visualize.py 2.3 KB

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  1. # copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
  2. #
  3. # Licensed under the Apache License, Version 2.0 (the "License");
  4. # you may not use this file except in compliance with the License.
  5. # You may obtain a copy of the License at
  6. #
  7. # http://www.apache.org/licenses/LICENSE-2.0
  8. #
  9. # Unless required by applicable law or agreed to in writing, software
  10. # distributed under the License is distributed on an "AS IS" BASIS,
  11. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12. # See the License for the specific language governing permissions and
  13. # limitations under the License.
  14. import os.path as osp
  15. import tqdm
  16. import numpy as np
  17. import matplotlib
  18. matplotlib.use('Agg')
  19. import matplotlib.pyplot as plt
  20. from .prune import cal_model_size
  21. from paddleslim.prune import load_sensitivities
  22. def visualize(model, sensitivities_file, save_dir='./'):
  23. """将模型裁剪率和每个参数裁剪后精度损失的关系可视化。
  24. 可视化结果纵轴为eval_metric_loss参数值,横轴为对应的模型被裁剪的比例
  25. Args:
  26. model (paddlex.cv.models): paddlex中的模型。
  27. sensitivities_file (str): 敏感度文件存储路径。
  28. """
  29. program = model.test_prog
  30. place = model.places[0]
  31. fig = plt.figure()
  32. plt.xlabel("model prune ratio")
  33. plt.ylabel("evaluation loss")
  34. title_name = osp.split(sensitivities_file)[-1].split('.')[0]
  35. plt.title(title_name)
  36. plt.grid(linestyle='--', linewidth=0.5)
  37. x = list()
  38. y = list()
  39. for loss_thresh in tqdm.tqdm(list(np.arange(0.05, 1, 0.05))):
  40. prune_ratio = 1 - cal_model_size(
  41. program, place, sensitivities_file, eval_metric_loss=loss_thresh)
  42. x.append(prune_ratio)
  43. y.append(loss_thresh)
  44. plt.plot(x, y, color='green', linewidth=0.5, marker='o', markersize=3)
  45. my_x_ticks = np.arange(
  46. min(np.array(x)) - 0.01,
  47. max(np.array(x)) + 0.01, 0.05)
  48. my_y_ticks = np.arange(0.05, 1, 0.05)
  49. plt.xticks(my_x_ticks, fontsize=3)
  50. plt.yticks(my_y_ticks, fontsize=3)
  51. for a, b in zip(x, y):
  52. plt.text(
  53. a,
  54. b, (float('%0.4f' % a), float('%0.3f' % b)),
  55. ha='center',
  56. va='bottom',
  57. fontsize=3)
  58. suffix = osp.splitext(sensitivities_file)[-1]
  59. plt.savefig('sensitivities.png', dpi=800)
  60. plt.close()