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D-X-Y
362
xautodl/models/cell_searchs/generic_model.py
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362
xautodl/models/cell_searchs/generic_model.py
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#####################################################
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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__(
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self,
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edge2index,
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op_names,
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max_nodes,
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lstm_size=32,
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lstm_num_layers=2,
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tanh_constant=2.5,
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temperature=5.0,
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):
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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(
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"input_vars", nn.Parameter(torch.Tensor(1, 1, lstm_size))
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)
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self.w_lstm = nn.LSTM(
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input_size=self.lstm_size,
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hidden_size=self.lstm_size,
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num_layers=self.lstm_N,
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)
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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 (
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torch.sum(torch.cat(log_probs)),
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torch.sum(torch.cat(entropys)),
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self.convert_structure(sampled_arch),
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)
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class GenericNAS201Model(nn.Module):
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def __init__(
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self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats
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):
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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), nn.BatchNorm2d(C)
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)
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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(
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zip(layer_channels, layer_reductions)
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):
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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(
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C_prev,
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C_curr,
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1,
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max_nodes,
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search_space,
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affine,
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track_running_stats,
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)
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if num_edge is None:
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num_edge, edge2index = cell.num_edges, cell.edge2index
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else:
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assert (
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num_edge == cell.num_edges and edge2index == cell.edge2index
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), "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(
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nn.BatchNorm2d(
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C_prev, affine=affine, track_running_stats=track_running_stats
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),
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nn.ReLU(inplace=True),
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)
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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(
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1e-3 * torch.randn(num_edge, len(search_space))
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)
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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(
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self.edge2index, self._op_names, self._max_nodes
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)
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else:
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self.arch_parameters = nn.Parameter(
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1e-3 * torch.randn(self._num_edge, len(self._op_names))
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)
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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":
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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 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(
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i, len(self._cells), cell.extra_repr()
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)
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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(
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nn.functional.softmax(self.arch_parameters, dim=-1).cpu()
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)
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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(
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name=self.__class__.__name__, **self.__dict__
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)
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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):
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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 (
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(torch.isinf(gumbels).any())
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or (torch.isinf(probs).any())
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or (torch.isnan(probs).any())
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):
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continue
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else:
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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:
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raise ValueError("invalid mode={:}".format(self.mode))
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else:
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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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