From bacee065a2a807ab14c3336138032342d06053eb Mon Sep 17 00:00:00 2001 From: Guanghua Yu <742925032@qq.com> Date: Fri, 20 Nov 2020 19:13:29 +0800 Subject: [PATCH] Cherry pick fix paddle.enable_static() check_version (#1728) * update solov2 some detail * fix check paddle version * fix version check error * fix some comment --- configs/solov2/README.md | 12 ++++++++++-- .../solov2_light_r50_vd_fpn_dcn_512_3x.yml | 5 +++-- deploy/python/infer.py | 3 ++- docs/images/instance_segmentation.png | Bin 0 -> 387228 bytes ppdet/data/tests/test_dataset.py | 4 ++-- ppdet/data/tests/test_loader.py | 4 ++-- ppdet/data/tests/test_loader_yaml.py | 4 ++-- ppdet/ext_op/test/test_corner_pool.py | 4 ++-- ppdet/modeling/anchor_heads/solov2_head.py | 2 +- ppdet/modeling/tests/test_architectures.py | 3 ++- ppdet/utils/check.py | 8 ++++++++ slim/distillation/distill.py | 4 ++-- .../distill_pruned_model.py | 4 ++-- slim/nas/train_nas.py | 4 ++-- slim/prune/eval.py | 4 ++-- slim/prune/export_model.py | 4 ++-- slim/prune/infer.py | 4 ++-- slim/prune/prune.py | 4 ++-- slim/quantization/eval.py | 4 ++-- slim/quantization/export_model.py | 4 ++-- slim/quantization/infer.py | 4 ++-- slim/quantization/train.py | 4 ++-- slim/sensitive/sensitive.py | 4 ++-- tools/eval.py | 4 ++-- tools/export_model.py | 4 ++-- tools/export_serving_model.py | 4 ++-- tools/face_eval.py | 4 ++-- tools/infer.py | 4 ++-- tools/train.py | 4 ++-- tools/train_multi_machine.py | 4 ++-- 30 files changed, 72 insertions(+), 53 deletions(-) create mode 100644 docs/images/instance_segmentation.png diff --git a/configs/solov2/README.md b/configs/solov2/README.md index ee39c5847..d33a5a3a3 100644 --- a/configs/solov2/README.md +++ b/configs/solov2/README.md @@ -9,10 +9,14 @@ SOLOv2 (Segmenting Objects by Locations) is a fast instance segmentation framewo - Performance: `Light-R50-VD-DCN-FPN` model reached 38.6 FPS on single Tesla V100, and mask ap on the COCO-val dataset reached 38.8, which increased inference speed by 24%, mAP increased by 2.4 percentage points. - Training Time: The training time of the model of `solov2_r50_fpn_1x` on Tesla v100 with 8 GPU is only 10 hours. +
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