From 8476fe2c5e238a6cd798beca26f9a7b0d02c3d1b Mon Sep 17 00:00:00 2001 From: chenhaozhe Date: Wed, 13 Jan 2021 14:07:39 +0800 Subject: [PATCH] Infer SSD with decoder --- model_zoo/official/cv/ssd/README.md | 50 +++++++++---------- model_zoo/official/cv/ssd/eval.py | 5 +- model_zoo/official/cv/ssd/export.py | 4 +- .../cv/ssd/scripts/run_distribute_train.sh | 2 +- .../ssd/scripts/run_distribute_train_gpu.sh | 2 +- model_zoo/official/cv/ssd/src/dataset.py | 8 +-- model_zoo/official/cv/ssd/src/eval_utils.py | 2 - model_zoo/official/cv/ssd/src/ssd.py | 36 +++++++++++++ 8 files changed, 74 insertions(+), 35 deletions(-) diff --git a/model_zoo/official/cv/ssd/README.md b/model_zoo/official/cv/ssd/README.md index bd23cb5dac..889c77e447 100644 --- a/model_zoo/official/cv/ssd/README.md +++ b/model_zoo/official/cv/ssd/README.md @@ -371,34 +371,34 @@ The ckpt_file parameter is required. #### Evaluation Performance -| Parameters | Ascend | GPU | -| -------------------------- | -------------------------------------------------------------| -------------------------------------------------------------| -| Model Version | SSD V1 | SSD V1 | -| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G | NV SMX2 V100-16G | -| uploaded Date | 09/15/2020 (month/day/year) | 09/24/2020 (month/day/year) | -| MindSpore Version | 1.0.0 | 1.0.0 | -| Dataset | COCO2017 | COCO2017 | -| Training Parameters | epoch = 500, batch_size = 32 | epoch = 800, batch_size = 32 | -| Optimizer | Momentum | Momentum | -| Loss Function | Sigmoid Cross Entropy,SmoothL1Loss | Sigmoid Cross Entropy,SmoothL1Loss | -| Speed | 8pcs: 90ms/step | 8pcs: 121ms/step | -| Total time | 8pcs: 4.81hours | 8pcs: 12.31hours | -| Parameters (M) | 34 | 34 | -| Scripts | | | +| Parameters | Ascend | GPU | Ascend | +| ------------------- | ----------------------------------------------------------------------------- | ----------------------------------------------------------------------------- | ----------------------------------------------------------------------------- | +| Model Version | SSD V1 | SSD V1 | SSD-Mobilenet-V1-Fpn | +| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G | NV SMX2 V100-16G | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G | +| uploaded Date | 09/15/2020 (month/day/year) | 09/24/2020 (month/day/year) | 01/13/2021 (month/day/year) | +| MindSpore Version | 1.0.0 | 1.0.0 | 1.1.0 | +| Dataset | COCO2017 | COCO2017 | COCO2017 | +| Training Parameters | epoch = 500, batch_size = 32 | epoch = 800, batch_size = 32 | epoch = 60, batch_size = 32 | +| Optimizer | Momentum | Momentum | Momentum | +| Loss Function | Sigmoid Cross Entropy,SmoothL1Loss | Sigmoid Cross Entropy,SmoothL1Loss | Sigmoid Cross Entropy,SmoothL1Loss | +| Speed | 8pcs: 90ms/step | 8pcs: 121ms/step | 8pcs: 547ms/step | +| Total time | 8pcs: 4.81hours | 8pcs: 12.31hours | 8pcs: 4.22hours | +| Parameters (M) | 34 | 34 | 48M | +| Scripts | | | | #### Inference Performance -| Parameters | Ascend | GPU | -| ------------------- | ----------------------------| ----------------------------| -| Model Version | SSD V1 | SSD V1 | -| Resource | Ascend 910 | GPU | -| Uploaded Date | 09/15/2020 (month/day/year) | 09/24/2020 (month/day/year) | -| MindSpore Version | 1.0.0 | 1.0.0 | -| Dataset | COCO2017 | COCO2017 | -| batch_size | 1 | 1 | -| outputs | mAP | mAP | -| Accuracy | IoU=0.50: 23.8% | IoU=0.50: 22.4% | -| Model for inference | 34M(.ckpt file) | 34M(.ckpt file) | +| Parameters | Ascend | GPU | Ascend | +| ------------------- | --------------------------- | --------------------------- | --------------------------- | +| Model Version | SSD V1 | SSD V1 | SSD-Mobilenet-V1-Fpn | +| Resource | Ascend 910 | GPU | Ascend 910 | +| Uploaded Date | 09/15/2020 (month/day/year) | 09/24/2020 (month/day/year) | 09/24/2020 (month/day/year) | +| MindSpore Version | 1.0.0 | 1.0.0 | 1.1.0 | +| Dataset | COCO2017 | COCO2017 | COCO2017 | +| batch_size | 1 | 1 | 1 | +| outputs | mAP | mAP | mAP | +| Accuracy | IoU=0.50: 23.8% | IoU=0.50: 22.4% | Iout=0.50: 30% | +| Model for inference | 34M(.ckpt file) | 34M(.ckpt file) | 48M(.ckpt file) | ## [Description of Random Situation](#contents) diff --git a/model_zoo/official/cv/ssd/eval.py b/model_zoo/official/cv/ssd/eval.py index 37e3f1efa2..0e06d08c4f 100644 --- a/model_zoo/official/cv/ssd/eval.py +++ b/model_zoo/official/cv/ssd/eval.py @@ -21,10 +21,11 @@ import time import numpy as np from mindspore import context, Tensor from mindspore.train.serialization import load_checkpoint, load_param_into_net -from src.ssd import SSD300, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn +from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn from src.dataset import create_ssd_dataset, create_mindrecord from src.config import config from src.eval_utils import metrics +from src.box_utils import default_boxes def ssd_eval(dataset_path, ckpt_path, anno_json): """SSD evaluation.""" @@ -35,6 +36,8 @@ def ssd_eval(dataset_path, ckpt_path, anno_json): net = SSD300(ssd_mobilenet_v2(), config, is_training=False) else: net = ssd_mobilenet_v1_fpn(config=config) + net = SsdInferWithDecoder(net, Tensor(default_boxes), config) + print("Load Checkpoint!") param_dict = load_checkpoint(ckpt_path) net.init_parameters_data() diff --git a/model_zoo/official/cv/ssd/export.py b/model_zoo/official/cv/ssd/export.py index cd31c5adc9..9a76420a21 100644 --- a/model_zoo/official/cv/ssd/export.py +++ b/model_zoo/official/cv/ssd/export.py @@ -19,8 +19,9 @@ import numpy as np import mindspore from mindspore import context, Tensor from mindspore.train.serialization import load_checkpoint, load_param_into_net, export -from src.ssd import SSD300, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn +from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn from src.config import config +from src.box_utils import default_boxes parser = argparse.ArgumentParser(description='SSD export') parser.add_argument("--device_id", type=int, default=0, help="Device id") @@ -41,6 +42,7 @@ if __name__ == '__main__': net = SSD300(ssd_mobilenet_v2(), config, is_training=False) else: net = ssd_mobilenet_v1_fpn(config=config) + net = SsdInferWithDecoder(net, Tensor(default_boxes), config) param_dict = load_checkpoint(args.ckpt_file) net.init_parameters_data() diff --git a/model_zoo/official/cv/ssd/scripts/run_distribute_train.sh b/model_zoo/official/cv/ssd/scripts/run_distribute_train.sh index 73c0613ded..f7a3694548 100644 --- a/model_zoo/official/cv/ssd/scripts/run_distribute_train.sh +++ b/model_zoo/official/cv/ssd/scripts/run_distribute_train.sh @@ -31,7 +31,7 @@ fi # Before start distribute train, first create mindrecord files. BASE_PATH=$(cd "`dirname $0`" || exit; pwd) cd $BASE_PATH/../ || exit -python train.py --only_create_dataset=True +python train.py --only_create_dataset=True --dataset=$4 echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt" diff --git a/model_zoo/official/cv/ssd/scripts/run_distribute_train_gpu.sh b/model_zoo/official/cv/ssd/scripts/run_distribute_train_gpu.sh index 5f27a22d32..9277e3de69 100644 --- a/model_zoo/official/cv/ssd/scripts/run_distribute_train_gpu.sh +++ b/model_zoo/official/cv/ssd/scripts/run_distribute_train_gpu.sh @@ -31,7 +31,7 @@ fi # Before start distribute train, first create mindrecord files. BASE_PATH=$(cd "`dirname $0`" || exit; pwd) cd $BASE_PATH/../ || exit -python train.py --only_create_dataset=True --run_platform="GPU" +python train.py --only_create_dataset=True --run_platform="GPU" --dataset=$4 echo "After running the scipt, the network runs in the background. The log will be generated in LOG/log.txt" diff --git a/model_zoo/official/cv/ssd/src/dataset.py b/model_zoo/official/cv/ssd/src/dataset.py index 738bb39784..d9b8cbf2ee 100644 --- a/model_zoo/official/cv/ssd/src/dataset.py +++ b/model_zoo/official/cv/ssd/src/dataset.py @@ -207,10 +207,10 @@ def create_voc_label(is_training): print(f'Label "{cls_name}" not in "{config.classes}"') continue bnd_box = obj.find('bndbox') - x_min = int(bnd_box.find('xmin').text) - 1 - y_min = int(bnd_box.find('ymin').text) - 1 - x_max = int(bnd_box.find('xmax').text) - 1 - y_max = int(bnd_box.find('ymax').text) - 1 + x_min = int(float(bnd_box.find('xmin').text)) - 1 + y_min = int(float(bnd_box.find('ymin').text)) - 1 + x_max = int(float(bnd_box.find('xmax').text)) - 1 + y_max = int(float(bnd_box.find('ymax').text)) - 1 labels.append([y_min, x_min, y_max, x_max, cls_map[cls_name]]) if not is_training: diff --git a/model_zoo/official/cv/ssd/src/eval_utils.py b/model_zoo/official/cv/ssd/src/eval_utils.py index 180069d185..e8e01b32c0 100644 --- a/model_zoo/official/cv/ssd/src/eval_utils.py +++ b/model_zoo/official/cv/ssd/src/eval_utils.py @@ -17,7 +17,6 @@ import json import numpy as np from .config import config -from .box_utils import ssd_bboxes_decode def apply_nms(all_boxes, all_scores, thres, max_boxes): @@ -81,7 +80,6 @@ def metrics(pred_data, anno_json): img_id = sample['img_id'] h, w = sample['image_shape'] - pred_boxes = ssd_bboxes_decode(pred_boxes) final_boxes = [] final_label = [] final_score = [] diff --git a/model_zoo/official/cv/ssd/src/ssd.py b/model_zoo/official/cv/ssd/src/ssd.py index d9e4b82ddf..ebaa9f6948 100644 --- a/model_zoo/official/cv/ssd/src/ssd.py +++ b/model_zoo/official/cv/ssd/src/ssd.py @@ -569,6 +569,42 @@ class SSDWithMobileNetV2(nn.Cell): return self.last_channel +class SsdInferWithDecoder(nn.Cell): + """ + SSD Infer wrapper to decode the bbox locations. + + Args: + network (Cell): the origin ssd infer network without bbox decoder. + default_boxes (Tensor): the default_boxes from anchor generator + config (dict): ssd config + Returns: + Tensor, the locations for bbox after decoder representing (y0,x0,y1,x1) + Tensor, the prediction labels. + + """ + def __init__(self, network, default_boxes, config): + super(SsdInferWithDecoder, self).__init__() + self.network = network + self.default_boxes = default_boxes + self.prior_scaling_xy = config.prior_scaling[0] + self.prior_scaling_wh = config.prior_scaling[1] + + def construct(self, x): + pred_loc, pred_label = self.network(x) + + default_bbox_xy = self.default_boxes[..., :2] + default_bbox_wh = self.default_boxes[..., 2:] + pred_xy = pred_loc[..., :2] * self.prior_scaling_xy * default_bbox_wh + default_bbox_xy + pred_wh = P.Exp()(pred_loc[..., 2:] * self.prior_scaling_wh) * default_bbox_wh + + pred_xy_0 = pred_xy - pred_wh / 2.0 + pred_xy_1 = pred_xy + pred_wh / 2.0 + pred_xy = P.Concat(-1)((pred_xy_0, pred_xy_1)) + pred_xy = P.Maximum()(pred_xy, 0) + pred_xy = P.Minimum()(pred_xy, 1) + return pred_xy, pred_label + + def ssd_mobilenet_v1_fpn(**kwargs): return SsdMobilenetV1Fpn(**kwargs)