File size: 2,044 Bytes
f7837d4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
from torch import nn
from transformers import PreTrainedModel
from transformers import PretrainedConfig
from torchvision import transforms
from transformers.models.auto.configuration_auto import CONFIG_MAPPING
from transformers.models.auto.modeling_auto import MODEL_MAPPING
class ImageToImageConfig(PretrainedConfig):
model_type = "upscaleing"
def __init__(self, in_channels=3, out_channels=3,in_resolution=256,out_resolution=768, **kwargs):
super().__init__(**kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.in_resolution = in_resolution
self.out_resolution = out_resolution
class ImageToImageModel(PreTrainedModel):
config_class = ImageToImageConfig
def __init__(self, config):
super().__init__(config)
self.model = nn.Sequential(
nn.Conv2d(in_channels=config.in_channels, out_channels=64, kernel_size=5, padding=2),
nn.ReLU(),
nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=32, out_channels=3 * 3 * 3, kernel_size=3, padding=1),
nn.PixelShuffle(3)
)
self.transform1 = transforms.Compose(
transforms=(
transforms.Resize((config.in_resolution,config.in_resolution)),
transforms.ToTensor()
)
)
self.transform2 = transforms.Compose(
transforms=(
transforms.ToPILImage(),
transforms.Resize((config.out_resolution,config.out_resolution))
)
)
def forward(self, image):
x = self.transform1(image)
x = self.model(x)
x = self.transform2(x)
return x
CONFIG_MAPPING.register("upscaleing", ImageToImageConfig)
MODEL_MAPPING.register(ImageToImageConfig, ImageToImageModel)
config = ImageToImageConfig()
model = ImageToImageModel(config)
model.save_pretrained("AIupscaleing")
|