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DEKHTIARJonathanDEKHTIARJonathan
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Adding NVIDIA Example Models
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‎tftrt/examples/benchmark_utils.py‎

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@@ -190,3 +190,35 @@ def aggregate_data(self, y_pred, y):
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self._predicted[key][idx_start:idx_stop] = y_pred[key]
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for key, val in self._expected.items():
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self._expected[key][idx_start:idx_stop] = y[key]
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def patch_dali_dataset(dataset):
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import nvidia.dali.plugin.tf as dali_tf
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if not isinstance(dataset, dali_tf.DALIDataset):
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raise TypeError(
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"Dataset supplied should be an instance of `DALIDataset`."
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f"Received: `{type(dataset)}`"
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)
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def take(self, limit):
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class _Dataset(self.__class__):
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def __init__(self, _ds, _limit):
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self._ds = _ds
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self._limit = _limit
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def __iter__(self):
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idx = 0
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for data in self._ds:
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if idx >= self._limit:
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break
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yield data
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idx += 1
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return _Dataset(self, limit)
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# Monkey Patch
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dataset.__class__.take = take
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return dataset

‎tftrt/examples/image_classification/image_classification.py‎

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@@ -219,7 +219,7 @@ def evaluate_model(self, predictions, expected, bypass_data_to_eval):
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This computes overall accuracy, mAP, etc. Returns the
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metric value and a metric_units string naming the metric.
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Note: script arguments can be accessed using `args.attr`
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Note: script arguments can be accessed using `self._args.attr`
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"""
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return (

‎tftrt/examples/nvidia_examples/bert_tf2/analysis.txt‎

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MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
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signature_def['__saved_model_init_op']:
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The given SavedModel SignatureDef contains the following input(s):
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The given SavedModel SignatureDef contains the following output(s):
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outputs['__saved_model_init_op'] tensor_info:
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dtype: DT_INVALID
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shape: unknown_rank
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name: NoOp
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Method name is:
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signature_def['serving_default']:
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The given SavedModel SignatureDef contains the following input(s):
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inputs['input_mask'] tensor_info:
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dtype: DT_INT32
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shape: (-1, 384)
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name: serving_default_input_mask:0
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inputs['input_type_ids'] tensor_info:
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dtype: DT_INT32
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shape: (-1, 384)
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name: serving_default_input_type_ids:0
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inputs['input_word_ids'] tensor_info:
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dtype: DT_INT32
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shape: (-1, 384)
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name: serving_default_input_word_ids:0
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The given SavedModel SignatureDef contains the following output(s):
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outputs['end_positions'] tensor_info:
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dtype: DT_FLOAT
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shape: (-1, 384)
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name: StatefulPartitionedCall:0
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outputs['start_positions'] tensor_info:
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dtype: DT_FLOAT
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shape: (-1, 384)
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name: StatefulPartitionedCall:1
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Method name is: tensorflow/serving/predict
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Defined Functions:
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Function Name: '__call__'
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Option #1
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Callable with:
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Argument #1
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DType: list
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Value: [TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_word_ids'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_mask'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_type_ids'), ]
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Argument #2
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DType: bool
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Value: True
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Argument #3
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DType: NoneType
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Value: None
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Option #2
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Callable with:
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Argument #1
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DType: list
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Value: [TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/0'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/1'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/2'), ]
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Argument #2
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DType: bool
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Value: True
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Argument #3
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DType: NoneType
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Value: None
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Option #3
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Callable with:
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Argument #1
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DType: list
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Value: [TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_word_ids'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_mask'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_type_ids'), ]
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Argument #2
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DType: bool
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Value: False
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Argument #3
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DType: NoneType
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Value: None
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Option #4
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Callable with:
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Argument #1
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DType: list
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Value: [TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/0'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/1'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/2'), ]
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Argument #2
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DType: bool
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Value: False
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Argument #3
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DType: NoneType
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Value: None
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Function Name: '_default_save_signature'
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Option #1
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Callable with:
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Argument #1
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DType: list
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Value: [TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_word_ids'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_mask'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_type_ids'), ]
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Function Name: 'call_and_return_all_conditional_losses'
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Option #1
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Callable with:
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Argument #1
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DType: list
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Value: [TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/0'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/1'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/2'), ]
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Argument #2
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DType: bool
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Value: True
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Argument #3
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DType: NoneType
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Value: None
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Option #2
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Callable with:
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Argument #1
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DType: list
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Value: [TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/0'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/1'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='inputs/2'), ]
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Argument #2
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DType: bool
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Value: False
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Argument #3
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DType: NoneType
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Value: None
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Option #3
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Callable with:
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Argument #1
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DType: list
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Value: [TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_word_ids'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_mask'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_type_ids'), ]
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Argument #2
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DType: bool
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Value: True
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Argument #3
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DType: NoneType
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Value: None
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Option #4
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Callable with:
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Argument #1
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DType: list
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Value: [TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_word_ids'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_mask'), TensorSpec(shape=(None, 384), dtype=tf.int32, name='input_type_ids'), ]
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Argument #2
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DType: bool
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Value: False
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Argument #3
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DType: NoneType
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Value: None

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