chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import tensorrt as trt
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from paddle.tensorrt.converter_utils import (
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WithFp16,
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get_trt_plugin,
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set_layer_name,
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unary_op_converter,
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)
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from paddle.tensorrt.register import converter_registry
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from paddle.tensorrt.util import (
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TensorRTConstantManager,
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)
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@converter_registry.register("pd_op.sqrt")
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@converter_registry.register("pd_op.sqrt_")
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@converter_registry.register("pd_op.floor")
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@converter_registry.register("pd_op.exp")
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@converter_registry.register("pd_op.abs")
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@converter_registry.register("pd_op.abs_")
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@converter_registry.register("pd_op.sin")
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@converter_registry.register("pd_op.cos")
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@converter_registry.register("pd_op.sinh")
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@converter_registry.register("pd_op.cosh")
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@converter_registry.register("pd_op.asinh")
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@converter_registry.register("pd_op.acosh")
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@converter_registry.register("pd_op.atanh")
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@converter_registry.register("pd_op.ceil")
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@converter_registry.register("pd_op.tan")
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@converter_registry.register("pd_op.asin")
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@converter_registry.register("pd_op.acos")
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@converter_registry.register("pd_op.atan")
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@converter_registry.register("pd_op.reciprocal")
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@converter_registry.register("pd_op.erf")
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@converter_registry.register("pd_op.rsqrt")
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@converter_registry.register("pd_op.sign", trt_version="trt_version_ge=8.2")
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@converter_registry.register("pd_op.round", trt_version="trt_version_ge=8.2")
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def UnaryOpConverter(network, paddle_op, inputs):
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layer_output = unary_op_converter(network, paddle_op, inputs)
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return layer_output
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@converter_registry.register("pd_op.roi_align")
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def roi_align_converter(network, paddle_op, inputs):
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x = inputs[0]
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rois = inputs[1]
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pooled_height = paddle_op.attrs().get("pooled_height")
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pooled_width = paddle_op.attrs().get("pooled_width")
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spatial_scale = paddle_op.attrs().get("spatial_scale")
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sampling_ratio = paddle_op.attrs().get("sampling_ratio")
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aligned = paddle_op.attrs().get("aligned")
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type_id = int(WithFp16())
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plugin_fields = [
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trt.PluginField(
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"type_id",
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np.array([type_id], dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"pooled_height",
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np.array(pooled_height, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"pooled_width",
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np.array(pooled_width, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"spatial_scale",
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np.array(spatial_scale, dtype=np.float32),
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trt.PluginFieldType.FLOAT32,
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),
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trt.PluginField(
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"sampling_ratio",
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np.array(sampling_ratio, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"aligned",
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np.array(aligned, dtype=np.bool_),
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trt.PluginFieldType.INT32,
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),
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]
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plugin_field_collection = trt.PluginFieldCollection(plugin_fields)
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plugin_name = "pir_roi_align_plugin_dynamic"
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plugin_version = "2"
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plugin = get_trt_plugin(
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plugin_name, plugin_field_collection, plugin_version
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)
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roi_align_inputs = [x, rois]
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roi_align_layer = network.add_plugin_v2(roi_align_inputs, plugin)
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set_layer_name(roi_align_layer, paddle_op)
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return roi_align_layer.get_output(0)
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@converter_registry.register("pd_op.yolo_box")
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def YoloBoxOpConverter(network, paddle_op, inputs):
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x, imgSize = inputs
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class_num = paddle_op.attrs().get("class_num")
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anchors = paddle_op.attrs().get("anchors")
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downsample_ratio = paddle_op.attrs().get("downsample_ratio")
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conf_thresh = paddle_op.attrs().get("conf_thresh")
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clip_bbox = paddle_op.attrs().get("clip_bbox")
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scale_x_y = paddle_op.attrs().get("scale_x_y")
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iou_aware = paddle_op.attrs().get("iou_aware")
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iou_aware_factor = paddle_op.attrs().get("iou_aware_factor")
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type_id = int(WithFp16())
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anchors = np.array(anchors, dtype=np.int32)
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plugin_fields = [
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trt.PluginField(
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"type_id",
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np.array([type_id], dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"anchors",
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anchors,
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"class_num",
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np.array(class_num, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"conf_thresh",
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np.array(conf_thresh, dtype=np.float32),
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trt.PluginFieldType.FLOAT32,
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),
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trt.PluginField(
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"downsample_ratio",
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np.array(downsample_ratio, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"clip_bbox",
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np.array(clip_bbox, dtype=np.bool_),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"scale_x_y",
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np.array(scale_x_y, dtype=np.float32),
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trt.PluginFieldType.FLOAT32,
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),
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trt.PluginField(
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"iou_aware",
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np.array(iou_aware, dtype=np.bool_),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"iou_aware_factor",
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np.array(iou_aware_factor, dtype=np.float32),
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trt.PluginFieldType.FLOAT32,
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),
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]
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plugin_field_collection = trt.PluginFieldCollection(plugin_fields)
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plugin_name = "yolo_box_plugin_dynamic"
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plugin_version = "1"
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plugin = get_trt_plugin(
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plugin_name, plugin_field_collection, plugin_version
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)
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yolo_box_inputs = [x, imgSize]
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yolo_box_layer = network.add_plugin_v2(yolo_box_inputs, plugin)
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set_layer_name(yolo_box_layer, paddle_op)
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out0 = yolo_box_layer.get_output(0)
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out1 = yolo_box_layer.get_output(1)
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return (out0, out1)
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@converter_registry.register(
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"pd_op.deformable_conv", trt_version="trt_version_ge=8.5"
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)
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def deformable_conv_converter(network, paddle_op, inputs):
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input = inputs[0]
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constant_manager = TensorRTConstantManager()
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offset = inputs[1]
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filter = inputs[2]
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mask = inputs[3]
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if isinstance(filter, trt.ITensor):
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filter_name = (
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paddle_op.operands()[2]
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.source()
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.get_defining_op()
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.attrs()['parameter_name']
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)
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filter = constant_manager.get_constant_value(filter_name)
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else:
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filter = filter.numpy()
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groups = paddle_op.attrs().get("groups")
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deformable_groups = paddle_op.attrs().get("deformable_groups")
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im2col_step = paddle_op.attrs().get("im2col_step")
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strides = paddle_op.attrs().get("strides")
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paddings = paddle_op.attrs().get("paddings")
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dilations = paddle_op.attrs().get("dilations")
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kernel_dims = paddle_op.operands()[2].source().shape
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plugin_fields = [
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trt.PluginField(
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"with_fp16",
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np.array([False], dtype=np.bool_),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"weights",
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filter,
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trt.PluginFieldType.FLOAT32,
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),
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trt.PluginField(
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"kernel_dims",
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np.array(kernel_dims, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"strides",
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np.array(strides, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"paddings",
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np.array(paddings, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"dilations",
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np.array(dilations, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"groups",
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np.array(groups, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"deformable_groups",
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np.array(deformable_groups, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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trt.PluginField(
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"im2col_step",
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np.array(im2col_step, dtype=np.int32),
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trt.PluginFieldType.INT32,
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),
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]
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plugin_field_collection = trt.PluginFieldCollection(plugin_fields)
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plugin_name = "pir_deformable_conv_plugin"
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plugin_version = "1"
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plugin = get_trt_plugin(
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plugin_name, plugin_field_collection, plugin_version
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)
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deformable_conv_layer = network.add_plugin_v2(
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[inputs[0], inputs[1], inputs[3]], plugin
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)
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set_layer_name(deformable_conv_layer, paddle_op)
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return deformable_conv_layer.get_output(0)
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