72 lines
3.1 KiB
Python
72 lines
3.1 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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#####################################################
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import torch
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import torch.nn as nn
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from copy import deepcopy
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from .cells import NASNetInferCell as InferCell, AuxiliaryHeadCIFAR
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# The macro structure is based on NASNet
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class NASNetonCIFAR(nn.Module):
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def __init__(self, C, N, stem_multiplier, num_classes, genotype, auxiliary, affine=True, track_running_stats=True):
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super(NASNetonCIFAR, self).__init__()
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self._C = C
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self._layerN = N
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self.stem = nn.Sequential(
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nn.Conv2d(3, C*stem_multiplier, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(C*stem_multiplier))
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# config for each layer
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layer_channels = [C ] * N + [C*2 ] + [C*2 ] * (N-1) + [C*4 ] + [C*4 ] * (N-1)
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layer_reductions = [False] * N + [True] + [False] * (N-1) + [True] + [False] * (N-1)
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C_prev_prev, C_prev, C_curr, reduction_prev = C*stem_multiplier, C*stem_multiplier, C, False
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self.auxiliary_index = None
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self.auxiliary_head = None
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self.cells = nn.ModuleList()
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for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
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cell = InferCell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, affine, track_running_stats)
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self.cells.append( cell )
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C_prev_prev, C_prev, reduction_prev = C_prev, cell._multiplier*C_curr, reduction
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if reduction and C_curr == C*4 and auxiliary:
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self.auxiliary_head = AuxiliaryHeadCIFAR(C_prev, num_classes)
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self.auxiliary_index = index
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self._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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self.drop_path_prob = -1
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def update_drop_path(self, drop_path_prob):
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self.drop_path_prob = drop_path_prob
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def auxiliary_param(self):
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if self.auxiliary_head is None: return []
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else: return list( self.auxiliary_head.parameters() )
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def get_message(self):
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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(i, len(self.cells), cell.extra_repr())
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return string
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def extra_repr(self):
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return ('{name}(C={_C}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
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def forward(self, inputs):
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stem_feature, logits_aux = self.stem(inputs), None
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cell_results = [stem_feature, stem_feature]
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for i, cell in enumerate(self.cells):
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cell_feature = cell(cell_results[-2], cell_results[-1], self.drop_path_prob)
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cell_results.append( cell_feature )
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if self.auxiliary_index is not None and i == self.auxiliary_index and self.training:
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logits_aux = self.auxiliary_head( cell_results[-1] )
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out = self.lastact(cell_results[-1])
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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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if logits_aux is None: return out, logits
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else: return out, [logits, logits_aux]
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