wehub-resource-sync
178 lines
4.9 KiB
C++
178 lines
4.9 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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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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*/
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#ifndef TRT_PLUGIN_MASKRCNN_CONFIG_H
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#define TRT_PLUGIN_MASKRCNN_CONFIG_H
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#include "NvInfer.h"
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#include <string>
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#include <vector>
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namespace MaskRCNNConfig
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{
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static const nvinfer1::Dims3 IMAGE_SHAPE{3, 1024, 1024};
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// Pooled ROIs
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static int32_t const POOL_SIZE = 7;
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static int32_t const MASK_POOL_SIZE = 14;
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// Threshold to determine the mask area out of final convolution output
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static float const MASK_THRESHOLD = 0.5F;
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// Bounding box refinement standard deviation for RPN and final detections.
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static float const RPN_BBOX_STD_DEV[] = {0.1F, 0.1F, 0.2F, 0.2F};
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static float const BBOX_STD_DEV[] = {0.1F, 0.1F, 0.2F, 0.2F};
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// Max number of final detections
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static int32_t const DETECTION_MAX_INSTANCES = 100;
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// Minimum probability value to accept a detected instance
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// ROIs below this threshold are skipped
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static float const DETECTION_MIN_CONFIDENCE = 0.7F;
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// Non-maximum suppression threshold for detection
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static float const DETECTION_NMS_THRESHOLD = 0.3F;
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// The strides of each layer of the FPN Pyramid. These values
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// are based on a Resnet101 backbone.
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static const std::vector<float> BACKBONE_STRIDES = {4.F, 8.F, 16.F, 32.F, 64.F};
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// Size of the fully-connected layers in the classification graph
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static int32_t const FPN_CLASSIF_FC_LAYERS_SIZE = 1024;
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// Size of the top-down layers used to build the feature pyramid
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static int32_t const TOP_DOWN_PYRAMID_SIZE = 256;
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// Number of classification classes (including background)
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static int32_t const NUM_CLASSES = 1 + 80; // COCO has 80 classes
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// Length of square anchor side in pixels
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static const std::vector<float> RPN_ANCHOR_SCALES = {32.F, 64.F, 128.F, 256.F, 512.F};
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// Ratios of anchors at each cell (width/height)
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// A value of 1 represents a square anchor, and 0.5 is a wide anchor
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static float const RPN_ANCHOR_RATIOS[] = {0.5F, 1.F, 2.F};
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// Anchor stride
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// If 1 then anchors are created for each cell in the backbone feature map.
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// If 2, then anchors are created for every other cell, and so on.
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static int32_t const RPN_ANCHOR_STRIDE = 1;
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// Although Python impementation uses 6000,
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// TRT fails if this number larger than kMAX_TOPK_K defined in engine/checkMacros.h
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static int32_t const MAX_PRE_NMS_RESULTS = 1024; // 3840;
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// Non-max suppression threshold to filter RPN proposals.
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// You can increase this during training to generate more propsals.
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static float const RPN_NMS_THRESHOLD = 0.7F;
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// ROIs kept after non-maximum suppression (training and inference)
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static int32_t const POST_NMS_ROIS_INFERENCE = 1000;
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// COCO Class names
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static const std::vector<std::string> CLASS_NAMES = {
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"BG",
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"person",
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"bicycle",
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"car",
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"motorcycle",
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"airplane",
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"bus",
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"train",
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"truck",
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"boat",
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"traffic light",
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"fire hydrant",
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"stop sign",
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"parking meter",
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"bench",
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"bird",
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"cat",
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"dog",
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"horse",
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"sheep",
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"cow",
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"elephant",
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"bear",
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"zebra",
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"giraffe",
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"backpack",
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"umbrella",
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"handbag",
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"tie",
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"suitcase",
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"frisbee",
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"skis",
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"snowboard",
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"sports ball",
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"kite",
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"baseball bat",
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"baseball glove",
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"skateboard",
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"surfboard",
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"tennis racket",
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"bottle",
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"wine glass",
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"cup",
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"fork",
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"knife",
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"spoon",
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"bowl",
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"banana",
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"apple",
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"sandwich",
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"orange",
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"broccoli",
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"carrot",
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"hot dog",
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"pizza",
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"donut",
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"cake",
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"chair",
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"couch",
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"potted plant",
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"bed",
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"dining table",
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"toilet",
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"tv",
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"laptop",
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"mouse",
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"remote",
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"keyboard",
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"cell phone",
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"microwave",
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"oven",
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"toaster",
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"sink",
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"refrigerator",
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"book",
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"clock",
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"vase",
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"scissors",
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"teddy bear",
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"hair drier",
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"toothbrush",
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};
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static const std::string MODEL_NAME = "mrcnn_nchw.uff";
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static const std::string MODEL_INPUT = "input_image";
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static const nvinfer1::Dims3 MODEL_INPUT_SHAPE = IMAGE_SHAPE;
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static const std::vector<std::string> MODEL_OUTPUTS = {"mrcnn_detection", "mrcnn_mask/Sigmoid"};
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static const nvinfer1::Dims2 MODEL_DETECTION_SHAPE{DETECTION_MAX_INSTANCES, 6};
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static const nvinfer1::Dims4 MODEL_MASK_SHAPE{DETECTION_MAX_INSTANCES, NUM_CLASSES, 28, 28};
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} // namespace MaskRCNNConfig
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#endif // TRT_PLUGIN_MASKRCNN_CONFIG_H
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