CIFAR2/02_counter.ipynb

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   "id": "f04439e8-3684-4e71-a58d-1b222008c74d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='0'\n",
    "os.environ[\"HF_HOME\"]=\"~/codes/.cache/huggingface\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "379a0758-ffa9-4c0f-8828-6976b5752918",
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7c672bc-d87e-450e-bf89-86799b5ba6ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from accelerate.utils import ProjectConfiguration, set_seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8820a3ed-9b69-423b-868b-79c11c67762e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "def set_seeds(seed):\n",
    "    set_seed(seed)\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    \n",
    "set_seeds(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e9bc9238-5dc8-4d43-8551-a2ce62bfd120",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "from diffusers import DDPMPipeline, DDIMPipeline, DDPMScheduler, DDIMScheduler, UNet2DModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af017ec5-da0e-43ed-b9d1-8544336a4736",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c7aa6f4-4adf-494a-bfd2-dd414ef7d8e6",
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    {
     "data": {
      "text/plain": [
       "'config.json'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Args():\n",
    "    \n",
    "    model_config_name_or_path=\"config.json\"\n",
    "    \n",
    "    dataset_name=\"cifar10\"\n",
    "\n",
    "    resolution=32\n",
    "    center_crop=True\n",
    "    random_flip=False\n",
    "\n",
    "    dataloader_num_workers=8\n",
    "    \n",
    "    seed=42\n",
    "    gen_seed=0    \n",
    "\n",
    "    train_batch_size=256\n",
    "\n",
    "    ddpm_num_steps=1000\n",
    "    ddpm_num_inference_steps=50\n",
    "    ddpm_beta_schedule='linear'\n",
    "\n",
    "args=Args()\n",
    "args.model_config_name_or_path"
   ]
  },
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     "execution_count": 7,
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   ],
   "source": [
    "with open('./data/indices/5000-0.5/counter/idx-gen-sampled.pkl', 'rb') as handle:\n",
    "    test_index = pickle.load(handle)\n",
    "test_index"
   ]
  },
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   "cell_type": "code",
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      "56 617\n",
      "config.json\n",
      "torch.float32\n",
      "./saved/5000-0.5/counter/ddpm-sub-56-1000-Random-42/unet/diffusion_pytorch_model.bin\n",
      "cuda:0\n",
      "DDIM!\n"
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      "./saved/5000-0.5/counter/ddpm-sub-56-1000-TRAK-42/unet/diffusion_pytorch_model.bin\n",
      "cuda:0\n",
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      "./saved/5000-0.5/counter/ddpm-sub-56-1000-Ours-42/unet/diffusion_pytorch_model.bin\n",
      "cuda:0\n",
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      "57 10\n",
      "config.json\n",
      "torch.float32\n",
      "./saved/5000-0.5/counter/ddpm-sub-57-1000-Random-42/unet/diffusion_pytorch_model.bin\n",
      "cuda:0\n",
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      "./saved/5000-0.5/counter/ddpm-sub-57-1000-Ours-42/unet/diffusion_pytorch_model.bin\n",
      "cuda:0\n",
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      "58 221\n",
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      "./saved/5000-0.5/counter/ddpm-sub-58-1000-Random-42/unet/diffusion_pytorch_model.bin\n",
      "cuda:0\n",
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      "./saved/5000-0.5/counter/ddpm-sub-58-1000-TRAK-42/unet/diffusion_pytorch_model.bin\n",
      "cuda:0\n",
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      "./saved/5000-0.5/counter/ddpm-sub-58-1000-Ours-42/unet/diffusion_pytorch_model.bin\n",
      "cuda:0\n",
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      "59 820\n",
      "config.json\n",
      "torch.float32\n",
      "./saved/5000-0.5/counter/ddpm-sub-59-1000-Random-42/unet/diffusion_pytorch_model.bin\n",
      "cuda:0\n",
      "DDIM!\n"
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     "text": [
      "config.json\n",
      "torch.float32\n",
      "./saved/5000-0.5/counter/ddpm-sub-59-1000-TRAK-42/unet/diffusion_pytorch_model.bin\n",
      "cuda:0\n",
      "DDIM!\n"
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    {
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     "output_type": "stream",
     "text": [
      "config.json\n",
      "torch.float32\n",
      "./saved/5000-0.5/counter/ddpm-sub-59-1000-Ours-42/unet/diffusion_pytorch_model.bin\n",
      "cuda:0\n",
      "DDIM!\n"
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   ],
   "source": [
    "index_image_list = []\n",
    "for index, my_i in enumerate(test_index):\n",
    "        \n",
    "    print(index, my_i)\n",
    "    k_image_list = []\n",
    "    for k in [\n",
    "        1000\n",
    "    ]:\n",
    "        method_image_list = []\n",
    "        for method in ['Random', 'TRAK', 'Ours']:\n",
    "            # If passed along, set the training seed now.\n",
    "            if args.seed is not None:\n",
    "                set_seeds(args.seed)\n",
    "            ####    \n",
    "            print(args.model_config_name_or_path)\n",
    "            config = UNet2DModel.load_config(args.model_config_name_or_path)\n",
    "            config['resnet_time_scale_shift'] = 'scale_shift'\n",
    "                \n",
    "            model = UNet2DModel.from_config(config)\n",
    "            print(model.dtype)\n",
    "            ####\n",
    "            noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)\n",
    "            ddim_noise_scheduler = DDIMScheduler.from_config(noise_scheduler.config)\n",
    "            ####    \n",
    "            args.model_path = \"./saved/5000-0.5/counter/ddpm-sub-{}-{}-{}-42\".format(index, k, method)\n",
    "            model_path = '{}/unet/diffusion_pytorch_model.bin'.format(args.model_path)\n",
    "            print(model_path)\n",
    "            model.load_state_dict(torch.load(model_path))\n",
    "            model.cuda()\n",
    "            model.eval()\n",
    "    ####\n",
    "            set_seeds(42)\n",
    "            pipeline = DDIMPipeline(\n",
    "                unet=model,\n",
    "                scheduler=noise_scheduler,\n",
    "            ).to('cuda')\n",
    "            print(pipeline.device)\n",
    "            print('DDIM!')\n",
    "            \n",
    "            generator = [torch.Generator('cpu').manual_seed(my_i)]\n",
    "                    \n",
    "            images = pipeline(\n",
    "                        generator=generator,\n",
    "                        batch_size=1,\n",
    "                        num_inference_steps=50,\n",
    "                        output_type=\"numpy\",\n",
    "                        eta=0.0,\n",
    "            ).images\n",
    "            images = pipeline.numpy_to_pil(images)\n",
    "        \n",
    "            method_image_list.append(images[0])\n",
    "        k_image_list.append(method_image_list)\n",
    "    index_image_list.append(k_image_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e7ec7ce-d988-4b88-935b-db4e48dcfc71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1d91bf6-3750-49de-b782-c6018d1721dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./counter/saved.pkl\", 'wb') as handle:\n",
    "    pickle.dump(index_image_list, handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df86fcb8-af99-499e-83f9-b60d74b7acc0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e6287de-5e8e-4c7f-8714-d307f1e0a27f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00ca9848-98bf-4019-85a5-70c7bbbe0308",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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