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.gitignore
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Executable file → Normal file
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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from .model_search import Network
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from .CifarNet import NetworkCIFAR
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from .ImageNet import NetworkImageNet
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn.parameter import Parameter
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from .head_utils import CifarHEAD, ImageNetHEAD
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from .operations import OPS, FactorizedReduce, ReLUConvBN
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from .genotypes import PRIMITIVES, Genotype
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class MixedOp(nn.Module):
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def __init__(self, C, stride):
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super(MixedOp, self).__init__()
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self._ops = nn.ModuleList()
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for primitive in PRIMITIVES:
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op = OPS[primitive](C, stride, False)
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self._ops.append(op)
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def forward(self, x, weights):
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return sum(w * op(x) for w, op in zip(weights, self._ops))
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class Cell(nn.Module):
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def __init__(self, steps, multiplier, C_prev_prev, C_prev, C, reduction, reduction_prev):
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super(Cell, self).__init__()
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self.reduction = reduction
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if reduction_prev:
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self.preprocess0 = FactorizedReduce(C_prev_prev, C, affine=False)
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else:
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self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, affine=False)
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self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, affine=False)
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self._steps = steps
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self._multiplier = multiplier
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self._ops = nn.ModuleList()
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for i in range(self._steps):
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for j in range(2+i):
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stride = 2 if reduction and j < 2 else 1
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op = MixedOp(C, stride)
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self._ops.append(op)
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def forward(self, s0, s1, weights):
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s0 = self.preprocess0(s0)
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s1 = self.preprocess1(s1)
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states = [s0, s1]
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offset = 0
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for i in range(self._steps):
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clist = []
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for j, h in enumerate(states):
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x = self._ops[offset+j](h, weights[offset+j])
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clist.append( x )
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s = sum(clist)
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offset += len(states)
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states.append(s)
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return torch.cat(states[-self._multiplier:], dim=1)
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class Network(nn.Module):
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def __init__(self, C, num_classes, layers, steps=4, multiplier=4, stem_multiplier=3, head='cifar'):
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super(Network, self).__init__()
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self._C = C
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self._num_classes = num_classes
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self._layers = layers
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self._steps = steps
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self._multiplier = multiplier
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C_curr = stem_multiplier*C
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if head == 'cifar':
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self.stem = nn.Sequential(
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nn.Conv2d(3, C_curr, 3, padding=1, bias=False),
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nn.BatchNorm2d(C_curr)
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)
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elif head == 'imagenet':
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self.stem = ImageNetHEAD(C_curr, stride=1)
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else:
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raise ValueError('Invalid head : {:}'.format(head))
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C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
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reduction_prev, cells = False, []
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for i in range(layers):
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if i in [layers//3, 2*layers//3]:
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C_curr *= 2
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reduction = True
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else:
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reduction = False
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cell = Cell(steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
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reduction_prev = reduction
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cells.append( cell )
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C_prev_prev, C_prev = C_prev, multiplier*C_curr
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self.cells = nn.ModuleList(cells)
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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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# initialize architecture parameters
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k = sum(1 for i in range(self._steps) for n in range(2+i))
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num_ops = len(PRIMITIVES)
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self.alphas_normal = Parameter(torch.Tensor(k, num_ops))
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self.alphas_reduce = Parameter(torch.Tensor(k, num_ops))
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nn.init.normal_(self.alphas_normal, 0, 0.001)
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nn.init.normal_(self.alphas_reduce, 0, 0.001)
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def set_tau(self, tau):
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return -1
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def get_tau(self):
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return -1
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def arch_parameters(self):
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return [self.alphas_normal, self.alphas_reduce]
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def base_parameters(self):
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lists = list(self.stem.parameters()) + list(self.cells.parameters())
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lists += list(self.global_pooling.parameters())
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lists += list(self.classifier.parameters())
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return lists
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def forward(self, inputs):
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batch, C, H, W = inputs.size()
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s0 = s1 = self.stem(inputs)
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for i, cell in enumerate(self.cells):
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if cell.reduction:
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weights = F.softmax(self.alphas_reduce, dim=-1)
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else:
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weights = F.softmax(self.alphas_normal, dim=-1)
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s0, s1 = s1, cell(s0, s1, weights)
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out = self.global_pooling(s1)
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out = out.view(batch, -1)
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logits = self.classifier(out)
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return logits
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def genotype(self):
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def _parse(weights):
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gene, n, start = [], 2, 0
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for i in range(self._steps):
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end = start + n
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W = weights[start:end].copy()
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edges = sorted(range(i + 2), key=lambda x: -max(W[x][k] for k in range(len(W[x])) if k != PRIMITIVES.index('none')))[:2]
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for j in edges:
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k_best = None
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for k in range(len(W[j])):
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if k != PRIMITIVES.index('none'):
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if k_best is None or W[j][k] > W[j][k_best]:
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k_best = k
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gene.append((PRIMITIVES[k_best], j, float(W[j][k_best])))
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start = end
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n += 1
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return gene
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with torch.no_grad():
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gene_normal = _parse(F.softmax(self.alphas_normal, dim=-1).cpu().numpy())
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gene_reduce = _parse(F.softmax(self.alphas_reduce, dim=-1).cpu().numpy())
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concat = range(2+self._steps-self._multiplier, self._steps+2)
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genotype = Genotype(
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normal=gene_normal, normal_concat=concat,
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reduce=gene_reduce, reduce_concat=concat
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)
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return genotype
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