118 lines
5.1 KiB
Python
118 lines
5.1 KiB
Python
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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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# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 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 .infer_cells import ResNetBasicblock
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from .search_cells import SearchCell
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from .genotypes import Structure
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class TinyNetworkGDAS(nn.Module):
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def __init__(self, C, N, max_nodes, num_classes, search_space):
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super(TinyNetworkGDAS, self).__init__()
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self._C = C
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self._layerN = N
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self.max_nodes = max_nodes
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self.stem = nn.Sequential(
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nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(C))
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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, num_edge, edge2index = C, None, 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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if reduction:
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cell = ResNetBasicblock(C_prev, C_curr, 2)
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else:
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cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space)
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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 = cell.out_dim
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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_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
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self.tau = 10
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self.nan_count = 0
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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 set_tau(self, tau, _nan_count=0):
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self.tau = tau
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self.nan_count = _nan_count
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def get_tau(self):
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return self.tau
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def get_alphas(self):
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return [self.arch_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}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
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def genotype(self):
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genotypes = []
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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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with torch.no_grad():
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weights = self.arch_parameters[ self.edge2index[node_str] ]
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op_name = self.op_names[ weights.argmax().item() ]
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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 forward(self, inputs):
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def gumbel_softmax(_logits, _tau):
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while True: # a trick to avoid the gumbels bug
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gumbels = -torch.empty_like(_logits).exponential_().log()
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new_logits = (_logits.log_softmax(dim=1) + gumbels) / _tau
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probs = nn.functional.softmax(new_logits, dim=1)
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index = probs.max(-1, keepdim=True)[1]
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if index[0].item() == self.op_names.index('none') and index[3].item() == self.op_names.index('none') and index[5].item() == self.op_names.index('none'): continue
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if index[1].item() == self.op_names.index('none') and index[2].item() == self.op_names.index('none') and index[3].item() == self.op_names.index('none') and index[4].item() == self.op_names.index('none'): continue
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if index[3].item() == self.op_names.index('none') and index[4].item() == self.op_names.index('none') and index[5].item() == self.op_names.index('none'): continue
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if index[3].item() == self.op_names.index('none') and index[0].item() == self.op_names.index('none') and index[1].item() == self.op_names.index('none'): continue
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one_h = torch.zeros_like(_logits).scatter_(-1, index, 1.0)
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xres = one_h - probs.detach() + probs
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if (not torch.isinf(gumbels).any()) and (not torch.isinf(probs).any()) and (not torch.isnan(probs).any()): break
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self.nan_count += 1
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return xres, index
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feature = self.stem(inputs)
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for i, cell in enumerate(self.cells):
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if isinstance(cell, SearchCell):
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alphas, IDX = gumbel_softmax(self.arch_parameters, self.tau)
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feature = cell.forward_gdas(feature, alphas, IDX.cpu())
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else:
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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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