Name	Size	Mode	Actions
__pycache__/	-	0755	rm
acc_ops_converters.py	54261	0644	editdlrm
activation.py	1988	0644	editdlrm
adaptive_avgpool.py	1097	0644	editdlrm
add.py	2261	0644	editdlrm
batchnorm.py	1737	0644	editdlrm
convolution.py	3368	0644	editdlrm
helper_functions.py	1826	0644	editdlrm
linear.py	2784	0644	editdlrm
maxpool.py	1374	0644	editdlrm
mul.py	1435	0644	editdlrm
quantization.py	1999	0644	editdlrm
transformation.py	1439	0644	editdlrm
__init__.py	439	0644	editdlrm

import torch import tensorrt as trt from torch.fx.experimental.fx2trt.fx2trt import tensorrt_converter from .helper_functions import mark_as_int8_layer, extend_attr_to_tuple def common_maxpool(network, mod, dimension, input_val, layer_name): kernel_size = extend_attr_to_tuple(mod, "kernel_size", dimension) stride = extend_attr_to_tuple(mod, "stride", dimension) padding = extend_attr_to_tuple(mod, "padding", dimension) layer = network.add_pooling( input=input_val, type=trt.PoolingType.MAX, window_size=kernel_size) layer.stride = stride layer.padding = padding layer.name = layer_name if mod.ceil_mode: layer.padding_mode = trt.PaddingMode.EXPLICIT_ROUND_UP if input_val.dynamic_range: mark_as_int8_layer(layer, input_val.dynamic_range) return layer.get_output(0) @tensorrt_converter(torch.nn.modules.pooling.MaxPool2d) def maxpool2d(network, submod, args, kwargs, layer_name): # args/kwargs should have already been normalized to kwargs assert len(args) == 0 input_val = kwargs["input"] if not isinstance(input_val, trt.tensorrt.ITensor): raise RuntimeError(f"MaxPool2d received input {input_val} that is not part " "of the TensorRT region!") return common_maxpool(network, submod, dimension=2, input_val=input_val, layer_name=layer_name)