198 lines
6.9 KiB
Python
198 lines
6.9 KiB
Python
###########################################################################
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# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
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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 .search_cells import NASNetSearchCell as SearchCell
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# The macro structure is based on NASNet
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class NASNetworkGDAS(nn.Module):
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def __init__(
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self,
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C,
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N,
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steps,
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multiplier,
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stem_multiplier,
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num_classes,
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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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super(NASNetworkGDAS, self).__init__()
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self._C = C
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self._layerN = N
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self._steps = steps
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self._multiplier = multiplier
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self.stem = nn.Sequential(
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nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(C * stem_multiplier),
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)
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# config for each layer
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layer_channels = (
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[C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1)
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)
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layer_reductions = (
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[False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1)
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)
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num_edge, edge2index = None, None
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C_prev_prev, C_prev, C_curr, reduction_prev = (
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C * stem_multiplier,
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C * stem_multiplier,
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C,
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False,
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)
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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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cell = SearchCell(
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search_space,
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steps,
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multiplier,
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C_prev_prev,
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C_prev,
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C_curr,
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reduction,
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reduction_prev,
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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_prev, C_prev, reduction_prev = C_prev, multiplier * C_curr, reduction
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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_normal_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.arch_reduce_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.tau = 10
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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 set_tau(self, tau):
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self.tau = tau
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def get_tau(self):
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return self.tau
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def get_alphas(self):
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return [self.arch_normal_parameters, self.arch_reduce_parameters]
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def show_alphas(self):
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with torch.no_grad():
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A = "arch-normal-parameters :\n{:}".format(
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nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu()
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)
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B = "arch-reduce-parameters :\n{:}".format(
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nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu()
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)
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return "{:}\n{:}".format(A, B)
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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}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, 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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def _parse(weights):
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gene = []
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for i in range(self._steps):
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edges = []
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for j in range(2 + i):
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node_str = "{:}<-{:}".format(i, j)
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ws = weights[self.edge2index[node_str]]
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for k, op_name in enumerate(self.op_names):
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if op_name == "none":
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continue
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edges.append((op_name, j, ws[k]))
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edges = sorted(edges, key=lambda x: -x[-1])
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selected_edges = edges[:2]
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gene.append(tuple(selected_edges))
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return gene
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with torch.no_grad():
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gene_normal = _parse(
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torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy()
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)
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gene_reduce = _parse(
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torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy()
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)
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return {
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"normal": gene_normal,
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"normal_concat": list(
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range(2 + self._steps - self._multiplier, self._steps + 2)
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),
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"reduce": gene_reduce,
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"reduce_concat": list(
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range(2 + self._steps - self._multiplier, self._steps + 2)
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),
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}
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def forward(self, inputs):
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def get_gumbel_prob(xins):
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while True:
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gumbels = -torch.empty_like(xins).exponential_().log()
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logits = (xins.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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return hardwts, index
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normal_hardwts, normal_index = get_gumbel_prob(self.arch_normal_parameters)
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reduce_hardwts, reduce_index = get_gumbel_prob(self.arch_reduce_parameters)
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s0 = s1 = self.stem(inputs)
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for i, cell in enumerate(self.cells):
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if cell.reduction:
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hardwts, index = reduce_hardwts, reduce_index
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else:
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hardwts, index = normal_hardwts, normal_index
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s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
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out = self.lastact(s1)
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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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