update

CDARTS/_init_paths.py

@@ -0,0 +1,19 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os.path as osp
+import sys
+
+
+def add_path(path):
+    if path not in sys.path:
+        sys.path.insert(0, path)
+
+
+this_dir = osp.dirname(__file__)
+lib_path = osp.join(this_dir, '..', 'lib')
+add_path(lib_path)
+
+lib_path = osp.join(this_dir, '..')
+add_path(lib_path)

CDARTS/cells/cifar_genotype.json

@@ -0,0 +1 @@
+{"0": "Genotype(normal=[[('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 2)], [('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('skip_connect', 0), ('skip_connect', 2)]], normal_concat=range(2, 6), reduce=[[('sep_conv_5x5', 1), ('max_pool_3x3', 0)], [('dil_conv_5x5', 2), ('skip_connect', 0)], [('sep_conv_3x3', 0), ('sep_conv_5x5', 1)], [('sep_conv_3x3', 0), ('dil_conv_5x5', 2)]], reduce_concat=range(2, 6))", "1": "Genotype(normal=[[('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 2)], [('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('skip_connect', 0), ('skip_connect', 2)]], normal_concat=range(2, 6), reduce=[[('sep_conv_5x5', 1), ('max_pool_3x3', 0)], [('dil_conv_5x5', 2), ('skip_connect', 0)], [('sep_conv_3x3', 0), ('sep_conv_5x5', 1)], [('sep_conv_3x3', 0), ('dil_conv_5x5', 2)]], reduce_concat=range(2, 6))", "2": "Genotype(normal=[[('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 2)], [('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('skip_connect', 0), ('skip_connect', 2)]], normal_concat=range(2, 6), reduce=[[('sep_conv_5x5', 1), ('max_pool_3x3', 0)], [('dil_conv_5x5', 2), ('skip_connect', 0)], [('sep_conv_3x3', 0), ('sep_conv_5x5', 1)], [('sep_conv_3x3', 0), ('dil_conv_5x5', 2)]], reduce_concat=range(2, 6))"}

CDARTS/cells/dartsv1_genotype.json

@@ -0,0 +1 @@
+{"0": "Genotype(normal=[[('sep_conv_3x3', 1), ('sep_conv_3x3', 0)], [('skip_connect', 0), ('sep_conv_3x3', 1)], [('skip_connect', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 0), ('skip_connect', 2)]], normal_concat=[2, 3, 4, 5], reduce=[[('max_pool_3x3', 0), ('max_pool_3x3', 1)], [('skip_connect', 2), ('max_pool_3x3', 0)], [('max_pool_3x3', 0), ('skip_connect', 2)], [('skip_connect', 2), ('avg_pool_3x3', 0)]], reduce_concat=[2, 3, 4, 5])", "1": "Genotype(normal=[[('sep_conv_3x3', 1), ('sep_conv_3x3', 0)], [('skip_connect', 0), ('sep_conv_3x3', 1)], [('skip_connect', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 0), ('skip_connect', 2)]], normal_concat=[2, 3, 4, 5], reduce=[[('max_pool_3x3', 0), ('max_pool_3x3', 1)], [('skip_connect', 2), ('max_pool_3x3', 0)], [('max_pool_3x3', 0), ('skip_connect', 2)], [('skip_connect', 2), ('avg_pool_3x3', 0)]], reduce_concat=[2, 3, 4, 5])", "2": "Genotype(normal=[[('sep_conv_3x3', 1), ('sep_conv_3x3', 0)], [('skip_connect', 0), ('sep_conv_3x3', 1)], [('skip_connect', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 0), ('skip_connect', 2)]], normal_concat=[2, 3, 4, 5], reduce=[[('max_pool_3x3', 0), ('max_pool_3x3', 1)], [('skip_connect', 2), ('max_pool_3x3', 0)], [('max_pool_3x3', 0), ('skip_connect', 2)], [('skip_connect', 2), ('avg_pool_3x3', 0)]], reduce_concat=[2, 3, 4, 5])"}

CDARTS/cells/dartsv2_genotype.json

@@ -0,0 +1 @@
+{"0": "Genotype(normal=[[('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('skip_connect', 0)], [('skip_connect', 0), ('dil_conv_3x3', 2)]], normal_concat=[2, 3, 4, 5], reduce=[[('max_pool_3x3', 0), ('max_pool_3x3', 1)], [('skip_connect', 2), ('max_pool_3x3', 1)], [('max_pool_3x3', 0), ('skip_connect', 2)], [('skip_connect', 2), ('max_pool_3x3', 1)]], reduce_concat=[2, 3, 4, 5])", "1": "Genotype(normal=[[('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('skip_connect', 0)], [('skip_connect', 0), ('dil_conv_3x3', 2)]], normal_concat=[2, 3, 4, 5], reduce=[[('max_pool_3x3', 0), ('max_pool_3x3', 1)], [('skip_connect', 2), ('max_pool_3x3', 1)], [('max_pool_3x3', 0), ('skip_connect', 2)], [('skip_connect', 2), ('max_pool_3x3', 1)]], reduce_concat=[2, 3, 4, 5])", "2": "Genotype(normal=[[('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 0), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('skip_connect', 0)], [('skip_connect', 0), ('dil_conv_3x3', 2)]], normal_concat=[2, 3, 4, 5], reduce=[[('max_pool_3x3', 0), ('max_pool_3x3', 1)], [('skip_connect', 2), ('max_pool_3x3', 1)], [('max_pool_3x3', 0), ('skip_connect', 2)], [('skip_connect', 2), ('max_pool_3x3', 1)]], reduce_concat=[2, 3, 4, 5])"}

CDARTS/cells/imagenet_genotype.json

@@ -0,0 +1 @@
+{"0": "Genotype(normal=[[('sep_conv_3x3', 1), ('sep_conv_3x3', 0)], [('sep_conv_3x3', 0), ('sep_conv_5x5', 2)], [('sep_conv_5x5', 2), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('sep_conv_5x5', 4)]], normal_concat=range(2, 6), reduce=[[('sep_conv_5x5', 1), ('avg_pool_3x3', 0)], [('max_pool_3x3', 1), ('dil_conv_5x5', 2)], [('sep_conv_5x5', 1), ('dil_conv_5x5', 2)], [('sep_conv_3x3', 3), ('dil_conv_3x3', 4)]], reduce_concat=range(2, 6))", "1": "Genotype(normal=[[('sep_conv_3x3', 1), ('sep_conv_3x3', 0)], [('sep_conv_3x3', 0), ('sep_conv_5x5', 2)], [('sep_conv_5x5', 2), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('sep_conv_5x5', 4)]], normal_concat=range(2, 6), reduce=[[('sep_conv_5x5', 1), ('avg_pool_3x3', 0)], [('max_pool_3x3', 1), ('dil_conv_5x5', 2)], [('sep_conv_5x5', 1), ('dil_conv_5x5', 2)], [('sep_conv_3x3', 3), ('dil_conv_3x3', 4)]], reduce_concat=range(2, 6))", "2": "Genotype(normal=[[('sep_conv_3x3', 1), ('sep_conv_3x3', 0)], [('sep_conv_3x3', 0), ('sep_conv_5x5', 2)], [('sep_conv_5x5', 2), ('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('sep_conv_5x5', 4)]], normal_concat=range(2, 6), reduce=[[('sep_conv_5x5', 1), ('avg_pool_3x3', 0)], [('max_pool_3x3', 1), ('dil_conv_5x5', 2)], [('sep_conv_5x5', 1), ('dil_conv_5x5', 2)], [('sep_conv_3x3', 3), ('dil_conv_3x3', 4)]], reduce_concat=range(2, 6))"}

CDARTS/cells/pcdarts_cifar_genotype.json

@@ -0,0 +1 @@
+{"0": "Genotype(normal=[[('sep_conv_3x3', 1), ('skip_connect', 0)], [('sep_conv_3x3', 0), ('dil_conv_3x3', 1)], [('sep_conv_5x5', 0), ('sep_conv_3x3', 1)], [('avg_pool_3x3', 0), ('dil_conv_3x3', 1)]], normal_concat=range(2, 6), reduce=[[('sep_conv_5x5', 1), ('max_pool_3x3', 0)], [('sep_conv_5x5', 1), ('sep_conv_5x5', 2)], [('sep_conv_3x3', 0), ('sep_conv_3x3', 3)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 2)]], reduce_concat=range(2, 6))", "1": "Genotype(normal=[[('sep_conv_3x3', 1), ('skip_connect', 0)], [('sep_conv_3x3', 0), ('dil_conv_3x3', 1)], [('sep_conv_5x5', 0), ('sep_conv_3x3', 1)], [('avg_pool_3x3', 0), ('dil_conv_3x3', 1)]], normal_concat=range(2, 6), reduce=[[('sep_conv_5x5', 1), ('max_pool_3x3', 0)], [('sep_conv_5x5', 1), ('sep_conv_5x5', 2)], [('sep_conv_3x3', 0), ('sep_conv_3x3', 3)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 2)]], reduce_concat=range(2, 6))", "2": "Genotype(normal=[[('sep_conv_3x3', 1), ('skip_connect', 0)], [('sep_conv_3x3', 0), ('dil_conv_3x3', 1)], [('sep_conv_5x5', 0), ('sep_conv_3x3', 1)], [('avg_pool_3x3', 0), ('dil_conv_3x3', 1)]], normal_concat=range(2, 6), reduce=[[('sep_conv_5x5', 1), ('max_pool_3x3', 0)], [('sep_conv_5x5', 1), ('sep_conv_5x5', 2)], [('sep_conv_3x3', 0), ('sep_conv_3x3', 3)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 2)]], reduce_concat=range(2, 6))"}

CDARTS/cells/pcdarts_imagenet_genotype.json

@@ -0,0 +1 @@
+{"0": "Genotype(normal=[[('skip_connect', 1), ('sep_conv_3x3', 0)], [('sep_conv_3x3', 0), ('skip_connect', 1)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 3)], [('sep_conv_3x3', 1), ('dil_conv_5x5', 4)]], normal_concat=range(2, 6), reduce=[[('sep_conv_3x3', 0), ('skip_connect', 1)], [('dil_conv_5x5', 2), ('max_pool_3x3', 1)], [('sep_conv_3x3', 2), ('sep_conv_3x3', 1)], [('sep_conv_5x5', 0), ('sep_conv_3x3', 3)]], reduce_concat=range(2, 6))", "1": "Genotype(normal=[[('skip_connect', 1), ('sep_conv_3x3', 0)], [('sep_conv_3x3', 0), ('skip_connect', 1)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 3)], [('sep_conv_3x3', 1), ('dil_conv_5x5', 4)]], normal_concat=range(2, 6), reduce=[[('sep_conv_3x3', 0), ('skip_connect', 1)], [('dil_conv_5x5', 2), ('max_pool_3x3', 1)], [('sep_conv_3x3', 2), ('sep_conv_3x3', 1)], [('sep_conv_5x5', 0), ('sep_conv_3x3', 3)]], reduce_concat=range(2, 6))", "2": "Genotype(normal=[[('skip_connect', 1), ('sep_conv_3x3', 0)], [('sep_conv_3x3', 0), ('skip_connect', 1)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 3)], [('sep_conv_3x3', 1), ('dil_conv_5x5', 4)]], normal_concat=range(2, 6), reduce=[[('sep_conv_3x3', 0), ('skip_connect', 1)], [('dil_conv_5x5', 2), ('max_pool_3x3', 1)], [('sep_conv_3x3', 2), ('sep_conv_3x3', 1)], [('sep_conv_5x5', 0), ('sep_conv_3x3', 3)]], reduce_concat=range(2, 6))"}

CDARTS/cells/pdarts_genotype.json

@@ -0,0 +1 @@
+{"0": "Genotype(normal=[[('skip_connect', 0), ('dil_conv_3x3', 1)], [('skip_connect', 0),('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 3)], [('sep_conv_3x3',0), ('dil_conv_5x5', 4)]], normal_concat=range(2, 6), reduce=[[('avg_pool_3x3', 0), ('sep_conv_5x5', 1)], [('sep_conv_3x3', 0), ('dil_conv_5x5', 2)], [('max_pool_3x3', 0), ('dil_conv_3x3', 1)], [('dil_conv_3x3', 1), ('dil_conv_5x5', 3)]], reduce_concat=range(2, 6))", "1": "Genotype(normal=[[('skip_connect', 0), ('dil_conv_3x3', 1)], [('skip_connect', 0),('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 3)], [('sep_conv_3x3',0), ('dil_conv_5x5', 4)]], normal_concat=range(2, 6), reduce=[[('avg_pool_3x3', 0), ('sep_conv_5x5', 1)], [('sep_conv_3x3', 0), ('dil_conv_5x5', 2)], [('max_pool_3x3', 0), ('dil_conv_3x3', 1)], [('dil_conv_3x3', 1), ('dil_conv_5x5', 3)]], reduce_concat=range(2, 6))", "2": "Genotype(normal=[[('skip_connect', 0), ('dil_conv_3x3', 1)], [('skip_connect', 0),('sep_conv_3x3', 1)], [('sep_conv_3x3', 1), ('sep_conv_3x3', 3)], [('sep_conv_3x3',0), ('dil_conv_5x5', 4)]], normal_concat=range(2, 6), reduce=[[('avg_pool_3x3', 0), ('sep_conv_5x5', 1)], [('sep_conv_3x3', 0), ('dil_conv_5x5', 2)], [('max_pool_3x3', 0), ('dil_conv_3x3', 1)], [('dil_conv_3x3', 1), ('dil_conv_5x5', 3)]], reduce_concat=range(2, 6))"}

CDARTS/retrain.py

@@ -0,0 +1,206 @@
+""" Retrain cell """
+import _init_paths
+import os
+import torch
+import json
+import torch.nn as nn
+import numpy as np
+import lib.utils.genotypes as gt
+
+from tensorboardX import SummaryWriter
+from lib.models.cdarts_controller import CDARTSController
+from lib.utils import utils
+from lib.config import AugmentConfig
+from lib.core.augment_function import train, validate
+
+# config
+config = AugmentConfig()
+
+# make apex optional
+if config.distributed:
+    # DDP = torch.nn.parallel.DistributedDataParallel
+    try:
+        import apex
+        from apex.parallel import DistributedDataParallel as DDP
+        from apex import amp, optimizers
+        from apex.fp16_utils import *
+    except ImportError:
+        raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this example.")
+
+
+# tensorboard
+writer = SummaryWriter(log_dir=os.path.join(config.path, "tb"))
+writer.add_text('config', config.as_markdown(), 0)
+
+logger = utils.get_logger(os.path.join(config.path, "{}.log".format(config.name)))
+if config.local_rank == 0:
+    config.print_params(logger.info)
+
+if 'cifar' in config.dataset:
+    from lib.datasets.cifar import get_augment_datasets
+elif 'imagenet' in config.dataset:
+    from lib.datasets.imagenet import get_augment_datasets
+else:
+    raise Exception("Not support dataset!")
+
+def main():
+    logger.info("Logger is set - training start")
+
+    # set seed
+    np.random.seed(config.seed)
+    torch.manual_seed(config.seed)
+    torch.cuda.manual_seed_all(config.seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = True
+
+    if config.distributed:
+        config.gpu = config.local_rank % torch.cuda.device_count()
+        torch.cuda.set_device(config.gpu)
+        # distributed init
+        torch.distributed.init_process_group(backend='nccl', init_method=config.dist_url,
+                world_size=config.world_size, rank=config.local_rank)
+
+        config.world_size = torch.distributed.get_world_size()
+
+        config.total_batch_size = config.world_size * config.batch_size
+    else:
+        config.total_batch_size = config.batch_size
+
+
+    loaders, samplers = get_augment_datasets(config)
+    train_loader, valid_loader = loaders
+    train_sampler, valid_sampler = samplers
+
+    net_crit = nn.CrossEntropyLoss().cuda()
+    controller = CDARTSController(config, net_crit, n_nodes=4, stem_multiplier=config.stem_multiplier)
+
+    file = open(config.cell_file, 'r') 
+    js = file.read()
+    r_dict = json.loads(js)
+    if config.local_rank == 0:  
+        logger.info(r_dict) 
+    file.close()
+    genotypes_dict = {}
+    for layer_idx, genotype in r_dict.items():
+        genotypes_dict[int(layer_idx)] = gt.from_str(genotype)
+
+    controller.build_augment_model(controller.init_channel, genotypes_dict)
+    resume_state = None
+    if config.resume:
+        resume_state = torch.load(config.resume_path,  map_location='cpu')
+        controller.model_main.load_state_dict(resume_state['model_main'])
+
+    controller.model_main = controller.model_main.cuda()
+    param_size = utils.param_size(controller.model_main)
+    logger.info("param size = %fMB", param_size)
+
+    # change training hyper parameters according to cell type
+    if 'cifar' in config.dataset:
+        if param_size < 3.0:
+            config.weight_decay = 3e-4
+            config.drop_path_prob = 0.2
+        elif param_size > 3.0 and param_size < 3.5:
+            config.weight_decay = 3e-4
+            config.drop_path_prob = 0.3
+        else:
+            config.weight_decay = 5e-4
+            config.drop_path_prob = 0.3
+
+    if config.local_rank == 0:
+        logger.info("Current weight decay: {}".format(config.weight_decay))
+        logger.info("Current drop path prob: {}".format(config.drop_path_prob))
+
+    controller.model_main = apex.parallel.convert_syncbn_model(controller.model_main)
+    # weights optimizer
+    optimizer = torch.optim.SGD(controller.model_main.parameters(), lr=config.lr, momentum=config.momentum, weight_decay=config.weight_decay)
+    # optimizer = torch.optim.SGD(controller.model_main.parameters(), lr=config.lr, momentum=config.momentum, weight_decay=config.weight_decay, nesterov=True)
+    lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, config.epochs)
+
+    if config.use_amp:
+        controller.model_main, optimizer = amp.initialize(controller.model_main, optimizer, opt_level=config.opt_level)
+
+    if config.distributed:
+        controller.model_main = DDP(controller.model_main, delay_allreduce=True)
+
+    best_top1 = 0.
+    best_top5 = 0.
+    sta_epoch = 0
+    # training loop
+    if config.resume:
+        optimizer.load_state_dict(resume_state['optimizer'])
+        lr_scheduler.load_state_dict(resume_state['lr_scheduler'])
+        best_top1 = resume_state['best_top1']
+        best_top5 = resume_state['best_top5']
+        sta_epoch = resume_state['sta_epoch']
+
+    epoch_pool = [220, 230, 235, 240, 245]
+    for epoch in range(sta_epoch, config.epochs):
+        # reset iterators
+        train_sampler.set_epoch(epoch)
+        valid_sampler.set_epoch(epoch)
+        current_lr = lr_scheduler.get_lr()[0]
+        # current_lr = utils.adjust_lr(optimizer, epoch, config)
+
+        if config.local_rank == 0:
+            logger.info('Epoch: %d lr %e', epoch, current_lr)
+        if epoch < config.warmup_epochs and config.total_batch_size > 256:
+            for param_group in optimizer.param_groups:
+                param_group['lr'] = current_lr * (epoch + 1) / 5.0
+            if config.local_rank == 0:
+                logger.info('Warming-up Epoch: %d, LR: %e', epoch, current_lr * (epoch + 1) / 5.0)
+
+        drop_prob = config.drop_path_prob * epoch / config.epochs
+        controller.model_main.module.drop_path_prob(drop_prob)
+
+        # training
+        train(train_loader, controller.model_main, optimizer, epoch, writer, logger, config)
+
+        # validation
+        cur_step = (epoch+1) * len(train_loader)
+        top1, top5 = validate(valid_loader, controller.model_main, epoch, cur_step, writer, logger, config)
+
+        if 'cifar' in config.dataset:
+            lr_scheduler.step()    
+        elif 'imagenet' in config.dataset:
+            lr_scheduler.step()
+            # current_lr = utils.adjust_lr(optimizer, epoch, config)
+        else:
+            raise Exception('Lr error!')
+
+        # save
+        if best_top1 < top1:
+            best_top1 = top1
+            best_top5 = top5
+            is_best = True
+        else:
+            is_best = False
+
+        # save
+        if config.local_rank == 0:
+            if ('imagenet' in config.dataset) and ((epoch+1) in epoch_pool) and (not config.resume) and (config.local_rank == 0):
+                torch.save({
+                    "model_main":controller.model_main.module.state_dict(),
+                    "optimizer":optimizer.state_dict(),
+                    "lr_scheduler":lr_scheduler.state_dict(),
+                    "best_top1":best_top1,
+                    "best_top5":best_top5,
+                    "sta_epoch":epoch + 1
+                }, os.path.join(config.path, "epoch_{}.pth.tar".format(epoch+1)))
+                utils.save_checkpoint(controller.model_main.module.state_dict(), config.path, is_best)
+
+            torch.save({
+                "model_main":controller.model_main.module.state_dict(),
+                "optimizer":optimizer.state_dict(),
+                "lr_scheduler":lr_scheduler.state_dict(),
+                "best_top1":best_top1,
+                "best_top5":best_top5,
+                "sta_epoch":epoch + 1
+            }, os.path.join(config.path, "retrain_resume.pth.tar"))
+            utils.save_checkpoint(controller.model_main.module.state_dict(), config.path, is_best)
+
+    if config.local_rank == 0:
+        logger.info("Final best Prec@1 = {:.4%}, Prec@5 = {:.4%}".format(best_top1, best_top5))
+
+
+if __name__ == "__main__":
+    main()

CDARTS/scripts/run_retrain_cifar_1gpu.sh

@@ -0,0 +1,14 @@
+NGPUS=1
+SGPU=0
+EGPU=$[NGPUS+SGPU-1]
+GPU_ID=`seq -s , $SGPU $EGPU`
+CUDA_VISIBLE_DEVICES=$GPU_ID python -m torch.distributed.launch --nproc_per_node=$NGPUS ./CDARTS/retrain.py \
+    --name cifar10-retrain --dataset cifar10 --model_type cifar \
+    --n_classes 10 --init_channels 36 --stem_multiplier 3 \
+    --cell_file './genotypes.json' \
+    --batch_size 128 --workers 1 --print_freq 100 \
+    --world_size $NGPUS --weight_decay 5e-4 \
+    --distributed  --dist_url 'tcp://127.0.0.1:26443' \
+    --lr 0.025 --warmup_epochs 0 --epochs 600 \
+    --cutout_length 16 --aux_weight 0.4 --drop_path_prob 0.3 \
+    --label_smooth 0.0 --mixup_alpha 0

CDARTS/scripts/run_retrain_cifar_4gpus.sh

@@ -0,0 +1,15 @@
+NGPUS=4
+SGPU=0
+EGPU=$[NGPUS+SGPU-1]
+GPU_ID=`seq -s , $SGPU $EGPU`
+CUDA_VISIBLE_DEVICES=$GPU_ID python -m torch.distributed.launch --nproc_per_node=$NGPUS ./CDARTS/retrain.py \
+    --name cifar10-retrain --dataset cifar10 --model_type cifar \
+    --n_classes 10 --init_channels 36 --stem_multiplier 3 \
+    --cell_file './genotypes.json' \
+    --batch_size 128 --workers 1 --print_freq 100 \
+    --world_size $NGPUS --weight_decay 5e-4 \
+    --distributed  --dist_url 'tcp://127.0.0.1:26443' \
+    --lr 0.1 --warmup_epochs 0 --epochs 600 \
+    --cutout_length 16 --aux_weight 0.4 --drop_path_prob 0.3 \
+    --label_smooth 0.0 --mixup_alpha 0
+

CDARTS/scripts/run_retrain_imagenet.sh

@@ -0,0 +1,15 @@
+NGPUS=8
+SGPU=0
+EGPU=$[NGPUS+SGPU-1]
+GPU_ID=`seq -s , $SGPU $EGPU`
+CUDA_VISIBLE_DEVICES=$GPU_ID python -m torch.distributed.launch --nproc_per_node=$NGPUS ./CDARTS/retrain.py \
+    --name imagenet-retrain --dataset imagenet --model_type imagenet \
+    --n_classes 1000 --init_channels 48 --stem_multiplier 1 \
+    --batch_size 128 --workers 4 --print_freq 100 \
+    --cell_file './genotypes.json' \
+    --world_size $NGPUS --weight_decay 3e-5 \
+    --distributed  --dist_url 'tcp://127.0.0.1:24443' \
+    --lr 0.5 --warmup_epochs 5 --epochs 250 \
+    --cutout_length 0 --aux_weight 0.4 --drop_path_prob 0.0 \
+    --label_smooth 0.1 --mixup_alpha 0 \
+    --resume_name "retrain_resume.pth.tar"