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Add mean IOU op. #10519

Merged
merged 10 commits into from
Jun 14, 2018
Merged

Add mean IOU op. #10519

merged 10 commits into from
Jun 14, 2018

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wanghaoshuang
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@wanghaoshuang wanghaoshuang commented May 9, 2018

Performance on GPU P40:

input size class_num GPU CPU
1024 * 2048 100 0.168812ms 13.0831ms
1024 * 2048 50 0.172748ms 13.5145ms
1024 * 2048 20 0.174807ms 14.4619ms
1024 * 2048 10 0.188483ms 16.1516ms
1024 * 2048 1 0.230743ms 12.7893ms
1024 * 2048 *2 100 0.308306ms 26.4576
1024 * 2048 * 2 50 0.326073ms 26.9835ms
1024 * 2048 *2 20 0.28971ms 29.0224ms
1024 * 2048* 2 10 0.267694ms 34.2029ms
1024 * 2048 * 2 1 0.295844ms 25.4808ms

test code:

import paddle.fluid as fluid
import numpy as np
from paddle.fluid import core

num_classes=1
input_size=1024 * 2048 * 2

images = fluid.layers.data(name='image', shape=[1], dtype='int32')
label = fluid.layers.data(name='label', shape=[1], dtype='int32')
fluid.layers.mean_iou(images, label, num_classes)

place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

image_t = core.LoDTensor()
label_t = core.LoDTensor()
image_t.set(np.random.randint(num_classes, size=[input_size]).astype("int32"), place)
label_t.set(np.random.randint(num_classes, size=[input_size]).astype("int32"), place)

with fluid.profiler.profiler('GPU', sorted_key='total'):
    for i in range(500):
        exe.run(feed={"image": image_t, "label":label_t})

@wanghaoshuang
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请先不要review,我再优化下GPU kernel.

1. Merge computing in GPU to two kernel.
2. Use wrong array and correct array instead of confusion matrix.
"A Tensor representing the"
" mean intersection-over-union.");
AddOutput("out_wrong", "A Tensor with shape [num_classes]. ");
AddOutput("out_correct", "A Tensor with shape [num_classes]. ");
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Thx. FIxed.

AddComment(R"DOC(
mean-IOU Operator.
Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes. IOU is defined as follows: IOU = true_positive / (true_positive + false_positive + false_negative). The predictions are accumulated in a confusion matrix and mean-IOU is then calculated from it.

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Since we have iou_similarity_op:

https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/detection/iou_similarity_op.cc#L71

The doc here better to give more details for the difference.

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Thx. FIxed.

.AsDispensable();
AddOutput("out_mean_iou",
"A Tensor representing the"
" mean intersection-over-union.");
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Also need to give the shape.

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Thx. FIxed.

REGISTER_OPERATOR(mean_iou, ops::MeanIoUOp, ops::MeanIoUOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(mean_iou, ops::MeanIoUKernel<int>,
ops::MeanIoUKernel<int64_t>);
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上面文档描述里是支持int32和int64,这里没有注册int32。

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Thx. FIxed.


namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(mean_iou, ops::MeanIoUCUDAOpKernel<int>,
ops::MeanIoUKernel<int64_t>);
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同上。

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Thx. FIxed.

: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("predictions",
"A Tensor of prediction results for semantic labels"
" with type int32 or int64.");
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Need to give the shape

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Thx. FIxed.

float* out_mean_iou_data =
out_mean_iou->mutable_data<float>(ctx.GetPlace());

// get eigen tensor
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eigen -> Eigen

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Thx. FIxed.

auto out_wrong_t = EigenTensor<int, 1>::From(*out_wrong);
auto out_correct_t = EigenTensor<int, 1>::From(*out_correct);

// Tmp tensor
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Dot not use Tmp

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Thx. FIxed.

.AsDispensable();
AddInput("in_mean_iou",
"A list of Tensor that Output(mean_iou) should "
"be added to. Empty list is also valid here.")
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in_wrongs, in_corrects, in_mean_iou是干啥的?和out_wrong/correct/mean_iou有啥区别?

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in_wrongs, in_corrects, in_mean_iou之前当前batch之前累计的数据,加上当前batch的统计结果,就得到:out_wrong/correct/mean_iou

for (int i = threadIdx.x; i < num_classes; i += blockDim.x) {
atomicAdd(wrong + i, wrong_c[i]);
atomicAdd(correct + i, correct_c[i]);
}
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如果num_classes较小, predictions的shape较大,会导致这个kernel的性能非常低效,其实感觉类似这样的kernel,先CPU即可,后续最好评估下时间。

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@wanghaoshuang wanghaoshuang Jun 12, 2018

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input size class_num GPU CPU
1024 * 2048 100 0.168812ms 13.0831ms
1024 * 2048 50 0.172748ms 13.5145ms
1024 * 2048 20 0.174807ms 14.4619ms
1024 * 2048 10 0.188483ms 16.1516ms
1024 * 2048 1 0.230743ms 12.7893ms
1024 * 2048 *2 100 0.308306ms 26.4576
1024 * 2048 * 2 50 0.326073ms 26.9835ms
1024 * 2048 *2 20 0.28971ms 29.0224ms
1024 * 2048* 2 10 0.267694ms 34.2029ms
1024 * 2048 * 2 1 0.295844ms 25.4808ms

'softmax_with_cross_entropy', 'smooth_l1', 'one_hot',
'autoincreased_step_counter', 'reshape', 'lod_reset', 'lrn', 'pad',
'label_smooth', 'roi_pool', 'dice_loss', 'image_resize',
'image_resize_short', 'resize_bilinear', 'gather', 'random_crop', 'mean_iou'
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This is due to he yapf version?

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@wanghaoshuang wanghaoshuang Jun 13, 2018

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The version of my yapf is 0.22. I'm not sure it is due to he yapf version. Which style is correct?

@wanghaoshuang wanghaoshuang merged commit 6fcdb24 into PaddlePaddle:develop Jun 14, 2018
@wanghaoshuang wanghaoshuang deleted the mean_iou branch May 20, 2022 03:59
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