Prototype generic nas model.
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D-X-Y
@@ -20,7 +20,7 @@ from .cell_searchs import CellStructure, CellArchitectures
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def get_cell_based_tiny_net(config):
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if isinstance(config, dict): config = dict2config(config, None) # to support the argument being a dict
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super_type = getattr(config, 'super_type', 'basic')
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group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS', 'RANDOM']
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group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS', 'RANDOM', 'generic']
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if super_type == 'basic' and config.name in group_names:
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from .cell_searchs import nas201_super_nets as nas_super_nets
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try:
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@@ -7,6 +7,7 @@ from .search_model_gdas import TinyNetworkGDAS
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from .search_model_setn import TinyNetworkSETN
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from .search_model_enas import TinyNetworkENAS
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from .search_model_random import TinyNetworkRANDOM
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from .generic_model import GenericNAS201Model
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from .genotypes import Structure as CellStructure, architectures as CellArchitectures
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# NASNet-based macro structure
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from .search_model_gdas_nasnet import NASNetworkGDAS
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@@ -18,7 +19,8 @@ nas201_super_nets = {'DARTS-V1': TinyNetworkDarts,
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"GDAS": TinyNetworkGDAS,
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"SETN": TinyNetworkSETN,
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"ENAS": TinyNetworkENAS,
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"RANDOM": TinyNetworkRANDOM}
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"RANDOM": TinyNetworkRANDOM,
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"generic": GenericNAS201Model}
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nasnet_super_nets = {"GDAS": NASNetworkGDAS,
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"DARTS": NASNetworkDARTS}
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200
lib/models/cell_searchs/generic_model.py
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200
lib/models/cell_searchs/generic_model.py
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@@ -0,0 +1,200 @@
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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 ..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 .search_model_enas_utils import Controller
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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), 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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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(len(self.edge2index), len(self._op_names))
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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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@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 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 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):
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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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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
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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
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def forward(self, inputs):
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alphas, alphas_cpu, index = 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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elif self.mode == 'select':
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feature = cell.forward_select(feature, alphas_cpu)
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elif self.mode == 'joint':
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feature = cell.forward_joint(feature, alphas)
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elif self.mode == 'dynamic':
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feature = cell.forward_dynamic(feature, self.dynamic_cell)
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elif self.mode == 'gdas':
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feature = cell.forward_gdas(feature, alphas, index)
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else: raise ValueError('invalid mode={:}'.format(self.mode))
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else: 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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@@ -1,5 +1,5 @@
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from .evaluation_utils import obtain_accuracy
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from .gpu_manager import GPUManager
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from .flop_benchmark import get_model_infos
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from .flop_benchmark import get_model_infos, count_parameters_in_MB
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from .affine_utils import normalize_points, denormalize_points
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from .affine_utils import identity2affine, solve2theta, affine2image
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