import torch import torch.nn as nn import torch.nn.functional as F from copy import deepcopy from .cell_operations import ResNetBasicblock from .search_cells import NAS201SearchCell as SearchCell from .genotypes import Structure from torch.autograd import Variable class TinyNetwork(nn.Module): def __init__(self, C, N, max_nodes, num_classes, criterion, search_space, args, affine=False, track_running_stats=True, stem_channels=3): super(TinyNetwork, self).__init__() self._C = C self._layerN = N self.max_nodes = max_nodes self._num_classes = num_classes self._criterion = criterion self._args = args self._affine = affine self._track_running_stats = track_running_stats self.stem = nn.Sequential( nn.Conv2d(stem_channels, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C)) layer_channels = [C ] * N + [C*2 ] + [C*2 ] * N + [C*4 ] + [C*4 ] * N layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N C_prev, num_edge, edge2index = C, None, None self.cells = nn.ModuleList() for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)): if reduction: cell = ResNetBasicblock(C_prev, C_curr, 2) else: cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space, affine, track_running_stats) if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges) self.cells.append( cell ) C_prev = cell.out_dim self.num_edge = num_edge self.num_op = len(search_space) self.op_names = deepcopy( search_space ) self._Layer = len(self.cells) self.edge2index = edge2index self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True)) self.global_pooling = nn.AdaptiveAvgPool2d(1) self.classifier = nn.Linear(C_prev, num_classes) # self._arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) ) self._arch_parameters = Variable(1e-3*torch.randn(num_edge, len(search_space)).cuda(), requires_grad=True) ## optimizer ## 记录的是m在内存中的地址，以示区分 arch_params = set(id(m) for m in self.arch_parameters()) self._model_params = [m for m in self.parameters() if id(m) not in arch_params] # 模型参数优化器 self.optimizer = torch.optim.SGD( self._model_params, args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay, nesterov= args.nesterov) def entropy_y_x(self, p_logit): p = F.softmax(p_logit, dim=1) return - torch.sum(p * F.log_softmax(p_logit, dim=1)) / p_logit.shape[0] def _loss(self, input, target, return_logits=False): logits = self(input) loss = self._criterion(logits, target) return (loss, logits) if return_logits else loss def get_weights(self): xlist = list( self.stem.parameters() ) + list( self.cells.parameters() ) xlist+= list( self.lastact.parameters() ) + list( self.global_pooling.parameters() ) xlist+= list( self.classifier.parameters() ) return xlist def arch_parameters(self): return [self._arch_parameters] def get_theta(self): return nn.functional.softmax(self._arch_parameters, dim=-1).cpu() def get_message(self): string = self.extra_repr() for i, cell in enumerate(self.cells): string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr()) return string def extra_repr(self): return ('{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__)) def genotype(self): genotypes = [] for i in range(1, self.max_nodes): xlist = [] for j in range(i): node_str = '{:}<-{:}'.format(i, j) with torch.no_grad(): weights = self._arch_parameters[ self.edge2index[node_str] ] op_name = self.op_names[ weights.argmax().item() ] xlist.append((op_name, j)) genotypes.append( tuple(xlist) ) return Structure( genotypes ) def forward(self, inputs, weights=None): sim_nn = [] weights = nn.functional.softmax(self._arch_parameters, dim=-1) if weights is None else weights if self.slim: weights[1].data.fill_(0) weights[3].data.fill_(0) weights[4].data.fill_(0) feature = self.stem(inputs) for i, cell in enumerate(self.cells): if isinstance(cell, SearchCell): feature = cell(feature, weights) else: feature = cell(feature) out = self.lastact(feature) out = self.global_pooling( out ) out = out.view(out.size(0), -1) logits = self.classifier(out) return logits def _save_arch_parameters(self): self._saved_arch_parameters = [p.clone() for p in self._arch_parameters] def project_arch(self): self._save_arch_parameters() for p in self.arch_parameters(): m, n = p.size() maxIndexs = p.data.cpu().numpy().argmax(axis=1) p.data = self.proximal_step(p, maxIndexs) def proximal_step(self, var, maxIndexs=None): values = var.data.cpu().numpy() m, n = values.shape alphas = [] for i in range(m): for j in range(n): if j == maxIndexs[i]: alphas.append(values[i][j].copy()) values[i][j] = 1 else: values[i][j] = 0 return torch.Tensor(values).cuda() def restore_arch_parameters(self): for i, p in enumerate(self._arch_parameters): p.data.copy_(self._saved_arch_parameters[i]) del self._saved_arch_parameters def new(self): model_new = TinyNetwork(self._C, self._layerN, self.max_nodes, self._num_classes, self._criterion, self.op_names, self._args, self._affine, self._track_running_stats).cuda() for x, y in zip(model_new.arch_parameters(), self.arch_parameters()): x.data.copy_(y.data) return model_new def step(self, input, target, args, shared=None, return_grad=False): Lt, logit_t = self._loss(input, target, return_logits=True) Lt.backward() if args.grad_clip != 0: nn.utils.clip_grad_norm_(self.get_weights(), args.grad_clip) self.optimizer.step() if return_grad: grad = torch.nn.utils.parameters_to_vector([p.grad for p in self.get_weights()]) return logit_t, Lt, grad else: return logit_t, Lt def printing(self, logging): logging.info(self.get_theta()) def set_arch_parameters(self, new_alphas): for alpha, new_alpha in zip(self.arch_parameters(), new_alphas): alpha.data.copy_(new_alpha.data) def save_arch_parameters(self): self._saved_arch_parameters = self._arch_parameters.clone() def restore_arch_parameters(self): self.set_arch_parameters(self._saved_arch_parameters) def reset_optimizer(self, lr, momentum, weight_decay): del self.optimizer self.optimizer = torch.optim.SGD( self.get_weights(), lr, momentum=momentum, weight_decay=weight_decay, nesterov= args.nesterov)