1/3/2023 0 Comments G clip pluginWith trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.UffParser() as parser:īuilder.int8_calibrator = None uff_path = model_to_uff(model_path) Output_uff_path = model_path_to_uff_path(model_path) Tmp = gs.create_node(name='tmp', op=None)ĭynamic_graph = gs.DynamicGraph(model_path)ĭynamic_llapse_namespaces(namespace_plugin_map)ĭynamic_graph.remove(removed_node_list, remove_exclusive_dependencies=False)ĭynamic_graph.forward_inputs(forward_nodel_list) Uff_path = os.path.splitext(model_path) + “.uff”ĭef model_to_uff(model_path): trt_clip = gs.create_plugin_node(name="trt_clip", op="Clip_TRT", clipMin=0.0, clipMax=6.0) WORKING_DIR = os.path.dirname(os.path.realpath( file)) (Github repo, Google Drive, Dropbox, etc.) Please attach or include links to any models, data, files, or scripts necessary to reproduce your issue. TensorFlow Version (if applicable): 1.12.0īaremetal or Container (if container which image + tag): None Relevant Files I wonder why the supported fusion did not happen, thx. But according to the profiling results (I used nsight system to profile), the convolution layer did not fuse the Clip activation, which is supported in the TensorRT fusion types. Here is the details, I followed the instructions on the plugin section of the TensorRT tutorial, replaced the Relu activation that follows a convolution layer with the Clip activation shipped with TensorRT, and it worked. I wonder how to make sure the supported fusion patterns, e.g., convolution followed by a clip activation, happen when I build an engine? And if there are more details about the exact requirements of the fusion in tensorRT?
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