78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
import torch
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import torch.nn as nn
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from .construct_utils import drop_path
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from .base_cells import InferCell
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from .head_utils import ImageNetHEAD, AuxiliaryHeadImageNet
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class NetworkImageNet(nn.Module):
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def __init__(self, C, N, auxiliary, genotype, num_classes):
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super(NetworkImageNet, self).__init__()
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self._C = C
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self._layerN = N
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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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self.stem0 = nn.Sequential(
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nn.Conv2d(3, C // 2, kernel_size=3, stride=2, padding=1, bias=False),
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nn.BatchNorm2d(C // 2),
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nn.ReLU(inplace=True),
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nn.Conv2d(C // 2, C, 3, stride=2, padding=1, bias=False),
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nn.BatchNorm2d(C),
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)
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self.stem1 = nn.Sequential(
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nn.ReLU(inplace=True),
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nn.Conv2d(C, C, 3, stride=2, padding=1, bias=False),
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nn.BatchNorm2d(C),
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)
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C_prev_prev, C_prev, C_curr, reduction_prev = C, C, C, True
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self.cells = nn.ModuleList()
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self.auxiliary_index = None
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for i, (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)
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reduction_prev = reduction
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self.cells += [cell]
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C_prev_prev, C_prev = C_prev, cell._multiplier * C_curr
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if reduction and C_curr == C*4:
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C_to_auxiliary = C_prev
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self.auxiliary_index = i
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self._NNN = len(self.cells)
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if auxiliary:
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self.auxiliary_head = AuxiliaryHeadImageNet(C_to_auxiliary, num_classes)
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else:
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self.auxiliary_head = None
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self.global_pooling = nn.AvgPool2d(7)
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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 extra_repr(self):
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return ('{name}(C={_C}, N=[{_layerN}, {_NNN}], aux-index={auxiliary_index}, drop-path={drop_path_prob})'.format(name=self.__class__.__name__, **self.__dict__))
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def get_message(self):
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return self.extra_repr()
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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 forward(self, inputs):
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s0 = self.stem0(inputs)
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s1 = self.stem1(s0)
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logits_aux = None
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for i, cell in enumerate(self.cells):
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s0, s1 = s1, cell(s0, s1, self.drop_path_prob)
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if i == self.auxiliary_index and self.auxiliary_head and self.training:
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logits_aux = self.auxiliary_head(s1)
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out = self.global_pooling(s1)
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logits = self.classifier(out.view(out.size(0), -1))
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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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