140 lines
6.1 KiB
Python
140 lines
6.1 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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######################################################################################
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# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
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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 typing import List, Text, Dict
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from .search_cells import NASNetSearchCell as SearchCell
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# The macro structure is based on NASNet
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class NASNetworkSETN(nn.Module):
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def __init__(self, C: int, N: int, steps: int, multiplier: int, stem_multiplier: int,
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num_classes: int, search_space: List[Text], affine: bool, track_running_stats: bool):
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super(NASNetworkSETN, self).__init__()
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self._C = C
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self._layerN = N
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self._steps = steps
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self._multiplier = multiplier
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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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num_edge, edge2index = None, None
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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.cells = nn.ModuleList()
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for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
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cell = SearchCell(search_space, steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, affine, track_running_stats)
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if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
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else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
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self.cells.append( cell )
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C_prev_prev, C_prev, reduction_prev = C_prev, multiplier*C_curr, reduction
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self.op_names = deepcopy( search_space )
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self._Layer = len(self.cells)
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self.edge2index = edge2index
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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.arch_normal_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
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self.arch_reduce_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
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self.mode = 'urs'
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self.dynamic_cell = None
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def set_cal_mode(self, mode, dynamic_cell=None):
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assert mode in ['urs', 'joint', 'select', 'dynamic']
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self.mode = mode
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if mode == 'dynamic':
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self.dynamic_cell = deepcopy(dynamic_cell)
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else:
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self.dynamic_cell = None
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def get_weights(self):
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xlist = list( self.stem.parameters() ) + list( self.cells.parameters() )
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xlist+= list( self.lastact.parameters() ) + list( self.global_pooling.parameters() )
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xlist+= list( self.classifier.parameters() )
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return xlist
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def get_alphas(self):
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return [self.arch_normal_parameters, self.arch_reduce_parameters]
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def show_alphas(self):
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with torch.no_grad():
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A = 'arch-normal-parameters :\n{:}'.format( nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu() )
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B = 'arch-reduce-parameters :\n{:}'.format( nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu() )
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return '{:}\n{:}'.format(A, B)
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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}, steps={_steps}, multiplier={_multiplier}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
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def dync_genotype(self, use_random=False):
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genotypes = []
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with torch.no_grad():
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alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
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for i in range(1, self.max_nodes):
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xlist = []
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for j in range(i):
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node_str = '{:}<-{:}'.format(i, j)
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if use_random:
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op_name = random.choice(self.op_names)
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else:
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weights = alphas_cpu[ self.edge2index[node_str] ]
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op_index = torch.multinomial(weights, 1).item()
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op_name = self.op_names[ op_index ]
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xlist.append((op_name, j))
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genotypes.append( tuple(xlist) )
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return Structure( genotypes )
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def genotype(self):
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def _parse(weights):
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gene = []
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for i in range(self._steps):
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edges = []
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for j in range(2+i):
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node_str = '{:}<-{:}'.format(i, j)
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ws = weights[ self.edge2index[node_str] ]
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for k, op_name in enumerate(self.op_names):
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if op_name == 'none': continue
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edges.append( (op_name, j, ws[k]) )
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edges = sorted(edges, key=lambda x: -x[-1])
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selected_edges = edges[:2]
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gene.append( tuple(selected_edges) )
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return gene
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with torch.no_grad():
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gene_normal = _parse(torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy())
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gene_reduce = _parse(torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy())
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return {'normal': gene_normal, 'normal_concat': list(range(2+self._steps-self._multiplier, self._steps+2)),
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'reduce': gene_reduce, 'reduce_concat': list(range(2+self._steps-self._multiplier, self._steps+2))}
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def forward(self, inputs):
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normal_hardwts = nn.functional.softmax(self.arch_normal_parameters, dim=-1)
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reduce_hardwts = nn.functional.softmax(self.arch_reduce_parameters, dim=-1)
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s0 = s1 = self.stem(inputs)
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for i, cell in enumerate(self.cells):
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# [TODO]
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raise NotImplementedError
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if cell.reduction: hardwts, index = reduce_hardwts, reduce_index
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else : hardwts, index = normal_hardwts, normal_index
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s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
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out = self.lastact(s1)
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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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