202 lines
7.1 KiB
Python
202 lines
7.1 KiB
Python
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 copy import deepcopy
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from .cell_operations import ResNetBasicblock
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from .search_cells import NAS201SearchCell as SearchCell
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from .genotypes import Structure
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from torch.autograd import Variable
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class TinyNetwork(nn.Module):
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def __init__(self, C, N, max_nodes, num_classes, criterion, search_space, args, affine=False, track_running_stats=True, stem_channels=3):
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super(TinyNetwork, 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._num_classes = num_classes
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self._criterion = criterion
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self._args = args
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self._affine = affine
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self._track_running_stats = track_running_stats
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self.stem = nn.Sequential(
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nn.Conv2d(stem_channels, 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, 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 = cell.out_dim
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self.num_edge = num_edge
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self.num_op = len(search_space)
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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._arch_parameters = Variable(1e-3*torch.randn(num_edge, len(search_space)).cuda(), requires_grad=True)
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## optimizer
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## 记录的是m在内存中的地址，以示区分
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arch_params = set(id(m) for m in self.arch_parameters())
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self._model_params = [m for m in self.parameters() if id(m) not in arch_params]
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# 模型参数优化器
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self.optimizer = torch.optim.SGD(
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self._model_params,
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args.learning_rate,
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momentum=args.momentum,
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weight_decay=args.weight_decay,
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nesterov= args.nesterov)
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def entropy_y_x(self, p_logit):
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p = F.softmax(p_logit, dim=1)
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return - torch.sum(p * F.log_softmax(p_logit, dim=1)) / p_logit.shape[0]
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def _loss(self, input, target, return_logits=False):
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logits = self(input)
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loss = self._criterion(logits, target)
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return (loss, logits) if return_logits else loss
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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 arch_parameters(self):
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return [self._arch_parameters]
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def get_theta(self):
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return nn.functional.softmax(self._arch_parameters, dim=-1).cpu()
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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, weights=None):
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sim_nn = []
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weights = nn.functional.softmax(self._arch_parameters, dim=-1) if weights is None else weights
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if self.slim:
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weights[1].data.fill_(0)
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weights[3].data.fill_(0)
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weights[4].data.fill_(0)
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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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feature = cell(feature, weights)
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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 logits
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def _save_arch_parameters(self):
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self._saved_arch_parameters = [p.clone() for p in self._arch_parameters]
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def project_arch(self):
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self._save_arch_parameters()
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for p in self.arch_parameters():
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m, n = p.size()
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maxIndexs = p.data.cpu().numpy().argmax(axis=1)
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p.data = self.proximal_step(p, maxIndexs)
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def proximal_step(self, var, maxIndexs=None):
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values = var.data.cpu().numpy()
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m, n = values.shape
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alphas = []
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for i in range(m):
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for j in range(n):
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if j == maxIndexs[i]:
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alphas.append(values[i][j].copy())
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values[i][j] = 1
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else:
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values[i][j] = 0
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return torch.Tensor(values).cuda()
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def restore_arch_parameters(self):
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for i, p in enumerate(self._arch_parameters):
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p.data.copy_(self._saved_arch_parameters[i])
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del self._saved_arch_parameters
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def new(self):
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model_new = TinyNetwork(self._C, self._layerN, self.max_nodes, self._num_classes, self._criterion,
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self.op_names, self._args, self._affine, self._track_running_stats).cuda()
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for x, y in zip(model_new.arch_parameters(), self.arch_parameters()):
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x.data.copy_(y.data)
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return model_new
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def step(self, input, target, args, shared=None, return_grad=False):
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Lt, logit_t = self._loss(input, target, return_logits=True)
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Lt.backward()
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if args.grad_clip != 0:
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nn.utils.clip_grad_norm_(self.get_weights(), args.grad_clip)
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self.optimizer.step()
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if return_grad:
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grad = torch.nn.utils.parameters_to_vector([p.grad for p in self.get_weights()])
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return logit_t, Lt, grad
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else:
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return logit_t, Lt
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def printing(self, logging):
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logging.info(self.get_theta())
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def set_arch_parameters(self, new_alphas):
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for alpha, new_alpha in zip(self.arch_parameters(), new_alphas):
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alpha.data.copy_(new_alpha.data)
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def save_arch_parameters(self):
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self._saved_arch_parameters = self._arch_parameters.clone()
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def restore_arch_parameters(self):
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self.set_arch_parameters(self._saved_arch_parameters)
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def reset_optimizer(self, lr, momentum, weight_decay):
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del self.optimizer
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self.optimizer = torch.optim.SGD(
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self.get_weights(),
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lr,
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momentum=momentum,
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weight_decay=weight_decay,
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nesterov= args.nesterov) |