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Mhrooz 2024-07-09 14:20:03 +02:00
parent dabc2495a9
commit 7a071fa658
3 changed files with 13 additions and 66 deletions
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8
.gitignore vendored
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@ -1,8 +1,10 @@
./flowers/*
.DS_Store
./UNet/train_image/*
./UNet/params/*
./UNet/__pycache__/*
UNet/train_image/*
UNet/params/*
UNet/__pycache__/*
UNet/test_image
data/
archive.zip
flowers/*
UNet/result/result.jpg

6
UNet/train.py
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@ -7,9 +7,9 @@ from net import *
from torchvision.utils import save_image
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
weight_path = r'/Users/hanzhangma/Nextcloud/mhz/Study/SS24/MasterThesis/UNet/params/unet.pth'
data_path = r'/Users/hanzhangma/Document/DataSet/VOC2007'
save_path = r'/Users/hanzhangma/Nextcloud/mhz/Study/SS24/MasterThesis/Unet/train_image'
weight_path = r'D:\\MasterThesis\\UNet\\params\\unet.pth'
data_path = r'D:\\MasterThesis\\data\\VOCdevkit\\VOC2007'
save_path = r'D:\\MasterThesis\\UNet\\train_image'
if __name__ == '__main__':
data_loader = DataLoader(MyDataset(data_path), batch_size= 4, shuffle=True)

65
diffusion.ipynb
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@ -665,6 +665,7 @@
"\n",
" num_resolutions = len(base_channels_multiples)\n",
"\n",
" # encoder blocks = resnetblock * 3 + \n",
" self.encoder_blocks = nn.ModuleList()\n",
" curr_channels = [base_channels]\n",
" in_channels = base_channels\n",
@ -799,6 +800,7 @@
" self.sqrt_one_minus_alpha_cumulative = torch.sqrt(1-self.alpha_cumulative)\n",
"\n",
" def get_betas(self):\n",
" \"\"\"linear schedule, proposed in original ddpm paper 线性在原ddpm论文中提出\"\"\"\n",
" scale = 1000 / self.num_diffusion_timesteps\n",
" beta_start = scale * 1e-4\n",
" beta_end = scale * 0.02\n",
@ -896,66 +898,6 @@
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"@dataclass\n",
"class ModelConfig:\n",
" BASE_CH = 64 # 64, 128, 256, 256\n",
" BASE_CH_MULT = (1, 2, 4, 4) # 32, 16, 8, 8 \n",
" APPLY_ATTENTION = (False, True, True, False)\n",
" DROPOUT_RATE = 0.1\n",
" TIME_EMB_MULT = 4 # 128"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"model = UNet(\n",
" input_channels = TrainingConfig.IMG_SHAPE[0],\n",
" output_channels = TrainingConfig.IMG_SHAPE[0],\n",
" base_channels = ModelConfig.BASE_CH,\n",
" base_channels_multiples = ModelConfig.BASE_CH_MULT,\n",
" apply_attention = ModelConfig.APPLY_ATTENTION,\n",
" dropout_rate = ModelConfig.DROPOUT_RATE,\n",
" time_multiple = ModelConfig.TIME_EMB_MULT,\n",
")\n",
"model.to(BaseConfig.DEVICE)\n",
"\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=TrainingConfig.LR)\n",
"\n",
"dataloader = get_dataloader(\n",
" dataset_name = BaseConfig.DATASET,\n",
" batch_size = TrainingConfig.BATCH_SIZE,\n",
" device = BaseConfig.DEVICE,\n",
" pin_memory = True,\n",
" num_workers = TrainingConfig.NUM_WORKERS,\n",
")\n",
"\n",
"loss_fn = nn.MSELoss()\n",
"\n",
"sd = SimpleDiffusion(\n",
" num_diffusion_timesteps = TrainingConfig.TIMESTEPS,\n",
" img_shape = TrainingConfig.IMG_SHAPE,\n",
" device = BaseConfig.DEVICE,\n",
")\n",
"\n",
"scaler = amp.GradScaler()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 101,
@ -1051,13 +993,16 @@
" for x0s, _ in loader:\n",
" tq.update(1)\n",
" \n",
" # 生成噪声\n",
" ts = torch.randint(low=1, high=training_config.TIMESTEPS, size=(x0s.shape[0],), device=base_config.DEVICE)\n",
" xts, gt_noise = forward_diffusion(sd, x0s, ts)\n",
"\n",
" # forward & get loss\n",
" with amp.autocast():\n",
" pred_noise = model(xts, ts)\n",
" loss = loss_fn(gt_noise, pred_noise)\n",
"\n",
" # 梯度缩放和反向传播\n",
" optimizer.zero_grad(set_to_none=True)\n",
" scaler.scale(loss).backward()\n",
"\n",