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train_search-wws.py
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train_search-wws.py
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import os
import sys
import time
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
import copy
from model_search import Network
from genotypes import PRIMITIVES
from genotypes import Genotype
import torchvision
import torchvision.transforms as transforms
from datasets.aircraft import FGVCAircraft
import datasets.tinyimagenet as tinyimagenet
import datasets.my_subset as my_subset
from sklearn.model_selection import train_test_split
from torch.utils.data import Subset
import collections
from datasets.my_subset import Subset2
from datasets.datasets import imagenet, dtd, birds, flower, omniglot
parser = argparse.ArgumentParser("cifar")
parser.add_argument('--workers', type=int, default=0, help='number of workers to load dataset')
parser.add_argument('--batch_size', type=int, default=96, help='batch size')
parser.add_argument('--learning_rate', type=float, default=0.025, help='init learning rate')
parser.add_argument('--learning_rate_min', type=float, default=0.0, help='min learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--weight_decay', type=float, default=3e-4, help='weight decay')
parser.add_argument('--report_freq', type=float, default=50, help='report frequency')
parser.add_argument('--epochs', type=int, default=25, help='num of training epochs')
parser.add_argument('--init_channels', type=int, default=16, help='num of init channels')
parser.add_argument('--layers', type=int, default=5, help='total number of layers')
parser.add_argument('--cutout', action='store_true', default=False, help='use cutout')
parser.add_argument('--cutout_length', type=int, default=16, help='cutout length')
parser.add_argument('--drop_path_prob', type=float, default=0.3, help='drop path probability')
parser.add_argument('--save', type=str, default='/tmp/checkpoints/', help='experiment path')
parser.add_argument('--seed', type=int, default=2, help='random seed')
parser.add_argument('--grad_clip', type=float, default=5, help='gradient clipping')
parser.add_argument('--train_portion', type=float, default=0.5, help='portion of training data')
parser.add_argument('--arch_learning_rate', type=float, default=6e-4, help='learning rate for arch encoding')
parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')
parser.add_argument('--tmp_data_dir', type=str, default='/tmp/cache/', help='temp data dir')
parser.add_argument('--note', type=str, default='try', help='note for this run')
parser.add_argument('--dropout_rate', action='append', default=[], help='dropout rate of skip connect')
parser.add_argument('--add_width', action='append', default=['0'], help='add channels')
parser.add_argument('--add_layers', action='append', default=['0'], help='add layers')
parser.add_argument('--cifar100', action='store_true', default=False, help='search with cifar100 dataset')
parser.add_argument('--dataset', type=str, default='CIFAR10', help='Select dataset: CIFAR10, CIFAR100, MNIST, FashionMNIST, SVHN')
parser.add_argument('--model_path', type=str, default='CIFAR10.pt', help='path of pretrained model')
parser.add_argument('--normal', type=str, default='CIFAR10', help='Select switch: CIFAR10, MNIST, SVHN')
parser.add_argument('--reduce', type=str, default='CIFAR10', help='Select switch: CIFAR10, MNIST, SVHN')
args = parser.parse_args()
args.save = '{}search-{}-{}'.format(args.save, args.note, time.strftime("%Y%m%d-%H%M%S"))
utils.create_exp_dir(args.save, scripts_to_save=glob.glob('*.py'))
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format=log_format, datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(args.save, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
if args.dataset == 'omniglot':
DATASET_CLASSES = 1623
elif args.dataset == 'Aircraft':
DATASET_CLASSES = 102
elif args.dataset == 'ImageNet':
DATASET_CLASSES = 200
elif args.dataset == 'dtd':
DATASET_CLASSES = 47
elif args.dataset == 'birds':
DATASET_CLASSES = 200
elif args.dataset == 'flower':
DATASET_CLASSES = 102
else:
raise ValueError(args.dataset)
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
# prepare dataset
if args.dataset == 'omniglot':
train_data = omniglot(args, "train")
val_data = omniglot(args, "val")
elif args.dataset == 'Aircraft':
train_transform, val_transform = utils._data_transforms(args, "Aircraft")
train_data = FGVCAircraft(root="/home/grobi/pdarts-v2/data/aircraft", split="train", download=True, transform=train_transform)
val_data = FGVCAircraft(root="/home/grobi/pdarts-v2/data/aircraft", split="val", download=True, transform=val_transform)
elif args.dataset == 'ImageNet':
train_data = imagenet(args, "train")
val_data = imagenet(args, "val")
elif args.dataset == 'dtd':
train_data = dtd(args, "train")
val_data = dtd(args, "val")
elif args.dataset == 'birds':
train_data = birds(args, "train")
val_data = birds(args, "val")
elif args.dataset == 'flower':
train_data = flower(args, "train")
val_data = flower(args, "val")
else:
raise ValueError(args.dataset)
input_channels = 3
logging.info('------- Dataset: --------: %s %i', args.dataset, input_channels)
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True,
pin_memory=True, num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
val_data, batch_size=args.batch_size, shuffle=False,
pin_memory=True, num_workers=args.workers)
# build Network
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
switches = []
for i in range(14):
switches.append([True for j in range(len(PRIMITIVES))])
switches_normal = copy.deepcopy(switches)
switches_reduce = copy.deepcopy(switches)
if len(args.dropout_rate) == 2:
add_layers = [6, 12]
eps_no_archs = [10, 10]
add_layers = [6, 12]
add_width = [0, 0]
num_to_keep = [3, 1]
num_to_drop = [2, 2]
drop_rate = [0.0, 0.7]
elif args.normal == 'img2_3':
add_layers = [12]
eps_no_archs = [10]
add_layers = [12]
add_width = [0]
num_to_keep = [1]
num_to_drop = [0]
drop_rate = [0.7]
elif len(args.dropout_rate) == 1:
add_layers = [12]
eps_no_archs = [10]
add_layers = [12]
add_width = [0]
num_to_keep = [1]
num_to_drop = [2]
drop_rate = args.dropout_rate
if args.normal == 'img2_2':
switches_normal = [[False, False, False, False, True, False, True, True], [True, False, False, True, False, False, True, False], [True, True, False, False, False, True, False, False], [True, False, False, False, False, True, True, False], [True, False, False, True, True, False, False, False], [False, False, False, False, True, True, False, True], [True, False, False, False, False, True, True, False], [True, False, False, True, False, True, False, False], [True, False, False, False, True, True, False, False], [False, False, False, False, True, True, True, False], [True, False, False, True, True, False, False, False], [True, False, False, False, False, True, True, False], [True, False, False, False, True, True, False, False], [True, False, False, False, True, True, False, False]]
elif args.normal == 'dtd2_2':
switches_normal = [[True, False, False, False, True, False, False, True], [True, False, False, False, True, False, True, False], [True, False, False, False, True, True, False, False], [True, False, False, False, True, True, False, False], [True, False, False, False, True, True, False, False], [True, False, False, False, False, False, True, True], [False, False, False, False, True, False, True, True], [True, False, False, False, False, False, True, True], [True, False, False, False, True, True, False, False], [True, False, False, False, True, False, False, True], [True, False, False, False, True, True, False, False], [False, False, False, False, True, True, False, True], [True, False, False, False, False, True, False, True], [True, False, False, False, True, True, False, False]]
elif args.normal == 'flower2_2':
switches_normal = [[False, False, False, False, True, True, True, False], [True, False, False, False, True, True, False, False], [False, True, False, False, False, True, False, True], [True, False, False, False, False, True, False, True], [False, False, False, False, False, True, True, True], [False, False, False, False, True, True, False, True], [False, True, False, False, False, True, False, True], [False, True, False, False, False, True, False, True], [True, False, False, False, False, True, False, True], [False, True, False, False, True, False, False, True], [True, True, False, False, False, False, True, False], [False, True, True, True, False, False, False, False], [True, False, False, True, False, True, False, False], [False, True, False, False, False, True, False, True]]
elif args.normal == 'flower2_2_skip':
switches_normal = [[False, True, False, False, True, True, False, False], [False, True, False, False, True, False, True, False], [True, True, False, False, True, False, False, False], [False, True, False, False, True, False, False, True], [True, True, False, True, False, False, False, False], [False, True, False, False, False, True, False, True], [True, False, False, False, False, False, True, True], [False, False, False, False, True, False, True, True], [False, True, False, False, False, True, False, True], [False, False, False, False, True, False, True, True], [False, True, False, False, True, False, False, True], [False, True, False, False, False, True, True, False], [False, True, True, True, False, False, False, False], [False, True, False, False, True, False, False, True]]
elif args.normal == 'meta':
switches_normal = [[False, True, False, True, True, False, False, False], [True, False, False, True, False, True, False, False], [True, False, False, False, True, True, False, False], [True, False, False, True, False, False, True, False], [True, False, False, True, False, True, False, False], [True, False, False, False, True, True, False, False], [False, False, False, True, True, True, False, False], [True, False, False, True, True, False, False, False], [True, False, False, False, True, True, False, False], [True, True, False, False, True, False, False, False], [True, False, False, True, False, True, False, False], [True, False, False, True, True, False, False, False], [True, False, False, False, True, False, False, True], [True, False, False, False, False, True, True, False]]
else:
raise ValueError(args.normal)
if args.reduce == 'img2_2':
switches_reduce = [[False, True, False, False, True, False, False, True], [True, True, False, False, True, False, False, False], [True, True, False, False, False, True, False, False], [True, False, True, False, False, True, False, False], [True, False, False, False, True, False, False, True], [True, True, False, False, False, False, False, True], [True, False, False, False, True, False, False, True], [True, False, False, False, False, True, False, True], [True, False, False, False, True, False, False, True], [False, True, False, False, True, False, True, False], [False, True, True, True, False, False, False, False], [False, True, False, True, False, True, False, False], [False, False, False, True, True, True, False, False], [True, False, False, False, True, True, False, False]]
elif args.normal == 'dtd2_2':
switches_reduce = [[True, False, False, True, False, True, False, False], [False, False, False, True, True, False, True, False], [False, False, False, False, True, True, False, True], [False, True, False, True, True, False, False, False], [True, True, False, False, True, False, False, False], [True, False, False, False, True, False, False, True], [False, False, True, False, True, False, False, True], [True, False, False, False, True, True, False, False], [False, False, False, False, True, True, False, True], [False, False, False, True, True, False, False, True], [False, False, True, False, True, False, True, False], [False, True, False, False, True, False, True, False], [True, False, False, False, True, False, False, True], [False, False, False, False, False, True, True, True]]
elif args.normal == 'flower2_2':
switches_reduce = [[True, False, False, True, True, False, False, False], [False, False, False, True, False, True, False, True], [True, False, False, True, False, False, True, False], [False, False, False, False, False, True, True, True], [False, False, False, True, False, True, False, True], [False, False, False, True, False, True, True, False], [False, True, True, False, False, False, False, True], [True, True, False, False, False, False, True, False], [False, True, False, False, True, False, False, True], [False, False, False, True, True, True, False, False], [False, True, False, False, True, False, False, True], [True, False, False, False, True, False, False, True], [False, True, False, False, True, False, False, True], [False, False, False, False, True, False, True, True]]
elif args.normal == 'flower2_2_skip':
switches_reduce = [[False, True, False, False, False, True, False, True], [False, False, True, False, False, True, False, True], [False, True, True, False, False, False, True, False], [True, False, False, True, True, False, False, False], [False, False, False, False, True, False, True, True], [False, True, False, False, False, True, False, True], [False, True, False, True, False, False, True, False], [True, True, False, False, False, True, False, False], [True, True, False, False, False, False, False, True], [False, True, True, False, True, False, False, False], [False, False, True, False, False, True, True, False], [True, False, False, False, True, False, True, False], [False, True, False, False, True, True, False, False], [False, False, False, True, True, False, False, True]]
elif args.normal == 'meta':
switches_reduce = [[False, False, False, True, False, True, False, True], [False, False, False, False, True, True, False, True], [True, False, False, True, False, True, False, False], [False, True, False, True, False, False, True, False], [True, False, False, False, True, False, True, False], [True, False, False, True, False, True, False, False], [False, False, False, False, True, False, True, True], [True, True, False, False, False, False, False, True], [False, False, False, False, True, True, False, True], [True, False, False, True, False, True, False, False], [False, False, True, False, False, True, True, False], [True, False, False, False, False, True, False, True], [False, False, False, False, True, True, True, False], [False, False, False, False, True, True, False, True]]
else:
raise ValueError(args.normal)
for sp in range(len(num_to_keep)):
if args.reduce == 'img2_2':
model = Network(args.init_channels + int(add_width[sp]), input_channels, 200, args.layers + int(add_layers[sp]), criterion, switches_normal=switches_normal, switches_reduce=switches_reduce, p=float(drop_rate[sp]))
model = nn.DataParallel(model)
try:
utils.load(model, "/home/grobi/pdarts-v2/log_pathsearch-imagenet2-20200806-215742/weights.pt")
model.module.classifier = nn.Linear(128, DATASET_CLASSES)
except:
print("egh!")
model = model.module
utils.load(model, "/home/grobi/pdarts-v2/log_pathsearch-imagenet2-20200806-215742/weights.pt")
model.module.classifier = nn.Linear(128, DATASET_CLASSES)
elif args.normal == 'dtd2_2':
model = Network(args.init_channels + int(add_width[sp]), input_channels, 47, args.layers + int(add_layers[sp]), criterion, switches_normal=switches_normal, switches_reduce=switches_reduce, p=float(drop_rate[sp]))
model = nn.DataParallel(model)
try:
utils.load(model, "/home/grobi/pdarts-v2/log_pathsearch-dtd-20200918-100748/weights.pt")
model.module.classifier = nn.Linear(128, DATASET_CLASSES)
except:
print("egh!")
model = model.module
utils.load(model, "/home/grobi/pdarts-v2/log_pathsearch-dtd-20200918-100748/weights.pt")
model.module.classifier = nn.Linear(128, DATASET_CLASSES)
elif args.normal == 'flower2_2':
model = Network(args.init_channels + int(add_width[sp]), input_channels, 102, args.layers + int(add_layers[sp]), criterion, switches_normal=switches_normal, switches_reduce=switches_reduce, p=float(drop_rate[sp]))
model = nn.DataParallel(model)
try:
utils.load(model, "/home/grobi/pdarts-v2/log_pathsearch-flower4-20201017-132056/weights.pt")
model.module.classifier = nn.Linear(128, DATASET_CLASSES)
except:
print("egh!")
model = model.module
utils.load(model, "/home/grobi/pdarts-v2/log_pathsearch-flower4-20201017-132056/weights.pt")
model.module.classifier = nn.Linear(128, DATASET_CLASSES)
else:
raise ValueError(args.normal)
model = model.cuda()
#print(model)
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
network_params = []
for k, v in model.named_parameters():
if not (k.endswith('alphas_normal') or k.endswith('alphas_reduce')):
network_params.append(v)
optimizer = torch.optim.SGD(
network_params,
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer_a = torch.optim.Adam(model.module.arch_parameters(),
lr=args.arch_learning_rate, betas=(0.5, 0.999), weight_decay=args.arch_weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
sm_dim = -1
epochs = args.epochs
eps_no_arch = eps_no_archs[sp]
scale_factor = 0.2
for epoch in range(epochs):
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('Epoch: %d lr: %e', epoch, lr)
epoch_start = time.time()
# training
if epoch < eps_no_arch:
model.module.p = float(drop_rate[sp]) * (epochs - epoch - 1) / epochs
model.module.update_p()
train_acc, train_obj = train(train_queue, valid_queue, model, network_params, criterion, optimizer, optimizer_a, lr, train_arch=False)
else:
model.module.p = float(drop_rate[sp]) * np.exp(-(epoch - eps_no_arch) * scale_factor)
model.module.update_p()
train_acc, train_obj = train(train_queue, valid_queue, model, network_params, criterion, optimizer, optimizer_a, lr, train_arch=True)
logging.info('Train_acc %f', train_acc)
epoch_duration = time.time() - epoch_start
logging.info('Epoch time: %ds', epoch_duration)
# validation
if epochs - epoch < 5:
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('Valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
print('------Dropping %d paths------' % num_to_drop[sp])
# Save switches info for s-c refinement.
if sp == len(num_to_keep) - 1:
switches_normal_2 = copy.deepcopy(switches_normal)
switches_reduce_2 = copy.deepcopy(switches_reduce)
# drop operations with low architecture weights
arch_param = model.module.arch_parameters()
normal_prob = F.softmax(arch_param[0], dim=sm_dim).data.cpu().numpy()
for i in range(14):
idxs = []
for j in range(len(PRIMITIVES)):
if switches_normal[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
# for the last stage, drop all Zero operations
drop = get_min_k_no_zero(normal_prob[i, :], idxs, num_to_drop[sp])
else:
drop = get_min_k(normal_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_normal[i][idxs[idx]] = False
reduce_prob = F.softmax(arch_param[1], dim=-1).data.cpu().numpy()
for i in range(14):
idxs = []
for j in range(len(PRIMITIVES)):
if switches_reduce[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
drop = get_min_k_no_zero(reduce_prob[i, :], idxs, num_to_drop[sp])
else:
drop = get_min_k(reduce_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_reduce[i][idxs[idx]] = False
logging.info('switches_normal = %s', switches_normal)
logging_switches(switches_normal)
logging.info('switches_reduce = %s', switches_reduce)
logging_switches(switches_reduce)
if sp == len(num_to_keep) - 1:
arch_param = model.module.arch_parameters()
normal_prob = F.softmax(arch_param[0], dim=sm_dim).data.cpu().numpy()
reduce_prob = F.softmax(arch_param[1], dim=sm_dim).data.cpu().numpy()
normal_final = [0 for idx in range(14)]
reduce_final = [0 for idx in range(14)]
# remove all Zero operations
for i in range(14):
if switches_normal_2[i][0] == True:
normal_prob[i][0] = 0
normal_final[i] = max(normal_prob[i])
if switches_reduce_2[i][0] == True:
reduce_prob[i][0] = 0
reduce_final[i] = max(reduce_prob[i])
# Generate Architecture, similar to DARTS
keep_normal = [0, 1]
keep_reduce = [0, 1]
n = 3
start = 2
for i in range(3):
end = start + n
tbsn = normal_final[start:end]
tbsr = reduce_final[start:end]
edge_n = sorted(range(n), key=lambda x: tbsn[x])
keep_normal.append(edge_n[-1] + start)
keep_normal.append(edge_n[-2] + start)
edge_r = sorted(range(n), key=lambda x: tbsr[x])
keep_reduce.append(edge_r[-1] + start)
keep_reduce.append(edge_r[-2] + start)
start = end
n = n + 1
# set switches according the ranking of arch parameters
for i in range(14):
if not i in keep_normal:
for j in range(len(PRIMITIVES)):
switches_normal[i][j] = False
if not i in keep_reduce:
for j in range(len(PRIMITIVES)):
switches_reduce[i][j] = False
# translate switches into genotype
genotype = parse_network(switches_normal, switches_reduce)
logging.info(genotype)
## restrict skipconnect (normal cell only)
logging.info('Restricting skipconnect...')
# generating genotypes with different numbers of skip-connect operations
for sks in range(0, 9):
max_sk = 8 - sks
num_sk = check_sk_number(switches_normal)
if not num_sk > max_sk:
continue
while num_sk > max_sk:
normal_prob = delete_min_sk_prob(switches_normal, switches_normal_2, normal_prob)
switches_normal = keep_1_on(switches_normal_2, normal_prob)
switches_normal = keep_2_branches(switches_normal, normal_prob)
num_sk = check_sk_number(switches_normal)
logging.info('Number of skip-connect: %d', max_sk)
genotype = parse_network(switches_normal, switches_reduce)
logging.info(genotype)
def train(train_queue, valid_queue, model, network_params, criterion, optimizer, optimizer_a, lr, train_arch=True):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
for step, (input, target) in enumerate(train_queue):
model.train()
n = input.size(0)
input = input.cuda()
target = target.cuda(non_blocking=True)
if train_arch:
# In the original implementation of DARTS, it is input_search, target_search = next(iter(valid_queue), which slows down
# the training when using PyTorch 0.4 and above.
try:
input_search, target_search = next(valid_queue_iter)
except:
valid_queue_iter = iter(valid_queue)
input_search, target_search = next(valid_queue_iter)
input_search = input_search.cuda()
target_search = target_search.cuda(non_blocking=True)
optimizer_a.zero_grad()
logits = model(input_search)
loss_a = criterion(logits, target_search)
loss_a.backward()
nn.utils.clip_grad_norm_(model.module.arch_parameters(), args.grad_clip)
optimizer_a.step()
optimizer.zero_grad()
logits = model(input)
loss = criterion(logits, target)
loss.backward()
nn.utils.clip_grad_norm_(network_params, args.grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % args.report_freq == 0:
logging.info('TRAIN Step: %03d Objs: %e R1: %f R5: %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = input.cuda()
target = target.cuda(non_blocking=True)
with torch.no_grad():
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg
def parse_network(switches_normal, switches_reduce):
def _parse_switches(switches):
n = 2
start = 0
gene = []
step = 4
for i in range(step):
end = start + n
for j in range(start, end):
for k in range(len(switches[j])):
if switches[j][k]:
gene.append((PRIMITIVES[k], j - start))
start = end
n = n + 1
return gene
gene_normal = _parse_switches(switches_normal)
gene_reduce = _parse_switches(switches_reduce)
concat = range(2, 6)
genotype = Genotype(
normal=gene_normal, normal_concat=concat,
reduce=gene_reduce, reduce_concat=concat
)
return genotype
def get_class_distribution(dataset_obj):
count_dict = {k:0 for k,v in dataset_obj.class_to_idx.items()}
for element in dataset_obj:
y_lbl = element[1]
y_lbl = idx2class[y_lbl]
count_dict[y_lbl] += 1
return count_dict
def get_min_k(input_in, k):
input = copy.deepcopy(input_in)
index = []
for i in range(k):
idx = np.argmin(input)
index.append(idx)
input[idx] = 1
return index
def get_min_k_no_zero(w_in, idxs, k):
w = copy.deepcopy(w_in)
index = []
if 0 in idxs:
zf = True
else:
zf = False
if zf:
w = w[1:]
index.append(0)
k = k - 1
for i in range(k):
idx = np.argmin(w)
w[idx] = 1
if zf:
idx = idx + 1
index.append(idx)
return index
def logging_switches(switches):
for i in range(len(switches)):
ops = []
for j in range(len(switches[i])):
if switches[i][j]:
ops.append(PRIMITIVES[j])
logging.info(ops)
def check_sk_number(switches):
count = 0
for i in range(len(switches)):
if switches[i][3]:
count = count + 1
return count
def delete_min_sk_prob(switches_in, switches_bk, probs_in):
def _get_sk_idx(switches_in, switches_bk, k):
if not switches_in[k][3]:
idx = -1
else:
idx = 0
for i in range(3):
if switches_bk[k][i]:
idx = idx + 1
return idx
probs_out = copy.deepcopy(probs_in)
sk_prob = [1.0 for i in range(len(switches_bk))]
for i in range(len(switches_in)):
idx = _get_sk_idx(switches_in, switches_bk, i)
if not idx == -1:
sk_prob[i] = probs_out[i][idx]
d_idx = np.argmin(sk_prob)
idx = _get_sk_idx(switches_in, switches_bk, d_idx)
probs_out[d_idx][idx] = 0.0
return probs_out
def keep_1_on(switches_in, probs):
switches = copy.deepcopy(switches_in)
for i in range(len(switches)):
idxs = []
for j in range(len(PRIMITIVES)):
if switches[i][j]:
idxs.append(j)
drop = get_min_k_no_zero(probs[i, :], idxs, 2)
for idx in drop:
switches[i][idxs[idx]] = False
return switches
def keep_2_branches(switches_in, probs):
switches = copy.deepcopy(switches_in)
final_prob = [0.0 for i in range(len(switches))]
for i in range(len(switches)):
final_prob[i] = max(probs[i])
keep = [0, 1]
n = 3
start = 2
for i in range(3):
end = start + n
tb = final_prob[start:end]
edge = sorted(range(n), key=lambda x: tb[x])
keep.append(edge[-1] + start)
keep.append(edge[-2] + start)
start = end
n = n + 1
for i in range(len(switches)):
if not i in keep:
for j in range(len(PRIMITIVES)):
switches[i][j] = False
return switches
if __name__ == '__main__':
start_time = time.time()
main()
end_time = time.time()
duration = end_time - start_time
logging.info('Total searching time: %ds', duration)