304 lines
11 KiB
Python
304 lines
11 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
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#####################################################
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import torch, random
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import torch.nn as nn
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from copy import deepcopy
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from typing import Text
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from torch.distributions.categorical import Categorical
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from ..cell_operations import ResNetBasicblock, drop_path
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from .search_cells import NAS201SearchCell as SearchCell
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from .genotypes import Structure
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class Controller(nn.Module):
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# we refer to https://github.com/TDeVries/enas_pytorch/blob/master/models/controller.py
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def __init__(self, edge2index, op_names, max_nodes, lstm_size=32, lstm_num_layers=2, tanh_constant=2.5, temperature=5.0):
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super(Controller, self).__init__()
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# assign the attributes
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self.max_nodes = max_nodes
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self.num_edge = len(edge2index)
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self.edge2index = edge2index
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self.num_ops = len(op_names)
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self.op_names = op_names
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self.lstm_size = lstm_size
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self.lstm_N = lstm_num_layers
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self.tanh_constant = tanh_constant
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self.temperature = temperature
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# create parameters
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self.register_parameter('input_vars', nn.Parameter(torch.Tensor(1, 1, lstm_size)))
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self.w_lstm = nn.LSTM(input_size=self.lstm_size, hidden_size=self.lstm_size, num_layers=self.lstm_N)
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self.w_embd = nn.Embedding(self.num_ops, self.lstm_size)
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self.w_pred = nn.Linear(self.lstm_size, self.num_ops)
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nn.init.uniform_(self.input_vars , -0.1, 0.1)
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nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1)
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nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1)
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nn.init.uniform_(self.w_embd.weight , -0.1, 0.1)
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nn.init.uniform_(self.w_pred.weight , -0.1, 0.1)
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def convert_structure(self, _arch):
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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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op_index = _arch[self.edge2index[node_str]]
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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 forward(self):
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inputs, h0 = self.input_vars, None
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log_probs, entropys, sampled_arch = [], [], []
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for iedge in range(self.num_edge):
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outputs, h0 = self.w_lstm(inputs, h0)
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logits = self.w_pred(outputs)
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logits = logits / self.temperature
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logits = self.tanh_constant * torch.tanh(logits)
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# distribution
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op_distribution = Categorical(logits=logits)
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op_index = op_distribution.sample()
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sampled_arch.append( op_index.item() )
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op_log_prob = op_distribution.log_prob(op_index)
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log_probs.append( op_log_prob.view(-1) )
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op_entropy = op_distribution.entropy()
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entropys.append( op_entropy.view(-1) )
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# obtain the input embedding for the next step
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inputs = self.w_embd(op_index)
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return torch.sum(torch.cat(log_probs)), torch.sum(torch.cat(entropys)), self.convert_structure(sampled_arch)
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class GenericNAS201Model(nn.Module):
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def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
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super(GenericNAS201Model, 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, 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._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, affine=affine, track_running_stats=track_running_stats), 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._num_edge = num_edge
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# algorithm related
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self.arch_parameters = nn.Parameter(1e-3*torch.randn(num_edge, len(search_space)))
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self._mode = None
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self.dynamic_cell = None
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self._tau = None
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self._algo = None
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self._drop_path = None
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self.verbose = False
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def set_algo(self, algo: Text):
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# used for searching
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assert self._algo is None, 'This functioin can only be called once.'
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self._algo = algo
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if algo == 'enas':
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self.controller = Controller(self.edge2index, self._op_names, self._max_nodes)
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else:
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self.arch_parameters = nn.Parameter( 1e-3*torch.randn(self._num_edge, len(self._op_names)) )
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if algo == 'gdas':
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self._tau = 10
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def set_cal_mode(self, mode, dynamic_cell=None):
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assert mode in ['gdas', 'enas', 'urs', 'joint', 'select', 'dynamic']
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self._mode = mode
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if mode == 'dynamic': self.dynamic_cell = deepcopy(dynamic_cell)
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else : self.dynamic_cell = None
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def set_drop_path(self, progress, drop_path_rate):
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if drop_path_rate is None:
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self._drop_path = None
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elif progress is None:
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self._drop_path = drop_path_rate
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else:
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self._drop_path = progress * drop_path_rate
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@property
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def mode(self):
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return self._mode
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@property
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def drop_path(self):
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return self._drop_path
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@property
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def weights(self):
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xlist = list(self._stem.parameters())
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xlist+= list(self._cells.parameters())
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xlist+= list(self.lastact.parameters())
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xlist+= 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):
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self._tau = tau
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@property
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def tau(self):
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return self._tau
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@property
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def alphas(self):
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if self._algo == 'enas':
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return list(self.controller.parameters())
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else:
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return [self.arch_parameters]
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@property
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def 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 show_alphas(self):
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with torch.no_grad():
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if self._algo == 'enas':
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return 'w_pred :\n{:}'.format(self.controller.w_pred.weight)
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else:
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return 'arch-parameters :\n{:}'.format(nn.functional.softmax(self.arch_parameters, dim=-1).cpu())
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def extra_repr(self):
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return ('{name}(C={_C}, Max-Nodes={_max_nodes}, N={_layerN}, L={_Layer}, alg={_algo})'.format(name=self.__class__.__name__, **self.__dict__))
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@property
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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 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 get_log_prob(self, arch):
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with torch.no_grad():
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logits = nn.functional.log_softmax(self.arch_parameters, dim=-1)
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select_logits = []
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for i, node_info in enumerate(arch.nodes):
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for op, xin in node_info:
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node_str = '{:}<-{:}'.format(i+1, xin)
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op_index = self._op_names.index(op)
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select_logits.append( logits[self.edge2index[node_str], op_index] )
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return sum(select_logits).item()
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def return_topK(self, K, use_random=False):
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archs = Structure.gen_all(self._op_names, self._max_nodes, False)
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pairs = [(self.get_log_prob(arch), arch) for arch in archs]
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if K < 0 or K >= len(archs): K = len(archs)
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if use_random:
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return random.sample(archs, K)
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else:
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sorted_pairs = sorted(pairs, key=lambda x: -x[0])
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return_pairs = [sorted_pairs[_][1] for _ in range(K)]
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return return_pairs
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def normalize_archp(self):
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if self.mode == 'gdas':
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while True:
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gumbels = -torch.empty_like(self.arch_parameters).exponential_().log()
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logits = (self.arch_parameters.log_softmax(dim=1) + gumbels) / self.tau
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probs = nn.functional.softmax(logits, dim=1)
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index = probs.max(-1, keepdim=True)[1]
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one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
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hardwts = one_h - probs.detach() + probs
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if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()):
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continue
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else: break
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with torch.no_grad():
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hardwts_cpu = hardwts.detach().cpu()
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return hardwts, hardwts_cpu, index, 'GUMBEL'
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else:
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alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
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index = alphas.max(-1, keepdim=True)[1]
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with torch.no_grad():
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alphas_cpu = alphas.detach().cpu()
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return alphas, alphas_cpu, index, 'SOFTMAX'
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def forward(self, inputs):
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alphas, alphas_cpu, index, verbose_str = self.normalize_archp()
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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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if self.mode == 'urs':
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feature = cell.forward_urs(feature)
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if self.verbose:
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verbose_str += '-forward_urs'
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elif self.mode == 'select':
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feature = cell.forward_select(feature, alphas_cpu)
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if self.verbose:
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verbose_str += '-forward_select'
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elif self.mode == 'joint':
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feature = cell.forward_joint(feature, alphas)
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if self.verbose:
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verbose_str += '-forward_joint'
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elif self.mode == 'dynamic':
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feature = cell.forward_dynamic(feature, self.dynamic_cell)
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if self.verbose:
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verbose_str += '-forward_dynamic'
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elif self.mode == 'gdas':
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feature = cell.forward_gdas(feature, alphas, index)
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if self.verbose:
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verbose_str += '-forward_gdas'
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else: raise ValueError('invalid mode={:}'.format(self.mode))
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else: feature = cell(feature)
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if self.drop_path is not None:
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feature = drop_path(feature, self.drop_path)
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if self.verbose and random.random() < 0.001:
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print(verbose_str)
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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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