Performance#

Profile Decoder#

The default Python and Cython decoder can be profiled with Python’s standard cprofile. The output can be a sorted table and a flame graph. Both are generated below:

%%bash
python -m openpifpaf.predict coco/000000081988.jpg --no-download-progress --debug --profile-decoder

INFO:__main__:neural network device: cpu (CUDA available: False, count: 0)
DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
DEBUG:openpifpaf.network.factory:Shell(
  (base_net): ShuffleNetV2K(
    (input_block): Sequential(
      (0): Sequential(
        (0): Conv2d(3, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
    )
    (stage2): Sequential(
      (0): InvertedResidualK(
        (branch1): Sequential(
          (0): Conv2d(24, 24, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=24, bias=False)
          (1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): Conv2d(24, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (3): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (4): ReLU(inplace=True)
        )
        (branch2): Sequential(
          (0): Conv2d(24, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(174, 174, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=174, bias=False)
          (4): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (1): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(174, 174, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=174, bias=False)
          (4): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (2): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(174, 174, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=174, bias=False)
          (4): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (3): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(174, 174, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=174, bias=False)
          (4): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
    )
    (stage3): Sequential(
      (0): InvertedResidualK(
        (branch1): Sequential(
          (0): Conv2d(348, 348, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=348, bias=False)
          (1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (3): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (4): ReLU(inplace=True)
        )
        (branch2): Sequential(
          (0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(348, 348, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=348, bias=False)
          (4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (1): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
          (4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (2): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
          (4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (3): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
          (4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (4): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
          (4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (5): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
          (4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (6): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
          (4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (7): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
          (4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
    )
    (stage4): Sequential(
      (0): InvertedResidualK(
        (branch1): Sequential(
          (0): Conv2d(696, 696, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=696, bias=False)
          (1): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (3): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (4): ReLU(inplace=True)
        )
        (branch2): Sequential(
          (0): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(696, 696, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=696, bias=False)
          (4): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (1): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(696, 696, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=696, bias=False)
          (4): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (2): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(696, 696, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=696, bias=False)
          (4): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
      (3): InvertedResidualK(
        (branch2): Sequential(
          (0): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
          (3): Conv2d(696, 696, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=696, bias=False)
          (4): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (5): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (6): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (7): ReLU(inplace=True)
        )
      )
    )
    (conv5): Sequential(
      (0): Conv2d(1392, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (1): BatchNorm2d(1392, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
  )
  (head_nets): ModuleList(
    (0): CompositeField4(
      (dropout): Dropout2d(p=0.0, inplace=False)
      (conv): Conv2d(1392, 340, kernel_size=(1, 1), stride=(1, 1))
      (upsample_op): PixelShuffle(upscale_factor=2)
    )
    (1): CompositeField4(
      (dropout): Dropout2d(p=0.0, inplace=False)
      (conv): Conv2d(1392, 608, kernel_size=(1, 1), stride=(1, 1))
      (upsample_op): PixelShuffle(upscale_factor=2)
    )
  )
)
DEBUG:openpifpaf.decoder.factory:head names = ['cif', 'caf']
DEBUG:openpifpaf.signal:subscribe to eval_reset
DEBUG:openpifpaf.decoder.pose_similarity:valid keypoints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
DEBUG:openpifpaf.visualizer.base:cif: indices = []
DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
DEBUG:openpifpaf.visualizer.base:cif: indices = []
DEBUG:openpifpaf.visualizer.base:caf: indices = []
DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
DEBUG:openpifpaf.visualizer.base:cif: indices = []
DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
DEBUG:openpifpaf.visualizer.base:cif: indices = []
DEBUG:openpifpaf.visualizer.base:caf: indices = []
DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
DEBUG:openpifpaf.decoder.factory:created 2 decoders
INFO:openpifpaf.decoder.factory:No specific decoder requested. Using the first one from:
  --decoder=cifcaf:0
  --decoder=posesimilarity:0
Use any of the above arguments to select one or multiple decoders and to suppress this message.
INFO:openpifpaf.predictor:neural network device: cpu (CUDA available: False, count: 0)
DEBUG:openpifpaf.transforms.pad:valid area before pad: [  0.   0. 639. 426.], image size = (640, 427)
DEBUG:openpifpaf.transforms.pad:pad with [0, 3, 1, 3]
DEBUG:openpifpaf.transforms.pad:valid area after pad: [  0.   3. 639. 426.], image size = (641, 433)
DEBUG:openpifpaf.decoder.decoder:nn processing time: 752.8ms
DEBUG:openpifpaf.decoder.decoder:parallel execution with worker <openpifpaf.decoder.decoder.DummyPool object at 0x7ff66aa51ca0>
DEBUG:openpifpaf.decoder.multi:task 0
DEBUG:openpifpaf.decoder.cifcaf:cpp annotations = 5 (12.7ms)
INFO:openpifpaf.decoder.cifcaf:annotations 5: [16, 14, 13, 12, 12]
INFO:openpifpaf.profiler:writing profile file profile_decoder.prof
         386 function calls in 0.015 seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.013    0.013    0.015    0.015 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/decoder/cifcaf.py:224(__call__)
        5    0.000    0.000    0.000    0.000 {method 'tolist' of 'numpy.ndarray' objects}
        5    0.000    0.000    0.001    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/annotation.py:17(__init__)
       10    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/numpy/core/fromnumeric.py:2188(sum)
       15    0.000    0.000    0.000    0.000 {method 'numpy' of 'torch._C._TensorBase' objects}
       10    0.000    0.000    0.000    0.000 {method 'reduce' of 'numpy.ufunc' objects}
       10    0.000    0.000    0.000    0.000 {built-in method numpy.asarray}
        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:288(__init__)
        2    0.000    0.000    0.000    0.000 {method 'flush' of '_io.TextIOWrapper' objects}
        1    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/visualizer/cif.py:47(predicted)
        1    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/decoder/cifcaf.py:276(<listcomp>)
        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:1689(isEnabledFor)
       10    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/numpy/core/fromnumeric.py:69(_wrapreduction)
        2    0.000    0.000    0.000    0.000 {method 'unbind' of 'torch._C._TensorBase' objects}
        1    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/visualizer/caf.py:47(predicted)
       15    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/torch/_tensor.py:952(__array__)
       10    0.000    0.000    0.000    0.000 {built-in method numpy.zeros}
        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:364(getMessage)
        1    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/torch/_tensor.py:890(__len__)
        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/torch/_tensor.py:906(__iter__)
        5    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/visualizer/base.py:114(indices)
        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:1514(findCaller)
       10    0.000    0.000    0.000    0.000 <__array_function__ internals>:177(sum)
        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/posixpath.py:140(basename)
       10    0.000    0.000    0.000    0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}
       24    0.000    0.000    0.000    0.000 {built-in method builtins.len}
       19    0.000    0.000    0.000    0.000 {built-in method builtins.isinstance}
        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:1073(emit)
        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:1565(_log)
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DEBUG:openpifpaf.decoder.decoder:time: nn = 753.1ms, dec = 16.8ms
INFO:openpifpaf.predictor:batch 0: coco/000000081988.jpg
src/openpifpaf/csrc/src/cif_hr.cpp:102: UserInfo: resizing cifhr buffer
src/openpifpaf/csrc/src/occupancy.cpp:53: UserInfo: resizing occupancy buffer

!flameprof profile_decoder.prof > profile_decoder_flame.svg

There is a second output that is generated from the Autograd Profiler. This can only be viewed in the Chrome browser:

open chrome://tracing
click “Load” in the top left corner
select decoder_profile.1.json

This is the same type of plot that is used to trace the training of a batch. An example of such a plot is shown below.

Profile Training#

For a training batch, the Chrome trace looks like this:

OpenPifPaf Guide

Performance

On this page

Performance#

Profile Decoder#

Profile Training#