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def | __init__ (self, model, input_record, name='gather_record', kwargs) |
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def | add_ops (self, net) |
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def | __init__ (self, model, prefix, input_record, predict_input_record_fields=None, tags=None, kwargs) |
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def | get_type (self) |
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def | predict_input_record (self) |
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def | input_record (self) |
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def | predict_output_schema (self) |
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def | predict_output_schema (self, output_schema) |
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def | output_schema (self) |
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def | output_schema (self, output_schema) |
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def | get_parameters (self) |
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def | get_fp16_compatible_parameters (self) |
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def | get_memory_usage (self) |
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def | add_init_params (self, init_net) |
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def | create_param (self, param_name, shape, initializer, optimizer, ps_param=None, regularizer=None) |
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def | get_next_blob_reference (self, name) |
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def | add_operators (self, net, init_net=None, context=InstantiationContext.TRAINING) |
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def | add_ops (self, net) |
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def | add_eval_ops (self, net) |
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def | add_train_ops (self, net) |
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def | add_ops_to_accumulate_pred (self, net) |
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def | add_param_copy_operators (self, net) |
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def | export_output_for_metrics (self) |
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def | export_params_for_metrics (self) |
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Given 1-D `indices` tensor, gather elements at `i` in `indices` from all the
blobs in `record`. If a blob is a values blob of a list, all the elements
included by the list's lengths blob are gathered. For example,
Input:
indices = [0, 2]
record:a = [[0, 1], [2, 3], [4, 5], [6, 7]]
record:b:lengths = [0, 1, 2, 3]
record:b:items = [0, 1, 2, 3, 4, 5]
Output:
a = [[0, 1], [4, 5]]
b:lengths = [0, 2]
b:items = [1, 2]
This supports nested list.
Definition at line 12 of file gather_record.py.