Detectron2 基准测试 | 十二

作者|facebookresearch 编译|Flin 来源|Github

基准测试

在这里,我们以一些其他流行的开源Mask R-CNN实现为基准,对Detectron2中Mask R-CNN的训练速度进行了基准测试。

设置

硬件：8个带有NVLink的NVIDIA V100。
软件: Python 3.7, CUDA 10.0, cuDNN 7.6.4, PyTorch 1.3.0 (链接(https://download.pytorch.org/whl/nightly/cu100/torch-1.3.0%2Bcu100-cp37-cp37m-linux_x86_64.whl)), TensorFlow 1.15.0rc2, Keras 2.2.5, MxNet 1.6.0b20190820.
模型：端到端R-50-FPN Mask-RCNN模型,使用与Detectron基线配置(https://github.com/facebookresearch/Detectron/blob/master/configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml)相同的超参数。
指标：我们使用100-500次迭代中的平均吞吐量来跳过GPU预热时间。请注意,对于R-CNN样式的模型,模型的吞吐量通常会在训练期间发生变化,因为它取决于模型的预测。因此,该指标不能直接与model zoo中的"训练速度"相比较,后者是整个训练过程的平均速度。

主要结果

<table> <tr> <td>工具</td><td>吞吐率(img / s)</td> </tr> <tr> <td>Detectron2</td><td>59</td> </tr> <tr> <td>maskrcnn-benchmark</td><td>51</td> </tr> <tr> <td>tensorpack </td><td>50</td> </tr> <tr> <td>mmdetection</td><td>41</td> </tr> <tr> <td>simpledet</td><td>39</td> </tr> <tr> <td>Detectron</td><td>19</td> </tr> <tr> <td>matterport/Mask_RCNN</td><td>14</td> </tr> </table> 每个实现的链接:

Detectron2:https://github.com/facebookresearch/detectron2/
maskrcnn-benchmark:https://github.com/facebookresearch/maskrcnn-benchmark/
tensorpack:https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN
mmdetection:https://github.com/open-mmlab/mmdetection/
simpledet:https://github.com/TuSimple/simpledet/
Detectron:https://github.com/facebookresearch/Detectron
matterport/Mask_RCNN:https://github.com/matterport/Mask_RCNN/

每个实现的详细信息：

Detectron2:

python tools/train_net.py  --config-file configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml --num-gpus 8

maskrcnn-benchmark: 通过sed -i ‘s/torch.uint8/torch.bool/g’ **/*.py使用commit 0ce8f6f与使其与最新的PyTorch兼容。然后,运行
```
python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
```
我们观察到的速度比其model zoo快,这可能是由于软件版本不同所致。

tensorpack: 在提交caafda,export TF_CUDNN_USE_AUTOTUNE=0, 然后运行

mpirun -np 8 ./train.py --config DATA.BASEDIR=/data/coco TRAINER=horovod BACKBONE.STRIDE_1X1=True TRAIN.STEPS_PER_EPOCH=50 --load ImageNet-R50-AlignPadding.npz

mmdetection: commit 4d9a5f,应用以下diff,然后运行

./tools/dist_train.sh configs/mask_rcnn_r50_fpn_1x.py 8

我们观察到的速度比其model zoo快,这可能是由于软件版本不同所致。

<details> <summary> (diff使其使用相同的超参数-单击展开) </summary>

diff --git i/configs/mask_rcnn_r50_fpn_1x.py w/configs/mask_rcnn_r50_fpn_1x.py
index 04f6d22..ed721f2 100644
--- i/configs/mask_rcnn_r50_fpn_1x.py
+++ w/configs/mask_rcnn_r50_fpn_1x.py
@@ -1,14 +1,15 @@
# model settings
model = dict(
  type='MaskRCNN',
-    pretrained='torchvision://resnet50',
+    pretrained='open-mmlab://resnet50_caffe',
  backbone=dict(
    type='ResNet',
    depth=50,
    num_stages=4,
    out_indices=(0, 1, 2, 3),
    frozen_stages=1,
-        style='pytorch'),
+        norm_cfg=dict(type="BN", requires_grad=False),
+        style='caffe'),
  neck=dict(
    type='FPN',
    in_channels=[256, 512, 1024, 2048],
@@ -115,7 +116,7 @@ test_cfg = dict(
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+    mean=[123.675, 116.28, 103.53], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
  dict(type='LoadImageFromFile'),
  dict(type='LoadAnnotations', with_bbox=True, with_mask=True),

</details>

SimpleDet: 在commit9187a1时运行

python detection_train.py --config config/mask_r50v1_fpn_1x.py

Detectron: 运行
```
python tools/train_net.py --cfg configs/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml
```
请注意,它的许多操作都在CPU上运行,因此性能受到限制。
matterport/Mask_RCNN:在commit时3deaec,应用以下diff ,export TF_CUDNN_USE_AUTOTUNE=0, 然后运行
```
python coco.py train --dataset=/data/coco/ --model=imagenet
```

请注意,此实现中的许多小细节可能与Detectron的标准不同。

<details> <summary> (diff使其使用相同的超参数-单击展开) </summary>

diff --git i/mrcnn/model.py w/mrcnn/model.py
index 62cb2b0..61d7779 100644
--- i/mrcnn/model.py
+++ w/mrcnn/model.py
@@ -2367,8 +2367,8 @@ class MaskRCNN():
      epochs=epochs,
      steps_per_epoch=self.config.STEPS_PER_EPOCH,
      callbacks=callbacks,
-            validation_data=val_generator,
-            validation_steps=self.config.VALIDATION_STEPS,
+            #validation_data=val_generator,
+            #validation_steps=self.config.VALIDATION_STEPS,
      max_queue_size=100,
      workers=workers,
      use_multiprocessing=True,
diff --git i/mrcnn/parallel_model.py w/mrcnn/parallel_model.py
index d2bf53b..060172a 100644
--- i/mrcnn/parallel_model.py
+++ w/mrcnn/parallel_model.py
@@ -32,6 +32,7 @@ class ParallelModel(KM.Model):
    keras_model: The Keras model to parallelize
    gpu_count: Number of GPUs. Must be > 1
    """
+        super().__init__()
    self.inner_model = keras_model
    self.gpu_count = gpu_count
    merged_outputs = self.make_parallel()
diff --git i/samples/coco/coco.py w/samples/coco/coco.py
index 5d172b5..239ed75 100644
--- i/samples/coco/coco.py
+++ w/samples/coco/coco.py
@@ -81,7 +81,10 @@ class CocoConfig(Config):
  IMAGES_PER_GPU = 2

  # Uncomment to train on 8 GPUs (default is 1)
-    # GPU_COUNT = 8
+    GPU_COUNT = 8
+    BACKBONE = "resnet50"
+    STEPS_PER_EPOCH = 50
+    TRAIN_ROIS_PER_IMAGE = 512

  # Number of classes (including background)
  NUM_CLASSES = 1 + 80  # COCO has 80 classes
@@ -496,29 +499,10 @@ if __name__ == '__main__':
    # *** This training schedule is an example. Update to your needs ***

    # Training - Stage 1
-        print("Training network heads")
    model.train(dataset_train, dataset_val,
          learning_rate=config.LEARNING_RATE,
          epochs=40,
-                    layers='heads',
-                    augmentation=augmentation)
-
-        # Training - Stage 2
-        # Finetune layers from ResNet stage 4 and up
-        print("Fine tune Resnet stage 4 and up")
-        model.train(dataset_train, dataset_val,
-                    learning_rate=config.LEARNING_RATE,
-                    epochs=120,
-                    layers='4+',
-                    augmentation=augmentation)
-
-        # Training - Stage 3
-        # Fine tune all layers
-        print("Fine tune all layers")
-        model.train(dataset_train, dataset_val,
-                    learning_rate=config.LEARNING_RATE / 10,
-                    epochs=160,
-                    layers='all',
+                    layers='3+',
          augmentation=augmentation)

  elif args.command == "evaluate":

</details>

原文链接：https://detectron2.readthedocs.io/notes/benchmarks.html

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来源：oschina

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标签

Mask_RCNN