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!16552 Enable acceleration by Graph Kernel for CycleGAN on GPU. 

From: @lishanni513
Reviewed-by: @gaoxiong1,@ckey_dou
Signed-off-by: @ckey_dou
This commit is contained in:
mindspore-ci-bot 2021-05-20 14:31:04 +08:00 committed by Gitee
parent b33a7c1fb3 282d46e5eb
commit e8a46db6cf
1 changed files with 83 additions and 79 deletions
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162
model_zoo/research/cv/CycleGAN/src/utils/args.py
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@ -22,87 +22,88 @@ from mindspore import context
from mindspore.communication.management import init, get_rank
parser = argparse.ArgumentParser(description='Cycle GAN')
# basic parameters
parser.add_argument('--platform', type=str, default='Ascend', help='only support GPU and Ascend')
parser.add_argument('--device_id', type=int, default=0, help='device id, default is 0.')
parser.add_argument('--device_num', type=int, default=1, help='device num, default is 1.')
parser.add_argument('--model', type=str, default='DepthResNet', choices=('DepthResNet', 'ResNet', 'UNet'), \
help='generator model')
parser.add_argument('--init_type', type=str, default='normal', choices=('normal', 'xavier'), \
help='network initialization, default is normal.')
parser.add_argument('--init_gain', type=float, default=0.02, \
help='scaling factor for normal, xavier and orthogonal, default is 0.02.')
parser.add_argument('--image_size', type=int, default=256, help='input image_size, default is 256.')
parser.add_argument('--batch_size', type=int, default=1, help='batch_size, default is 1.')
parser.add_argument('--pool_size', type=int, default=50, \
help='the size of image buffer that stores previously generated images')
parser.add_argument('--beta1', type=float, default=0.5, help='Adam beta1, default is 0.5.')
parser.add_argument('--lr', type=float, default=0.0002, help='learning rate, default is 0.0002.')
parser.add_argument('--lr_policy', type=str, default='linear', choices=('linear', 'constant'), \
help='learning rate policy, default is linear')
parser.add_argument('--max_epoch', type=int, default=200, help='epoch size for training, default is 200.')
parser.add_argument('--n_epochs', type=int, default=100, \
help='number of epochs with the initial learning rate, default is 100')
# model parameters
parser.add_argument('--in_planes', type=int, default=3, help='input channels, default is 3.')
parser.add_argument('--ngf', type=int, default=64, help='generator model filter numbers, default is 64.')
parser.add_argument('--gl_num', type=int, default=9, help='generator model residual block numbers, default is 9.')
parser.add_argument('--ndf', type=int, default=64, help='discriminator model filter numbers, default is 64.')
parser.add_argument('--dl_num', type=int, default=3, \
help='discriminator model residual block numbers, default is 3.')
parser.add_argument('--slope', type=float, default=0.2, help='leakyrelu slope, default is 0.2.')
parser.add_argument('--norm_mode', type=str, default='batch', choices=('batch', 'instance'), \
help='norm mode, default is batch.')
parser.add_argument('--lambda_A', type=float, default=10.0, \
help='weight for cycle loss (A -> B -> A), default is 10.')
parser.add_argument('--lambda_B', type=float, default=10.0, \
help='weight for cycle loss (B -> A -> B), default is 10.')
parser.add_argument('--lambda_idt', type=float, default=0.5, \
help='use identity mapping. Setting lambda_identity other than 0 has an effect of scaling the '
'weight of the identity mapping loss. For example, if the weight of the identity loss '
'should be 10 times smaller than the weight of the reconstruction loss,'
'please set lambda_identity = 0.1, default is 0.5.')
parser.add_argument('--gan_mode', type=str, default='lsgan', choices=('lsgan', 'vanilla'), \
help='the type of GAN loss, default is lsgan.')
parser.add_argument('--pad_mode', type=str, default='CONSTANT', choices=('CONSTANT', 'REFLECT', 'SYMMETRIC'), \
help='the type of Pad, default is CONSTANT.')
# additional parameters
parser.add_argument('--dataroot', default='./data/horse2zebra/', \
help='path of images (should have subfolders trainA, trainB, testA, testB, etc).')
parser.add_argument('--outputs_dir', type=str, default='./outputs', \
help='models are saved here, default is ./outputs.')
parser.add_argument('--load_ckpt', type=ast.literal_eval, default=False, \
help='whether load pretrained ckpt')
parser.add_argument('--G_A_ckpt', type=str, default='./outputs/ckpt/G_A_200.ckpt', \
help='checkpoint file path of G_A.')
parser.add_argument('--G_B_ckpt', type=str, default='./outputs/ckpt/G_B_200.ckpt', \
help='checkpoint file path of G_B.')
parser.add_argument('--D_A_ckpt', type=str, default='./outputs/ckpt/D_A_200.ckpt', \
help='checkpoint file path of D_A.')
parser.add_argument('--D_B_ckpt', type=str, default='./outputs/ckpt/D_B_200.ckpt', \
help='checkpoint file path of D_B.')
parser.add_argument('--save_checkpoint_epochs', type=int, default=10, \
help='Save checkpoint epochs, default is 10.')
parser.add_argument('--print_iter', type=int, default=100, help='log print iter, default is 100.')
parser.add_argument('--need_profiler', type=ast.literal_eval, default=False, \
help='whether need profiler, default is False.')
parser.add_argument('--save_graphs', type=ast.literal_eval, default=False, \
help='whether save graphs, default is False.')
parser.add_argument('--save_imgs', type=ast.literal_eval, default=True, \
help='whether save imgs when epoch end')
parser.add_argument('--use_random', type=ast.literal_eval, default=True, \
help='whether use random when training, default is True.')
parser.add_argument('--need_dropout', type=ast.literal_eval, default=False, \
help='whether need dropout, default is True.')
parser.add_argument('--max_dataset_size', type=int, default=None, \
help='max images pre epoch, default is None.')
args = parser.parse_args()
def get_args(phase):
"""Define the common options that are used in both training and test."""
parser = argparse.ArgumentParser(description='Cycle GAN')
# basic parameters
parser.add_argument('--platform', type=str, default='Ascend', help='only support GPU and Ascend')
parser.add_argument('--device_id', type=int, default=0, help='device id, default is 0.')
parser.add_argument('--device_num', type=int, default=1, help='device num, default is 1.')
parser.add_argument('--model', type=str, default='DepthResNet', choices=('DepthResNet', 'ResNet', 'UNet'), \
help='generator model')
parser.add_argument('--init_type', type=str, default='normal', choices=('normal', 'xavier'), \
help='network initialization, default is normal.')
parser.add_argument('--init_gain', type=float, default=0.02, \
help='scaling factor for normal, xavier and orthogonal, default is 0.02.')
parser.add_argument('--image_size', type=int, default=256, help='input image_size, default is 256.')
parser.add_argument('--batch_size', type=int, default=1, help='batch_size, default is 1.')
parser.add_argument('--pool_size', type=int, default=50, \
help='the size of image buffer that stores previously generated images')
parser.add_argument('--beta1', type=float, default=0.5, help='Adam beta1, default is 0.5.')
parser.add_argument('--lr', type=float, default=0.0002, help='learning rate, default is 0.0002.')
parser.add_argument('--lr_policy', type=str, default='linear', choices=('linear', 'constant'), \
help='learning rate policy, default is linear')
parser.add_argument('--max_epoch', type=int, default=200, help='epoch size for training, default is 200.')
parser.add_argument('--n_epochs', type=int, default=100, \
help='number of epochs with the initial learning rate, default is 100')
# model parameters
parser.add_argument('--in_planes', type=int, default=3, help='input channels, default is 3.')
parser.add_argument('--ngf', type=int, default=64, help='generator model filter numbers, default is 64.')
parser.add_argument('--gl_num', type=int, default=9, help='generator model residual block numbers, default is 9.')
parser.add_argument('--ndf', type=int, default=64, help='discriminator model filter numbers, default is 64.')
parser.add_argument('--dl_num', type=int, default=3, \
help='discriminator model residual block numbers, default is 3.')
parser.add_argument('--slope', type=float, default=0.2, help='leakyrelu slope, default is 0.2.')
parser.add_argument('--norm_mode', type=str, default='batch', choices=('batch', 'instance'), \
help='norm mode, default is batch.')
parser.add_argument('--lambda_A', type=float, default=10.0, \
help='weight for cycle loss (A -> B -> A), default is 10.')
parser.add_argument('--lambda_B', type=float, default=10.0, \
help='weight for cycle loss (B -> A -> B), default is 10.')
parser.add_argument('--lambda_idt', type=float, default=0.5, \
help='use identity mapping. Setting lambda_identity other than 0 has an effect of scaling the '
'weight of the identity mapping loss. For example, if the weight of the identity loss '
'should be 10 times smaller than the weight of the reconstruction loss,'
'please set lambda_identity = 0.1, default is 0.5.')
parser.add_argument('--gan_mode', type=str, default='lsgan', choices=('lsgan', 'vanilla'), \
help='the type of GAN loss, default is lsgan.')
parser.add_argument('--pad_mode', type=str, default='CONSTANT', choices=('CONSTANT', 'REFLECT', 'SYMMETRIC'), \
help='the type of Pad, default is CONSTANT.')
# additional parameters
parser.add_argument('--dataroot', default='./data/horse2zebra/', \
help='path of images (should have subfolders trainA, trainB, testA, testB, etc).')
parser.add_argument('--outputs_dir', type=str, default='./outputs', \
help='models are saved here, default is ./outputs.')
parser.add_argument('--load_ckpt', type=ast.literal_eval, default=False, \
help='whether load pretrained ckpt')
parser.add_argument('--G_A_ckpt', type=str, default='./outputs/ckpt/G_A_200.ckpt', \
help='checkpoint file path of G_A.')
parser.add_argument('--G_B_ckpt', type=str, default='./outputs/ckpt/G_B_200.ckpt', \
help='checkpoint file path of G_B.')
parser.add_argument('--D_A_ckpt', type=str, default='./outputs/ckpt/D_A_200.ckpt', \
help='checkpoint file path of D_A.')
parser.add_argument('--D_B_ckpt', type=str, default='./outputs/ckpt/D_B_200.ckpt', \
help='checkpoint file path of D_B.')
parser.add_argument('--save_checkpoint_epochs', type=int, default=10, \
help='Save checkpoint epochs, default is 10.')
parser.add_argument('--print_iter', type=int, default=100, help='log print iter, default is 100.')
parser.add_argument('--need_profiler', type=ast.literal_eval, default=False, \
help='whether need profiler, default is False.')
parser.add_argument('--save_graphs', type=ast.literal_eval, default=False, \
help='whether save graphs, default is False.')
parser.add_argument('--save_imgs', type=ast.literal_eval, default=True, \
help='whether save imgs when epoch end')
parser.add_argument('--use_random', type=ast.literal_eval, default=True, \
help='whether use random when training, default is True.')
parser.add_argument('--need_dropout', type=ast.literal_eval, default=False, \
help='whether need dropout, default is True.')
parser.add_argument('--max_dataset_size', type=int, default=None, \
help='max images pre epoch, default is None.')
args = parser.parse_args()
if args.device_num > 1:
context.set_context(mode=context.GRAPH_MODE, device_target=args.platform, save_graphs=args.save_graphs)
context.reset_auto_parallel_context()
@ -116,6 +117,9 @@ def get_args(phase):
args.rank = 0
args.device_num = 1
if args.platform == "GPU":
context.set_context(enable_graph_kernel=True)
if args.platform == "Ascend":
args.pad_mode = "CONSTANT"