapes = max_shapes
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self.dynamic_shapes = dynamic_shapes
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return self.min_shapes, self.max_shapes, self.dynamic_shapes
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return dynamic_shapes, min_shapes, max_shapes
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def num_classes(self):
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"""
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@ -254,8 +254,9 @@ class DatasetHelper:
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"""Get the types and shape of current batch."""
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return self.iter.get_data_info()
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def get_dynamic_min_max_shape(self):
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return self.iter.get_dynamic_min_max_shape()
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def dynamic_min_max_shapes(self):
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"""Get shape range(min shape, max shape) of dynamic data."""
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return self.iter.dynamic_min_max_shapes()
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class _DatasetIter:
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"""Base iter for dataset helper"""
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@ -285,7 +286,7 @@ class _DatasetIter:
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self.release = dataset.__transfer_dataset__.release
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self.continue_send = dataset.__transfer_dataset__.continue_send
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self.get_data_info = dataset.__transfer_dataset__.get_data_info
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self.get_dynamic_min_max_shape = dataset.__transfer_dataset__.get_dynamic_min_max_shape
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self.dynamic_min_max_shapes = dataset.__transfer_dataset__.dynamic_min_max_shapes
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self.dataset_types, self.dataset_shapes = _get_types_and_shapes(dataset)
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def __iter__(self):
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@ -73,7 +73,7 @@ class MindData:
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def get_data_info(self):
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pass
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def get_dynamic_min_max_shape(self):
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def dynamic_min_max_shapes(self):
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pass
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def __len__(self):
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@ -23,16 +23,17 @@ def generator0():
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yield (np.ones((32, i)), np.zeros((16, i, i, 3)), np.ones((i)))
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def test_get_dynamic_min_max_shape_0():
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logger.info("Test get_dynamic_min_max_shape with dynamic shape columns")
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def test_get_dynamic_min_max_shapes_0():
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logger.info("Test dynamic_min_max_shapes with dynamic shape columns")
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dataset = ds.GeneratorDataset(generator0, ["data1", "data2", "data3"])
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# config dynamic shape
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ds.config.set_dynamic_columns(columns={"data1": [32, None], "data2": [16, None, None, 3], "data3": [None]})
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dataset.set_dynamic_columns(columns={"data1": [32, None], "data2": [16, None, None, 3], "data3": [None]})
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# get dynamic information
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min_shapes, max_shapes, dynamic_shapes = dataset.get_dynamic_min_max_shape()
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min_shapes, max_shapes = dataset.dynamic_min_max_shapes()
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dynamic_shapes = dataset.output_shapes()
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# check result
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np.testing.assert_array_equal(min_shapes, [[32, 1], [16, 1, 1, 3], [1]])
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@ -45,16 +46,17 @@ def generator1():
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yield (np.ones((16, i, 83)), np.array((i)))
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def test_get_dynamic_min_max_shape_1():
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logger.info("Test get_dynamic_min_max_shape with dynamic shape column and fix shape column")
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def test_get_dynamic_min_max_shapes_1():
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logger.info("Test dynamic_min_max_shapes with dynamic shape column and fix shape column")
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dataset = ds.GeneratorDataset(generator1, ["data1", "data2"])
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# config dynamic shape
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ds.config.set_dynamic_columns(columns={"data1": [16, None, 83], "data2": []})
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dataset.set_dynamic_columns(columns={"data1": [16, None, 83], "data2": []})
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# get dynamic information
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min_shapes, max_shapes, dynamic_shapes = dataset.get_dynamic_min_max_shape()
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dynamic_shapes = dataset.output_shapes()
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min_shapes, max_shapes = dataset.dynamic_min_max_shapes()
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# check result
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# raise a warning to tell user "data2" is not dynamic
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@ -63,14 +65,15 @@ def test_get_dynamic_min_max_shape_1():
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np.testing.assert_array_equal(dynamic_shapes, [[16, -1, 83], []])
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def test_get_dynamic_min_max_shape_2():
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logger.info("Test get_dynamic_min_max_shape with all dynamic config")
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def test_get_dynamic_min_max_shapes_2():
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logger.info("Test dynamic_min_max_shapes with all dynamic config")
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dataset = ds.GeneratorDataset(generator1, ["data1", "data2"])
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# config all dims have dynamic shape
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ds.config.set_dynamic_columns(columns={"data1": [None, None, None]})
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min_shapes, max_shapes, dynamic_shapes = dataset.get_dynamic_min_max_shape()
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dataset.set_dynamic_columns(columns={"data1": [None, None, None]})
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dynamic_shapes = dataset.output_shapes()
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min_shapes, max_shapes = dataset.dynamic_min_max_shapes()
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# check result
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# Although shape[0] of data1 is fix in given data, user think it is dynamic, so shape[0] is dynamic
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@ -84,16 +87,17 @@ def generator2():
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yield (np.ones((16, i, 83)), np.ones((5, 5)))
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def test_get_dynamic_min_max_shape_3():
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logger.info("Test get_dynamic_min_max_shape with only config dynamic column")
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def test_get_dynamic_min_max_shapes_3():
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logger.info("Test dynamic_min_max_shapes with only config dynamic column")
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dataset = ds.GeneratorDataset(generator2, ["data1", "data2"])
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# only dynamic shape is required to config
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ds.config.set_dynamic_columns(columns={"data1": [16, None, 83]})
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dataset.set_dynamic_columns(columns={"data1": [16, None, 83]})
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# get dynamic information
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min_shapes, max_shapes, dynamic_shapes = dataset.get_dynamic_min_max_shape()
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dynamic_shapes = dataset.output_shapes()
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min_shapes, max_shapes = dataset.dynamic_min_max_shapes()
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# check result
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# column with fix shape will be also appended to shapes list
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@ -102,51 +106,51 @@ def test_get_dynamic_min_max_shape_3():
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np.testing.assert_array_equal(dynamic_shapes, [[16, -1, 83], [5, 5]])
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def test_get_dynamic_min_max_shape_4():
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logger.info("Test get_dynamic_min_max_shape with unexpected column setting")
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def test_get_dynamic_min_max_shapes_4():
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logger.info("Test dynamic_min_max_shapes with unexpected column setting")
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dataset = ds.GeneratorDataset(generator1, ["data1", "data2"])
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with pytest.raises(TypeError) as info:
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# dynamic column is not in dict
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ds.config.set_dynamic_columns(columns=list())
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dataset.set_dynamic_columns(columns=list())
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assert "Pass a dict to set dynamic shape" in str(info.value)
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with pytest.raises(RuntimeError) as info:
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# dynamic column is not set
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ds.config.set_dynamic_columns(columns=dict())
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dataset.get_dynamic_min_max_shape()
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dataset.set_dynamic_columns(columns=dict())
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dataset.dynamic_min_max_shapes()
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assert "dynamic_columns is not set, call set_dynamic_columns() first" in str(info.value)
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with pytest.raises(RuntimeError) as info:
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# dynamic column is not set
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ds.config.set_dynamic_columns(columns={"data2": []})
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dataset.get_dynamic_min_max_shape()
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dataset.set_dynamic_columns(columns={"data2": []})
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dataset.dynamic_min_max_shapes()
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assert "column [data1] has dynamic shape but not set by set_dynamic_columns()" in str(info.value)
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with pytest.raises(RuntimeError) as info:
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# column does not exist
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ds.config.set_dynamic_columns(columns={"data3": [16, None, 83]})
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dataset.get_dynamic_min_max_shape()
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dataset.set_dynamic_columns(columns={"data3": [16, None, 83]})
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dataset.dynamic_min_max_shapes()
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assert "dynamic column [data3] does not match any column in dataset" in str(info.value)
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with pytest.raises(RuntimeError) as info:
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# unexpected column shape
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ds.config.set_dynamic_columns(columns={"data1": [16, 83, None]})
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dataset.get_dynamic_min_max_shape()
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dataset.set_dynamic_columns(columns={"data1": [16, 83, None]})
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dataset.dynamic_min_max_shapes()
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assert "shape [16, 83, None] does not match dataset column [data1] with shape [16, 1, 83]" in str(info.value)
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with pytest.raises(RuntimeError) as info:
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# unexpected column shape
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ds.config.set_dynamic_columns(columns={"data1": [16, None]})
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dataset.get_dynamic_min_max_shape()
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dataset.set_dynamic_columns(columns={"data1": [16, None]})
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dataset.dynamic_min_max_shapes()
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assert "shape [16, None] does not match dataset column [data1] with shape [16, 1, 83]" in str(info.value)
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if __name__ == "__main__":
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test_get_dynamic_min_max_shape_0()
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test_get_dynamic_min_max_shape_1()
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test_get_dynamic_min_max_shape_2()
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test_get_dynamic_min_max_shape_3()
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test_get_dynamic_min_max_shape_4()
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test_get_dynamic_min_max_shapes_0()
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test_get_dynamic_min_max_shapes_1()
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test_get_dynamic_min_max_shapes_2()
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test_get_dynamic_min_max_shapes_3()
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test_get_dynamic_min_max_shapes_4()
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