179 lines
6.8 KiB
Python
179 lines
6.8 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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######################################################################################
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# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
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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 ..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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class TinyNetworkSETN(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(TinyNetworkSETN, 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(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(
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1e-3 * torch.randn(num_edge, len(search_space))
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)
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self.mode = "urs"
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self.dynamic_cell = None
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def set_cal_mode(self, mode, dynamic_cell=None):
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assert mode in ["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 get_cal_mode(self):
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return self.mode
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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(
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self.global_pooling.parameters()
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)
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xlist += list(self.classifier.parameters())
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return xlist
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def get_alphas(self):
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return [self.arch_parameters]
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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(
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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 extra_repr(self):
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return "{name}(C={_C}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format(
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name=self.__class__.__name__, **self.__dict__
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)
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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):
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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 forward(self, inputs):
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alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
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with torch.no_grad():
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alphas_cpu = alphas.detach().cpu()
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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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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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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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