Caffe Parser¶
-
class
tensorrt.
IBlobNameToTensor
¶ This class is used to store and query
ITensor
s after they have been extracted from a Caffe model using theCaffeParser
.-
find
(self: tensorrt.tensorrt.IBlobNameToTensor, name: str) → tensorrt.tensorrt.ITensor¶ Given a blob name, this function returns an
ITensor
object.Parameters: name – Caffe blob name for which the user wants the corresponding ITensor
.Returns: A ITensor
object corresponding to the queried name. If no suchITensor
exists, then an empty object is returned.
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-
class
tensorrt.
CaffeParser
(self: tensorrt.tensorrt.CaffeParser) → None¶ This class is used for parsing Caffe models. It allows users to export models trained using Caffe to TRT.
Variables: - plugin_factory –
ICaffePluginFactory
The ICaffePluginFactory used to create the user defined plugins. - plugin_factory_ext –
ICaffePluginFactoryExt
The ICaffePluginFactoryExt used to create the user defined pluginExts. - plugin_factory_v2 –
ICaffePluginFactoryV2
The ICaffePluginFactory used to create the user defined plugins. - plugin_namespace –
str
The namespace used to lookup and create plugins in the network. - protobuf_buffer_size –
int
The buffer size for the parsing and storage of the learned model.
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__del__
(self: tensorrt.tensorrt.CaffeParser) → None¶
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__exit__
(exc_type, exc_value, traceback)¶ Destroy this object, freeing all memory associated with it. This should be called to ensure that the object is cleaned up properly. Equivalent to invoking
__del__()
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__init__
(self: tensorrt.tensorrt.CaffeParser) → None¶
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parse
(self: tensorrt.tensorrt.CaffeParser, deploy: str, model: str, network: tensorrt.tensorrt.INetworkDefinition, dtype: tensorrt.tensorrt.DataType) → tensorrt.tensorrt.IBlobNameToTensor¶ Parse a prototxt file and a binaryproto Caffe model to extract network definition and weights associated with the network, respectively.
Parameters: - deploy – The plain text, prototxt file used to define the network definition.
- model – The binaryproto Caffe model that contains the weights associated with the network.
- network – Network in which the CaffeParser will fill the layers.
- dtype – The type to which the weights will be transformed.
Returns: An
IBlobNameToTensor
object that contains the extracted data.
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parse_binary_proto
(self: tensorrt.tensorrt.CaffeParser, filename: str) → array¶ Parse and extract data stored in binaryproto file. The binaryproto file contains data stored in a binary blob.
parse_binary_proto()
converts it to annumpy.ndarray
object.Parameters: filename – Path to file containing binary proto. Returns: numpy.ndarray
An array that contains the extracted data.
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parse_buffer
(self: tensorrt.tensorrt.CaffeParser, deploy_buffer: buffer, model_buffer: buffer, network: tensorrt.tensorrt.INetworkDefinition, dtype: tensorrt.tensorrt.DataType) → tensorrt.tensorrt.IBlobNameToTensor¶ Parse a prototxt file and a binaryproto Caffe model to extract network definition and weights associated with the network, respectively.
Parameters: - deploy_buffer – The memory buffer containing the plain text deploy prototxt used to define the network definition.
- model_buffer – The binaryproto Caffe memory buffer that contains the weights associated with the network.
- network – Network in which the CaffeParser will fill the layers.
- dtype – The type to which the weights will be transformed.
Returns: An
IBlobNameToTensor
object that contains the extracted data.
- plugin_factory –
-
tensorrt.
shutdown_protobuf_library
() → None¶ Shuts down protocol buffers library.