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celinelee
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python_fn_calls

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f.static_function(2147483647+1)
assert(f.int == 13)
assert(f.int64 == 31)
print(f'Bar: {dir(pyBar.Bar)
pyBar.free_function(2147483647)
pyBar.free_function(2147483647+1)
pyBar.Bar()
print(f'class Bar: {dir(b)
b.static_function(1)
b.static_function(2147483647)
b.static_function(2147483647+1)
assert(b.int == 13)
assert(b.int64 == 31)
print(f'FooBar: {dir(pyFooBar.FooBar)
pyFooBar.free_function(2147483647)
pyFooBar.free_function(2147483647+1)
pyFooBar.FooBar()
print(f'class FooBar: {dir(fb)
fb.static_function(1)
fb.static_function(2147483647)
fb.static_function(2147483647+1)
assert(fb.int == 30)
assert(fb.int64 == 68)
os.makedirs('images', exist_ok=True)
argparse.ArgumentParser()
parser.add_argument('--n_epochs', type=int, default=200, help='number of epochs of training')
parser.add_argument('--batch_size', type=int, default=64, help='size of the batches')
parser.add_argument('--lr', type=float, default=0.0002, help='adam: learning rate')
parser.add_argument('--b1', type=float, default=0.5, help='adam: decay of first order momentum of gradient')
parser.add_argument('--b2', type=float, default=0.999, help='adam: decay of first order momentum of gradient')
parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation')
parser.add_argument('--n_residual_blocks', type=int, default=1, help='number of residual blocks in generator')
parser.add_argument('--latent_dim', type=int, default=10, help='dimensionality of the noise input')
parser.add_argument('--img_size', type=int, default=32, help='size of each image dimension')
parser.add_argument('--channels', type=int, default=3, help='number of image channels')
parser.add_argument('--n_classes', type=int, default=10, help='number of classes in the dataset')
parser.add_argument('--sample_interval', type=int, default=300, help='interval betwen image samples')
parser.parse_args()
print(opt)
discriminator (PatchGAN)
int(opt.img_size / 2**4)
torch.cuda.is_available()
print("cuda : {}".format(cuda)
weights_init_normal(m)
print("classname : {}".format(classname)
classname.find('Conv')
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
classname.find('BatchNorm')
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
ResidualBlock_back(nn.Module)
__init__(self, in_features=64, out_features=64)
super(ResidualBlock, self)
__init__()
nn.Conv2d(in_features, in_features, 3, 1, 1)
nn.BatchNorm2d(in_features)
nn.ReLU(inplace=True)
nn.Conv2d(in_features, in_features, 3, 1, 1)
nn.BatchNorm2d(in_features)
forward(self, x)
self.block(x)
sencode_ResidualBlock(nn.Module)
__init__(self, in_features=64, out_features=64)
super(sencode_ResidualBlock, self)
__init__()
nn.Conv2d(in_channels=1*in_features,out_channels=4*in_features,kernel_size=(3, 3)
nn.BatchNorm2d(4*in_features)
nn.LeakyReLU(inplace=True)
nn.Conv2d(in_channels=4*in_features,out_channels=8*in_features,kernel_size=(3, 3)
nn.BatchNorm2d(8*in_features)
nn.LeakyReLU(inplace=True)
forward(self, x)
self.sencode_block(x)
sdecode_ResidualBlock(nn.Module)
__init__(self, in_features=64, out_features=64)
super(sdecode_ResidualBlock, self)
__init__()
nn.ConvTranspose2d(in_channels=8*in_features,out_channels=4*in_features,kernel_size=(3, 3)
nn.BatchNorm2d(4*in_features)
nn.LeakyReLU(inplace=True)
nn.ConvTranspose2d(in_channels=4*in_features,out_channels=1*in_features,kernel_size=(3, 3)
nn.BatchNorm2d(1*in_features)
nn.LeakyReLU(inplace=True)
forward(self, encode_x)
self.sdecode_block(encode_x)
F.sigmoid(decode_x)
tencode_ResidualBlock(nn.Module)
__init__(self, in_features=64, out_features=64)
super(tencode_ResidualBlock, self)
__init__()
nn.Conv2d(in_channels=1*in_features,out_channels=4*in_features,kernel_size=(3, 3)
nn.BatchNorm2d(4*in_features)
nn.LeakyReLU(inplace=True)
nn.Conv2d(in_channels=4*in_features,out_channels=8*in_features,kernel_size=(3, 3)
nn.BatchNorm2d(8*in_features)
nn.LeakyReLU(inplace=True)
forward(self, x)
self.tencode_block(x)
tdecode_ResidualBlock(nn.Module)
__init__(self, in_features=64, out_features=64)