val_loss,
val_r_sq,
val_accu
)
# print loss
if epoch % PRINT_EVERY == 0:
print('='*20 + '\nEpoch %d / %d\n' % (epoch, NUM_EPOCHS) + '='*20)
print('[%s (%d %.1f%%)]' % (train_utils.time_since(START), epoch, float(epoch) / NUM_EPOCHS * 100))
print('[%s %0.5f, %s %0.5f, %s %0.5f]'% ('Train Loss: ', train_loss, ' R-sq: ', train_r_sq, ' Accu:', train_accu))
print('[%s %0.5f, %s %0.5f, %s %0.5f]'% ('Valid Loss: ', val_loss, ' R-sq: ', val_r_sq, ' Accu:', val_accu))
# save model if best validation loss
if val_loss < best_val_loss:
n = file_info + '_best'
train_utils.save(n, perf_model)
best_val_loss = val_loss
print("Saving...")
train_utils.save(file_info, perf_model, log_parameters)
except KeyboardInterrupt:
print("Saving before quit...")
train_utils.save(file_info, perf_model, log_parameters)
# RUN VALIDATION SET ON THE BEST MODEL
# read the best model
filename = file_info + '_best' + '_Reg'
if torch.cuda.is_available():
perf_model.cuda()
perf_model.load_state_dict(torch.load('saved/' + filename + '.pt'))
log_value('val_r_sq', val_r_sq, epoch)
log_value('train_accu', train_accu, epoch)
log_value('val_accu', val_accu, epoch)
log_value('train_accu2', train_accu2, epoch)
log_value('val_accu2', val_accu2, epoch)
#####
# print loss
if epoch % PRINT_EVERY == 0:
print('[%s (%d %.1f%%)]' % (train_utils.time_since(START), epoch, float(epoch) / NUM_EPOCHS * 100))
print('[%s %0.5f, %s %0.5f, %s %0.5f %0.5f]'% ('Train Loss: ', train_loss, ' R-sq: ', train_r_sq, ' Accu:', train_accu, train_accu2))
print('[%s %0.5f, %s %0.5f, %s %0.5f %0.5f]'% ('Valid Loss: ', val_loss, ' R-sq: ', val_r_sq, ' Accu:', val_accu, val_accu2))
# save model if best validation loss
if val_loss.item() < best_val_loss:
n = NAME + '_best'
train_utils.save(n, perf_model)
best_val_loss = val_loss.item()
best_epoch = epoch
# store the best r-squared value from training
if val_r_sq > best_valrsq:
best_valrsq = val_r_sq
if best_epoch < epoch - 200:
break
print("Saving...")
train_utils.save(NAME, perf_model)
except KeyboardInterrupt:
print("Saving before quit...")
train_utils.save(NAME, perf_model)
print('BEST R^2 VALUE: ' + str(best_valrsq))
def main(args):
s3_bucket = args.run.s3_bucket
log = os.path.join(os.getcwd(), "log.txt")
if s3_bucket is not None:
aws_utils.download_from_s3(log, s3_bucket, log)
train_utils.set_up_logging(log)
CIFAR_CLASSES = 10
if not torch.cuda.is_available():
logging.info("no gpu device available")
sys.exit(1)
try:
aws_utils.download_from_s3("cnn_genotypes.txt", s3_bucket,
"/tmp/cnn_genotypes.txt")
with open("/code/nas-theory/cnn/search_spaces/darts/genotypes.py",
"a") as f:
with open("/tmp/cnn_genotypes.txt") as archs:
f.write("\n")
for line in archs:
if "Genotype" in line:
f.write(line)
print("Downloaded genotypes from aws.")
except Exception as e:
print(e)
# Importing here because need to get the latest genotypes before importing.
from search_spaces.darts import genotypes
rng_seed = train_utils.RNGSeed(args.run.seed)
torch.cuda.set_device(args.run.gpu)
logging.info("gpu device = %d" % args.run.gpu)
logging.info("args = %s", args.pretty())
print(dir(genotypes))
genotype = eval("genotypes.%s" % args.train.arch)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
model = Network(
args.train.init_channels,
CIFAR_CLASSES,
args.train.layers,
args.train.auxiliary,
genotype,
)
model = model.cuda()
optimizer, scheduler = train_utils.setup_optimizer(model, args)
logging.info("param size = %fMB",
train_utils.count_parameters_in_MB(model))
total_params = sum(x.data.nelement() for x in model.parameters())
logging.info("Model total parameters: {}".format(total_params))
try:
start_epochs, _ = train_utils.load(os.getcwd(),
rng_seed,
model,
optimizer,
s3_bucket=s3_bucket)
scheduler.last_epoch = start_epochs - 1
except Exception as e:
print(e)
start_epochs = 0
num_train, num_classes, train_queue, valid_queue = train_utils.create_data_queues(
args, eval_split=True)
for epoch in range(start_epochs, args.run.epochs):
logging.info("epoch %d lr %e", epoch, scheduler.get_lr()[0])
model.drop_path_prob = args.train.drop_path_prob * epoch / args.run.epochs
train_acc, train_obj = train(args, train_queue, model, criterion,
optimizer)
logging.info("train_acc %f", train_acc)
valid_acc, valid_obj = train_utils.infer(
valid_queue, model, criterion, report_freq=args.run.report_freq)
logging.info("valid_acc %f", valid_acc)
train_utils.save(os.getcwd(),
epoch + 1,
rng_seed,
model,
optimizer,
s3_bucket=s3_bucket)
scheduler.step()
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
log = os.path.join(args.save, 'log.txt')
if args.s3_bucket is not None:
aws_utils.download_from_s3(log, args.s3_bucket, log)
rng_seed = train_utils.RNGSeed(args.seed, deterministic=False)
logging.info("args = %s", args)
#dataset_dir = '/cache/'
#pre.split_dataset(dataset_dir)
#sys.exit(1)
# dataset prepare
data_dir = os.path.join(args.tmp_data_dir, 'imagenet_search')
traindir = os.path.join(data_dir, 'train')
valdir = data_dir = os.path.join(data_dir, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
#dataset split
train_data1 = dset.ImageFolder(traindir, transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_data2 = dset.ImageFolder(valdir, transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
valid_data = dset.ImageFolder(valdir, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]))
num_train = len(train_data1)
num_val = len(train_data2)
print('# images to train network: %d' % num_train)
print('# images to validate network: %d' % num_val)
model = Network(args.init_channels, CLASSES, args.layers, criterion)
model = torch.nn.DataParallel(model)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
architect = Architect(model, criterion, args)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
history = []
try:
start_epochs, history = train_utils.load(
args.save, rng_seed, model, optimizer, args.s3_bucket
)
print(history)
scheduler.last_epoch = start_epochs - 1
except Exception as e:
print(e)
start_epochs = 0
test_queue = torch.utils.data.DataLoader(
valid_data,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=args.workers)
train_queue = torch.utils.data.DataLoader(
train_data1, batch_size=args.batch_size, shuffle=True,
pin_memory=True, num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
train_data2, batch_size=args.batch_size, shuffle=True,
pin_memory=True, num_workers=args.workers)
if args.debug:
train_queue = valid_queue
valid_queue = test_queue
lr=args.learning_rate
for epoch in range(start_epochs, args.epochs):
scheduler.step()
current_lr = scheduler.get_lr()[0]
logging.info('Epoch: %d lr: %e', epoch, current_lr)
if epoch < 5 and args.batch_size > 256:
for param_group in optimizer.param_groups:
param_group['lr'] = lr * (epoch + 1) / 5.0
logging.info('Warming-up Epoch: %d, LR: %e', epoch, lr * (epoch + 1) / 5.0)
print(optimizer)
genotype = model.module.genotype()
logging.info('genotype = %s', genotype)
arch_param = model.module.arch_parameters()
logging.info(arch_param[0])
logging.info(arch_param[1])
logging.info(arch_param[2])
logging.info(arch_param[3])
# training
train_acc, train_obj = train(train_queue, valid_queue, model, optimizer, architect, criterion, lr,epoch)
logging.info('Train_acc %f', train_acc)
# validation
if epoch>= 47:
valid_acc, valid_obj = infer(valid_queue, model, criterion)
#test_acc, test_obj = infer(test_queue, model, criterion)
logging.info('Valid_acc %f', valid_acc)
#logging.info('Test_acc %f', test_acc)
history.append([p.data.cpu().numpy() for p in model.module.arch_parameters()])
logging.info("saving checkpoint")
train_utils.save(args.save, epoch+1, rng_seed, model, optimizer, history, args.s3_bucket)
if args.s3_bucket is not None:
filename = "cnn_genotypes.txt"
aws_utils.download_from_s3(filename, args.s3_bucket, filename)
with open(filename, "a+") as f:
f.write("\n")
f.write("{}{} = {}".format('edarts', args.seed, genotype))
aws_utils.upload_to_s3(filename, args.s3_bucket, filename)
aws_utils.upload_to_s3(log, args.s3_bucket, log)
# print loss
if epoch % PRINT_EVERY == 0:
print('[%s (%d %.1f%%)]' % (train_utils.time_since(START), epoch, float(epoch) / NUM_EPOCHS * 100))
#print('[%s %0.5f, %s %0.5f, %s %0.5f %0.5f]'% ('Train Loss: ', train_loss, ' R-sq: ', train_r_sq, ' Accu:', train_accu, train_accu2))
#print('[%s %0.5f, %s %0.5f, %s %0.5f %0.5f]'% ('Valid Loss: ', val_loss, ' R-sq: ', val_r_sq, ' Accu:', val_accu, val_accu2))
if contrastive:
#print('[%s %0.5f, %s %0.5f, %s %0.5f]'%('Train Contrastive Loss: ', loss_contrastive_train,'Train CE Loss:', ce_loss_train, 'Train Accuracy: ', acc_contrastive_train))
#print('[%s %0.5f, %s %0.5f, %s %0.5f]'%('Validation Contrastive Loss: ', loss_contrastive_val,'Validation CE Loss:', ce_loss_val, 'Validation Accuracy: ', acc_contrastive_val))
print('[%s %0.5f]'%('Train Contrastive Loss: ', loss_contrastive_train))
print('[%s %0.5f]'%('Validation Contrastive Loss: ', loss_contrastive_val))
# save model if best validation accuracy
if loss_contrastive_val.item() < best_loss_contrastive_val: #acc_contrastive_val > best_acc_contrastive_val: #
n = 'pc_contrastive_runs/' + NAME + '_best'
train_utils.save(n, perf_model)
#best_acc_contrastive_val = acc_contrastive_val
best_loss_contrastive_val = loss_contrastive_val.item()
best_epoch = epoch
# store the best r-squared value from training
if val_r_sq > best_valrsq:
best_valrsq = val_r_sq
if best_epoch < epoch - 250 and earlystop:
break
train_utils.save('pc_contrastive_runs/'+NAME, perf_model)
else:
try:
perf_model.load_state_dict(torch.load('pc_contrastive_runs/' + NAME))
except:
pass
log_value('train_accu2', train_accu2, epoch)
log_value('val_accu2', val_accu2, epoch)
# print loss
if epoch % PRINT_EVERY == 0:
print('[%s (%d %.1f%%)]' % (train_utils.time_since(START), epoch,
float(epoch) / NUM_EPOCHS * 100))
print('[%s %0.5f, %s %0.5f, %s %0.5f %0.5f]' %
('Train Loss: ', train_loss, ' R-sq: ', train_r_sq, ' Accu:',
train_accu, train_accu2))
print('[%s %0.5f, %s %0.5f, %s %0.5f %0.5f]' %
('Valid Loss: ', val_loss, ' R-sq: ', val_r_sq, ' Accu:',
val_accu, val_accu2))
# save model if best validation loss
if val_loss < best_val_loss:
n = file_info + '_best'
train_utils.save(n, perf_model)
best_val_loss = val_loss
print("Saving...")
train_utils.save(file_info, perf_model)
except KeyboardInterrupt:
print("Saving before quit...")
train_utils.save(file_info, perf_model)
# test
# test of full length data
test_loss, test_r_sq, test_accu, test_accu2 = eval_utils.eval_model(
perf_model, criterion, tef, METRIC, MTYPE, CTYPE)
print('[%s %0.5f, %s %0.5f, %s %0.5f %0.5f]' %
('Testing Loss: ', test_loss, ' R-sq: ', test_r_sq, ' Accu:', test_accu,
test_accu2))
def main(args):
"""Performs NAS.
"""
np.set_printoptions(precision=3)
save_dir = os.getcwd()
log = os.path.join(save_dir, "log.txt")
# Setup SummaryWriter
summary_dir = os.path.join(save_dir, "summary")
if not os.path.exists(summary_dir):
os.mkdir(summary_dir)
writer = SummaryWriter(summary_dir)
# own writer that I use to keep track of interesting variables
own_writer = SummaryWriter(os.path.join(save_dir, 'tensorboard'))
if args.run.s3_bucket is not None:
aws_utils.download_from_s3(log, args.run.s3_bucket, log)
train_utils.copy_code_to_experiment_dir(
"/home/julienf/git/gaea_release/cnn", save_dir)
aws_utils.upload_directory(os.path.join(save_dir, "scripts"),
args.run.s3_bucket)
train_utils.set_up_logging(log)
if not torch.cuda.is_available():
logging.info("no gpu device available")
sys.exit(1)
torch.cuda.set_device(args.run.gpu)
logging.info("Search hyperparameters:")
#logging.info("gpu device = %d" % args.run.gpu)
logging.info(args.pretty())
# Set random seeds for random, numpy, torch and cuda
rng_seed = train_utils.RNGSeed(args.run.seed)
# Load respective architect
if args.search.method in ["edarts", "gdarts", "eedarts"]:
if args.search.fix_alphas:
from architect.architect_edarts_edge_only import (
ArchitectEDARTS as Architect, )
else:
from architect.architect_edarts import ArchitectEDARTS as Architect
elif args.search.method in ["darts", "fdarts"]:
from architect.architect_darts import ArchitectDARTS as Architect
elif args.search.method == "egdas":
from architect.architect_egdas import ArchitectEGDAS as Architect
else:
raise NotImplementedError
# Load respective search spaces
if args.search.search_space in ["darts", "darts_small"]:
from search_spaces.darts.model_search import DARTSNetwork as Network
elif "nas-bench-201" in args.search.search_space:
from search_spaces.nasbench_201.model_search import (
NASBENCH201Network as Network, )
elif args.search.search_space == "pcdarts":
from search_spaces.pc_darts.model_search import PCDARTSNetwork as Network
else:
raise NotImplementedError
if args.train.smooth_cross_entropy:
criterion = train_utils.cross_entropy_with_label_smoothing
else:
criterion = nn.CrossEntropyLoss()
#num_train, num_classes, train_queue, valid_queue = train_utils.create_data_queues(
# args
#)
num_classes, (train_queue, train_2_queue), valid_queue, test_queue, (
number_train, number_valid,
number_test) = train_utils.create_cifar10_data_queues_own(args)
logging.info(f"Dataset: {args.run.dataset}, num_classes: {num_classes}")
logging.info(f"Number of training images: {number_train}")
if args.search.single_level:
logging.info(
f"Number of validation images (unused during search): {number_valid}"
)
else:
logging.info(
f"Number of validation images (used during search): {number_valid}"
)
logging.info(
f"Number of test images (unused during search): {number_test}")
model = Network(
args.train.init_channels, num_classes, args.search.nodes,
args.train.layers, criterion, **{
"auxiliary": args.train.auxiliary,
"search_space_name": args.search.search_space,
"exclude_zero": args.search.exclude_zero,
"track_running_stats": args.search.track_running_stats,
})
#if args.run.dataset == 'cifar10':
# random_img = np.random.randint(0, 255, size=(1, 3, 32, 32))
# own_writer.add_graph(model, input_to_model=torch.from_numpy(random_img))
model = model.cuda()
logging.info("param size = %fMB",
train_utils.count_parameters_in_MB(model))
optimizer, scheduler = train_utils.setup_optimizer(model, args)
# TODO: separate args by model, architect, etc
# TODO: look into using hydra for config files
architect = Architect(model, args, writer)
# Try to load a previous checkpoint
try:
start_epochs, history, _, _ = train_utils.load(save_dir, rng_seed,
model, optimizer,
architect,
args.run.s3_bucket)
scheduler.last_epoch = start_epochs - 1
#(
# num_train,
# num_classes,
# train_queue,
# valid_queue,
#) = train_utils.create_data_queues(args)
# TODO: why are data queues reloaded?
num_classes, (train_queue, train_2_queue), valid_queue, test_queue, (
number_train, number_valid,
number_test) = train_utils.create_cifar10_data_queues_own(args)
logging.info(
'Resumed training from a previous checkpoint. Runtime measurement will be wrong.'
)
train_start_time = 0
except Exception as e:
logging.info(e)
start_epochs = 0
train_start_time = timer()
best_valid = 0 # for single-level search, corresponds to best train accuracy observed so far
epoch_best_valid = 0 # for single-level search, corresponds to the epoch of the best observed train accuracy so far
overall_visualization_time = 0 # don't count visualization into runtime
for epoch in range(start_epochs, args.run.epochs):
lr = scheduler.get_lr()[0]
logging.info(f"| Epoch: {epoch:3d} / {args.run.epochs} | lr: {lr} |")
model.drop_path_prob = args.train.drop_path_prob * epoch / args.run.epochs
# training returns top1, loss and top5
train_acc, train_obj, train_top5 = train(
args,
train_queue,
valid_queue if train_2_queue == None else
train_2_queue, # valid_queue for bi-level search, train_2_queue for single-level search
model,
architect,
criterion,
optimizer,
lr,
)
architect.baseline = train_obj
architect.update_history()
architect.log_vars(epoch, writer)
if "update_lr_state" in dir(scheduler):
scheduler.update_lr_state(train_obj)
logging.info(f"| train_acc: {train_acc} |")
# History tracking
for vs in [("alphas", architect.alphas), ("edges", architect.edges)]:
for ct in vs[1]:
v = vs[1][ct]
logging.info("{}-{}".format(vs[0], ct))
logging.info(v)
# Calling genotypes sets alphas to best arch for EGDAS and MEGDAS
# so calling here before infer.
genotype = architect.genotype()
logging.info("genotype = %s", genotype)
# log epoch values to tensorboard
own_writer.add_scalar('Loss/train', train_obj, epoch)
own_writer.add_scalar('Top1/train', train_acc, epoch)
own_writer.add_scalar('Top5/train', train_top5, epoch)
own_writer.add_scalar('lr', lr, epoch)
# visualize Genotype
start_visualization = timer()
genotype_graph_normal = visualize.plot(genotype.normal,
"",
return_type="graph",
output_format='png')
binary_normal = genotype_graph_normal.pipe()
stream_normal = io.BytesIO(binary_normal)
graph_normal = np.array(PIL.Image.open(stream_normal).convert("RGB"))
own_writer.add_image("Normal_Cell",
graph_normal,
epoch,
dataformats="HWC")
#del genotype_graph_normal
#del binary_normal
#del stream_normal
#del graph_normal
genotype_graph_reduce = visualize.plot(genotype.reduce,
"",
return_type="graph",
output_format='png')
binary_reduce = genotype_graph_reduce.pipe()
stream_reduce = io.BytesIO(binary_reduce)
graph_reduce = np.array(PIL.Image.open(stream_reduce).convert("RGB"))
own_writer.add_image("Reduce_Cell",
graph_reduce,
epoch,
dataformats="HWC")
#del genotype_graph_reduce
#del binary_reduce
#del stream_reduce
#del graph_reduce
end_visualization = timer()
overall_visualization_time += (end_visualization - start_visualization)
# log validation metrics, but don't utilize them for decisions during single-level search
valid_acc, valid_obj, valid_top5 = train_utils.infer(
valid_queue,
model,
criterion,
report_freq=args.run.report_freq,
discrete=args.search.discrete,
)
own_writer.add_scalar('Loss/valid', valid_obj, epoch)
own_writer.add_scalar('Top1/valid', valid_acc, epoch)
own_writer.add_scalar('Top5/valid', valid_top5, epoch)
logging.info(f"| valid_acc: {valid_acc} |")
if not args.search.single_level:
if valid_acc > best_valid:
best_valid = valid_acc
best_genotype = architect.genotype()
epoch_best_valid = epoch
else:
if train_acc > best_valid:
best_valid = train_acc
best_genotype = architect.genotype()
epoch_best_valid = epoch
train_utils.save(
save_dir,
epoch + 1,
rng_seed,
model,
optimizer,
architect,
save_history=True,
s3_bucket=args.run.s3_bucket,
)
scheduler.step()
train_end_time = timer()
logging.info(
f"Visualization of cells during search took a total of {timedelta(seconds=overall_visualization_time)} (hh:mm:ss)."
)
logging.info(f"This time is not included in the runtime given below.\n")
logging.info(
f"Training finished after {timedelta(seconds=((train_end_time - train_start_time) - overall_visualization_time))}(hh:mm:ss)."
) # Performing validation of final epoch...")
#valid_acc, valid_obj, valid_top5 = train_utils.infer(
# valid_queue,
# model,
# criterion,
# report_freq=args.run.report_freq,
# discrete=args.search.discrete,
#)
#own_writer.add_scalar('Loss/valid', valid_obj, args.run.epochs-1)
#own_writer.add_scalar('Top1/valid', valid_acc, args.run.epochs-1)
#own_writer.add_scalar('Top5/valid', valid_top5, args.run.epochs-1)
#logging.info(f"| valid_acc: {valid_acc} |")
#if not args.search.single_level:
# if valid_acc > best_valid:
# best_valid = valid_acc
# best_genotype = architect.genotype()
# epoch_best_valid = args.run.epochs-1
#else:
# if train_acc > best_valid:
# best_valid = train_acc
# best_genotype = architect.genotype()
# epoch_best_valid = args.run.epochs-1
if args.search.single_level:
logging.info((
f"\nBecause single-level search is performed, the best genotype was not selected according to the best achieved validation accuracy "
f"but according to the best train accuracy."))
logging.info(
f"\nOverall best found genotype with validation accuracy of {best_valid} (found in epoch {epoch_best_valid}):"
)
logging.info(f"{best_genotype}")
# dump best genotype to json file, so that we can load it during evaluation phase (in train_final.py)
genotype_dict = best_genotype._asdict()
for key, val in genotype_dict.items():
if type(val) == range:
genotype_dict[key] = [node for node in val]
if os.path.splitext(args.run.genotype_path)[1] != '.json':
args.run.genotype_path += '.json'
with open(args.run.genotype_path, 'w') as genotype_file:
json.dump(genotype_dict, genotype_file, indent=4)
logging.info(
f"Search finished. Dumped best genotype into {args.run.genotype_path}")
if args.run.s3_bucket is not None:
filename = "cnn_genotypes.txt"
aws_utils.download_from_s3(filename, args.run.s3_bucket, filename)
with open(filename, "a+") as f:
f.write("\n")
f.write("{}{}{}{} = {}".format(
args.search.search_space,
args.search.method,
args.run.dataset.replace("-", ""),
args.run.seed,
best_genotype,
))
aws_utils.upload_to_s3(filename, args.run.s3_bucket, filename)
aws_utils.upload_to_s3(log, args.run.s3_bucket, log)
def main(args):
np.set_printoptions(precision=3)
save_dir = os.getcwd()
log = os.path.join(save_dir, "log.txt")
# Setup SummaryWriter
summary_dir = os.path.join(save_dir, "summary")
if not os.path.exists(summary_dir):
os.mkdir(summary_dir)
writer = SummaryWriter(summary_dir)
if args.run.s3_bucket is not None:
aws_utils.download_from_s3(log, args.run.s3_bucket, log)
train_utils.copy_code_to_experiment_dir("/code/nas-theory/cnn",
save_dir)
aws_utils.upload_directory(os.path.join(save_dir, "scripts"),
args.run.s3_bucket)
train_utils.set_up_logging(log)
if not torch.cuda.is_available():
logging.info("no gpu device available")
sys.exit(1)
torch.cuda.set_device(args.run.gpu)
logging.info("gpu device = %d" % args.run.gpu)
logging.info("args = %s", args.pretty())
rng_seed = train_utils.RNGSeed(args.run.seed)
if args.search.method in ["edarts", "gdarts", "eedarts"]:
if args.search.fix_alphas:
from architect.architect_edarts_edge_only import (
ArchitectEDARTS as Architect, )
else:
from architect.architect_edarts import ArchitectEDARTS as Architect
elif args.search.method in ["darts", "fdarts"]:
from architect.architect_darts import ArchitectDARTS as Architect
elif args.search.method == "egdas":
from architect.architect_egdas import ArchitectEGDAS as Architect
else:
raise NotImplementedError
if args.search.search_space in ["darts", "darts_small"]:
from search_spaces.darts.model_search import DARTSNetwork as Network
elif "nas-bench-201" in args.search.search_space:
from search_spaces.nasbench_201.model_search import (
NASBENCH201Network as Network, )
elif args.search.search_space == "pcdarts":
from search_spaces.pc_darts.model_search import PCDARTSNetwork as Network
else:
raise NotImplementedError
if args.train.smooth_cross_entropy:
criterion = train_utils.cross_entropy_with_label_smoothing
else:
criterion = nn.CrossEntropyLoss()
num_train, num_classes, train_queue, valid_queue = train_utils.create_data_queues(
args)
print("dataset: {}, num_classes: {}".format(args.run.dataset, num_classes))
model = Network(
args.train.init_channels, num_classes, args.search.nodes,
args.train.layers, criterion, **{
"auxiliary": args.train.auxiliary,
"search_space_name": args.search.search_space,
"exclude_zero": args.search.exclude_zero,
"track_running_stats": args.search.track_running_stats,
})
model = model.cuda()
logging.info("param size = %fMB",
train_utils.count_parameters_in_MB(model))
optimizer, scheduler = train_utils.setup_optimizer(model, args)
# TODO: separate args by model, architect, etc
# TODO: look into using hydra for config files
architect = Architect(model, args, writer)
# Try to load a previous checkpoint
try:
start_epochs, history = train_utils.load(save_dir, rng_seed, model,
optimizer, architect,
args.run.s3_bucket)
scheduler.last_epoch = start_epochs - 1
(
num_train,
num_classes,
train_queue,
valid_queue,
) = train_utils.create_data_queues(args)
except Exception as e:
logging.info(e)
start_epochs = 0
best_valid = 0
for epoch in range(start_epochs, args.run.epochs):
lr = scheduler.get_lr()[0]
logging.info("epoch %d lr %e", epoch, lr)
model.drop_path_prob = args.train.drop_path_prob * epoch / args.run.epochs
# training
train_acc, train_obj = train(
args,
train_queue,
valid_queue,
model,
architect,
criterion,
optimizer,
lr,
)
architect.baseline = train_obj
architect.update_history()
architect.log_vars(epoch, writer)
if "update_lr_state" in dir(scheduler):
scheduler.update_lr_state(train_obj)
logging.info("train_acc %f", train_acc)
# History tracking
for vs in [("alphas", architect.alphas), ("edges", architect.edges)]:
for ct in vs[1]:
v = vs[1][ct]
logging.info("{}-{}".format(vs[0], ct))
logging.info(v)
# Calling genotypes sets alphas to best arch for EGDAS and MEGDAS
# so calling here before infer.
genotype = architect.genotype()
logging.info("genotype = %s", genotype)
if not args.search.single_level:
valid_acc, valid_obj = train_utils.infer(
valid_queue,
model,
criterion,
report_freq=args.run.report_freq,
discrete=args.search.discrete,
)
if valid_acc > best_valid:
best_valid = valid_acc
best_genotype = architect.genotype()
logging.info("valid_acc %f", valid_acc)
train_utils.save(
save_dir,
epoch + 1,
rng_seed,
model,
optimizer,
architect,
save_history=True,
s3_bucket=args.run.s3_bucket,
)
scheduler.step()
valid_acc, valid_obj = train_utils.infer(
valid_queue,
model,
criterion,
report_freq=args.run.report_freq,
discrete=args.search.discrete,
)
if valid_acc > best_valid:
best_valid = valid_acc
best_genotype = architect.genotype()
logging.info("valid_acc %f", valid_acc)
if args.run.s3_bucket is not None:
filename = "cnn_genotypes.txt"
aws_utils.download_from_s3(filename, args.run.s3_bucket, filename)
with open(filename, "a+") as f:
f.write("\n")
f.write("{}{}{}{} = {}".format(
args.search.search_space,
args.search.method,
args.run.dataset.replace("-", ""),
args.run.seed,
best_genotype,
))
aws_utils.upload_to_s3(filename, args.run.s3_bucket, filename)
aws_utils.upload_to_s3(log, args.run.s3_bucket, log)