115 lines
4.2 KiB
Python
115 lines
4.2 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##########################################################################
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# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
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##########################################################################
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import torch
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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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from .search_model_enas_utils import Controller
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class TinyNetworkENAS(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(TinyNetworkENAS, 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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# to maintain the sampled architecture
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self.sampled_arch = None
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def update_arch(self, _arch):
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if _arch is None:
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self.sampled_arch = None
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elif isinstance(_arch, Structure):
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self.sampled_arch = _arch
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elif isinstance(_arch, (list, tuple)):
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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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self.sampled_arch = Structure(genotypes)
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else:
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raise ValueError("invalid type of input architecture : {:}".format(_arch))
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return self.sampled_arch
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def create_controller(self):
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return Controller(len(self.edge2index), len(self.op_names))
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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 forward(self, inputs):
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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.forward_dynamic(feature, self.sampled_arch)
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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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