206 lines
7.4 KiB
Python
206 lines
7.4 KiB
Python
#####################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
|
|
######################################################################################
|
|
# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
|
|
######################################################################################
|
|
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
|