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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
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import torch
import torch.nn as nn
from copy import deepcopy
from typing import List, Text, Dict
from .search_cells import NASNetSearchCell as SearchCell
# The macro structure is based on NASNet
class NASNetworkSETN(nn.Module):
def __init__(
self,
C: int,
N: int,
steps: int,
multiplier: int,
stem_multiplier: int,
num_classes: int,
search_space: List[Text],
affine: bool,
track_running_stats: bool,
):
super(NASNetworkSETN, self).__init__()
self._C = C
self._layerN = N
self._steps = steps
self._multiplier = multiplier
self.stem = nn.Sequential(
nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(C * stem_multiplier),
)
# config for each layer
layer_channels = (
[C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1)
)
layer_reductions = (
[False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1)
)
num_edge, edge2index = None, None
C_prev_prev, C_prev, C_curr, reduction_prev = (
C * stem_multiplier,
C * stem_multiplier,
C,
False,
)
self.cells = nn.ModuleList()
for index, (C_curr, reduction) in enumerate(
zip(layer_channels, layer_reductions)
):
cell = SearchCell(
search_space,
steps,
multiplier,
C_prev_prev,
C_prev,
C_curr,
reduction,
reduction_prev,
affine,
track_running_stats,
)
if num_edge is None:
num_edge, edge2index = cell.num_edges, cell.edge2index
else:
assert (
num_edge == cell.num_edges and edge2index == cell.edge2index
), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
self.cells.append(cell)
C_prev_prev, C_prev, reduction_prev = C_prev, multiplier * C_curr, reduction
self.op_names = deepcopy(search_space)
self._Layer = len(self.cells)
self.edge2index = edge2index
self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
self.global_pooling = nn.AdaptiveAvgPool2d(1)
self.classifier = nn.Linear(C_prev, num_classes)
self.arch_normal_parameters = nn.Parameter(
1e-3 * torch.randn(num_edge, len(search_space))
)
self.arch_reduce_parameters = nn.Parameter(
1e-3 * torch.randn(num_edge, len(search_space))
)
self.mode = "urs"
self.dynamic_cell = None
def set_cal_mode(self, mode, dynamic_cell=None):
assert mode in ["urs", "joint", "select", "dynamic"]
self.mode = mode
if mode == "dynamic":
self.dynamic_cell = deepcopy(dynamic_cell)
else:
self.dynamic_cell = None
def get_weights(self):
xlist = list(self.stem.parameters()) + list(self.cells.parameters())
xlist += list(self.lastact.parameters()) + list(
self.global_pooling.parameters()
)
xlist += list(self.classifier.parameters())
return xlist
def get_alphas(self):
return [self.arch_normal_parameters, self.arch_reduce_parameters]
def show_alphas(self):
with torch.no_grad():
A = "arch-normal-parameters :\n{:}".format(
nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu()
)
B = "arch-reduce-parameters :\n{:}".format(
nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu()
)
return "{:}\n{:}".format(A, B)
def get_message(self):
string = self.extra_repr()
for i, cell in enumerate(self.cells):
string += "\n {:02d}/{:02d} :: {:}".format(
i, len(self.cells), cell.extra_repr()
)
return string
def extra_repr(self):
return "{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})".format(
name=self.__class__.__name__, **self.__dict__
)
def dync_genotype(self, use_random=False):
genotypes = []
with torch.no_grad():
alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
for i in range(1, self.max_nodes):
xlist = []
for j in range(i):
node_str = "{:}<-{:}".format(i, j)
if use_random:
op_name = random.choice(self.op_names)
else:
weights = alphas_cpu[self.edge2index[node_str]]
op_index = torch.multinomial(weights, 1).item()
op_name = self.op_names[op_index]
xlist.append((op_name, j))
genotypes.append(tuple(xlist))
return Structure(genotypes)
def genotype(self):
def _parse(weights):
gene = []
for i in range(self._steps):
edges = []
for j in range(2 + i):
node_str = "{:}<-{:}".format(i, j)
ws = weights[self.edge2index[node_str]]
for k, op_name in enumerate(self.op_names):
if op_name == "none":
continue
edges.append((op_name, j, ws[k]))
edges = sorted(edges, key=lambda x: -x[-1])
selected_edges = edges[:2]
gene.append(tuple(selected_edges))
return gene
with torch.no_grad():
gene_normal = _parse(
torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy()
)
gene_reduce = _parse(
torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy()
)
return {
"normal": gene_normal,
"normal_concat": list(
range(2 + self._steps - self._multiplier, self._steps + 2)
),
"reduce": gene_reduce,
"reduce_concat": list(
range(2 + self._steps - self._multiplier, self._steps + 2)
),
}
def forward(self, inputs):
normal_hardwts = nn.functional.softmax(self.arch_normal_parameters, dim=-1)
reduce_hardwts = nn.functional.softmax(self.arch_reduce_parameters, dim=-1)
s0 = s1 = self.stem(inputs)
for i, cell in enumerate(self.cells):
# [TODO]
raise NotImplementedError
if cell.reduction:
hardwts, index = reduce_hardwts, reduce_index
else:
hardwts, index = normal_hardwts, normal_index
s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
out = self.lastact(s1)
out = self.global_pooling(out)
out = out.view(out.size(0), -1)
logits = self.classifier(out)
return out, logits