xautodl/xautodl/models/cell_searchs/search_model_setn.py
2021-05-18 14:08:00 +00:00

179 lines
6.8 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, random
import torch.nn as nn
from copy import deepcopy
from ..cell_operations import ResNetBasicblock
from .search_cells import NAS201SearchCell as SearchCell
from .genotypes import Structure
class TinyNetworkSETN(nn.Module):
def __init__(
self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats
):
super(TinyNetworkSETN, self).__init__()
self._C = C
self._layerN = N
self.max_nodes = max_nodes
self.stem = nn.Sequential(
nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C)
)
layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
C_prev, num_edge, edge2index = C, None, None
self.cells = nn.ModuleList()
for index, (C_curr, reduction) in enumerate(
zip(layer_channels, layer_reductions)
):
if reduction:
cell = ResNetBasicblock(C_prev, C_curr, 2)
else:
cell = SearchCell(
C_prev,
C_curr,
1,
max_nodes,
search_space,
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 = cell.out_dim
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_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_cal_mode(self):
return self.mode
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_parameters]
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}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format(
name=self.__class__.__name__, **self.__dict__
)
def genotype(self):
genotypes = []
for i in range(1, self.max_nodes):
xlist = []
for j in range(i):
node_str = "{:}<-{:}".format(i, j)
with torch.no_grad():
weights = self.arch_parameters[self.edge2index[node_str]]
op_name = self.op_names[weights.argmax().item()]
xlist.append((op_name, j))
genotypes.append(tuple(xlist))
return Structure(genotypes)
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 get_log_prob(self, arch):
with torch.no_grad():
logits = nn.functional.log_softmax(self.arch_parameters, dim=-1)
select_logits = []
for i, node_info in enumerate(arch.nodes):
for op, xin in node_info:
node_str = "{:}<-{:}".format(i + 1, xin)
op_index = self.op_names.index(op)
select_logits.append(logits[self.edge2index[node_str], op_index])
return sum(select_logits).item()
def return_topK(self, K):
archs = Structure.gen_all(self.op_names, self.max_nodes, False)
pairs = [(self.get_log_prob(arch), arch) for arch in archs]
if K < 0 or K >= len(archs):
K = len(archs)
sorted_pairs = sorted(pairs, key=lambda x: -x[0])
return_pairs = [sorted_pairs[_][1] for _ in range(K)]
return return_pairs
def forward(self, inputs):
alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
with torch.no_grad():
alphas_cpu = alphas.detach().cpu()
feature = self.stem(inputs)
for i, cell in enumerate(self.cells):
if isinstance(cell, SearchCell):
if self.mode == "urs":
feature = cell.forward_urs(feature)
elif self.mode == "select":
feature = cell.forward_select(feature, alphas_cpu)
elif self.mode == "joint":
feature = cell.forward_joint(feature, alphas)
elif self.mode == "dynamic":
feature = cell.forward_dynamic(feature, self.dynamic_cell)
else:
raise ValueError("invalid mode={:}".format(self.mode))
else:
feature = cell(feature)
out = self.lastact(feature)
out = self.global_pooling(out)
out = out.view(out.size(0), -1)
logits = self.classifier(out)
return out, logits