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sequential.py 3.1 kB

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  1. # -*- coding: utf-8 -*-
  2. # MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
  3. #
  4. # Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
  5. #
  6. # Unless required by applicable law or agreed to in writing,
  7. # software distributed under the License is distributed on an
  8. # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  9. from collections import OrderedDict
  10. from .module import Module
  11. class Sequential(Module):
  12. r"""A sequential container.
  13. Modules will be added to it in the order they are passed in the constructor.
  14. Alternatively, an ordered dict of modules can also be passed in.
  15. To make it easier to understand, here is a small example:
  16. Examples:
  17. .. testcode::
  18. import numpy as np
  19. import megengine as mge
  20. import megengine.module as M
  21. import megengine.functional as F
  22. from collections import OrderedDict
  23. batch_size = 64
  24. data = mge.tensor(np.zeros((batch_size, 1, 28, 28)), dtype=np.float32)
  25. label = mge.tensor(np.zeros(batch_size,), dtype=np.int32)
  26. data = data.reshape(batch_size, -1)
  27. net0 = M.Sequential(
  28. M.Linear(28 * 28, 320),
  29. M.Linear(320, 10)
  30. )
  31. pred0 = net0(data)
  32. modules = OrderedDict()
  33. modules["fc0"] = nn.Linear(28 * 28, 320)
  34. modules["fc1"] = nn.Linear(320, 10)
  35. net1 = nn.Sequential(modules)
  36. pred1 = net1(data)
  37. """
  38. def __init__(self, *args):
  39. super().__init__()
  40. self.layer_keys = []
  41. if len(args) == 1 and isinstance(args[0], OrderedDict):
  42. for key, module in args[0].items():
  43. # self.add_module(key, module)
  44. setattr(self, key, module)
  45. self.layer_keys.append(key)
  46. else:
  47. for idx, module in enumerate(args):
  48. # self.add_module(str(idx), module)
  49. setattr(self, str(idx), module)
  50. self.layer_keys.append(str(idx))
  51. def __getitem__(self, idx):
  52. if isinstance(idx, slice):
  53. return self.__class__(
  54. OrderedDict(zip(self.layer_keys[idx], self.layer_values[idx]))
  55. )
  56. else:
  57. return getattr(self, self.layer_keys[idx])
  58. def __setitem__(self, idx, module):
  59. key = self.layer_keys[idx]
  60. return setattr(self, key, module)
  61. def __delitem__(self, idx):
  62. if isinstance(idx, slice):
  63. for key in self.layer_keys[idx]:
  64. delattr(self, key)
  65. del self.layer_keys[idx]
  66. else:
  67. delattr(self, self.layer_keys[idx])
  68. del self.layer_keys[idx]
  69. def __len__(self):
  70. return len(self.layer_keys)
  71. def __iter__(self):
  72. return iter(self.layer_values)
  73. @property
  74. def layer_values(self):
  75. return [getattr(self, key) for key in self.layer_keys]
  76. def forward(self, inp):
  77. for layer in self.layer_values:
  78. inp = layer(inp)
  79. return inp

MegEngine 安装包中集成了使用 GPU 运行代码所需的 CUDA 环境,不用区分 CPU 和 GPU 版。 如果想要运行 GPU 程序,请确保机器本身配有 GPU 硬件设备并安装好驱动。 如果你想体验在云端 GPU 算力平台进行深度学习开发的感觉,欢迎访问 MegStudio 平台