diff options
| -rw-r--r-- | data/aligned_data_loader.py | 1 | ||||
| -rw-r--r-- | data/unaligned_data_loader.py | 1 | ||||
| -rw-r--r-- | models/networks.py | 8 | ||||
| -rw-r--r-- | options/train_options.py | 4 |
4 files changed, 8 insertions, 6 deletions
diff --git a/data/aligned_data_loader.py b/data/aligned_data_loader.py index bea3531..c5305fc 100644 --- a/data/aligned_data_loader.py +++ b/data/aligned_data_loader.py @@ -4,6 +4,7 @@ import torchvision.transforms as transforms from data.base_data_loader import BaseDataLoader from data.image_folder import ImageFolder from pdb import set_trace as st +# pip install future --upgrade from builtins import object class PairedData(object): diff --git a/data/unaligned_data_loader.py b/data/unaligned_data_loader.py index 4a06510..f37f6ce 100644 --- a/data/unaligned_data_loader.py +++ b/data/unaligned_data_loader.py @@ -2,6 +2,7 @@ import torch.utils.data import torchvision.transforms as transforms from data.base_data_loader import BaseDataLoader from data.image_folder import ImageFolder +# pip install future --upgrade from builtins import object from pdb import set_trace as st diff --git a/models/networks.py b/models/networks.py index 60e1777..2aea150 100644 --- a/models/networks.py +++ b/models/networks.py @@ -13,7 +13,7 @@ def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv') != -1: m.weight.data.normal_(0.0, 0.02) - elif classname.find('BatchNorm') != -1 or classname.find('InstanceNorm') != -1: + elif classname.find('BatchNorm2d') != -1 or classname.find('InstanceNormalization') != -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) @@ -162,7 +162,7 @@ class ResnetGenerator(nn.Module): self.model = nn.Sequential(*model) def forward(self, input): - if isinstance(input.data, torch.cuda.FloatTensor) and self.gpu_ids: + if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.model, input, self.gpu_ids) else: return self.model(input) @@ -222,7 +222,7 @@ class UnetGenerator(nn.Module): self.model = unet_block def forward(self, input): - if isinstance(input.data, torch.cuda.FloatTensor) and self.gpu_ids: + if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.model, input, self.gpu_ids) else: return self.model(input) @@ -323,7 +323,7 @@ class NLayerDiscriminator(nn.Module): self.model = nn.Sequential(*sequence) def forward(self, input): - if isinstance(input.data, torch.cuda.FloatTensor) and self.gpu_ids: + if len(self.gpu_ids) and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.model, input, self.gpu_ids) else: return self.model(input) diff --git a/options/train_options.py b/options/train_options.py index b241863..981126f 100644 --- a/options/train_options.py +++ b/options/train_options.py @@ -10,8 +10,8 @@ class TrainOptions(BaseOptions): self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') - self.parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate') - self.parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero') + self.parser.add_argument('--niter', type=int, default=200, help='# of iter at starting learning rate') + self.parser.add_argument('--niter_decay', type=int, default=0, help='# of iter to linearly decay learning rate to zero') self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') self.parser.add_argument('--ntrain', type=int, default=float("inf"), help='# of examples per epoch.') self.parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') |