def __init__(self, gpu=0):
print("start")
self.root = "./images/"
self.batchsize = 1
self.outdir = self.root + "out/"
self.outdir_min = self.root + "out_min/"
self.gpu = gpu
self._dtype = np.float32
print("load model")
if self.gpu >= 0:
cuda.get_device(self.gpu).use()
cuda.set_max_workspace_size(64 * 1024 * 1024) # 64MB
chainer.Function.type_check_enable = False
self.cnn_128 = unet.UNET()
self.cnn_512 = unet.UNET()
if self.gpu >= 0:
self.cnn_128.to_gpu()
self.cnn_512.to_gpu()
#lnn = lnet.LNET()
#serializers.load_npz("./cgi-bin/wnet/models/model_cnn_128_df_4", cnn_128)
#serializers.load_npz("./cgi-bin/paint_x2_unet/models/model_cnn_128_f3_2", cnn_128)
serializers.load_npz(
"./cgi-bin/paint_x2_unet/models/unet_128_standard", self.cnn_128)
#serializers.load_npz("./cgi-bin/paint_x2_unet/models/model_cnn_128_ua_1", self.cnn_128)
#serializers.load_npz("./cgi-bin/paint_x2_unet/models/model_m_1.6", self.cnn)
serializers.load_npz(
"./cgi-bin/paint_x2_unet/models/unet_512_standard", self.cnn_512)
def __init__(self, gpu=0):
current_path = os.path.dirname(__file__)
print("start")
two_up = os.path.abspath(os.path.join(__file__, "../../../"))
# print(two_up)
self.root = os.path.join(two_up + "/static/paintschainer/images/")
self.batchsize = 1
self.outdir = self.root + "out/"
self.outdir_min = self.root + "out_min/"
self.gpu = gpu
self._dtype = np.float32
print("load model")
if self.gpu >= 0:
cuda.get_device(self.gpu).use()
cuda.set_max_workspace_size(64 * 1024 * 1024) # 64MB
chainer.Function.type_check_enable = False
self.cnn_128 = unet.UNET()
self.cnn_512 = unet.UNET()
if self.gpu >= 0:
self.cnn_128.to_gpu()
self.cnn_512.to_gpu()
#lnn = lnet.LNET()
#serializers.load_npz("./cgi-bin/wnet/models/model_cnn_128_df_4", cnn_128)
#serializers.load_npz("./cgi-bin/paint_x2_unet/models/model_cnn_128_f3_2", cnn_128)
serializers.load_npz(
os.path.join(current_path + "/models/unet_128_standard"),
self.cnn_128)
#serializers.load_npz("./cgi-bin/paint_x2_unet/models/model_cnn_128_ua_1", self.cnn_128)
#serializers.load_npz("./cgi-bin/paint_x2_unet/models/model_m_1.6", self.cnn)
serializers.load_npz(
os.path.join(current_path + "/models/unet_512_standard"),
self.cnn_512)
def __init__(self, gpu=0):
self.log_debug('加载模型...')
self.batchsize = 1
self.gpu = gpu
self._dtype = np.float32
if not os.path.isfile(
"./genColorForAnimation/models/unet_128_standard"):
self.log_error(
'无法加载模型“./genColorForAnimation/models/unet_128_standard”')
return
if not os.path.isfile(
"./genColorForAnimation/models/unet_512_standard"):
self.log_error(
'无法加载模型“./genColorForAnimation/models/unet_512_standard”')
return
self.log_debug('成功加载模型')
if self.gpu >= 0:
self.log_debug('使用GPU: %d' % gpu)
cuda.get_device(self.gpu).use()
cuda.set_max_workspace_size(64 * 1024 * 1024) # 64MB
chainer.Function.type_check_enable = False
self.cnn_128 = unet.UNET()
self.cnn_512 = unet.UNET()
if self.gpu >= 0:
self.cnn_128.to_gpu()
self.cnn_512.to_gpu()
serializers.load_npz("./genColorForAnimation/models/unet_128_standard",
self.cnn_128)
serializers.load_npz("./genColorForAnimation/models/unet_512_standard",
self.cnn_512)
def __init__(self, gpu=0, colormode='LAB', normalized=False):
print("start")
self.root = "./images/"
self.batchsize = 1
self.outdir = self.root + "out/"
self.outdir_min = self.root + "out_min/"
self.gpu = gpu
self._dtype = np.float32
self._colormode = colormode
self._norm = normalized
print("load model")
if self.gpu >= 0:
cuda.get_device(self.gpu).use()
cuda.set_max_workspace_size(64 * 1024 * 1024) # 64MB
chainer.Function.type_check_enable = False
if self._colormode == 'YUV':
self.cnn_128 = unet.UNET()
# self.cnn_512 = unet.UNET()
elif self._colormode == 'LAB':
self.cnn_128 = unet.UNET(inputChannel=3, outputChannel=2)
# self.cnn_512 = unet.UNET()
if self.gpu >= 0:
self.cnn_128.to_gpu()
# self.cnn_512.to_gpu()
if self._colormode == 'YUV':
serializers.load_npz("./src/colorize/models/unet_128_standard-YUV", self.cnn_128)
elif self._colormode == 'LAB':
serializers.load_npz("./src/colorize/models/cnn_128_iter_370000", self.cnn_128)
def __init__(self, gpu=0):
rootPath = os.path.dirname(
os.path.abspath(
os.path.dirname(os.path.abspath(os.path.dirname(__file__)))))
print("start")
self.root = str(rootPath)
self.batchsize = 1
self.outdir = self.root + "out/"
self.outdir_min = self.root + "out_min/"
self.gpu = gpu
self._dtype = np.float32
print(rootPath)
if not os.path.isfile(
str(rootPath) + "/downloads/painting_model/unet_128_standard"):
print(
"./models/unet_128_standard not found. Please download them from http://paintschainer.preferred.tech/downloads/"
)
if not os.path.isfile(
str(rootPath) + "/downloads/painting_model/unet_512_standard"):
print(
"./models/unet_512_standard not found. Please download them from http://paintschainer.preferred.tech/downloads/"
)
print("load model")
if self.gpu >= 0:
cuda.get_device(self.gpu).use()
cuda.set_max_workspace_size(64 * 1024 * 1024) # 64MB
chainer.Function.type_check_enable = False
self.cnn_128 = unet.UNET()
self.cnn_512 = unet.UNET()
if self.gpu >= 0:
self.cnn_128.to_device(self.gpu)
self.cnn_512.to_device(self.gpu)
#lnn = lnet.LNET()
#serializers.load_npz("./cgi-bin/wnet/models/model_cnn_128_df_4", cnn_128)
#serializers.load_npz("./cgi-bin/paint_x2_unet/models/model_cnn_128_f3_2", cnn_128)
serializers.load_npz(
str(rootPath) + "/downloads/painting_model/unet_128_standard",
self.cnn_128)
#serializers.load_npz("./cgi-bin/paint_x2_unet/models/model_cnn_128_ua_1", self.cnn_128)
#serializers.load_npz("./cgi-bin/paint_x2_unet/models/model_m_1.6", self.cnn)
serializers.load_npz(
str(rootPath) + "/downloads/painting_model/unet_512_standard",
self.cnn_512)
def __init__(self, gpu=0):
print("start")
self.root = "./images/"
self.gpu = gpu
print("load model")
if self.gpu >= 0:
cuda.get_device(self.gpu).use()
cuda.set_max_workspace_size(64 * 1024 * 1024) # 64MB
chainer.Function.type_check_enable = False
self.cnn_128 = unet.UNET()
self.cnn = unet.UNET()
if self.gpu >= 0:
self.cnn_128.to_gpu()
self.cnn.to_gpu()
serializers.load_npz(
"./cgi-bin/paint_x2_unet/models/unet_128_standard", self.cnn_128)
serializers.load_npz(
"./cgi-bin/paint_x2_unet/models/unet_512_standard", self.cnn)
def __init__(self, gpu=-1):
print("start")
self.root = "./"
self.batchsize = 1
self.outdir = self.root
self.outdir_min = self.root + "out_min/"
self.gpu = gpu
self._dtype = np.float32
print("load model")
if self.gpu >= 0:
cuda.get_device(self.gpu).use()
cuda.set_max_workspace_size(1024 * 1024 * 1024) # 64MB
chainer.Function.type_check_enable = False
self.cnn_128 = unet.UNET()
self.cnn_512 = unet.UNET()
if self.gpu >= 0:
self.cnn_128.to_gpu()
self.cnn_512.to_gpu()
serializers.load_npz("./db/unet_128_standard", self.cnn_128)
serializers.load_npz("./db/unet_512_standard", self.cnn_512)
def test_size(self):
size = 1024
cuda.set_max_workspace_size(size)
self.assertEqual(size, cuda.get_max_workspace_size())
def tearDown(self):
cuda.set_max_workspace_size(self.space)
def test_size(self):
size = 1024
cuda.set_max_workspace_size(size)
self.assertEqual(size, cuda.get_max_workspace_size())
def tearDown(self):
cuda.set_max_workspace_size(self.space)
'measured with corresponding cache enabled. ')
parser.add_argument('--batchsize', '-b', type=int, default=32,
help='Batchsize')
args = parser.parse_args()
print(args)
numpy.random.seed(args.seed)
if args.gpu >= 0:
cuda.cupy.random.seed(args.seed)
if args.gpu >= 0:
if args.workspace_ratio < 0.0 or args.workspace_ratio > 1.0:
raise ValueError('Invalid workspace ratio:{} (valid interval:[0.0,1.0])'.format(args.workspace_ratio))
_free_mem, total_mem = cuda.cupy.cuda.runtime.memGetInfo()
size = long(total_mem * args.workspace_ratio)
cuda.set_max_workspace_size(size)
in_channels = 3
label_num = 100
if args.predictor == 'inception-v3':
predictor = net.inception_v3.InceptionV3(use_cudnn=args.cudnn)
model = net.inception_v3.InceptionV3Classifier(predictor)
in_size = 299
elif args.predictor == 'alex-owt':
predictor = net.alex_owt.AlexOWT(use_cudnn=args.cudnn)
model = L.Classifier(predictor)
in_size = 224
elif args.predictor == 'vgg-d':
predictor = net.vgg_d.VGG_D(use_cudnn=args.cudnn)
model = L.Classifier(predictor)
import os
import random
import warnings
import yaml
import chainer
import chainermn
import numpy
import tgan2
from chainer import cuda
from chainer import training
from chainer.training import extensions
from tgan2.utils import make_instance
cuda.set_max_workspace_size(1024 * 1024 * 1024)
chainer.global_config.autotune = True
chainer.config.comm = None
def get_device_communicator(gpu, communicator, seed, batchsize):
if gpu:
if communicator == 'naive':
print('Error: \'naive\' communicator does not support GPU.\n')
exit(-1)
comm = chainermn.create_communicator(communicator)
device = comm.intra_rank
else:
if communicator != 'naive':
print('Warning: using naive communicator '
'because only naive supports CPU-only execution')
