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A v2 API like data feeder for book demos. We can feed data directly from reader.
QiJune
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Dec 1, 2017
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| class DataToLoDTensorConverter(object): | ||
| def __init__(self, place, lod_level, shape, batch_size_dim, dtype): |
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It seems that batch_size_dim here is no use.
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Yes. batch_size_dim could be removed.
| elif dtype == core.DataType.FP64: | ||
| self.dtype = 'float64' | ||
| elif dtype == core.DataType.INT32: | ||
| self.dtype = 'int32' |
| arr = numpy.array(self.data, dtype=self.dtype).reshape(self.shape) | ||
| t = core.LoDTensor() | ||
| t.set(arr, self.place) | ||
| if self.lod_level != 0: |
| self.feed_names.append(each_var.name) | ||
| shape = each_var.shape | ||
| batch_size_dim = -1 | ||
| for i, s in enumerate(shape): |
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Is not batch_size_dim always in the first dimension?
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No, batch_size_dim is not the first dimension if we use static RNN
| converter = [] | ||
| for lod_level, shape, dtype in six.zip( | ||
| self.feed_lod_level, self.feed_shapes, self.feed_dtypes): | ||
| batch_size_dim, shape = shape |
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This line take batch_size_dim as the first dimension.
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No. shape is a tuple before.
Also add __all__ to data_feeder.py
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A v2 API like data feeder for book demos.
We can feed data directly from reader.
Fix #6101