Pred = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream, batch_size=ModelData. There is also a wave display that shows what effect the clipping curve is having on the. A graph showing the shape of the clipping function is displayed on the GUI. The clipping function used can be altered to have a hard- or soft-knee. # The common do_inference function will return a list of outputs - we only have one in this case. It prevents the input signal level from exceeding a specified maximum. With engine.create_execution_context() as context: Inputs, outputs, bindings, stream = common.allocate_buffers(engine, ModelData.BATCH_SIZE)ĭummy_data = np.random.randn(ModelData.BATCH_SIZE * 201000) # Build an engine and retrieve the image mean from the model. Trt.init_libnvinfer_plugins(TRT_LOGGER, '') "For more information, see the included README.md" "Please use 'python lenet5.py' to train and save the model.", "Failed to load library ().".format(MODEL_PATH), Return builder.build_cuda_engine(network)ĭef main(): if not os.path.isfile(CLIP_PLUGIN_LIBRARY):
Parser.register_output(ModelData.OUTPUT_NAME) Parser.register_input(ModelData.INPUT_NAME, ModelData.INPUT_SHAPE) With 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)) My initial concept was to simulate analog limiter and ADC clipping after it. (Github repo, Google Drive, Dropbox, etc.) Gonna put it through the paces against the other limiters in my plugins. 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?