65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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#####################################################
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from typing import List, Text, Any
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import torch.nn as nn
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from models.cell_operations import ResNetBasicblock
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from models.cell_infers.cells import InferCell
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class DynamicShapeTinyNet(nn.Module):
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def __init__(self, channels: List[int], genotype: Any, num_classes: int):
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super(DynamicShapeTinyNet, self).__init__()
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self._channels = channels
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if len(channels) % 3 != 2:
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raise ValueError("invalid number of layers : {:}".format(len(channels)))
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self._num_stage = N = len(channels) // 3
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self.stem = nn.Sequential(
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nn.Conv2d(3, channels[0], kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(channels[0]),
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)
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# layer_channels = [C ] * N + [C*2 ] + [C*2 ] * N + [C*4 ] + [C*4 ] * N
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layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
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c_prev = channels[0]
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self.cells = nn.ModuleList()
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for index, (c_curr, reduction) in enumerate(zip(channels, layer_reductions)):
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if reduction:
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cell = ResNetBasicblock(c_prev, c_curr, 2, True)
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else:
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cell = InferCell(genotype, c_prev, c_curr, 1)
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self.cells.append(cell)
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c_prev = cell.out_dim
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self._num_layer = len(self.cells)
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self.lastact = nn.Sequential(nn.BatchNorm2d(c_prev), nn.ReLU(inplace=True))
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self.global_pooling = nn.AdaptiveAvgPool2d(1)
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self.classifier = nn.Linear(c_prev, num_classes)
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def get_message(self) -> Text:
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string = self.extra_repr()
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for i, cell in enumerate(self.cells):
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string += "\n {:02d}/{:02d} :: {:}".format(
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i, len(self.cells), cell.extra_repr()
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)
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return string
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def extra_repr(self):
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return "{name}(C={_channels}, N={_num_stage}, L={_num_layer})".format(
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name=self.__class__.__name__, **self.__dict__
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)
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def forward(self, inputs):
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feature = self.stem(inputs)
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for i, cell in enumerate(self.cells):
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feature = cell(feature)
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out = self.lastact(feature)
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out = self.global_pooling(out)
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out = out.view(out.size(0), -1)
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logits = self.classifier(out)
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return out, logits
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