From 262185a20edc7b90dbeee0b8bca5272b0d87a319 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Thu, 5 Sep 2024 15:02:38 +0100 Subject: [PATCH 01/12] Adding 3D DDPM tutorial for a separate later PR Signed-off-by: Eric Kerfoot --- generation/3d_ddpm/3d_ddpm_tutorial.ipynb | 915 ++++++++++++++++++++++ 1 file changed, 915 insertions(+) create mode 100644 generation/3d_ddpm/3d_ddpm_tutorial.ipynb diff --git a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb new file mode 100644 index 000000000..0cdd80d7c --- /dev/null +++ b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -0,0 +1,915 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "fa57bdf5", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) MONAI Consortium\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "id": "6286986e", + "metadata": {}, + "source": [ + "# Denoising Diffusion Probabilistic Model on 3D data\n", + "\n", + "This tutorial illustrates how to use MONAI for training a denoising diffusion probabilistic model (DDPM)[1] to create synthetic 3D images.\n", + "\n", + "[1] - [Ho et al. \"Denoising Diffusion Probabilistic Models\"](https://arxiv.org/abs/2006.11239)\n", + "\n", + "\n", + "## Setup environment" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f96b6f31", + "metadata": {}, + "outputs": [], + "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tqdm]\"\n", + "!python -c \"import matplotlib\" || pip install -q matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "cbc01d24", + "metadata": {}, + "source": [ + "## Setup imports" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "cdea37d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MONAI version: 1.4.0rc6\n", + "Numpy version: 1.26.4\n", + "Pytorch version: 2.3.1+cu121\n", + "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", + "MONAI rev id: 6a0e1b043ba2890e1463fa49df76f66e56a68b08\n", + "MONAI __file__: /home//miniconda3/envs/monai/lib/python3.11/site-packages/monai/__init__.py\n", + "\n", + "Optional dependencies:\n", + "Pytorch Ignite version: 0.4.11\n", + "ITK version: 5.4.0\n", + "Nibabel version: 5.2.1\n", + "scikit-image version: 0.23.2\n", + "scipy version: 1.13.1\n", + "Pillow version: 10.3.0\n", + "Tensorboard version: 2.17.0\n", + "gdown version: 5.2.0\n", + "TorchVision version: 0.18.1+cu121\n", + "tqdm version: 4.66.4\n", + "lmdb version: 1.4.1\n", + "psutil version: 5.9.0\n", + "pandas version: 2.2.2\n", + "einops version: 0.8.0\n", + "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n", + "mlflow version: 2.14.0\n", + "pynrrd version: 1.0.0\n", + "clearml version: 1.16.2rc0\n", + "\n", + "For details about installing the optional dependencies, please visit:\n", + " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", + "\n" + ] + } + ], + "source": [ + "import os\n", + "import tempfile\n", + "import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from torch.amp import GradScaler, autocast\n", + "from tqdm import tqdm\n", + "\n", + "from monai.apps import DecathlonDataset\n", + "from monai.config import print_config\n", + "from monai.data import DataLoader\n", + "from monai.transforms import (\n", + " EnsureChannelFirstd,\n", + " CenterSpatialCropd,\n", + " Compose,\n", + " Lambdad,\n", + " LoadImaged,\n", + " Resized,\n", + " ScaleIntensityd,\n", + " ScaleIntensityRangePercentilesd\n", + ")\n", + "from monai.utils import set_determinism\n", + "from monai.inferers import DiffusionInferer\n", + "from monai.networks.nets import DiffusionModelUNet\n", + "from monai.networks.schedulers import DDPMScheduler, DDIMScheduler\n", + "\n", + "print_config()" + ] + }, + { + "cell_type": "markdown", + "id": "50e37a43", + "metadata": {}, + "source": [ + "## Setup data directory\n", + "\n", + "You can specify a directory with the MONAI_DATA_DIRECTORY environment variable.\n", + "\n", + "This allows you to save results and reuse downloads.\n", + "\n", + "If not specified a temporary directory will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c38b4c33", + "metadata": {}, + "outputs": [], + "source": [ + "directory = os.path.abspath(\"./data\") # os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "# print(root_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "41af1391", + "metadata": {}, + "source": [ + "## Set deterministic training for reproducibility" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "515d8583", + "metadata": {}, + "outputs": [], + "source": [ + "set_determinism(42)" + ] + }, + { + "cell_type": "markdown", + "id": "29d8c601", + "metadata": {}, + "source": [ + "## Setup Decathlon Dataset and training and validation data loaders\n", + "\n", + "In this tutorial, we will use the 3D T1 weighted brain images from the [2016 and 2017 Brain Tumor Segmentation (BraTS) challenges](https://www.med.upenn.edu/sbia/brats2017/data.html). This dataset can be easily downloaded using the [DecathlonDataset](https://docs.monai.io/en/stable/apps.html#monai.apps.DecathlonDataset) from MONAI (`task=\"Task01_BrainTumour\"`). To load the training and validation images, we are using the `data_transform` transformations that are responsible for the following:\n", + "\n", + "1. `LoadImaged`: Loads the brain images from files.\n", + "2. `Lambdad`: Choose channel 1 of the image, which is the T1-weighted image.\n", + "3. `EnsureChannelFirstd`: Add the channel dimension of the input data.\n", + "4. `ScaleIntensityd`: Apply a min-max scaling in the intensity values of each image to be in the `[0, 1]` range.\n", + "5. `CenterSpatialCropd`: Crop the background of the images using a roi of size `[160, 200, 155]`.\n", + "6. `Resized`: Resize the images to a volume with size `[32, 40, 32]`.\n", + "\n", + "For the data loader, we are using mini-batches of 8 images, which consumes about 21GB of GPU memory during training. Please, reduce this value to run on smaller GPUs." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "f640d7ac", + "metadata": {}, + "outputs": [], + "source": [ + "data_transform = Compose(\n", + " [\n", + " LoadImaged(keys=[\"image\"]),\n", + " Lambdad(keys=\"image\", func=lambda x: x[:, :, :, 1]),\n", + " EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n", + " CenterSpatialCropd(keys=[\"image\"], roi_size=[160, 200, 155]),\n", + " Resized(keys=[\"image\"], spatial_size=(32, 40, 32)),\n", + " ScaleIntensityd(keys=[\"image\"]),\n", + " # ScaleIntensityRangePercentilesd(keys=[\"image\"], lower=3, upper=97, b_min=0, b_max=1),\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "ddd61e60", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-09-05 13:14:05,902 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", + "2024-09-05 13:14:05,905 - INFO - File exists: /home/ek10/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour.tar, skipped downloading.\n", + "2024-09-05 13:14:05,907 - INFO - Non-empty folder exists in /home/ek10/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour, skipped extracting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|██████████| 388/388 [02:46<00:00, 2.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-09-05 13:17:05,728 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", + "2024-09-05 13:17:05,731 - INFO - File exists: /home/ek10/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour.tar, skipped downloading.\n", + "2024-09-05 13:17:05,733 - INFO - Non-empty folder exists in /home/ek10/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour, skipped extracting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|██████████| 96/96 [00:37<00:00, 2.54it/s]\n" + ] + } + ], + "source": [ + "batch_size = 16\n", + "num_workers = 8\n", + "\n", + "train_ds = DecathlonDataset(\n", + " root_dir=root_dir,\n", + " task=\"Task01_BrainTumour\",\n", + " transform=data_transform,\n", + " section=\"training\",\n", + " download=True,\n", + " num_workers=num_workers,\n", + ")\n", + "\n", + "train_loader = DataLoader(\n", + " train_ds, batch_size=batch_size, shuffle=True, num_workers=num_workers, persistent_workers=True\n", + ")\n", + "\n", + "val_ds = DecathlonDataset(\n", + " root_dir=root_dir,\n", + " task=\"Task01_BrainTumour\",\n", + " transform=data_transform,\n", + " section=\"validation\",\n", + " download=True,\n", + " num_workers=num_workers,\n", + ")\n", + "\n", + "val_loader = DataLoader(val_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers, persistent_workers=True)" + ] + }, + { + "cell_type": "markdown", + "id": "50efe5ef", + "metadata": {}, + "source": [ + "### Visualization of the training images" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "bffb4abc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(1, 4, figsize=(10, 6))\n", + "for i in range(4):\n", + " plt.subplot(1, 4, i + 1)\n", + " plt.imshow(train_ds[i * 20][\"image\"][0, :, :, 15].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.axis(\"off\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "efc86997", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/ek10/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour/imagesTr/BRATS_400.nii.gz\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from monai.utils import first\n", + "\n", + "img = first(item[\"image\"] for item in train_ds if \"400\" in item[\"image\"].meta[\"filename_or_obj\"])\n", + "print(img.meta[\"filename_or_obj\"])\n", + "imgn = img[0].detach().cpu().numpy()\n", + "f, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)\n", + "ax0.imshow(imgn[..., 15], vmin=0, vmax=1, cmap=\"gray\")\n", + "ax1.imshow(np.max(imgn, axis=0), vmin=0, vmax=1, cmap=\"gray\")\n", + "ax2.imshow(np.max(imgn, axis=1), vmin=0, vmax=1, cmap=\"gray\")\n", + "ax3.imshow(np.max(imgn, axis=2), vmin=0, vmax=1, cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "id": "d22296e5", + "metadata": {}, + "source": [ + "### Define network, scheduler, optimizer, and inferer\n", + "\n", + "We will use a DDPM in this example; for that, we need to define a `DiffusionModelUNet` network that will have as input the noisy images and the values for the timestep `t`, and it will predict the noise that is present in the image.\n", + "\n", + "In this example, we have a network with three levels (with 256, 256, and 512 channels in each). In every level, we will have two residual blocks, and only the last one will have an attention block with a single attention head (with 512 channels)." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "d499f7b1", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\")\n", + "\n", + "model = DiffusionModelUNet(\n", + " spatial_dims=3,\n", + " in_channels=1,\n", + " out_channels=1,\n", + " channels=[64, 64, 128],\n", + " attention_levels=[False, False, True],\n", + " num_head_channels=[0, 0, 128],\n", + " num_res_blocks=2,\n", + ").to(device)" + ] + }, + { + "cell_type": "markdown", + "id": "47ad91ff", + "metadata": {}, + "source": [ + "Together with our U-net, we need to define the Noise Scheduler for the diffusion model. This scheduler is responsible for defining the amount of noise that should be added in each timestep `t` of the diffusion model's Markov chain. Besides that, it has the operations to perform the reverse process, which will remove the noise of the images (a.k.a. denoising process). In this case, we are using a `DDPMScheduler`. Here we are using 1000 timesteps and a `scaled_linear` profile for the beta values (proposed in [Rombach et al. \"High-Resolution Image Synthesis with Latent Diffusion Models\"](https://arxiv.org/abs/2112.10752)). This profile had better results than the `linear, proposed in the original DDPM's paper. In `beta_start` and `beta_end`, we define the limits for the beta values. These are important to determine how accentuated is the addition of noise in the image." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "6c1de5ad", + "metadata": {}, + "outputs": [], + "source": [ + "num_train_timesteps = 1000\n", + "scheduler = DDPMScheduler(\n", + " num_train_timesteps=num_train_timesteps #, schedule=\"scaled_linear_beta\", beta_start=0.0005, beta_end=0.0195, clip_sample=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "36d3e99a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'alpha cumprod')" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(scheduler.alphas_cumprod.cpu(), color=(2 / 255, 163 / 255, 163 / 255), linewidth=2)\n", + "plt.xlabel(\"Timestep [t]\")\n", + "plt.ylabel(\"alpha cumprod\")" + ] + }, + { + "cell_type": "markdown", + "id": "9125f7c8", + "metadata": {}, + "source": [ + "Finally, we define the Inferer, which contains functions that will help during the training and sampling of the model, and the optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "8685da6e", + "metadata": {}, + "outputs": [], + "source": [ + "inferer = DiffusionInferer(scheduler)\n", + "optimizer = torch.optim.Adam(params=model.parameters(), lr=5e-5)" + ] + }, + { + "cell_type": "markdown", + "id": "9f371ad8", + "metadata": {}, + "source": [ + "## Model training\n", + "\n", + "In this part, we will train the diffusion model to predict the noise added to the images. For this, we are using an MSE loss between the prediction and the original noise. During the training, we are also sampling brain images to evaluate the evolution of the model. In this training, we use Automatic Mixed Precision to save memory and speed up the training." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "bd10b595", + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 00: 0%| | 0/25 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 08: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0209]\n", + "Epoch 09: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0210]\n", + "Epoch 10: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0184]\n", + "Epoch 11: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0177]\n", + "Epoch 12: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0115]\n", + "Epoch 13: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0170]\n", + "Epoch 14: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0152]\n", + "Epoch 15: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0140]\n", + "100%|██████████| 1000/1000 [00:16<00:00, 62.41it/s]\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 16: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0117]\n", + "Epoch 17: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0109]\n", + "Epoch 18: 100%|██████████| 25/25 [00:09<00:00, 2.51it/s, loss=0.0147]\n", + "Epoch 19: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0130]\n", + "Epoch 20: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0124]\n", + "Epoch 21: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0109]\n", + "Epoch 22: 100%|██████████| 25/25 [00:10<00:00, 2.43it/s, loss=0.0113]\n", + "Epoch 23: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0112]\n", + "100%|██████████| 1000/1000 [00:15<00:00, 63.25it/s]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 24: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0111]\n", + "Epoch 25: 100%|██████████| 25/25 [00:10<00:00, 2.48it/s, loss=0.0098]\n", + "Epoch 26: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0106]\n", + "Epoch 27: 100%|██████████| 25/25 [00:09<00:00, 2.51it/s, loss=0.0103]\n", + "Epoch 28: 100%|██████████| 25/25 [00:09<00:00, 2.51it/s, loss=0.0103]\n", + "Epoch 29: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0094]\n", + "Epoch 30: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0123]\n", + "Epoch 31: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0134]\n", + "100%|██████████| 1000/1000 [00:15<00:00, 62.71it/s]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 32: 100%|██████████| 25/25 [00:09<00:00, 2.53it/s, loss=0.0126]\n", + "Epoch 33: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0095]\n", + "Epoch 34: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0136]\n", + "Epoch 35: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0086]\n", + "Epoch 36: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0107]\n", + "Epoch 37: 100%|██████████| 25/25 [00:09<00:00, 2.51it/s, loss=0.0084]\n", + "Epoch 38: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0098]\n", + "Epoch 39: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0103]\n", + "100%|██████████| 1000/1000 [00:15<00:00, 63.37it/s]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train completed, total time: 485.4429187774658.\n" + ] + } + ], + "source": [ + "max_epochs = 40\n", + "val_interval = max_epochs // 5\n", + "use_amp = False\n", + "epoch_loss_list = []\n", + "val_epoch_loss_list = []\n", + "\n", + "scaler = GradScaler()\n", + "total_start = time.time()\n", + "for epoch in range(max_epochs):\n", + " model.train()\n", + " epoch_loss = 0\n", + " progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n", + " progress_bar.set_description(f\"Epoch {epoch:02}\")\n", + " for step, batch in progress_bar:\n", + " images = batch[\"image\"].to(device)\n", + " optimizer.zero_grad(set_to_none=True)\n", + "\n", + " with autocast(device_type=\"cuda\", enabled=use_amp):\n", + " # Generate random noise\n", + " noise = torch.randn_like(images).to(device)\n", + "\n", + " # Create timesteps\n", + " timesteps = torch.randint(0, num_train_timesteps, (images.shape[0],), device=images.device).long()\n", + "\n", + " # Get model prediction\n", + " noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n", + "\n", + " loss = F.mse_loss(noise_pred.float(), noise.float())\n", + "\n", + " if use_amp:\n", + " scaler.scale(loss).backward()\n", + " scaler.step(optimizer)\n", + " scaler.update()\n", + " else:\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " epoch_loss += loss.item()\n", + "\n", + " progress_bar.set_postfix({\"loss\": f\"{epoch_loss / (step + 1):.4f}\"})\n", + " epoch_loss_list.append(epoch_loss / (step + 1))\n", + "\n", + " if (epoch + 1) % val_interval == 0:\n", + " model.eval()\n", + "\n", + " torch.save(model.state_dict(),f\"model_{epoch:04}.pth\")\n", + "\n", + " val_epoch_loss = 0\n", + " for step, batch in enumerate(val_loader):\n", + " images = batch[\"image\"].to(device)\n", + " noise = torch.randn_like(images).to(device)\n", + " with torch.no_grad(), autocast(device_type=\"cuda\", enabled=use_amp):\n", + " timesteps = torch.randint(0, num_train_timesteps, (images.shape[0],), device=images.device).long()\n", + "\n", + " # Get model prediction\n", + " noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n", + " val_loss = F.mse_loss(noise_pred.float(), noise.float())\n", + "\n", + " val_epoch_loss += val_loss.item()\n", + " progress_bar.set_postfix({\"val_loss\": val_epoch_loss / (step + 1)})\n", + " val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n", + "\n", + " # Sampling image during training\n", + " image = torch.randn((1, 1, 32, 40, 32))\n", + " image = image.to(device)\n", + " scheduler.set_timesteps(num_inference_steps=1000)\n", + " with autocast(device_type=\"cuda\", enabled=use_amp):\n", + " image = inferer.sample(input_noise=image, diffusion_model=model, scheduler=scheduler)\n", + "\n", + " plt.figure(figsize=(2, 2))\n", + " plt.imshow(image[0, 0, :, :, 15].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "\n", + "total_time = time.time() - total_start\n", + "print(f\"train completed, total time: {total_time}.\")" + ] + }, + { + "cell_type": "markdown", + "id": "3e263b67", + "metadata": {}, + "source": [ + "### Learning curves" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "c7520419", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use(\"seaborn-v0_8\")\n", + "plt.title(\"Learning Curves\", fontsize=20)\n", + "plt.plot(np.linspace(1, max_epochs, max_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n", + "plt.plot(\n", + " np.linspace(val_interval, max_epochs, int(max_epochs / val_interval)),\n", + " val_epoch_loss_list,\n", + " color=\"C1\",\n", + " linewidth=2.0,\n", + " label=\"Validation\",\n", + ")\n", + "plt.yticks(fontsize=12)\n", + "plt.xticks(fontsize=12)\n", + "plt.xlabel(\"Epochs\", fontsize=16)\n", + "plt.ylabel(\"Loss\", fontsize=16)\n", + "plt.legend(prop={\"size\": 14})\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "38724c9b", + "metadata": {}, + "source": [ + "## Sampling Brain Image\n", + "\n", + "In order to sample the brain images, we need to pass the model an image containing just noise and use it to remove the noise of the image iteratively. For that, we will use the `.sample()` function of the `inferer`." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "092eb6a0", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1000/1000 [00:15<00:00, 63.78it/s]\n" + ] + } + ], + "source": [ + "model.eval()\n", + "model.load_state_dict(torch.load(\"model_0039.pth\"))\n", + "noise = torch.randn((1, 1, 32, 40, 32))\n", + "noise = noise.to(device)\n", + "scheduler.set_timesteps(num_inference_steps=1000)\n", + "image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "5dc3e69d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use(\"default\")\n", + "plotting_image_0 = np.concatenate([image[0, 0, :, :, 15].cpu(), np.flipud(image[0, 0, :, 20, :].cpu().T)], axis=1)\n", + "plotting_image_1 = np.concatenate([np.flipud(image[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n", + "plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), vmin=0, vmax=1, cmap=\"gray\")\n", + "plt.tight_layout()\n", + "plt.axis(\"off\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f0acc27a", + "metadata": {}, + "source": [ + "### Sampling with Denoising Diffusion Implicit Model Scheduler\n", + "\n", + "Recent papers have proposed different ways to improve the sampling speed by using fewer steps in the denoising process. In this example, we are using a `DDIMScheduler` (from [Song et al. \"Denoising Diffusion Implicit Models\"](https://arxiv.org/abs/2010.02502)) to reduce the original number of steps from 1000 to 250." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "e3e43b95", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 250/250 [00:03<00:00, 63.95it/s]\n" + ] + } + ], + "source": [ + "scheduler_ddim = DDIMScheduler(\n", + " num_train_timesteps=num_train_timesteps,\n", + " schedule=\"scaled_linear_beta\",\n", + " beta_start=0.0005,\n", + " beta_end=0.0195,\n", + " clip_sample=False,\n", + ")\n", + "\n", + "scheduler_ddim.set_timesteps(num_inference_steps=250)\n", + "\n", + "model.eval()\n", + "noise = torch.randn((1, 1, 32, 40, 32))\n", + "noise = noise.to(device)\n", + "\n", + "image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler_ddim)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "89f93ab8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use(\"default\")\n", + "plotting_image_0 = np.concatenate([image[0, 0, :, :, 15].cpu(), np.flipud(image[0, 0, :, 20, :].cpu().T)], axis=1)\n", + "plotting_image_1 = np.concatenate([np.flipud(image[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n", + "plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), vmin=0, vmax=1, cmap=\"gray\")\n", + "plt.tight_layout()\n", + "plt.axis(\"off\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "py:percent,ipynb" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e5e2c843bb31a8168e4fbc09a8c0f37106d4f0f8 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Wed, 6 Nov 2024 16:21:37 +0000 Subject: [PATCH 02/12] Example works now Signed-off-by: Eric Kerfoot --- generation/3d_ddpm/3d_ddpm_tutorial.ipynb | 240 ++++++++++------------ 1 file changed, 109 insertions(+), 131 deletions(-) diff --git a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb index 0cdd80d7c..3b6d19a96 100644 --- a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 1, "id": "cdea37d5", "metadata": {}, "outputs": [ @@ -64,32 +64,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "MONAI version: 1.4.0rc6\n", + "MONAI version: 1.4.0\n", "Numpy version: 1.26.4\n", - "Pytorch version: 2.3.1+cu121\n", + "Pytorch version: 2.4.0+cu121\n", "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", - "MONAI rev id: 6a0e1b043ba2890e1463fa49df76f66e56a68b08\n", - "MONAI __file__: /home//miniconda3/envs/monai/lib/python3.11/site-packages/monai/__init__.py\n", + "MONAI rev id: 46a5272196a6c2590ca2589029eed8e4d56ff008\n", + "MONAI __file__: /home//miniconda3/envs/monai/lib/python3.9/site-packages/monai/__init__.py\n", "\n", "Optional dependencies:\n", "Pytorch Ignite version: 0.4.11\n", - "ITK version: 5.4.0\n", + "ITK version: 5.3.0\n", "Nibabel version: 5.2.1\n", - "scikit-image version: 0.23.2\n", - "scipy version: 1.13.1\n", + "scikit-image version: 0.22.0\n", + "scipy version: 1.13.0\n", "Pillow version: 10.3.0\n", - "Tensorboard version: 2.17.0\n", + "Tensorboard version: 2.16.2\n", "gdown version: 5.2.0\n", - "TorchVision version: 0.18.1+cu121\n", + "TorchVision version: NOT INSTALLED or UNKNOWN VERSION.\n", "tqdm version: 4.66.4\n", "lmdb version: 1.4.1\n", "psutil version: 5.9.0\n", "pandas version: 2.2.2\n", "einops version: 0.8.0\n", - "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n", - "mlflow version: 2.14.0\n", + "transformers version: 4.40.2\n", + "mlflow version: 2.12.2\n", "pynrrd version: 1.0.0\n", - "clearml version: 1.16.2rc0\n", + "clearml version: 1.16.0rc0\n", "\n", "For details about installing the optional dependencies, please visit:\n", " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", @@ -146,14 +146,22 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "c38b4c33", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/localek10/workspace/monai/tutorials_mine/generation/3d_ddpm/data\n" + ] + } + ], "source": [ "directory = os.path.abspath(\"./data\") # os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", "root_dir = tempfile.mkdtemp() if directory is None else directory\n", - "# print(root_dir)" + "print(root_dir)" ] }, { @@ -195,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 4, "id": "f640d7ac", "metadata": {}, "outputs": [], @@ -208,49 +216,25 @@ " CenterSpatialCropd(keys=[\"image\"], roi_size=[160, 200, 155]),\n", " Resized(keys=[\"image\"], spatial_size=(32, 40, 32)),\n", " ScaleIntensityd(keys=[\"image\"]),\n", - " # ScaleIntensityRangePercentilesd(keys=[\"image\"], lower=3, upper=97, b_min=0, b_max=1),\n", + " ScaleIntensityRangePercentilesd(keys=[\"image\"], lower=3, upper=97, b_min=0, b_max=1),\n", " ]\n", ")" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 9, "id": "ddd61e60", "metadata": { "lines_to_next_cell": 2 }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-09-05 13:14:05,902 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", - "2024-09-05 13:14:05,905 - INFO - File exists: /home/ek10/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour.tar, skipped downloading.\n", - "2024-09-05 13:14:05,907 - INFO - Non-empty folder exists in /home/ek10/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour, skipped extracting.\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "Loading dataset: 100%|██████████| 388/388 [02:46<00:00, 2.33it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-09-05 13:17:05,728 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", - "2024-09-05 13:17:05,731 - INFO - File exists: /home/ek10/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour.tar, skipped downloading.\n", - "2024-09-05 13:17:05,733 - INFO - Non-empty folder exists in /home/ek10/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour, skipped extracting.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading dataset: 100%|██████████| 96/96 [00:37<00:00, 2.54it/s]\n" + "Loading dataset: 100%|██████████| 388/388 [01:53<00:00, 3.41it/s]\n", + "Loading dataset: 100%|██████████| 96/96 [00:28<00:00, 3.35it/s]\n" ] } ], @@ -263,7 +247,7 @@ " task=\"Task01_BrainTumour\",\n", " transform=data_transform,\n", " section=\"training\",\n", - " download=True,\n", + " download=False, #True,\n", " num_workers=num_workers,\n", ")\n", "\n", @@ -276,7 +260,7 @@ " task=\"Task01_BrainTumour\",\n", " transform=data_transform,\n", " section=\"validation\",\n", - " download=True,\n", + " download=False, #True,\n", " num_workers=num_workers,\n", ")\n", "\n", @@ -293,13 +277,13 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 10, "id": "bffb4abc", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -320,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 11, "id": "efc86997", "metadata": {}, "outputs": [ @@ -328,22 +312,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "/home/ek10/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour/imagesTr/BRATS_400.nii.gz\n" + "/home/localek10/workspace/monai/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour/imagesTr/BRATS_400.nii.gz\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 47, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -379,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 12, "id": "d499f7b1", "metadata": { "lines_to_next_cell": 2 @@ -409,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 13, "id": "6c1de5ad", "metadata": {}, "outputs": [], @@ -422,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 14, "id": "36d3e99a", "metadata": {}, "outputs": [ @@ -432,13 +416,13 @@ "Text(0, 0.5, 'alpha cumprod')" ] }, - "execution_count": 73, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -463,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 15, "id": "8685da6e", "metadata": {}, "outputs": [], @@ -484,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 16, "id": "bd10b595", "metadata": { "lines_to_next_cell": 0 @@ -494,27 +478,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 00: 0%| | 0/25 [00:00" ] @@ -526,20 +503,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 08: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0209]\n", - "Epoch 09: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0210]\n", - "Epoch 10: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0184]\n", - "Epoch 11: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0177]\n", - "Epoch 12: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0115]\n", - "Epoch 13: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0170]\n", - "Epoch 14: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0152]\n", - "Epoch 15: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0140]\n", - "100%|██████████| 1000/1000 [00:16<00:00, 62.41it/s]\n" + "Epoch 08: 100%|██████████| 25/25 [00:36<00:00, 1.48s/it, loss=0.0163]\n", + "Epoch 09: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0174]\n", + "Epoch 10: 100%|██████████| 25/25 [00:37<00:00, 1.48s/it, loss=0.0164]\n", + "Epoch 11: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0165]\n", + "Epoch 12: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0137]\n", + "Epoch 13: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0172]\n", + "Epoch 14: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0163]\n", + "Epoch 15: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0173]\n", + "100%|██████████| 1000/1000 [00:34<00:00, 28.68it/s]\n" ] }, { "data": { - "image/png": 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" ] @@ -551,20 +528,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 16: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0117]\n", - "Epoch 17: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0109]\n", - "Epoch 18: 100%|██████████| 25/25 [00:09<00:00, 2.51it/s, loss=0.0147]\n", - "Epoch 19: 100%|██████████| 25/25 [00:10<00:00, 2.49it/s, loss=0.0130]\n", - "Epoch 20: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0124]\n", - "Epoch 21: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0109]\n", - "Epoch 22: 100%|██████████| 25/25 [00:10<00:00, 2.43it/s, loss=0.0113]\n", - "Epoch 23: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0112]\n", - "100%|██████████| 1000/1000 [00:15<00:00, 63.25it/s]\n" + "Epoch 16: 100%|██████████| 25/25 [00:36<00:00, 1.47s/it, loss=0.0144]\n", + "Epoch 17: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0158]\n", + "Epoch 18: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0105]\n", + "Epoch 19: 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" ] @@ -576,20 +553,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 24: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0111]\n", - "Epoch 25: 100%|██████████| 25/25 [00:10<00:00, 2.48it/s, loss=0.0098]\n", - "Epoch 26: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0106]\n", - "Epoch 27: 100%|██████████| 25/25 [00:09<00:00, 2.51it/s, loss=0.0103]\n", - "Epoch 28: 100%|██████████| 25/25 [00:09<00:00, 2.51it/s, loss=0.0103]\n", - "Epoch 29: 100%|██████████| 25/25 [00:09<00:00, 2.50it/s, loss=0.0094]\n", - "Epoch 30: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0123]\n", - "Epoch 31: 100%|██████████| 25/25 [00:10<00:00, 2.50it/s, loss=0.0134]\n", - "100%|██████████| 1000/1000 [00:15<00:00, 62.71it/s]\n" + "Epoch 24: 100%|██████████| 25/25 [00:36<00:00, 1.47s/it, loss=0.0142]\n", + "Epoch 25: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0107]\n", + "Epoch 26: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0137]\n", + "Epoch 27: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0156]\n", + "Epoch 28: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0104]\n", + "Epoch 29: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0131]\n", + "Epoch 30: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0127]\n", + "Epoch 31: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0136]\n", + "100%|██████████| 1000/1000 [00:34<00:00, 28.60it/s]\n" ] }, { "data": { - "image/png": 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100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0114]\n", + "Epoch 36: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0152]\n", + "Epoch 37: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0138]\n", + "Epoch 38: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0100]\n", + "Epoch 39: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0113]\n", + "100%|██████████| 1000/1000 [00:35<00:00, 28.48it/s]\n" ] }, { "data": { - "image/png": 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" ] @@ -626,7 +603,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "train completed, total time: 485.4429187774658.\n" + "train completed, total time: 1676.2901484966278.\n" ] } ], @@ -720,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 17, "id": "c7520419", "metadata": { "lines_to_next_cell": 2 @@ -728,7 +705,7 @@ "outputs": [ { "data": { - "image/png": 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4qinYAFSOMb0A6mz37t2Vhs/yLr/8crVt21a/+tWvtH79en3xxRdauXKldu3apTFjxqhXr16Kjo5WXl6eDh06pI0bN2rt2rUKDQ3Vvffe636eCy+8UN26ddO+ffs0d+5c5eTk6NZbb1ViYqJOnDihpUuXatWqVerbt2+9/9m5KXj00Ue1cuVKd+/rTz/9pJEjRyo+Pl5HjhzRRx99pK1bt6pXr17avn27JFW4CEZ1RERE6N1339XEiRN1+PBhzZ8/X2vXrtXIkSPVv39/tWrVSoZhKD09XT/88INWr16tY8eOVfhcV199tdq1a6fU1FS9/vrrOn36tG688UbZbDb30tK7du1Snz59qpy3uTZGjhypb7/9VsePH9d7770nqXRaN1/jolu2bKn/+7//0+OPP6709HTdcccduu2223TVVVepTZs2cjgcOnHihLZv365Vq1bpl19+0fTp09W9e3dJpR/qHnroIbVr10433XSTevXqpXbt2slisSg9PV1ffvml5s+fL0lq3bp1QC73DDQVhF4AdbZmzZpq9apOmzZNbdu2lclk0t///nc9//zzmjNnjo4ePaqXX3650vPOnVrLZDLppZde0n333afs7GytWLHCa2GBbt266fXXX9fgwYNr96aasLi4OM2YMUO/+c1vlJmZqSVLlnitAnb77bfr0ksvdYfeuqzO1qFDB82dO1cvvPCCli1bpoyMDM2cOVMzZ86s8PiQkBDdfffdXqvahYaG6qWXXtJvf/tbFRQUeD2HxWLRM888o+zs7AYJvTfeeKOeffZZFRUVuccVV2fRkptuuklvv/22nn76aWVlZWnOnDmVDq0xm80VTkeXmpqqDz/8sNLXSExM1Ntvv+0e9wyg5gi9APwiJCREzz77rH71q19p/vz52rBhg44fP66CggJFRESoffv2uuiii3TVVVfp2muv9Tq/R48eWrx4sd59912tW7dOaWlpioyM1Hnnnadhw4bpnnvuqfMyu01Z9+7dtXz5cr3//vtau3atjh07psjISHXr1k133XWXRowY4REoy8YB11ZcXJxeeuklPfDAA/r888/13XffKSUlRadPn5bFYlGLFi2UnJysyy+/XCNHjvSagq5Mv3799K9//UvTp0/Xd999p9OnTysuLk59+/bV+PHj1bdvX7355pt1qrUyUVFRuvbaa90foCwWi4YPH16tc6+77jqtWbNG8+bN09dff60DBw4oOztbFotFLVu2VNeuXXX55ZdryJAhSkpKcp/Xrl07zZ8/X//5z3+0detWpaamKiMjQwUFBYqOjlaXLl107bXXasyYMYqKimqQ9w0EC5NR1eSVAIBm6Q9/+IMWLFigNm3a6Ouvv/Z3OQDQoLiRDQCCUFFRkXtISqAtKgEADYHQCwDN0NGjRyudEcDpdOrZZ591T3912223NWZpAOAXjOkFgGbo7bff1vbt23XzzTerd+/eSkhIUFFRkfbu3av58+e7F6644oormBEAQFAg9AJAM3Xw4EGfN3317dtXr732Wq2nKwOApoQb2QCgGTp06JBWr16t9evXKzU1VZmZmXI4HIqLi1PPnj118803a/jw4TKbGeUGIDgQegEAANDs8REfAAAAzR5jequQnp5bo+PNZpPi4yOVmZkvl4tO9IrQRr7RPr7RPr7RPlWjjXyjfXyjfXzzV/skJla9wA49vfXMbDbJZDLJbObGkMrQRr7RPr7RPr7RPlWjjXyjfXyjfXwL5PYh9AIAAKDZI/QCAACg2SP0AgAAoNkj9AIAAKDZI/QCAACg2SP0AgAAoNkj9AIAAKDZI/QCAACg2SP0AgAAoNkj9AIAAKDZI/QCAACg2SP0AgAAoNkj9AIAAKDZI/QCAACg2SP0AgAAoNkj9AIAAKDZI/QGiJOnC/TC7M2auWK3DMPwdzkAAKCBDRrUT48+OsnfZQQNq78LQKkNu07qQGq2DqRm6+o+7XR+Uoy/SwIAoNkbNKhfjY7//vstDVQJGhqhN0BYLWc73TOyiwi9AAA0gt/85rde2+bP/1R5eXkV7qtPn3yyQDZbWIO+Bs4i9AaI2MhQ9+PsfLsfKwEAIHjcf/8DXttWrPhMeXl5Fe6rTx07dmrQ54cnQm+AIPQCABC4jh8/pjvvvEU33zxSjz76kP7v/17W1q1blJOTrfnzlyopqa2+/vpLrV37hfbs2aVTp9JltVrVuXNX3XXXr3TNNdd7PeegQf3Up09fvfXWe+5tzz//rFas+Ezz5i3Rt9+u06JF83X8+DHFxydo+PBbNH78RJnN3JJVG4TeABFTLvTm5Bf7sRIAAFCZlJRfdNddd+mCC7po2LARysnJVkhIiCTp3XffUkhIiHr16qOEhJbKyjqtb775j/74x8l64on/p9Gj767267z99uv68cctuuKKwRowYKDWrftK//jHeyopKdEDDzzSMG+umSP0BojYKJv7cVYePb0AAASi7dt/1COPPKKxY++Xw+Hy2Pfyy6+rXbv2HtsKCgr00EMTNGPGdI0YMUphYdUbw7t3717NnDlHLVu2lCSNHz9Rd999m/71r3maMGGSO2ij+gi9ASI6PEQmk2QYDG8AAASOH/akafG6QyqyO/1dSoXCQi26bfAF6te9VaO8XkJCSz344IPKzy/x2ndu4JWkiIgIDRs2Qm+9NVW7d/+kSy65tFqvM378/e7AK0lxcXEaPPhqrVjxmY4ePaLOnbvU/k0EKUJvgDCbTYqJCFV2vl05hF4AQIBYueGIjmcU+LsMn1ZsONpoobdr164KDQ2tMPSePp2pjz+eqe+/X68TJ46ruNhzuOKpU+nVfp3k5B5e2xITS99jXl5uDauGROgNKLGRZ0OvyzBkNpn8XRIAIMgNu6yjFgV4T++wy85rtNeLj0+ocHtOTrYmThynkydP6OKLe6tfvwGKioqW2WzWgQP7tG7d1yop8Q7KlYmMjPTaZrFYJElOZ2D+LgIdoTeAxESFSmmS02Uov7BE0RGhVZ8EAEAD6te9VaP1ojYNFXdIffbZEp08eUITJz6o8eMneuybPXum1q37ujGKgw8BO+eF3W7Xyy+/rEGDBqlXr16688479e2331br3PXr12vs2LG67LLL1K9fP40ePVqLFy9u2ILrQVzk2ZvZGNcLAEDTkZqaIkkaPPgar33bt29t5GpQkYANvVOmTNHMmTM1cuRI/eEPf5DFYtGkSZO0adMmn+etWbNGEyZMUElJiR577DH97ne/U1hYmCZPnqyZM2c2TvG1FBvFXL0AADRFbdokSSqd3aG81atX6rvvqtdph4YVkMMbtm/fruXLl+upp57S/fffL0kaNWqURowYoVdeeUVz5syp9NxPPvlEiYmJ+uijjxQaWhoix4wZo2HDhmnhwoUaP358Y7yFWvGYq5dpywAAaDKGDLlZn3wyS1OnvqwtWzapTZskHTiwT5s3/6Crr75WX3/9pb9LDHoB2dO7cuVKWSwWjRkzxr3NZrNp9OjR2rp1q44fP17puXl5eYqNjXUHXkmyWq1q0aJFtefG8xdWZQMAoGlq1aq13nzzPV16aX9t2rRRS5YsVElJiV577S1deeVV/i4PCtCe3t27d6tTp06Kiory2N6rVy/3/qSkpArPHTBggN5//31NnTpVt912m0wmk5YtW6adO3dq6tSpDV16nXiGXlZlAwDAHxYsWOa1LSmprb75ZpOs1sr7C7t27abXXnurwn033zzSa9s333gP2fzDH57VH/7wbIXPcf/9D+j++x+o9PXhW0CG3vT0dCUmJnptL9uWlpZW6bkPP/ywUlJSNH36dL3zzjuSpPDwcL3xxhu64YYbalyL2WyS2Vz9qcMsFrPH95qIjz3bE51bUOLzP6ymrC5tFAxoH99oH99on6rRRr7RPr7RPr4FcvsEZOgtKiryGJ5QxmazufdXJjQ0VJ06ddKQIUN00003yel0at68efrv//5vffjhh+rTp0+NaomPj5SpFvPlxsSE1/gcW/jZ95xf7FCLFt5z9DUntWmjYEL7+Eb7+Eb7VI028o328Y328S0Q2ycgQ29YWJjsdu8xrWUrm/gam/vcc89p27ZtWrRokczm0k8Zw4YN04gRI/T8889r/vz5NaolMzO/xj29MTHhyskplNPpqvqEcgzDUKjVLLvDpVNZhTp9Or9G5zcVdWmjYED7+Eb7+Eb7VI028o328Y328c1f7VOdjsKADL2JiYk6efKk1/b09NLl+1q1qniSbLvdrn/961+aOHGiO/BKUkhIiAYPHqxPPvlEdru9wl7kyrhchlwuo4bvQHI6XXI4av7LjokM1ansImXn2Wt1flNS2zYKFrSPb7SPb7RP1Wgj32gf32gf3wKxfQJvwIWk7t276/Dhw8rLy/PYvm3bNklSjx7e61FLUlZWlhwOR4XL8zkcDrlcLrlcgfULOFfZXL15hSVy8AkSAACgXgRk6B06dKicTqfmzp3r3ma327Vw4UL17t3bPXPDsWPHdPDgQfcxCQkJiomJ0RdffOExPCI/P19ffvmlLrjggiYwbdnZVdlymLYMAACgXgTk8IbevXtr6NCheu2115SRkaGOHTtq0aJFSk1N1fPPP+8+bvLkydq4caP27t0rSbJYLJowYYKmTp2qMWPG6NZbb5XL5dKCBQt04sQJvfzyy/56S9V27ly98TGBHdIBAACagoAMvZL00ksvaerUqVq6dKmys7OVnJys6dOnq3///j7Pe+ihh9S+fXt99NFHmjZtmux2u5KTk/XGG29oyJAhjVR97bFABQAAQP0L2NBrs9k0efJkTZ48udJjZs+eXeH2kSNHauRI70mgm4KYqHJLERN6AQAA6kVAjukNZuV7erPyWJUNAACgPhB6A0z5G9kY3gAAAFA/CL0BpnxPb04eoRcAAKA+EHoDTAw3sgEAANQ7Qm+ACbGaFRlWen9hdj5jegEAaMo+/3yZBg3qp88/X+axffTokRo9uvo33Vf2PPXpgw/e1aBB/bRly6YGew1/IvQGoLLe3ux8uwyj5ksgAwCA6nn22T9o0KB++uKLlT6Py8/P0/XXX6kbbrhKRUVFjVRd/dqyZZMGDeqnDz5419+l+AWhNwCVjeu1l7hUZPdeUhkAANSPESNulSQtX77U53FffLFKxcXFuvHGofWyuuvrr7+j119/p87PU5/uuGOMPvlkgS68sKe/S2kQATtPbzCLi/Jcijjcxq8JAICGcOml/ZWU1E5btmzSiRMn1KZNmwqPKwvFt9wyql5et1279vXyPPUpLi5OcXFx/i6jwZCmAtC5N7O1jo/wYzUAADRfJpNJw4eP1IwZ0/X550s1YcIkr2MOHTqo3bt/UufOXdWhQwe99957Wrv2Sx09elTZ2VmKjY1Tv34DNGHCpGqH2bLxvAsWeI7RzcnJ1rvvTtN//vOVCgrydf75nTV27G8qfZ7PPluib775WgcO7FdmZoZstjD16HGh7r13vPr27ec+7oMP3tWHH74vSfrww/fdjyVp/vylSkpq6z7mjTeme5wrSd988x/NnfuJ9u3bo5IShzp0OE/Dhg3X6NF3y2o9GyePHTumyy8foZtvHqlx4yZo2rTXtXXrZjkcJbrool569NEn1LVrt2q1UX1jeEMAio1iBgcAABrLsGEjZDabtWLFZxXeS1N289iIEbfq8OGf9cYbb8hms+mqq67VXXf9St2799C//71Kv/3tfTpx4nit6ygqKtJjjz2gJUsWql279rrzzl/pvPM66n//92l99dWaCs957bWXlJmZqX79Buiuu36tK64YpJ07d+iJJx7WunVfuY+75JJLNWzYCElSnz599Zvf/Nb9FRUV7bOuOXM+1pQpv9fBgwd0441Ddfvtd6q4uFhvvTVV//M/Uypss+PHj+mBB8YrNzdHw4ffov79L9PmzRv1+OMPKjMzo7ZNVCf09Aag8nP1ZrMqGwAADap16zbq3/9ybdiwXps3/6B+/Qa49zkcDq1evUKhoaEaMmSYQkOt+uabb2QYIXI4XO7jtmzZpCeeeFizZn2gyZP/WKs6Pvlklg4ePKCRI2/T5Ml/cG8fMuRmPfnkYxWe8/HH89S2bTuPbadOndLEiWM1bdobGjz4Gkly99yuWPGZLrnkUt1//wPVqik1NUXvvPOmWrSI14wZH6l169LhH5MmPewO1qtWfa6hQ4d7nLd162Y9+OCjuvfe8e5t77//jmbN+kDLly/T2LHj1dgIvQGIVdkAAIFiS9p2fXZotYqdgdkJY7PYNOKCm9S3Va86Pc+IEbdow4b1Wr58qUfoXb/+G2VmZui6625UTEysrFaz4uIidfp0vsf5ffv20/nnX6BNmzbWuoaVKz9XSEiIJk70DKSXXTZQl146QJs3ez/3uYFXklq2bKlrrrlOCxbM1YkTx9WmTVKta/rii5VyOp26++573IFXkkJDQ/XQQ4/poYfu14oVn3mF3rZt2+nXvx7nsW3EiFs1a9YH2rPnp1rXUxeE3gAUywIVAIAA8e8jX+tkQZq/y/Dp30e/rnPoHTz4GsXFtdB//vOl8vLyFBUVJUlavnyJpLOzPEjShg0bNGPGP/TTTzuUlZUlp/PsTEshISG1ev38/DwdP56qTp0uUEJCS6/9vXv3qTD0pqam6OOPZ2rz5h906lS67HbP3HDqVHqdQu++fXsllQ6POFfPnr0UGmrT/v37vPZ17dpNZrPnKNrExFaSpLy8vFrXUxeE3gAUU25Mbw6hFwDgRzd0vDrge3pvOO/qOj+P1WrVkCE3a+7cT/TFFyt1222jlZFxSt9/v16tW7dx9/6uWfOF/vjHKQoPj9CAAZcrKamtewqzFSs+q/WY3vz80p7jFi1aVLg/Pj7ea1tKyi/67W/vU0FBvi655FJdeeVVioyMlMlk0tatm/Xjj1u8QnBt64qPT/DaZzKZFB8fr/R07w9FkZFRXtvKbngr/yGhMRF6A1BUeIjMJpNchqHsPEIvAMB/+rbqVede1KZixIhbNXfuJ/rssyW67bbRWrXqczmdTt1880h3r+WMGe/KZrNp5sxP1Lat50wNa9asrvVrR0ZGSpJOnz5d4f7MzEyvbXPn/lO5uTn6n/95TkOG3Oyx7+WXX9CPP26pdT3n1pWZmeHVY2wYhjIzMysMuIGI2RsCkNlkUkxk6Z9HWIoYAIDGcf75F+iiiy7W3r27deDAfn3++bIzU5rd4j4mNTVFnTt31nnnnedx7qlTp3TsWGqtXzsyMkpJSe2UmvqLMjJOee3ftu1Hr22pqSmSpMGDPXu6DcPQjh3bvI63WCySJJfL5bWvMt26JUsqvTHtXD/9tFN2e7HfpiCrKUJvgCq7mS0nv0QuliIGAKBRlI3dffXVF3X48M/q12+ARw9nmzZJOnLkiDIyzk67VVxcrFdf/ZscDkedXnvo0JtVUlKiGTM8lwneuPH7Csfzli2kcW4g/vjjmTp06KDX8dHRMZKktLST1a7pxhuHymKxaO7cf+rUqXT39pKSEk2f/qYkuadCC3QMbwhQsVGh0knJZRjKKyxRTERo1ScBAIA6uf76G/XGG6+6e0rL38AmSXfeOUavvvqS7rvvV7rmmuvlcDi1adMGGYahLl266cAB75u6quvXvx6nr79eq2XLFunw4YPq3buv0tJOau3aL3TFFYO0fv03HsePGnWHPv98mf74x6fcs0vs2rVDe/furfD4jh07qWXLRK1Zs1ohISFq1aq1JGn06LvdN+6dq1279nroocf01ltTdd99d+vaa29UeHi4vv32Pzp69IgGD77aa2hFoKKnN0CVX5Uth3G9AAA0ioiISF177Q2SpJiYWPc8t2VGjx6jP//5z4qJidXSpYu1bt2X6tOnr6ZP/7DS4Fhd4eHheuut93TLLbfpl19+0bx5n+rIkcP685//pmuuud7r+G7duuu1195St27d9fXXX2r58qWKiorWO+98oOTkHl7HWywWPf/8y7rwwp76979Xa8aM6ZoxY7pyc3N81nX33ffqxRdf1fnnd9bq1Sv0r3/NVUhIiB599An95S//J5PJVKf33VhMRkXLaMAtPT23RsdbrWa1aFE6f1/5Satr6l9fH9Ty745Ikp4c00cXne9912ZTVV9t1FzRPr7RPr7RPlWjjXyjfXyjfXzzV/skJvpeVU6ipzdglZ+rN4tV2QAAAOqE0BugYqPOrsrGXL0AAAB1Q+gNUKzKBgAAUH8IvQGK0AsAAFB/CL0BqvzsDdmM6QUAAKgTQm+ACgu1KDSk9NdDTy8AAEDdEHoDlMlkcg9x4EY2AACAuiH0BrCypYjzixwqYS5AAACAWiP0BrDYqHKrstHbCwAAUGuE3gDGDA4AAAD1g9AbwDxDLzM4AAAA1BahN4CVX5WNnl4AAIDaI/QGsPJz9ebkEXoBAABqi9AbwBjTCwAAUD8IvQGM0AsAAFA/CL0BLIYb2QAAAOoFoTeAWS1mRYWHSJKyGdMLAABQa4TeAFd+KWLDMPxcDQAAQNNE6A1wZUMc7A6XiuxOP1cDAADQNBF6A1z5pYiz8hjXCwAAUBuE3gBXfgaHHGZwAAAAqBVCb4CLjWRVNgAAgLoi9AY4j7l6mcEBAACgVgi9AS4migUqAAAA6orQG+BiWaACAACgzgi9AY6liAEAAOqO0BvgIsNDZDGbJEk5jOkFAACoFUJvgDObTO4FKujpBQAAqB1CbxNQFnpzCuxyuViKGAAAoKYIvU1A3JnQaxhSbmGJn6sBAABoegi9TUD5pYizWYoYAACgxgi9TUBMuVXZWIoYAACg5gi9TQDTlgEAANQNobcJIPQCAADUDaG3CfAc00voBQAAqClCbxPAUsQAAAB1Q+htAmLKhV5uZAMAAKg5Qm8TEBZqlS3UIokxvQAAALVB6G0iyoY4MKYXAACg5gi9TURZ6C0odshe4vRzNQAAAE0LobeJiGVcLwAAQK0RepuI2HKrsjGuFwAAoGYIvU1ETBQLVAAAANQWobeJYFU2AACA2iP0NhEeoTePBSoAAABqgtDbRJRfipgb2QAAAGqG0NtEcCMbAABA7RF6m4joiBD3Y0IvAABAzRB6mwirxayo8NLgy6psAAAANUPobULKxvVm59tlGIafqwEAAGg6CL1NSNyZGRwcTpcKix1+rgYAAKDpIPQ2ITHczAYAAFArhN4mpPy0ZYzrBQAAqD5CbxPCqmwAAAC1Q+htQgi9AAAAtUPobUI8Qy9LEQMAAFQXobcJiYk6eyNbDmN6AQAAqo3Q24QwvAEAAKB2CL1NSGSYVRazSZKURU8vAABAtQVs6LXb7Xr55Zc1aNAg9erVS3feeae+/fbbap//+eefa8yYMerTp4/69eunu+++W999910DVtzwTCaTe9qyHMb0AgAAVJvV3wVUZsqUKVq1apXGjRunTp06adGiRZo0aZJmzZqlfv36+Tz3zTff1LRp0zRkyBDddtttcjgc2rdvn06ePNlI1Tec2MhQZeYUK7egRE6XSxZzwH5uAQAACBgBGXq3b9+u5cuX66mnntL9998vSRo1apRGjBihV155RXPmzKn03B9//FHTpk3TlClTNH78+EaquPHERtok5cqQlFtQorhyN7cBAACgYgHZTbhy5UpZLBaNGTPGvc1ms2n06NHaunWrjh8/Xum5s2bNUsuWLTVu3DgZhqH8/PzGKLnRxESyKhsAAEBNBWTo3b17tzp16qSoqCiP7b169XLvr8x3332niy++WB999JEuv/xy9e3bV4MGDdLHH3/coDU3FmZwAAAAqLmAHN6Qnp6uxMREr+1l29LS0io8Lzs7W6dPn9aWLVv0/fff69FHH1VSUpIWLlyov/zlL7Jarbr77rtrVIvZbJL5zIwJ1WGxmD2+17cWMWeHM+QVlshqDcjPLT41dBs1dbSPb7SPb7RP1Wgj32gf32gf3wK5fQIy9BYVFSk0NNRru81mc++vSEFBgSQpKytLf//733XzzTdLkoYOHaqRI0fqnXfeqXHojY+PlMlU/dBbJiYmvMbnVEe71jHux3aXoRYtIhvkdRpDQ7VRc0H7+Eb7+Eb7VI028o328Y328S0Q2ycgQ29YWJjsdu8/3RcXF7v3V6QsFIeEhGjIkCHu7WazWcOGDdObb76pY8eOqW3bttWuJTMzv8Y9vTEx4crJKZTT6ar2edVlleF+fCI9T6dPN70xyw3dRk0d7eMb7eMb7VM12sg32sc32sc3f7VPdToBAzL0JiYmVji9WHp6uiSpVatWFZ4XFxcnm82mmJgYWSwWj30JCQmSpJycnBqFXpfLkMtlVH3gOZxOlxyO+v9lR4aHuB+fzi1ukNdoLA3VRs0F7eMb7eMb7VM12sg32sc32se3QGyfwBtwIal79+46fPiw8vLyPLZv27ZNktSjR48KzzObzerRo4cyMzO9eorLxgG3aNGiASpuPLER3MgGAABQUwEZeocOHSqn06m5c+e6t9ntdi1cuFC9e/dWUlKSJOnYsWM6ePCgx7nDhg2T0+nU4sWL3duKi4u1bNkydenSRa1bt26U99BQbKEWhYWW9mITegEAAKonIIc39O7dW0OHDtVrr72mjIwMdezYUYsWLVJqaqqef/5593GTJ0/Wxo0btXfvXve2u+++WwsWLNBzzz2nn3/+WW3bttWSJUt07NgxvfPOO/54O/UuNjJURfZCliIGAACopoAMvZL00ksvaerUqVq6dKmys7OVnJys6dOnq3///j7PCwsL06xZs/Tyyy9r4cKFKigoUI8ePfTuu+9q8ODBjVR9w4qNsunk6UIVFjtVXOKULcRS9UkAAABBLGBDr81m0+TJkzV58uRKj5k9e3aF2xMSEvTiiy82VGl+V36Bipx8uxLjAm9aEAAAgEASkGN64RursgEAANQMobcJio0qF3rzCL0AAABVIfQ2QTEewxu4mQ0AAKAqhN4mKDbS5n7M8AYAAICqEXqbIMb0AgAA1AyhtwliTC8AAEDNEHqboOiIEJnOPM5mTC8AAECVCL1NkMVsVnREiCSGNwAAAFQHobeJijlzM1t2nl2GYfi5GgAAgMBG6G2iysb1Ol2G8oscfq4GAAAgsBF6myhmcAAAAKg+Qm8TVT705uRxMxsAAIAvhN4mip5eAACA6iP0NlExUYReAACA6iL0NlEsRQwAAFB9hN4mymN4A6uyAQAA+ETobaLKL0Wcw6psAAAAPhF6m6gIm1VWS+mvj+ENAAAAvhF6myiTyeQe4kDoBQAA8I3Q24SVDXHIKyiRw+nyczUAAACBi9DbhJX19BqScgtK/FsMAABAACP0NmEeq7IxxAEAAKBShN4mLMZjVTZmcAAAAKgMobcJi40qt0AFc/UCAABUitDbhJUf3pDF8AYAAIBKEXqbMI8xvfT0AgAAVIrQ24TFMqYXAACgWgi9TZjnjWz09AIAAFSG0NuEhYZYFG6zSiL0AgAA+ELobeJYihgAAKBqhN4mriz0FtudKrI7/FwNAABAYCL0NnGxUazKBgAAUBVCbxPHzWwAAABVI/Q2cR7TljFXLwAAQIUIvU1cbGS5pYjp6QUAAKgQobeJKz+ml9ALAABQMUJvE+exFDGrsgEAAFSI0NvExUWdHd6QmUPoBQAAqAiht4mLjgiRLcQiSUrLKvRzNQAAAIGJ0NvEmUwmtWoRLknKyC6S0+Xyc0UAAACBh9DbDLSKKw29TpehDIY4AAAAeCH0NgNlPb2SlHa6wI+VAAAABCZCbzNQPvSmn2ZcLwAAwLkIvc1A2fAGSTpJ6AUAAPBC6G0GWrWIcD9OI/QCAAB4IfQ2Ay2ibbJaTJKkdKYtAwAA8ELobQbMZpMSzwxxSMsqlMsw/FwRAABAYCH0NhNl43pLHC5l59n9XA0AAEBgIfQ2E4lMWwYAAFApQm8z0brczWzM4AAAAOCJ0NtMJJabtoyb2QAAADwRepuJ1i2YqxcAAKAyhN5mIiE2TGbTmWnLCL0AAAAeCL3NhNViVnyMTZKUllUgg2nLAAAA3Ai9zUjZEIfCYqdyC0v8XA0AAEDgIPQ2I4nlZnBgiAMAAMBZhN5mpFVc+bl6Cb0AAABlrHU52el0qrCwUGFhYbJazz5VUVGRZsyYod27d6tdu3a6//771bp16zoXC9/Kz+CQxrRlAAAAbnUKvdOmTdM777yj2bNnq1+/fpIkwzA0duxY7dy5U4ZhyGQy6YsvvtDixYsVGxtbL0WjYqzKBgAAULE6DW/47rvv1LJlS3fglaS1a9dqx44d6tixo5555hldeeWVOnHihObNm1fnYuFbIsMbAAAAKlSn0JuSkqILLrjAY9uaNWtkMpn0yiuvaNy4cZo+fbri4+O1atWqOhWKqtlCLIqLCpXE8AYAAIDy6hR6s7Ky1LJlS49tW7ZsUevWrdWzZ09JktVqVe/evXX8+PG6vBSqqdWZGRxyC0pUWOzwczUAAACBoU6h12q1qrDwbI9idna2jhw5or59+3ocFxkZqdzc3Lq8FKqpVQuGOAAAAJyrTqG3ffv22rZtm1wulyTpyy+/lGEYuvTSSz2Oy8zMVHx8fF1eCtXkMW0ZQxwAAAAk1TH0XnfddcrIyNDDDz+sWbNm6ZVXXpHFYtG1117rPsYwDO3atUvt27evc7GoWitmcAAAAPBSpynLfvvb32rt2rX66quv9NVXX0mSJk2apLZt27qP2bx5s06fPu3V+4uGwfAGAAAAb3UKvVFRUZo/f75WrlypjIwMXXzxxRowYIDHMVlZWRo3bpyGDRtWp0JRPazKBgAA4K1OoVeSwsLCNGrUqEr333DDDbrhhhvq+jKopoiwEEWFhyivsIQxvQAAAGfUaUxvVXJzc2UYRkO+BCpQNsThdG6x7CVOP1cDAADgf3UKvfv27dNHH32kn3/+2WP7999/r+uuu04DBgzQwIEDtXDhwjoViZopP643PbvIj5UAAAAEhjqF3tmzZ+vFF19UWFiYe9vp06f1yCOP6NixYzIMQ1lZWfrjH/+oXbt21blYVI/nuF5mcAAAAKhT6N2yZYu6dOmipKQk97YlS5YoPz9fY8aM0aZNm/R///d/crlcmj17dp2LRfUwgwMAAICnOoXeU6dOeUxPJknr16+XxWLRE088oaioKN1666268MIL9eOPP9blpVADZUsRSyxQAQAAINUx9Obn5ysqKspj27Zt29S9e3e1aNHCva1jx446efJkXV4KNcC0ZQAAAJ7qFHojIyM9wuzBgweVnZ2tSy65xOtYk8lUl5dCDURHhCgs1CJJSif0AgAA1C309ujRQ1u3btWRI0ckSQsWLJDJZPJaoCIlJUWJiYl1eSnUgMlkcvf2nsouksPp8nNFAAAA/lWn0DtmzBg5HA7dfvvtGjVqlGbOnKmEhARdc8017mPy8vK0e/dudevWra61ogbKbmZzGYYycpi2DAAABLc6hd5hw4bp0UcfldPp1J49e9S2bVtNnTpVoaGh7mNWrFghh8Oh/v3717lYVF/5m9kY4gAAAIJdnZchfvTRRzVp0iTl5eUpPj7ea/+VV16pxYsXq0OHDnV9KdRA+WnLTp4uVE8/1gIAAOBvdQ69khQaGlph4JWktm3bek1rhoZXfgaHdKYtAwAAQa5OwxvKs9vt2rp1q1auXKmVK1dq69atstvtdXq+l19+WYMGDVKvXr1055136ttvv63x8/zmN79RcnKynnvuuVrX0hSxQAUAAMBZde7pdTgceuutt/Txxx8rPz/fY19kZKTGjh2rRx55RFZrzV5qypQpWrVqlcaNG6dOnTpp0aJFmjRpkmbNmqV+/fpV6zlWr14dtItixEXbZLWY5XC6dJKliAEAQJCrU0+vy+XSQw89pHfffVd5eXmKiYlRjx491KNHD8XExCgvL0/Tp0/Xww8/LJer+tNmbd++XcuXL9fvf/97TZ48WWPGjNGsWbPUtm1bvfLKK9V6juLiYr344ouaOHFibd9ek2Y2mdy9velZRXIZhp8rAgAA8J86hd758+dr3bp1atu2rd544w1t2LBBCxcu1MKFC7Vhwwa9+eabatu2rdatW6cFCxZU+3lXrlwpi8WiMWPGuLfZbDaNHj1aW7du1fHjx6t8jvfff1+GYej++++v1XtrDsrG9TqcLmXlFvu5GgAAAP+p0/CGxYsXKywsTLNmzVL79u299t94443q3r27RowYoUWLFumuu+6q1vPu3r1bnTp18lriuFevXu79SUlJlZ5/7Ngxvf/++3rhhRcUFhZWg3fkzWw2yWyu/mpyFovZ47s/tU6IkA6UPs7IKVKr+AjfJzSSQGqjQET7+Eb7+Eb7VI028o328Y328S2Q26dOoXf//v0aMGBAhYG3TIcOHXT55Zdr8+bN1X7e9PT0CldwK9uWlpbm8/wXX3xRPXr00PDhw6v9mpWJj4+s1RLKMTHhVR/UwM5vG+t+nFvsUosWkX6sxlsgtFEgo318o318o32qRhv5Rvv4Rvv4FojtU6fQa7fbFR0dXeVxkZGRNZrJoaioyGOBizI2m829vzLff/+9Vq9erXnz5lX79XzJzMyvcU9vTEy4cnIK5fTz8r9RYWd/vYdTs3T6dEs/VnNWILVRIKJ9fKN9fKN9qkYb+Ub7+Eb7+Oav9qlOx16dQm9SUpK2bt0qp9Mpi8VS4TFOp1M//vij2rRpU+3nDQsLqzAkFxcXu/dXxOFw6Pnnn9ett97qHgpRVy6XIZer5jeBOZ0uORz+/Y8hIcbmfnwiI9/v9ZwrENookNE+vtE+vtE+VaONfKN9fKN9fAvE9qnTgItBgwbp+PHjev7551VSUuK13263669//auOHz+uq666qtrPm5iYqPT0dK/tZdtatWpV4XmLFy/Wzz//rDFjxiglJcX9JUn5+flKSUlRYWHwzFmbEBMm85mhGczVCwAAglmdenonTZqkzz77TJ9++qnWrFmjm2++2T2+NyUlRZ9//rnS0tIUGxur3/72t9V+3u7du2vDhg3Ky8vzuJlt27ZtkqQePXpUeN7x48dVUlKiX/3qV177Fi9erMWLF2vatGm64YYbavI2myyrxayWsWFKyypUWlahDMOo1fhkAACApq5Oobd169Z6//339cQTT+jYsWOaOXOmx37DMNzTmbVu3brazzt06FD94x//0Ny5c91Tjtntdi1cuFC9e/d2z9xw7NgxFRYWqnPnzpKkm2++ucJA/Mgjj+jqq6/WXXfdVW/DHpqKxBbhSssqVJHdqdyCEsVEeo+VBgAAaO7qvCJbr1693EsPb9y4USdPnpRUGogHDBigoUOH6uDBg/rhhx/Uv3//aj1n7969NXToUL322mvKyMhQx44dtWjRIqWmpur55593Hzd58mRt3LhRe/fulSR17tzZHYDP1b59+6Dp4S2vVYtw/fRz6eO0rEJCLwAACEp1Dr2SFBoaqltuuUW33HJLhfufffZZ7dixQ7t27ar2c7700kuaOnWqli5dquzsbCUnJ2v69OnVDs4oVbZAhSSlnS5Ql3axPo4GAABonuol9FaHUcNlcG02myZPnqzJkydXeszs2bOr9VxlPcHBqGwpYomb2QAAQPAKvOUyUK9atTi7CltaFqEXAAAEJ0JvM5cYe3ZOY3p6AQBAsCL0NnOhIRa1iC5dpILQCwAAghWhNwiU3cyWV1iigiLvRUQAAACaO0JvEPC4mY1xvQAAIAjVaPaGxYsX1+pFMjMza3Ue6se5Mzh0ahPjx2oAAAAaX41C75QpU2q1jC3L3/qXxwwOjOsFAABBqEaht23btg1VBxqQxwIVDG8AAABBqEahd+3atQ1VBxoQC1QAAIBgx41sQSDcZlV0RIik0qWIAQAAgg2hN0iU9fZm5dlVXOL0czUAAACNi9AbJMqP601nXC8AAAgyhN4gUX4Gh3TG9QIAgCBD6A0S5Xt6TxJ6AQBAkCH0BglWZQMAAMGM0BskyofedGZwAAAAQYbQGySiwkMUbrNIYngDAAAIPoTeIGEymdQqrvRmtoycIjmcLj9XBAAA0HgIvUEk8cwQB8OQMrKL/FwNAABA4yH0BpHW3MwGAACCFKE3iJSftiyNcb0AACCIEHqDSPkZHE4ygwMAAAgihN4gwqpsAAAgWBF6g0hsVKhCrKW/csb0AgCAYELoDSJmk8k9rjc9q1Aul+HnigAAABoHoTfIlI3rdTgNnc4t9nM1AAAAjYPQG2QSPWZw4GY2AAAQHAi9QYa5egEAQDAi9AaZxBbM1QsAAIIPoTfIlJ+2jJ5eAAAQLAi9QSYhxiaL2SSJnl4AABA8CL1BxmI2KyE2TFJp6DUMpi0DAADNH6E3CJVNW1Zc4lROQYmfqwEAAGh4hN4g1IppywAAQJAh9AYhj5vZGNcLAACCAKE3CLVi2jIAABBkCL1ByGN4A9OWAQCAIEDoDUKJcWEynXlMTy8AAAgGhN4gFGK1qEWMTRI3sgEAgOBA6A1SZUMc8oscyi9i2jIAANC8EXqDFDezAQCAYELoDVJMWwYAAIIJoTdIMYMDAAAIJoTeIOU5vIGb2QAAQPNG6A1SieV6etMZ3gAAAJo5Qm+QCrdZFRMRIkk6yfAGAADQzBF6g1jZzWzZeXYV251+rgYAAKDhEHqDWJv4szM4/JKW58dKAAAAGhahN4h1bhfjfrwvJct/hQAAADQwQm8Q69Yhzv143y9ZfqsDAACgoRF6g1ib+AhFhZfezHYgJVsuw/BzRQAAAA2D0BvETCaTuraPlSQVFDt0LD3fzxUBAAA0DEJvkPMY4sC4XgAA0EwReoNc1/Zx7sf7U7L9VwgAAEADIvQGufNaRyk0pPQy2PdLlgzG9QIAgGaI0BvkrBazOrctHdd7OrdYGdlFfq4IAACg/hF64TGulyEOAACgOSL0wj2Dg8TNbAAAoHki9EKd28bKYjZJYpEKAADQPBF6IVuoRee1jpYkHc8oUG6B3c8VAQAA1C9CLyR5DnE4wLheAADQzBB6IYlFKgAAQPNG6IUkqUu5nl5mcAAAAM0NoReSpJiIUCUlREiSjpzIVbHd6eeKAAAA6g+hF25lSxI7XYYOHaO3FwAANB+EXrh161B+vl5CLwAAaD4IvXAr6+mVpP3czAYAAJoRQi/cWsaGqUW0TZJ0MDVHTpfLzxUBAADUD0Iv3Ewmk3u+3uISp46ezPNzRQAAAPWD0AsPHkMcWJIYAAA0E4ReePBcpIKb2QAAQPNA6IWHdomRirBZJZXezGYYhp8rAgAAqDtCLzyYTSb36my5BSU6kVng54oAAADqjtALL11ZkhgAADQzhF548RjXy81sAACgGSD0wkunNjGyWkovDRapAAAAzQGhF15CrGZdkBQtSUrPKtLp3GI/VwQAAFA3hF5UqGu5IQ709gIAgKaO0IsKlR/Xu/8XbmYDAABNm9XfBVTGbrfr9ddf15IlS5STk6Pk5GQ98cQTuvLKK32et3r1an3++efasWOHTp06pTZt2ujaa6/Vww8/rJiYmEaqvunr3DZWJkmGpH309AIAgCYuYHt6p0yZopkzZ2rkyJH6wx/+IIvFokmTJmnTpk0+z/uf//kfHTx4ULfccov++Mc/avDgwfr44481ZswYFRUVNVL1TV9EmFUdWkVJklLS8lRQVOLnigAAAGovIHt6t2/fruXLl+upp57S/fffL0kaNWqURowYoVdeeUVz5syp9Nw33nhDl112mce2nj17avLkyVq2bJnuvPPOBq29OenaIU5H0/JkSDqQmq1enVv6uyQAAIBaCcie3pUrV8pisWjMmDHubTabTaNHj9bWrVt1/PjxSs89N/BK0g033CBJOnjwYP0X24yxSAUAAGguAjL07t69W506dVJUVJTH9l69ern318SpU6ckSS1atKifAoMEi1QAAIDmIiCHN6SnpysxMdFre9m2tLS0Gj3f+++/L4vFoiFDhtS4FrPZJLPZVO3jLWcWdSj73pS1jAtXqxbhSjtdqJ+P58glQ6FWS52ftzm1UUOgfXyjfXyjfapGG/lG+/hG+/gWyO0TkKG3qKhIoaGhXtttNpt7f3UtW7ZMCxYs0MSJE9WpU6ca1xIfHymTqfqht0xMTHiNzwlEPTu31NpNv8jhNHQqt0QXXVB/M2A0lzZqKLSPb7SPb7RP1Wgj32gf32gf3wKxfQIy9IaFhclut3ttLy4udu+vjk2bNukPf/iDBg0apN/97ne1qiUzM7/GPb0xMeHKySmU0+mq1WsGkvNbnx1isnnXcbVtUb2296W5tVF9o318o318o32qRhv5Rvv4Rvv45q/2adEisspjAjL0JiYm6uTJk17b09PTJUmtWrWq8jn27Nmjhx56SF27dtUbb7whq7V2b9XlMuRyGTU+z+l0yeFo+v8xdG539ma2PUeyNOyy+ntPzaWNGgrt4xvt4xvtUzXayDfaxzfax7dAbJ/AG3AhqXv37jp8+LDy8vI8tm/btk2S1KNHD5/nHz16VBMnTlR8fLzef/99RUZWnf5RsdYtwhUTESKpdNqy2nwAAAAA8LeADL1Dhw6V0+nU3Llz3dvsdrsWLlyo3r17KykpSZJ07Ngxr2nI0tPTNWHCBJlMJn3wwQeKj49v1NqbG5PJpK7t4yRJhcUOpaTn+T4BAAAgAAXk8IbevXtr6NCheu2115SRkaGOHTtq0aJFSk1N1fPPP+8+bvLkydq4caP27t3r3jZx4kT98ssvmjhxojZv3qzNmze797Vs2bLKZYzhrWuHOG3eVzq0ZH9Kts5rHe3nigAAAGomIEOvJL300kuaOnWqli5dquzsbCUnJ2v69Onq37+/z/P27NkjSZoxY4bXvgEDBhB6a6Fbh/KLVGTp+kvb+7EaAACAmgvY0Guz2TR58mRNnjy50mNmz57tta18ry/qR4dWUbKFWlRsd2rfL1kyDKNW07gBAAD4S0CO6UVgsZjN6tK2dH7erDy70rOrP08yAABAICD0olq6lluSeD9LEgMAgCaG0ItqKZvBQSod1wsAANCUEHpRLRe0jZHlzMp0+37J9nM1AAAANUPoRbXYQizq1KZ0qrITmQXKyfdeJhoAACBQEXpRbZ5DHOjtBQAATQehF9XW9Zz5egEAAJoKQi+qrXxP7z5mcAAAAE0IoRfVFhUeorYtIyVJR0/mqcju8HNFAAAA1UPoRY10a186xMFlGDp4LMfP1QAAAFQPoRc1wiIVAACgKSL0oka6MYMDAABoggi9qJGE2DDFx9gkSQePZcvhdPm5IgAAgKoRelFjZb299hKXjpzM9W8xAAAA1UDoRY2VH9e772iW3+oAAACoLkIvaqz7eXHux//Zflwul+G/YgAAAKqB0IsaS0qIdAffk5kF2ro/3b8FAQAAVIHQi1oZellH9+PPvz8qw6C3FwAABC5CL2rl4gvi1T4xSpL08/Ec7WVsLwAACGCEXtSKyWTSsMvPc//8+YYjfqwGAADAN0Ivaq1/91ZKiAmTJO08lKlf0vL8XBEAAEDFCL2oNavFrJsGdHD/vILeXgAAEKAIvaiTq3q1VVR4iCRp4640ncoq9HNFAAAA3gi9qBNbqEXX9W0nSXIZhlb98IufKwIAAPBG6EWdXX9pe4VaSy+ldduOKbfA7ueKAAAAPBF6UWfREaEa3LutJMnucGntllQ/VwQAAOCJ0It6MaR/B5lNJknSms0pKrY7/VwRAADAWYRe1IuWceEa0KOVJCmvsETrth/zc0UAAABnEXpRb4ZednaxilUbf5HT5fJjNQAAAGcRelFvzmsdrZ4XxEuSMnKK9MPuND9XBAAAUIrQi3o17LKO7scrNhyVYRh+rAYAAKAUoRf1qvt5cTo/KVqS9Etannb+nOnnigAAAAi9qGcmk8mzt/d7liYGAAD+R+hFvevbLVGtW4RLkvYczdLPx3P8XBEAAAh2hF7UO7PZ5DGTw+f09gIAAD8j9KJBXNGzjWIiQyVJW/am60RmgZ8rAgAAwYzQiwYRYrXoxn7tJUmGpFUbj/q3IAAAENQIvWgw117STmGhFknStztOKDuv2M8VAQCAYEXoRYOJCAvRNZe0kyQ5nC59sSnFzxUBAIBgRehFg7qxXwdZzCZJ0pdbU1VY7PBzRQAAIBgRetGgWkTbNLBnG0lSYbFDX/2Y6ueKAABAMCL0osENu+w8mc48/uKHX1TicPm1HgAAEHwIvWhwSQmR6tO1pSQpK8+u9TuP+7kiAAAQbAi9aBTDLj+7NPHn3x2Ry2X4sRoAABBsCL1oFF3axapb+1hJ0vGMAm346YSfKwIAAMGE0ItGU763d96afXK6GNsLAAAaB6EXjaZX5wS1S4yUJB34JUuzV+6VYTDMAQAANDxCLxqNyWTSuCHJslpK53JYuyVVq3/4xc9VAQCAYEDoRaPq2j5O94+40P3zvLUHtGVfuh8rAgAAwYDQi0Z35cVJ+tVNyZIkQ9J7S3/Sz8dz/FsUAABo1gi98Itf3ZSsK86s1GZ3uPTGgu3KyC7yc1UAAKC5IvTCL0wmk+4fcaG6npnGLDvfrqkLtqmw2OHnygAAQHNE6IXfhFjNeuyOXmrVIlySlJqer3eW7GQqMwAAUO8IvfCrqPAQPXFnb0WGWSVJOw9l6pMv9jOVGQAAqFeEXvhdm/gIPXr7xbKYS6cy+2orU5kBAID6RehFQEg+r4Um3NzD/TNTmQEAgPpE6EXAGNizjW65spOkM1OZLWMqMwAAUD8IvQgotw46X5df2FqSZC9hKjMAAFA/CL0IKCaTSb+5uYfHVGavM5UZAACoI0IvAk6I1axHb79YreJKpzJLYSozAABQR4ReBKToiFA9cZfnVGb/ZCozAABQS4ReBKxzpzL7cmuqvmAqMwAAUAuEXgS05PNaaPyw7u6f5649oDcWbNfOQxly0esLAACqyervAoCqXHlxktJOF2rZ+sMyJP144JR+PHBKrVuE69pL2unKXkmKDAvxd5kAACCAEXrRJIwafL4iw0O0auNRnc4tliSdPF2oOWsPaOF/DumyC1vrur7t1bFNtJ8rBQAAgYjQiybBZDLppv4ddP2l7fTj/lNauyVVu4+cliTZHS6t235c67YfV+d2Mbqub3v1S26lECujdwAAQClCL5oUi9msS5Nb6dLkVjqeka+1W1K1fudxFRY7JUkHU3N0MHWX5qzZr6t6t9U1fdopITbMz1UDAAB/I/SiyUpKiNQ9N3bTHVdfoO9/Oqm1W1KUkp4vScotKNHy747o8++PqE+Xlrq2bztd2CleZpPJz1UDAAB/IPSiyQsLteqaS9rp6j5ttT8lW2u3pGjz3nQ5XYYMQ9q6/5S27j+lFtE2XdotUf26t1KX9rEEYAAAggihF82GyWRStw5x6tYhTtl5xfp62zF9/eMx941vp3OL9e/NKfr35hTFRYXq0uRW6k8ABgAgKBB60SzFRtl0y5Xna/jAjvpx/yl9s/24fjqcKYezdG7frDy71mxO0ZrNKYqNCtWl3RLVv3srdW0fJ7OZAAwAQHND6EWzVv7Gt4Iih348kK5Ne9K18+cMdwDOzrNr7ZZUrd2SqtjIUPVNTlT/5Fbq1oEADABAc0HoRdCICLPqip5JuqJnkgqKHNp24JR+2JOmnT9nyuF0SZKy8+36ckuqvtySqpiIEPVNbqUeHVsoMsyqcJtVEbbS7+E2K1OiAQDQhBB6EZQiwqwa2LONBvZso8LiswF4x6GzATinoERfbU3VV1tTK3yOEKvZHYAjbBaPQBxusyos1CKTySTDKL2hrrRf+cxjQzLOPFa5x4YhmcxSbHSYQi0mRYZZFRMRqpiIUEVHhio6PITeZwAAaoHQi6AXbrPq8ova6PKLzgTgg6e0aU+6dvycLiXtlikyWzJMkmE+8730yzDMKpRJhYZJmWXbi8xSoUmG+zjvcyST5DKVfi93jFHB8eeeY5JJEbZQRYWVfkVH2BQdblNMuE0xkTZFhdlks1oVYrEoxGqWxWyWxWKS1WKW1WKSxWKW1XzOzxaTzCaTTNzMBwBoxgi9QDnhNqsuv7CNLr+wjbae2KUZu1b6uyQvTknZZ77c7Ge+yjFcZ4KzPMN0ZeHaarbIFhqi8NAQRYSGKDIsVDZriMwmsywms8wmiyym0iBtNlnc2y1ntpvLjjGbz+w7u91issgks/ILHTqdY1dmjl2nc0tklkm2kBCFh4QoLNSqsJAQRYSGKtwWoghb6eMIW4gibaEKDwtRiNmqUFkV4QiR0+Us7RknrAMAqoHQC1SiW0IndYhup19yKx7eEOhM5jNjJySVRuUz2ys53iWpUFKhS8osklTUoOVJEef8XBbc82v4PEZpD7hJZveX2WSWuez7OaHdbDbJYjbJbC7t3XZnZsM4+5TnvoTXlnL7jIr3nXuOx0/GufsMuQzJ6XTJ6TLkdLnkdJY+g9kkd52mM4/NJklnvnu8B5X+fs1mk5yu0vEy3tX5eJ/VfC++z6n4+b2ew/Cxr7Lzzj3MJPeHKrPJ+8NW+X3uD2BmS+kHPFuInCWG+zopv7/i5zrzoc/k+aHPbPZ+Pa+fzZXtt3hem+Ve32Iyl17XfKgLOE6XS0V2h7/LQC0QeoFKRIZEaHK/x2XIkNNwyelyymW45DRKv5c9dpY9LrffWW6/575KznedPccwuRRqsyq/sEglTseZ811yGZ7PUeJ0qrjEIbvTIbvDoRKn0328y3DJJZeMM99dhkuGyr4Mz8cmlyRX5Wk40JkM93vyUj73NzXlfx9N+X2gTqoM8uZzg73nX1jMlfx1pvwHwnN/tlqtiooIU3GRQzJMlQR1s5xOqajYpaJipwqLXCoodqqoyCWnS3I4JKez9MtRYsjhlJwOqcRhyHHme0mJoRJH6X57iUsmmWW1mhViMctiMSvkzBCs0p9NlW4PDbEoLipUcVE2xUXZFBsVqhZRNsVEhspqqfkNx4ZhKL/IofSswnJfRTqVXfo4I7tYLsNQi2ibkhIilJQQqbYJEWpz5ntMZCgfVgIUoRfwwWQ604doMivE3Dj/uVitZrVoEanTp/PlcFQQ5BqIw+nU8Yx8HT6ZrcMnc3T0ZLZSMvJUXOKQyeSSTEa5r9KfTRVsk4wz+86eYzIZioq0Ki4qRLHRIYqODFF0hEWGDNkdJbI7nCp2lMjudKrE4ZTd6ZDDVRriHU6nnC6XHIZTznIfDlyGSyazy/161a6t2nz8o+X1NKZKfjKVji6pqAfVqO4/ijWpozbn1e4fZ8Mo1/us0sfGuZ2ylfxseGysTPXqMlVy/ZVta+rZo+xaL/F3ITVlVbUThllS2JnHhsskp0xyGibZfQ7LKncvhN0sZZikU57bTYZZVotFNqtVoVarwkKsCgsJVXioVeG2UIWHhsjpkAqKnCoociq/oPQrr7BEdrvO3lNR9poySS6zFGWS2TApy2VWVppJu0961hYWEqLW8ZFKahGlpPgotU2IUruEKCXGRXjciGwYpWE/v6hE+UUO5Ree+V5UovyiEhWc2ZZX5FBBUYlcLkOR4SGKOvMVGXb2cVR4iKIiSr9H2KzVvuHZ5TJUXOJUkd0p+5nvxSVnvuxOlThcchmGnC5DLpfh9djl8t4nmdQiJkz9khMVFxla/WumEZiMyv6e5Wd2u12vv/66lixZopycHCUnJ+uJJ57QlVdeWeW5J0+e1AsvvKBvv/1WLpdLl112mZ555hl16NChxnWkp+fW6Hh/BZamhDbyLZDax2UYOplZoMMncnWk7OtkrorszkrPibBZ1b5VlDokRql9q0i1bxWldi0jFRZaPx8aytonMzNPhUUO2R0u2UuclX4vOfO9uKT0e36RQ3mFpf+o5BWWfuUXlv4D44//M7RazEqIDVPLGJsSYsOUEBuuljFhSogNU4jVrKJihwrtThUWO0q/7E6vbUXFjtJeNnvp9mK7wz0PNcqFYJULxx4fjM75UKeyfd4f9kwez1XZ81X0IdDH81Xng5sMqdyHvIqf78x2BCTDkExlNyYbZvd9FxXfZ2Gu5Ibo8tvP2SeTDJdZJsMkq9UimyVEoSEWhVpCZLhMcjhLe9nLetodJSr9/4nKPkxUa/uZnyv4kNqrc4KeuLN3o7VvYmJ0lccEbE/vlClTtGrVKo0bN06dOnXSokWLNGnSJM2aNUv9+vWr9Lz8/HyNGzdOubm5euCBBxQSEqKZM2fq3nvv1eLFi9WiRYtGfBdA02Y2mZSUEKmkhEgNvKiNpNIgnHa6UIdP5OjIiVzlFpQoKSFC7ROj1KFVlFpE2xrlT3smU+mfNUNDLFJ4SJ2fz+UyVFDscAfhsjBc9rjY7pTD6VKJs3S8bYnTJYfDJYfLKP3udJX+o3LmmLKfXS5DsVGh7iCbEBumhDOPW8aGKyYipF7bq/yHAnuJU06ncWacsFFuzHDlP9sdLhXZSwN0kb20t6fsZ/fjknN+tjtldzhLx0qbzo6ZLvtuNpsr33dmXHJZ/4t7er8z45HLeoiN0v8p3XZm49ljK9nv3iev53e6DBUWO9y/13M13ehYUYg+J7BX+gHAO0SXfgDw3G4LNSnMZlGYzaSwULNCQ82yhZoUGmJWiLV02kWT+cyQI7MhU1k9OjPcSoZc7qFdVQ0Jc7qHdDld5faf+d6UmEwqbWNJklMmy5ntDfR6xWe+KmM581UfSqffLBeOHSEKCau6k7KxBWTo3b59u5YvX66nnnpK999/vyRp1KhRGjFihF555RXNmTOn0nP/+c9/6vDhw5o/f7569eolSRo8eLBGjhypDz/8UL///e8b5T0AzZXZZFKb+Ai1iY/Q5Re28Xc59cZsNrn/TNgcmEym0inrWEPFy7l/TXG6XLKXuDz+rGsvcanY4ZS9/J97S1xyuQxZLSb32FOrxawQ65nvZ7aXbSvbX7qtNNq4XKVh3XVm/u7Sn8/8qdgoDedex5x746N7mIjneJFzh424jNIPMiVOQyXuD2ausx/Yyn14O7ut9INQVKRN4SFmRUWEKDYyVLGRNsVGhiomMjRgFuYpbTfXmcDs9Lhn4uxQKKdKXE7lFdmVnVek7IJi5RYWKaewWHlFdoWGmBQdYVV0hFWR4RaFhZklGR5huzSYlwVwp2QyZA01q6CwqHT4Vfl7NM6EdbvToUJ7iYrsJSosKR3CZXeUqMTllMlkyGw23B8Oyj5wGGc+GJR+XKj8r2mBqDTQGyq7adpkdaikxSG/1lSRgAy9K1eulMVi0ZgxY9zbbDabRo8erddee03Hjx9XUlJSheeuWrVKF198sTvwSlLnzp01cOBArVixgtALAPBgMZsVbitdbAaBNcTKF5PpzE12skiq4sNqtKTE+nndurSPYRjV/suO+8Zlj17vslDv3evtLHffgzvwOx0qtDskk0tms+RSxeefe7O1Z+Av97plHzC8zj/7wcMllyJCwzSk0zW1aN2GFZD/he/evVudOnVSVFSUx/ayILt79+4KQ6/L5dLevXt1xx13eO27+OKL9c033ygvL8/reQEAABpaTYYylc2o0Vg3UdeXQP7QFJAtmZ6ersRE749kZdvS0tIqPC8rK0t2u73Kc2sSes1nxp1Vl+XM3xIt/E2xUrSRb7SPb7SPb7RP1Wgj32gf32gf3wK5fQIy9BYVFSk01HuaC5vN5t5fkeLi0iHbvs4tO6a64uMja3WTSUxMeI3PCTa0kW+0j2+0j2+0T9VoI99oH99oH98CsX0CMvSGhYXJbrd7bS8LrGFhYV77pLPB1te5ZcdUV2Zmfo17emNiwpWTUyhnBXcEgzaqCu3jG+3jG+1TNdrIN9rHN9rHN3+1T4sWkVUeE5ChNzExUSdPnvTanp6eLklq1apVhefFxcUpNDTUfVxNzq2M68ykyzXlPHMnLCpHG/lG+/hG+/hG+1SNNvKN9vGN9vEtENsn8AZcSOrevbsOHz6svLw8j+3btm2TJPXo0aPC88xms7p166adO3d67du+fbs6dOjATWwAAABBKCBD79ChQ+V0OjV37lz3NrvdroULF6p3797umRuOHTumgwcPepw7ZMgQ7dixQzt27HBvO3TokL7//nsNHTq0cd4AAAAAAkpADm/o3bu3hg4dqtdee00ZGRnq2LGjFi1apNTUVD3//PPu4yZPnqyNGzdq79697m2//vWvNX/+fD3wwAOaMGGCrFarZs6cqYSEBE2YMMEfbwcAAAB+FpChV5JeeuklTZ06VUuXLlV2draSk5M1ffp09e/f3+d5UVFRmj17tl544QW98847crlcuuyyy/T0008rPj6+kaoHAABAIDEZhlHzu7SCSHp6bo2OD+RJmQMFbeQb7eMb7eMb7VM12sg32sc32sc3f7VPYmJ0lccE5JheAAAAoD4RegEAANDsEXoBAADQ7BF6AQAA0OwRegEAANDsEXoBAADQ7BF6AQAA0OwRegEAANDssTgFAAAAmj16egEAANDsEXoBAADQ7BF6AQAA0OwRegEAANDsEXoBAADQ7BF6AQAA0OwRegEAANDsEXoBAADQ7BF6AQAA0OwRegEAANDsWf1dQHNht9v1+uuva8mSJcrJyVFycrKeeOIJXXnllf4uLSBs2LBB48aNq3Df3Llz1adPn8YtyI/y8/P1wQcfaNu2bdqxY4eys7P1t7/9TbfffrvXsQcPHtQLL7ygLVu2KCQkRFdffbWefvppxcfH+6HyxlHd9pkyZYoWLVrkdf7555+vlStXNla5jW779u1avHixNmzYoNTUVMXFxal379564okndP7553scG4zXT3XbJ1ivH0nav3+/3nzzTf300086deqUwsLC1KVLF91///267rrrPI4Nxmuouu0TzNfQud555x1NnTpVXbt21Weffeaxb8uWLXr55Ze1a9cuRUVFadiwYfrd736nyMjIRq+T0FtPpkyZolWrVmncuHHq1KmTFi1apEmTJmnWrFnq16+fv8sLGGPHjtXFF1/sse28887zUzX+cfr0aU2bNk1t27ZVcnKyNm7cWOFxJ06c0D333KPo6Gj97ne/U0FBgf7xj39o3759mj9/vkJDQxu58sZR3faRpNDQUP31r3/12BYdHd3QJfrVjBkztGXLFg0dOlTJyclKT0/XJ598ottvv11z585Vt27dJAXv9VPd9pGC8/qRpGPHjik/P1+33XabWrVqpcLCQq1evVoPPfSQnnvuOY0ZM0ZS8F5D1W0fKXivofJOnDihd999VxEREV77du/erfHjx6tz586aMmWKTpw4oX/84x86fPiwZsyY0fjFGqizbdu2Gd26dTNmzJjh3lZUVGTccMMNxpgxY/xYWeD4/vvvjW7duhkrVqzwdyl+V1xcbKSlpRmGYRjbt283unXrZvzrX//yOu5///d/jV69ehmpqanubd9++63RrVs3Y86cOY1Wb2OrbvtMnjzZ6NOnT2OX53ebN282iouLPbb9/PPPRs+ePY0nn3zSvS1Yr5/qtk+wXj+VcTgcxi233GIMGTLEvS1Yr6GKVNQ+XEOlnnjiCWPcuHHGvffeawwfPtxj38SJE40rr7zSyM3NdW+bN2+e0a1bN2PdunWNXarBmN56sHLlSlksFo9PfzabTaNHj9bWrVt1/PhxP1YXePLy8uRwOPxdht+EhoYqMTGxyuNWr16ta665Rm3btnVvu+KKK9SpUyetWLGiIUv0q+q2Txmn06m8vLwGrCiw9O3b16uHrVOnTuratasOHTrk3has109126dMsF0/lbFYLEpKSlJubq57W7BeQxWpqH3KBPM19MMPP2jVqlV65plnvPbl5eVp/fr1uuWWWxQVFeXefuuttyoiIsIv1xDDG+rB7t271alTJ49fqiT16tXLvT8pKckfpQWcp59+WgUFBbJYLLr00kv11FNPeQ13gHTy5EllZGSoZ8+eXvt69eql//znP36oKvAUFhbq0ksvVWFhoWJjYzV8+HD9v//3//wyVsyfDMPQqVOn1LVrV0lcP+c6t33KBPv1U1BQoKKiIuXl5Wnt2rX6z3/+o2HDhkniGpJ8t0+ZYL6GnE6n/vKXv2j06NFKTk722r937145HA6vayg0NFQ9evTQ7t27G6tUN0JvPUhPT6+wZ6psW1paWmOXFHBCQkI0ZMgQXXXVVWrRooUOHjyoDz74QPfcc4/mzJmjCy+80N8lBpSya6ay6yorK0t2u73ZjqmrjsTERE2cOFEXXnihDMPQunXr9M9//lN79uzR7NmzZbUGz/+9LV26VCdPntTjjz8uievnXOe2j8T1I0kvvvii5s6dK0kym8268cYb9ac//UkS15Dku30krqE5c+bo2LFjmjlzZoX709PTJUmtWrXy2peYmKjNmzc3ZHkVat6/kUZSVFRU4X/4NpvNvT/Y9e3bV3379nX/fP3112vIkCG65ZZb9Oqrr+qDDz7wY3WBp7i4WJKqvK6a8z84VXnyySc9fh4+fLg6deqkv//971q1apWGDx/up8oa18GDB/Xcc8/pkksu0W233SaJ66e8itpH4vqRpPvuu09Dhw5VWlqaVqxYIZfLpZKSEklcQ5Lv9pGC+xo6ffq03njjDT388MOVzuRRln0qu4b8kY0Y01sPwsLCZLfbvbaX/Z9GWFhYY5fUJHTs2FHXX3+9NmzYIKfT6e9yAkrZPypcVzUzfvx4mc1mrV+/3t+lNIr09HQ98MADio6O1uuvvy6LxSKJ66dMZe1TmWC7fjp37qwrrrhCo0aN0rvvvquCggI9+OCDMgyDa0i+26cywXINTZ06VbGxsbr33nsrPabs+qjsGvLH9UPorQeJiYnubvzyfHXto1SbNm1UUlKiwsJCf5cSUMqumcquq7i4uGbdw1JbYWFhiouLU3Z2tr9LaXC5ubn67W9/q9zcXM2YMUOtW7d27+P68d0+lQmm66ciQ4YM0Y4dO/Tzzz9zDVWgfPtUJhiuocOHD2vevHkaO3as0tLSlJKSopSUFBUXF6ukpEQpKSnKysryOcQzPT3dL9mI0FsPunfvrsOHD3vdvblt2zZJUo8ePfxRVpOQkpIim81W4fx+wax169aKj4/Xzp07vfZt375d3bt390NVgS8vL0+nT59u1hPnS6W9JA8++KAOHz6s6dOnq0uXLh77g/36qap9KhMs109lyv7cnJeXF/TXUEXKt09lguEaOnnypFwul/7617/q+uuvd39t27ZNhw8f1vXXX69p06apW7duslqtXteQ3W7X7t27/XINEXrrwdChQ+V0Ot0D3qXSX+rChQvVu3dvZm6QlJmZ6bVtz549Wrt2ra688kqZzVyK57rpppv01VdfeUx599133+nw4cMaOnSoHyvzv+Li4gr/4Xn77bdlGIYGDx7sh6oah9Pp1BNPPKEff/xRr7/+ui655JIKjwvW66c67RPM148kZWRkeG0rKSnRkiVLFBYWps6dO0sK3muoOu0TzNdQ165dNW3aNK+vrl27qm3btpo2bZpGjx6t6OhoDRw4UEuXLvVoqyVLlqigoMAv15DJ8DU4BdX2X//1X/r3v/+t++67Tx07dtSiRYu0Y8cOzZw5U/379/d3eX43btw4hYWF6ZJLLlFCQoIOHDigefPmyWq1au7cue7/kw0WH3/8sXJycpSWlqZPP/1UN910k/svAmPHjlV0dLSOHz+uUaNGKSYmRuPGjVNBQYE++OADtW7dWv/617+a9Z8Wq2qf7Oxs3XbbbRo+fLguuOACSdI333yjr7/+WoMHD9Z7773XbD9IPf/88/roo4907bXXek2fJJXOgSkpaK+f6rRPSkpK0F4/kvTII48oLy9P/fv3V+vWrZWenq5ly5bp0KFDmjJlin7zm99ICt5rqDrtE+zXUEXGjh2r06dPeyxD/NNPP+nuu+9Wly5ddNddd+nEiRP68MMP1b9/f7/cwE7orSfFxcWaOnWqli1bpuzsbCUnJ+u//uu/mvWnvZr46KOPtGzZMh09elR5eXlq0aKFBg4cqEcffVQdO3b0d3mN7rrrrlNqamqF+9asWaP27dtLKl0D/sUXX9TmzZvd695PmTJFLVu2bMxyG11V7RMTE6O//OUv2rZtm9LS0uR0OtWxY0eNHDlSEyZMUEhISCNX3HjGjh3rc2nmvXv3uh8H4/VTnfbJyckJ2utHkpYvX64FCxZo3759ysrKUmRkpC666CLde++9uv766z2ODcZrqDrtE+zXUEUqCr2StGnTJr3yyivatWuXIiMjNWzYMP3+97/3WtugMRB6AQAA0OwFV987AAAAghKhFwAAAM0eoRcAAADNHqEXAAAAzR6hFwAAAM0eoRcAAADNHqEXAAAAzR6hFwAAAM2e1d8FAEBz5WtlufL+9re/6fbbb2+EiuouOTlZkufKbwDQFBB6AaCB9e3b1+dy2+edd14jVgMAwYnQCwAN7M4772wyPbkA0FwxphcAAADNHj29ABBAyo+ZnTdvnubMmaNDhw7JarXqkksu0SOPPKI+ffpUeG5WVpb+8Y9/aM2aNUpJSZHZbNb555+vYcOGaezYsQoLC6vwvJMnT2rmzJn65ptvlJKSIsMw1Lp1a11yySW666671Ldv3wrPW7VqlWbOnKm9e/fK5XKpe/fueuihh3T11Vd7HZuWlqb33ntP69at07Fjx2Q2mxUXF6dOnTrpqquu0v3331+7BgOAajIZhmH4uwgAaI7KbmSryY1qZaF3/PjxmjVrlvr27aukpCTt27dP+/btk9Vq1dSpU3XjjTd6nPfLL7/ovvvuU2pqquLj49W/f3+VlJRow4YNys/P10UXXaQPP/xQsbGxHud99913evzxx5WTk6OEhAT16dNHISEhSk1N1Z49ezRixAi9+OKLXvU98sgjevvtt3XJJZeoTZs2OnTokPbs2SOTyaQ333zTo7709HTdfvvtSktLU9u2bdWjRw/ZbDalpaXpwIEDcjqd2rRpU63aGACqi55eAAhAc+bM0YcffqiBAwe6t82YMUMvv/yynn76afXt21cJCQnufU8++aRSU1N13XXX6dVXX1VERIQkKTMzUxMnTtRPP/2k5557Tq+++qr7nOPHj+uxxx5Tbm6uJk2apMcee0yhoaHu/RkZGfr5558rrG/27NmaO3euevfu7d725ptv6q233tIrr7ziEXrnzp2rtLQ0jRkzRn/+859lMpnc+0pKSgi8ABoFY3oBoIE9/fTTSk5OrvQrJyfH65wxY8Z4BF5Jmjhxonr27Knc3FzNnz/fvX3Tpk3atm2bwsPD9Ze//MUdeCUpPj5ezz33nCTp888/14kTJ9z7PvzwQ+Xm5uraa6/Vk08+6RF4JSkhIUH9+vWr8D09/vjjHoFXkh544AFFR0fr8OHDOn78uHt7RkaGJGnw4MEegVeSQkJCvN4nADQEenoBoIFVNWVZSEiI17bbbrutwmNHjRqlnTt3auPGjXrwwQclSRs3bpRUGipbtmzpdU7Pnj3VvXt37dmzRxs3btQtt9wiSVq3bp2k0oBdU9dee63XttDQUHXo0EG7du3SyZMnlZSUJEnq1auX/vnPf+qVV16RYRi68sorFRkZWePXBIC6IPQCQAOrzZRl7du397m9fI/tyZMnfZ4jlc4FvGfPHvexknTs2DFJ0gUXXFCj2iSpbdu2FW6PioqSJBUXF7u33Xrrrfr222+1bNkyPfbYY7JYLOrcubMuvfRSDRkyhJ5eAI2C4Q0A0AT5+x5ks7n6/3yYzWa98sorWr58uf77v/9b11xzjdLT0/Xpp59q/PjxevDBB+V0OhuwWgAg9AJAQEpJSalwe9myxm3atHFva926taTSGRwqU7av7FhJ7uEHhw4dqlux1dSlSxdNnDhRb7/9tr777jvNnDlTCQkJ+vLLL7V48eJGqQFA8CL0AkAAWrJkic/tAwYMcG8re7xu3TqdOnXK65xdu3Zp9+7dMpvN6t+/v3v74MGDJUnz5s2rt7qry2QyaeDAgRoxYoQkaffu3Y1eA4DgQugFgAD06aefasOGDR7bZs6cqe3btysyMlKjR492b+/Xr5969+6toqIi/elPf1JhYaF7X2Zmpv70pz9Jkm6++WZ3764k/eY3v1FkZKTWrl2rv//97yopKfF4vYyMjHqZTmzx4sXauXOn1/a8vDz3TXjt2rWr8+sAgC/cyAYADWz+/PnucFeRK6+8UiNHjvTYNmbMGN13333q16+fWrdu7V6cwmKx6IUXXlBiYqLH8a+++qruu+8+rVmzRtdff7369esnh8OhDRs2KC8vTxdddJE7/JZp27at3njjDT3++OOaPn26FixYoD59+shqterYsWPavXu3RowYUem0ZdW1evVqTZ48Wa1atVKPHj0UExOjnJwcbdmyRbm5uerWrZvuvPPOOr0GAFSF0AsADWzLli3asmVLpfujo6O9Qu8zzzyj888/X3PnztWOHTtktVo1ePBgPfzwwxUuC9yhQwctXLhQ//jHP/Tvf/9bX331lccyxOPGjatwGeJBgwbps88+04cffqh169Zp3bp1slgsatWqlW655RbddddddX7/EyZMUPv27bV161bt2rVLWVlZiouLU5cuXTRixAjdfvvtHnMLA0BDYBliAAggZcv87t2718+VAEDzwpheAAAANHuEXgAAADR7hF4AAAA0e4zpBQAAQLNHTy8AAACaPUIvAAAAmj1CLwAAAJo9Qi8AAACaPUIvAAAAmj1CLwAAAJo9Qi8AAACaPUIvAAAAmj1CLwAAAJq9/w9CRFuPbqg8JAAAAABJRU5ErkJggg==", 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", "text/plain": [ "
" ] @@ -768,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 18, "id": "092eb6a0", "metadata": { "lines_to_next_cell": 2 @@ -778,7 +755,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1000/1000 [00:15<00:00, 63.78it/s]\n" + "You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + "100%|██████████| 1000/1000 [00:30<00:00, 32.99it/s]\n" ] } ], @@ -793,13 +771,13 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 19, "id": "5dc3e69d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -830,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 20, "id": "e3e43b95", "metadata": {}, "outputs": [ @@ -838,7 +816,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 250/250 [00:03<00:00, 63.95it/s]\n" + "100%|██████████| 250/250 [00:07<00:00, 32.82it/s]\n" ] } ], @@ -862,13 +840,13 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 21, "id": "89f93ab8", "metadata": {}, "outputs": [ { "data": { - "image/png": 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Nd3pYLfPxvtu8k9TqPq9Ro4as+fLLL824twAlb968Mjdr1iyZU7zT3GoaRC05CSGEUaNGmXGvtfTv31/mdu7cacarVq0qa7Kzs2VOLdnxrkFRi5B+6hq8E+9KTlozv4EDABAhGjgAABGigQMAECEaOAAAEaKBAwAQIRo4AAARyvEYWYMGDWQuMzMz4Sfu1q2bGa9SpYqsef/992Xu2LFjCV9DqhUvXtyMN23aVNa0atVK5tSIiLeEpUKFCjJ3+PBhM+6NqXz11VdmvG/fvrJmzpw5CT/ef/7nf8oatXwkBD1y4o37NWzY0IyPHDlS1iTjhhtukLn//d//lTm16Gf79u2yJi0tzYyrxRAhhPC73/1O5k6ePGnG1TKOEEKYMWOGzHXv3t2Mewtk1D2mRuZC8O/lr7/+2ox7o2f/9V//JXOvvfaaGe/YsaOs2bx5sxnfsmWLrPHGPNUiHW9ZzlVXXSVzf/nLX8y4tyTm5ptvNuPe65o/f36ZU2OeS5culTXeSGTPnj3N+E033SRrVE/zRjmvu+46mRs+fLjMKYyRAQCQS9HAAQCIEA0cAIAI0cABAIgQDRwAgAjRwAEAiFCOx8iKFCkicy1atDDj3lhJ7dq1zbg3TuFt+alUqZIZ9zZCqe1cnldeeUXm1IiB2tYTQgjTpk1L+Bo83lYjNZ7kbUv74YcfzPjx48dljTdyojYhqW1VIYTQunVrmWvevLkZnzp1qqxRW+NSTW2/CiGEFStWyNyVV15pxj/55JOffU05pbYuzZ49O6nHU2OjEydOlDVlypQx47t375Y1hQsXlrkBAwaYcW9joTdGlpGRYca9TXjnnWf/zjR48GBZ8+6778qccscdd8ic9x32zjvvmHE1rhZCCO3btzfjJUqUkDWLFi2SudWrV5txdQ95NSGEMHr0aDPubUDctm2bGb///vtlzeuvvy5zive6evf5P/AbOAAAEaKBAwAQIRo4AAARooEDABAhGjgAABHK8Sn0e++9V+aGDBmS8BPfeOONZnzMmDEJP1YI+hS6txzFW4qglkN4fwB//vz5Ztz7mdLT02Vu8uTJZjyHb9n/MXToUDP+8ccfyxp1QnfYsGGyRp0MD0HfR96pWY9a1uEtUpg+fboZV8teQgihcuXKMqfusUmTJskaNREQQginT58241lZWbJGefTRR2Vu4MCBCT9estSkindqfO7cuWbc+0x7S5fUqV7vRLS3ZOeLL74w49nZ2bJm+fLlZty7HzZs2CBz6iT1rl27ZI234ObAgQMJP97KlSvNuLeEZezYsTKnnstbWLJjxw6ZU9/L3oTSpZdeasb37t0ra9QyqxD0d7mHZSYAAORSNHAAACJEAwcAIEI0cAAAIkQDBwAgQjRwAAAilOMxsnr16sncqlWrzPgf//hHWaNGGWrVqiVr1q1bJ3PqD8l7I1K///3vZU790Xq1aCIEvQjAG6fwcuPHjzfjanlACP5ihmXLliX0PCGE8Pjjj5tx7w/tz5kzR+aeeOIJMz5ixAhZo8ZAQtDjiHXq1JE1ixcvNuOdOnWSNd6YW548ecx4tWrVZI03GqR0795d5iZMmJDw47Vq1Urmfv3rX5txNdoVQgjjxo2TuVOnTuX4uv5BjYZ6i2+6dOkic9u3bzfj3jIfb9RUfR+df/75suaaa64x423btpU1akwrBL0MyVso1LdvX5lTS4COHj0qa3bu3ClzyqBBg2RuypQpZrxUqVKyxrsnLr/8cjPufUfMnDnTjH/zzTeyRi23SRZjZAAA5FI0cAAAIkQDBwAgQjRwAAAiRAMHACBCNHAAACKU4zEyNSoTQgiFChUy4xUqVJA1ahPMvHnzZE358uVlrmPHjmbcG3/wRnkyMzPN+JNPPilr1Gv07LPPyhpPo0aNzHjdunVlTf/+/WXuuuuuM+NqFCuEEBYuXGjGt23bJmv69esnc2o7ljf+5m0WU+M3s2fPljX169c34507d5Y13giLd08kQ72Hr7zySsKP1a1bN5krWbKkzKmRq40bN8qaI0eOyJzaFpXM2Kh3r3jbolq3bp3QtYUQwsUXXyxzo0aNMuPehkE19vXf//3fsmbPnj0yp74j/vznP8sate0uBP39631HqBpve5635W306NFmXI32hhDC6tWrZW7Lli1mvESJErJGbRZTGx1/StGiRc14w4YNZY03jvsP/AYOAECEaOAAAESIBg4AQIRo4AAARIgGDgBAhFJyCl3xTkKqk5/ewpL77rtP5rp27WrGixUrJmu8BQvHjh2TOeX55583419++aWs8RYBqD/Qv3//flnjnaRWpy69k/qzZs0y42lpabLm1ltvlbns7Gwz7i3W6N27t8y1a9fOjNesWVPWqBOh6sR9CCE89dRTMjd27FgzPnz4cFlTsWJFmVMn9Z977jlZoz7G3qls7z3s0aOHGVdLHkLwT3On8mS9p02bNjKnTl+rU8oh+NMW6vO+YMECWaMmc8qVKydrnnnmGZkbMGCAGfdOtb/wwgsyp76Pli5dKms++OADM/7ggw/KGu80d9WqVc24Wpr1U/r06WPG1XdHCCFMmjTJjHv3v/e9rH4m76Q+y0wAAMilaOAAAESIBg4AQIRo4AAARIgGDgBAhGjgAABEKCVjZM2bNzfja9eulTXDhg0z494IkjdqpJYieGNV3h/o79mzpxn3xpMGDRpkxu+++25Zc9FFF8mcGrU4cOCArPFePzU+NWTIEFmjFnU88MADCT9PCHqcQi2PCSGERx55RObU6NehQ4dkTYsWLcy49zrkzZtX5n744QeZU+666y6Ze+edd8x4s2bNZM13331nxr1RGY9aBtOpUydZo0YOQ9CLiLwxLcW7x70FLcuWLTPjM2bMSPgaPL/5zW9kTo1jeQtGvIUv6n2/5ZZbZI03PnjixAkz3rhxY1nz8MMPm3FvREqNdoUQwvvvv2/GvcUy3vdH7dq1zXjfvn1ljRpn9r4jvCVTyYzAMUYGAEAuRQMHACBCNHAAACJEAwcAIEI0cAAAIpSSU+jqhKm33OCGG24w461bt5Y1Dz30kMz99a9/NePeaWnPG2+8YcbXrFkja+rXr2/GP/30U1njvUbqxKpa3BJCCIsXL5Y5deLdO908bdo0M75ixQpZ452aVQsg1EKEn3q86dOnm/GmTZvKmuPHj5vxM2fOyBrv5L96zatUqSJrChcuLHPqM6CmHEIIIV++fGbcO93sTVSoJSje6+CdAFfv4fz582WNUqlSJZnzJgLUKeFFixbJmrZt28qc+k5UizBC0BM73v3gne4/V9LT02Vu9+7dZtxbDqRehxD099SRI0dkjfqeCiGEGjVqmPGTJ0/KmquuusqMe6fQU41T6AAA5FI0cAAAIkQDBwAgQjRwAAAiRAMHACBCNHAAACJkz54YbrvtNplTI0CNGjWSNWPHjjXjDRs2lDUDBgyQOfUH8L0xEG+RyL333mvGvT9YX6BAgYTiIehRsRBCKFSokBn3Rm9Kly4tc2oxyXvvvSdr1LiYN9KkRvpCCGH8+PFmfOXKlbJm+/btMrdkyRIzXq5cOVmjxqfat28va7zxQTVyddlll8maDRs2yJx6bdVoSwghTJ061Yx7C4DU6FkIIaxfv17mlH379snc+eefb8bVCGoIesTymWeekTVvvvmmzO3Zs8eMe+N+3hIb9V3gjU/VqVPHjG/atEnWeIoVK2bG1esdgj/uV6ZMGTPufU+pkTDvtbv99ttl7r777pM5RY0lh6B7jbegJdXjYh06dDDjP3dEkN/AAQCIEA0cAIAI0cABAIgQDRwAgAjRwAEAiBANHACACKVkG1kybr31VjPubaWqUKGCzH311VdmfMGCBbLmlltukblu3bqZ8dmzZ8saNZbmbXDavHmzzJ04ccKMe2NpqiaEEDp27GjGvU1IBw8eNONpaWmy5tSpUzI3Y8YMM16tWjVZ443Y5M+f34x7m+vU+MiWLVtkjfc+ff3112a8ePHisqZevXoyp0ZYLr74YlmTmZkpc0qfPn1kTo1lerzvCDWG6o2rffPNN2ZcjU6F4G8j279/vxn3Ru0OHTokc8lcX8GCBc24N/bojWOpz+Hy5ctljadIkSJm3BuJ9LavKR999JHMqe9l733yxjzVeKMa0w1Bbyx8/PHHZc2LL74oc8mMkbGNDACAXIoGDgBAhGjgAABEiAYOAECEaOAAAEToX3YKPRmdOnWSubVr15rxgQMHypqXX35Z5tRJb7XkJIQQevXqZcYfe+wxWfP999/L3Mcff2zGmzVrJmvOO0//n0wth+jbt6+s+dOf/mTGr7vuOlmzePFimVPLVrwToZUrV5Y5ddLbO+WanZ1txk+fPi1rjh49KnNqOYq3zOezzz6TuWSopT1z5sxJ6fOoZRchhLB79+6UPpe6x9RJ6RBCmDJlisyp77Bt27bJGu+z0aJFCzN+5513yhr1PlWqVEnWHD58WObU6etkltEkKz093Yx7kxtZWVkyN3r0aDN+zz33yBpvgqREiRJmvF27drJmwoQJMqeo74EQQmjatKkZ/+STT2QNp9ABAMilaOAAAESIBg4AQIRo4AAARIgGDgBAhGjgAABEKMdjZDVq1JC5ihUrmvF8+fLJGrV8xPuD9UeOHJE5tZDj22+/lTUeNarlLTfYs2ePGfcWAXgLWtTyBW806OGHH5a5P/zhD2bcW5bQv39/M67evxBCuPDCC2Vu5syZMqeo0bMQ9Gu0a9cuWfPWW2+ZcW+0S43/hBDCgw8+aMa9kT7vNVLLW7wlMT179jTj48ePlzXe4g/lXI6RKd5olzeWo8ZQvftr3rx5MqeWF40dO1bW/BI0adJE5tR31bJly2RNw4YNzXhGRoas8b7LFy1aZMa9JTFq3DUEvZDJWySiFgepRUMhhHDy5EmZe+KJJ8z4c889J2sYIwMAIJeigQMAECEaOAAAEaKBAwAQIRo4AAARooEDABChHI+RFS1aVObUCJc39qI22Hz44Yeypn379jL33XffmXE1DhBCCA0aNJC5YcOGyZzy4osvmvEXXnhB1jzyyCMyN3v2bDO+YcMGWXPixAmZUyN6Xbt2lTXjxo0z45dccklS16CuXY1OhRBCrVq1ZE69796Wt44dO5rxkiVLypqqVavKnHrfvddIjV6GoDch3XjjjbJmzJgxZvyCCy6QNd5IpOJt2vI+M926dTPj3tasVatWmXFvK+GSJUtk7j/+4z/MeDKfdU/BggVlrnr16gk/njfmmZmZacbV6x1CCBMnTkz4GrzPxr59+8y42tYWgv/5vPzyy824d69415eWlmbG1XdbCHrjXe/evWWN9zNNnjxZ5hTGyAAAyKVo4AAARIgGDgBAhGjgAABEiAYOAECE9LaR/0/9+vVlrly5cmZcnaYNQZ829/5gvbdAQ/GWmdSuXVvmpk6dasY/+OADWfP444+b8eLFi8uarVu3ypxadLJy5UpZ4512VCfyvdd87969ZrxOnTqy5m9/+5vMbd682Yx37txZ1hw4cEDmsrOzzfjx48dljXr9duzYkfDzeNRShhBCyMrKkjk18eGdsFbLTLwFLS+//LLMPfTQQ2Z89erVssajrt27/ytVqmTGv/76a1lTqlQpmUv1aXPFu1fUifKyZcvKmhkzZsjcFVdcYcZ37twpazyNGjUy4+pzG4JeePTee+/JmhIlSsicWkS0cOFCWZMMb6GQOrn++eefyxrvNTpb+A0cAIAI0cABAIgQDRwAgAjRwAEAiBANHACACNHAAQCIUI6XmeTJk+dsX0sIwf+j9EeOHJG5kydPmnFvCcvhw4dlTo2jqLEqT48ePWTOG/tS116jRg1Z441avPbaa2Z86NChsqZ06dJm3BsjW758uczNmTNH5pKhxj2OHTsma9R4nlqiEIK/ZEctPihUqJCs2b17t8ypkc01a9bIGjWquGfPHlnjqVmzphn3Fkp41CKdefPmyRq1FMRblpPDr7Mc80YsDx48mNLnSqU2bdrI3Ny5cxN+vLp168qcWjrjfUd444jqc5M/f35Z06xZM5lbu3atGfdGGNXCoxUrVsgaNf7mady4scx5Y6P/wG/gAABEiAYOAECEaOAAAESIBg4AQIRo4AAAROgXdwo9WU2aNDHjy5YtkzXNmzeXuZYtW5pxdZI7BH1Ksm3btrLGO7m4b98+M/7DDz/IGu8Uf8WKFRO+hszMTDOulh6EEMIFF1wgcxdddJEZHz58uKzxqBP+48ePT+rxktG1a1czvmXLFlnjndQ/V7zpg8cee8yMq3syWd5Si/3795vxZ599VtZ4i36mTJlixr3JkmuvvVbmxo0bZ8a975Xt27ebce9EdDJ69eolc95EhVK1alWZU4tYvIkYNZUQQghLly4149dcc42sGT16tMwlo3379mbcW6jlLfzyTq8rOWnN/AYOAECEaOAAAESIBg4AQIRo4AAARIgGDgBAhGjgAABEKKoxMm85xPHjxxN+PG/cwxuBUJJZ2NC5c2eZU2Mvnt69e8vc4sWLzbg30pSenm7GJ02alNiF/QzeyIn32iodOnQw42pkLoTkloKk+v5KNbWoJgT983rjRFlZWTKnFsg88sgjsuahhx6SuWQULlzYjB89ejSlz1OrVi2ZW7duXUqfS6levbrMVapUSeaSWTakekO1atVkzcaNGxN+nnNJLWLxlrAkw+tp3kKmf+A3cAAAIkQDBwAgQjRwAAAiRAMHACBCNHAAACJEAwcAIEI5HiO75ZZbZG7UqFEpu6Bk5cuXz4x7Yy8bNmyQObVpyNtOhF8ObzwvLS3NjKv3/GwoUKCAzJ04ccKMX3fddbLm008/NePeOFEyozze6Jm3hU79TN5Wu3M1qnjeefr3mDNnzsic2vyX6o1tasthCHrToRqVDMHfqDVjxoycX9iP2rVrZ8a91rJz506ZU9eutp6F4F/3jh07zLh3/5cqVcqMe5vr1JhiCMmNKrKNDACAXIoGDgBAhGjgAABEiAYOAECEaOAAAEQoJctMVC6HD51j7du3lznvZGUq1a1bV+ZWrVplxitXrixrtmzZ8rOvKSYtW7Y0495J29mzZ5+ty/knV155pcx98803MqdOmBYtWlTWHD58OOcX9iN16jmEEHr27GnGT548KWumTZsmc9u3b8/5hf1ITYKEEMLp06cTfjz1eU/1Z71Hjx4y5y1oUaeO1eKWEPSJ9zFjxsgajzrFX758eVmTzJIk795T97J3uv/qq6+WuYYNG5rxlStXyhpvqmPu3Llm/FwtlvEUL15c5vbv3/+T9fwGDgBAhGjgAABEiAYOAECEaOAAAESIBg4AQIRo4AAARCglY2TJaNCggRlfvny5rOnVq5fMffjhh2a8UqVKsubAgQMyl52dbcabNGkiaxYtWmTGa9WqJWtSPcrQvXt3mZswYYIZHzlypKzp3bu3GW/evLms8caJ1GjE6tWrZU2hQoVk7vjx4zIXq2rVqpnxyy67TNbcdtttZtwbjUtGxYoVZW7btm0pfS7g3xnLTAAAyKVo4AAARIgGDgBAhGjgAABEiAYOAECEaOAAAEQox2NkTZs2lbklS5ak6nrCVVddJXOZmZkyp7Yn1atXT9asWLEi5xf2I2+cTm3RycjISPh5zob09HQzPmnSJFlTtWpVM+5tUWvVqpXMqc1AHu/x5s2bl/Dj5UZlypQx4xdccIGsWb9+vcypTVJnzpxJ7MIAJIUxMgAAcikaOAAAEaKBAwAQIRo4AAARooEDABChHJ9C95Z47N6924ynerlB48aNZU6dqD1y5IisUQtVQvCXqvySee9TKqcFPB06dJC5/Pnzm/Fp06bJmrp168rcqlWrcn5hP8E77V6gQAGZUwtVli5dKmtOnDiR8wvLgQEDBpjxxx57LKnHK168uBlXkwwhhDBq1KikngvA/8UpdAAAcikaOAAAEaKBAwAQIRo4AAARooEDABAhGjgAABHK8RhZ5cqVZU4tEjmXiw/UeEubNm1kTZEiRWSuf//+P/ua/kGN5IQQQvny5WVu9erVZtx7y5599lmZy5cvnxn/wx/+IGsefvhhmVMGDRqUcI2nRYsWMqcW3BQtWlTWlCpVyox///33subYsWMy98gjj5hxb6zKWwZTvXr1hOIhhDBz5kyZS4b6bHhjmQBShzEyAAByKRo4AAARooEDABAhGjgAABGigQMAEKEcn0LPkyePzNWqVcuMly5dOuELysjIkDnvVHt2dnbCz5Vqffr0MeObN2+WNTNmzJC5tm3bmvF58+bJmvvuu0/m1MIQNUUQgl664d0P3pKYkiVLmvFJkybJGrUAJYQQatasaca9JSdqaYn3unrUlIE3fbBx40aZS+X1natFMABSi1PoAADkUjRwAAAiRAMHACBCNHAAACJEAwcAIEI0cAAAIpSSMbIyZcqYcW/sa+/evTl52hy7//77zfjrr7+e1OOpEa45c+bImipVqphxb4ysc+fOMjdlyhQz3rx5c1mjFpaEEEKJEiXMuLfE49ChQ2b83XfflTVe7ptvvjHjV1xxhax57733ZE4tEtm/f7+sWb9+vRn3RvrUa/dTz6WoUbEQ9LiYGpkLQf9M3gjeqVOnZE65+uqrZe7zzz9P+PEA2BgjAwAgl6KBAwAQIRo4AAARooEDABAhGjgAABGigQMAEKEcj5F540lqfMrbuKQ0atRI5lauXClzaiRGbb8KIYR9+/bl/MJ+hoEDB8rco48+KnOvvfaaGf/zn/8sa7yxNDVSlJmZKWseeOABM/7CCy/ImtOnT8vc8uXLzXi1atVkzaZNm2Tu66+/NuO33XabrFEjV+pnDcEfe/zggw9kTklPT5c5tZnNGz2rUKGCGf/ss88Su7Af9erVy4x/+OGHST0egMQwRgYAQC5FAwcAIEI0cAAAIkQDBwAgQjRwAAAilJJlJurkeEZGRsIXVKRIEZk7cuRIwo+XrPLly5vxHTt2pPR52rdvL3N33XWXGe/Tp09SzzVz5kwz7i2hGDRokBnv27evrHnrrbdkTk0FeNMC69atk7lu3bqZcW9Rx4EDB8z4/PnzZc25VKxYMTN+8OBBWVO/fn0zvmLFipRcE4Bzi1PoAADkUjRwAAAiRAMHACBCNHAAACJEAwcAIEI0cAAAIqQ3lPx/LrnkEpm76aabzPgTTzwha06cOGHGGzduLGuOHj0qc1u2bDHje/bskTWe3/3ud2Z84sSJsmbOnDlmPG/evLLmu+++k7lPP/1U5pRmzZrJnFp08uqrr8qacuXKmfFZs2bJmiVLlsjcjTfeaMYfe+wxWfPGG2/IXNmyZc24N0Z26NAhM96lSxdZ4y3SUeNd3tiXN5bp1SmpHhcrXry4GVcjeADOPX4DBwAgQjRwAAAiRAMHACBCNHAAACJEAwcAIEI0cAAAIpSSbWS/BAUKFDDjalztbOjXr58ZV9ulQghh4cKFMrdmzRoz7o3aeRvbvvnmGzOuXrsQ9JjWl19+KWvq1Kkjc+q5vPGkBx98UObGjBljxjt16iRrNm3aZMZnz54ta1577TWZUyOH6enpssYbtdu+fbsZr1KliqzZvHmzzAGID9vIAADIpWjgAABEiAYOAECEaOAAAESIBg4AQIRyvMzE8+ijj5rxgQMHyppGjRqZ8YyMjKSuoUePHmb8888/lzVdu3aVuV27dplxtbAkhBDmzZtnxnfu3ClrLrvsMpk7efKkGd+3b5+s2bhxo8z96le/MuMTJkyQNWqpxdChQ2XN8ePHZa5GjRoJ13iTBNu2bTPj552n/2967NgxM+4tM1EnzUMI4e677zbjb7/9tqxJRjInzb2JhaVLl/6cywHwL8Zv4AAARIgGDgBAhGjgAABEiAYOAECEaOAAAESIBg4AQIRyvMykYMGCMqeWVyQ7EpYMdQ2//vWvZc2zzz6b8PP07dtX5pYvX27G27dvL2uqVq0qcw888IAZ936mkSNHylytWrXMuFpYEoIeZcuXT08gej+vGi2sVKmSrOnfv7/MvfTSS2ZcLQQJIYT8+fObcTWKGIL/Gm3ZssWM7969W9Z416eWrQD498EyEwAAcikaOAAAEaKBAwAQIRo4AAARooEDABChHJ9Cz5MnT8IP7p1UbtasmRmfP3++rPnTn/4kc5MmTTLjc+fOlTUetQyjTJkysmb8+PFm3Ftm4v28Bw4cMONjxoyRNeXLl5c5tZjkq6++kjXp6elmXL3eP3UNO3bskDll8ODBMjdgwAAz7i3+KFCggBn3lqY0aNBA5i699FIzPnz4cFnTs2dPmTt06JAZnz59uqxR0tLSZO7JJ5+UuVdffdWMe/cygNThFDoAALkUDRwAgAjRwAEAiBANHACACNHAAQCIEA0cAIAIndUxsmTUr19f5ryFKt9//70Zv+mmm2TN3r17ZU6NarVr107WZGVlmfGrrrpK1niLKypUqGDGFy1aJGtWrFghczVr1jTjt956q6xZsmSJGfdGBEuVKiVzahRKjU6F4I87qdeocuXKskYtLWndurWsUSOCIYRwww03mPHFixfLmnXr1skcADBGBgBALkUDBwAgQjRwAAAiRAMHACBCNHAAACJEAwcAIEJ6FigFvPGpqVOnmvGKFSvKmmnTpiV8Dd74jze61LdvXzP+1ltvyZqbb77ZjHuvwxdffCFz3jarZKjRL7Wdy8t5G9E8alNZ4cKFZc3+/ftlbuXKlWbcG/dT99iuXbtkjSczM9OMt2zZUtZcf/31Mrd161Yz/uGHH8qaM2fOmPH7779f1rz++usypxQrVkzmDh48mPDjAUgev4EDABAhGjgAABGigQMAECEaOAAAEaKBAwAQoX/ZMpM777zTjA8bNkzW9O/fX+aWLVtmxkuXLi1rDhw4IHOTJ082488++6ysmTVrlhn3lnt4p9DVqd5GjRrJmvXr18vc0aNHzXjz5s0TfrzDhw/Lmrvuukvm3nzzTZlTnnnmmYRzV1xxhay5+OKLzfhrr70ma7zXPCMjw4xXqlRJ1nj35dKlS2XuXKlevboZ79Chg6wZMWLEWboa4N8Py0wAAMilaOAAAESIBg4AQIRo4AAARIgGDgBAhGjgAABEKMdjZPfee6/MDRkyJOEnvvHGG814sksy1MhOlSpVZI23zCQtLc2M33TTTbJm/vz5Ztz7mdLT02VOjbLl8C37P4YOHWrGP/74Y1mjxom8cT9vLE3dR3fccYes8Tz99NNmfNOmTbJm+vTpZtwbjatcubLMqXts0qRJsubCCy+UudOnT5vxrKwsWaM8+uijMjdw4MCEHw/AucEYGQAAuRQNHACACNHAAQCIEA0cAIAI0cABAIgQDRwAgAid1W1k3gjX5s2bz8njeWNfBQoUkLmyZcuacW88SY2L9ejRQ9bs2rVL5ubNm2fG1YhbCCH86le/krmFCxea8e7du8ua4cOHm/EmTZrImtmzZ8tcMlq1aiVza9asMePemJZ6Hd5++21Zc/fdd8uc4m2h8zbh/fDDDwk/l+K9Dhs2bJC5Bg0amPHly5f/7GsC8NMYIwMAIJeigQMAECEaOAAAEaKBAwAQIRo4AAARSskp9J49e5rxXr16yRp1Yttb/JE3b16Zq1Chghn3fjzv9PX48ePNeHZ2tqzZt2+fGc+fP7+sUUtYQgihcOHCZjwzM1PWeAYMGGDGr7/+ellTq1YtM+4t97joootk7vjx42a8Tp06smbEiBEy16lTJzOekZEha0qUKGHGvev2FpMko0iRIjJ38uRJM163bl1Zs27dOjN+7NgxWdO4cWOZW7p0qcwBOPs4hQ4AQC5FAwcAIEI0cAAAIkQDBwAgQjRwAAAiRAMHACBCOR4ju/zyy2Vu//79Znzq1Kmypk+fPmZ87dq1skaNyoSgl494SzeOHDkic3PnzjXjzZs3lzXqdVBjdiGEMGjQIJlTvPGfSy+9VOaWLVtmxnfs2CFrWrdubcarV68uawYOHChzxYoVM+Pt2rWTNRMmTJC5wYMHm3G1hCWEEDp06GDG58yZI2vKly8vc+PGjZO5ZFSsWNGMb9u2Tdao+9wbV/OWzlx55ZVm3Bsv2717t8wBSAxjZAAA5FI0cAAAIkQDBwAgQjRwAAAiRAMHACBCKVlmUqhQITOuFleEoJePbN++XdaMHj1a5tSCiqefflrWeCeLzz//fDPuLbU4fPiwzCWjZs2aZnznzp2yRp0aD0FPBTRq1EjWeEtBlE8++UTm1EKVhQsXyhq1UCWEEPLly2fG69WrJ2s+++wzmVPUyfUQQpg1a5YZb9asmazZtWuXzG3ZsiXnF/Yz1K9fX+aqVatmxlO91AWAjVPoAADkUjRwAAAiRAMHACBCNHAAACJEAwcAIEI0cAAAImTP4BjS0tJkTo3LeMsSvDEaZd68eTKnxonUOFgI/kKOPXv2mHFvVKxkyZJmXI3MhRBCw4YNZU6N+Tz11FOyxlsgo5x3nv5/3M0332zGp02bJmu88UE1LlaqVClZ4y2xeeSRR8y4NyrWuXNnM75o0SJZs2nTJpm7/fbbzfh7770na7wRkXLlyplxb3xQvU+jRo2SNQULFpQ5tRwIwC8Hv4EDABAhGjgAABGigQMAECEaOAAAEaKBAwAQIRo4AAARSsk2sgYNGphxb3wqmXGndu3aydyxY8fM+I4dO2RNxYoVZU6NhE2ZMkXWqK1Z3uiZ2uQWgh4b6tKli6xRI0ghhDBx4kQz7o0TFStWzIx7I1ee/Pnzm/EaNWrIGm+Ecf/+/Wa8ffv2smbkyJFmvGvXrrJm8uTJMpcMb7vZt99+a8azs7NljRqN8+5XdY+HEMK+fftkDsDZxzYyAAByKRo4AAARooEDABAhGjgAABGigQMAEKGUnEJv1aqVGV+1apWsOXDgQE6e9p+o0+4hhHDq1Ckz7l23d/q6RIkSZnzx4sWy5qGHHjLjb7zxhqzxTsKnp6ebcfWzhhDCwIEDZU55/PHHZU4tw9i4cWPCz5Msb7GGWgYzffr0lF5DixYtZG7+/Plm3DtpPmvWrJ99TTlx3333ydzgwYMTfjy1YCeEEFasWJHw4wGwcQodAIBcigYOAECEaOAAAESIBg4AQIRo4AAARIgGDgBAhFIyRpZK3jKO66+/XubGjh1rxl988UVZs3LlSpl76aWXzLhaGhGCHiPbtm2brLn99tsTztWpU0fWeEti1DiWGhULIYSWLVuacbVwI4QQbrjhBplbsGCBGVfLaEIIYdeuXTKnqOsOQV97vnz5ZI13X27dujXnFwYAOcAYGQAAuRQNHACACNHAAQCIEA0cAIAI0cABAIgQDRwAgAilZIzsrrvuMuPvvPNOclclFClSROaOHDmS8OM9+OCDMjd79mwzXqtWLVmjtpt5m9e8DWurV6824964kzcS1qRJEzN+/PjxhK+hW7dusmbixIkJX8OSJUtkjTc2p67PozZqpXqbltpoF0II+/fvT+lzqa1jJ0+elDVvv/22zHXp0sWMf/HFF4ldGICkMEYGAEAuRQMHACBCNHAAACJEAwcAIEI0cAAAIqSPMyfg3nvvNePeidXNmzcn/Dx58+ZNuKZo0aIyt3btWpnLzs424zt27JA1hQoVMuM7d+6UNSdOnJC5r7/+2oz369dP1ngaNmxoxkeOHJnwY3k/k8c7ba4kc9K8R48eMjd+/Hgznuoph5tuuknmhgwZInNq0mHdunWyZvDgwWbcW5aTnp4uc5MmTTLjVatWlTVZWVkyByD1+A0cAIAI0cABAIgQDRwAgAjRwAEAiBANHACACNHAAQCIUI6Xmdxyyy0y5y3Q+CVLS0uTuUaNGpnxMmXKyJpTp06Z8W+//VbWeEstfv/735vx887T/++aMmWKzKkxN7W4JYQQ2rRpI3PK0aNHE65ZunSpzCUz7pRqyVyD99rNnTv3Z19TTpQvX17mvJFIAP9aLDMBACCXooEDABAhGjgAABGigQMAECEaOAAAEcrxKfQ8efIk/ODNmzeXufPPP9+Me0tODh48KHOnT58241deeaWsUUstQgjh4osvNuOZmZmyZvLkyWa8e/fussY7JdyqVSszPnbsWFnToEEDmVu+fLkZr1u3rqxZtWqVGe/Vq5es8RaWbN++3Yw3bdpU1kyfPl3mUqly5coyV6xYMZnz7olkqCUoo0ePljVly5Y149695y2J8SYTAJx9nEIHACCXooEDABAhGjgAABGigQMAECEaOAAAEaKBAwAQoZSMkalFD+vXr5c1a9asycnTRqVmzZpm3Hsd+vTpI3OlSpUy46+88kpiF3YWVK9eXeY2btyY0udSI4chhHDy5MmU1XhjZFu2bJE5JZlr8HhLbM6cOWPGvYUqe/fulTk1YnbttdfKmnHjxskcgMQwRgYAQC5FAwcAIEI0cAAAIkQDBwAgQjRwAAAiRAMHACBCOR4ja926tcwdOnTIjB85ckTWZGVl5eRp/0mjRo1kLiMjI+HHu/DCC2Xu8OHDZnz37t2ypmDBgmbc24hWtGhRmRs1apQZz5s3r6zJnz+/zGVnZ8tcop5++mmZ++Mf/5jw46lRxBBCWLBggcyp0S+1nS4EvZUtWeo+2r9/f8I1Iej3cM+ePbJm27ZtZvzYsWOyxqO2mE2YMCGpxwOQGMbIAADIpWjgAABEiAYOAECEaOAAAESIBg4AQIRSssxEKVeunMzt3Lkz4cdLRqFChZLKqVO4I0aM+NnX9HO1aNFC5ubPn39OrqFz584yN2XKlIQfL9XLUbyJBXVCvX///rJm2LBhMjdv3rycX9iPypYtK3O7du1K+PHOFW9qQk1uAEgcp9ABAMilaOAAAESIBg4AQIRo4AAARIgGDgBAhGjgAABEKMdjZAAA4JeD38ABAIgQDRwAgAjRwAEAiBANHACACNHAAQCIEA0cAIAI0cABAIgQDRwAgAjRwAEAiND/A7qLQOgfSJgOAAAAAElFTkSuQmCC", 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", 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" ] @@ -893,7 +871,7 @@ "formats": "py:percent,ipynb" }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "monai", "language": "python", "name": "python3" }, @@ -907,7 +885,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.9.19" } }, "nbformat": 4, From 31e5a737e696fdab015b5bb01fdfbc501239eb44 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Wed, 6 Nov 2024 16:22:09 +0000 Subject: [PATCH 03/12] Example works now Signed-off-by: Eric Kerfoot --- generation/3d_ddpm/3d_ddpm_tutorial.ipynb | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb index 3b6d19a96..8eb2e75a8 100644 --- a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "cdea37d5", "metadata": {}, "outputs": [ @@ -120,7 +120,7 @@ " LoadImaged,\n", " Resized,\n", " ScaleIntensityd,\n", - " ScaleIntensityRangePercentilesd\n", + " ScaleIntensityRangePercentilesd,\n", ")\n", "from monai.utils import set_determinism\n", "from monai.inferers import DiffusionInferer\n", @@ -159,7 +159,7 @@ } ], "source": [ - "directory = os.path.abspath(\"./data\") # os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", "root_dir = tempfile.mkdtemp() if directory is None else directory\n", "print(root_dir)" ] @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "ddd61e60", "metadata": { "lines_to_next_cell": 2 @@ -247,7 +247,7 @@ " task=\"Task01_BrainTumour\",\n", " transform=data_transform,\n", " section=\"training\",\n", - " download=False, #True,\n", + " download=True,\n", " num_workers=num_workers,\n", ")\n", "\n", @@ -260,7 +260,7 @@ " task=\"Task01_BrainTumour\",\n", " transform=data_transform,\n", " section=\"validation\",\n", - " download=False, #True,\n", + " download=True,\n", " num_workers=num_workers,\n", ")\n", "\n", @@ -393,14 +393,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "6c1de5ad", "metadata": {}, "outputs": [], "source": [ "num_train_timesteps = 1000\n", "scheduler = DDPMScheduler(\n", - " num_train_timesteps=num_train_timesteps #, schedule=\"scaled_linear_beta\", beta_start=0.0005, beta_end=0.0195, clip_sample=False\n", + " num_train_timesteps=num_train_timesteps # , schedule=\"scaled_linear_beta\", beta_start=0.0005, beta_end=0.0195, clip_sample=False\n", ")" ] }, @@ -468,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "bd10b595", "metadata": { "lines_to_next_cell": 0 @@ -653,7 +653,7 @@ " if (epoch + 1) % val_interval == 0:\n", " model.eval()\n", "\n", - " torch.save(model.state_dict(),f\"model_{epoch:04}.pth\")\n", + " torch.save(model.state_dict(), f\"model_{epoch:04}.pth\")\n", "\n", " val_epoch_loss = 0\n", " for step, batch in enumerate(val_loader):\n", From 0782fa2275a4c60f061bf2aa87f6156f664547f6 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Wed, 6 Nov 2024 16:29:40 +0000 Subject: [PATCH 04/12] Update Signed-off-by: Eric Kerfoot --- generation/3d_ddpm/3d_ddpm_tutorial.ipynb | 117 ++-------------------- 1 file changed, 6 insertions(+), 111 deletions(-) diff --git a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb index 8eb2e75a8..0c770df97 100644 --- a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -153,15 +153,12 @@ { "name": "stdout", "output_type": "stream", - "text": [ - "/home/localek10/workspace/monai/tutorials_mine/generation/3d_ddpm/data\n" - ] + "text": [] } ], "source": [ "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", - "root_dir = tempfile.mkdtemp() if directory is None else directory\n", - "print(root_dir)" + "root_dir = tempfile.mkdtemp() if directory is None else directory" ] }, { @@ -304,16 +301,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "efc86997", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": [ - "/home/localek10/workspace/monai/tutorials_mine/generation/3d_ddpm/data/Task01_BrainTumour/imagesTr/BRATS_400.nii.gz\n" - ] + "text": [] }, { "data": { @@ -340,7 +335,6 @@ "from monai.utils import first\n", "\n", "img = first(item[\"image\"] for item in train_ds if \"400\" in item[\"image\"].meta[\"filename_or_obj\"])\n", - "print(img.meta[\"filename_or_obj\"])\n", "imgn = img[0].detach().cpu().numpy()\n", "f, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)\n", "ax0.imshow(imgn[..., 15], vmin=0, vmax=1, cmap=\"gray\")\n", @@ -486,107 +480,8 @@ "Epoch 05: 100%|██████████| 25/25 [00:37<00:00, 1.48s/it, loss=0.0503]\n", "Epoch 06: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0308]\n", "Epoch 07: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0231]\n", - "100%|██████████| 1000/1000 [00:34<00:00, 28.60it/s]\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 16: 100%|██████████| 25/25 [00:36<00:00, 1.47s/it, loss=0.0144]\n", - "Epoch 17: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0158]\n", - "Epoch 18: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0105]\n", - "Epoch 19: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0125]\n", - "Epoch 20: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0130]\n", - "Epoch 21: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0130]\n", - "Epoch 22: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0121]\n", - "Epoch 23: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0121]\n", - "100%|██████████| 1000/1000 [00:34<00:00, 28.79it/s]\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 32: 100%|██████████| 25/25 [00:37<00:00, 1.48s/it, loss=0.0116]\n", - "Epoch 33: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0118]\n", - "Epoch 34: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0116]\n", - "Epoch 35: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0114]\n", - "Epoch 36: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0152]\n", - "Epoch 37: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0138]\n", - "Epoch 38: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0100]\n", - "Epoch 39: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0113]\n", - "100%|██████████| 1000/1000 [00:35<00:00, 28.48it/s]\n" + "100%|██████████| 1000/1000 [00:34<00:00, 28.60it/s]\n", + "...\n" ] }, { From b99aca55c1f1a2fc5a2a3265cd51a4b1f5b407f3 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Wed, 6 Nov 2024 16:31:37 +0000 Subject: [PATCH 05/12] Update Signed-off-by: Eric Kerfoot --- generation/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/generation/README.md b/generation/README.md index 351416fd1..f2932f6b8 100644 --- a/generation/README.md +++ b/generation/README.md @@ -49,6 +49,9 @@ Examples show how to train Vector Quantized Variation Autoencoder on [2D](./2d_v ## [Training a 2D Denoising Diffusion Probabilistic Model](./2d_ddpm/2d_ddpm_tutorial.ipynb): Example shows how to easily train a DDPM on medical data (MedNIST). +## [Training a 3D Denoising Diffusion Probabilistic Model](./3d_ddpm/3d_ddpm_tutorial.ipynb): +Example shows how to easily train a DDPM on medical data (Decathlon Task 01). + ## [Comparing different noise schedulers](./2d_ddpm/2d_ddpm_compare_schedulers.ipynb): Example compares the performance of different noise schedulers. This shows how to sample a diffusion model using the DDPM, DDIM, and PNDM schedulers and how different numbers of timesteps affect the quality of the samples. From 3090438a42b7ceb081142b546f1061877e1e423c Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Wed, 6 Nov 2024 16:40:03 +0000 Subject: [PATCH 06/12] Update Signed-off-by: Eric Kerfoot --- generation/3d_ddpm/3d_ddpm_tutorial.ipynb | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb index 0c770df97..a0c83ad60 100644 --- a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -122,7 +122,7 @@ " ScaleIntensityd,\n", " ScaleIntensityRangePercentilesd,\n", ")\n", - "from monai.utils import set_determinism\n", + "from monai.utils import first, set_determinism\n", "from monai.inferers import DiffusionInferer\n", "from monai.networks.nets import DiffusionModelUNet\n", "from monai.networks.schedulers import DDPMScheduler, DDIMScheduler\n", @@ -332,8 +332,6 @@ } ], "source": [ - "from monai.utils import first\n", - "\n", "img = first(item[\"image\"] for item in train_ds if \"400\" in item[\"image\"].meta[\"filename_or_obj\"])\n", "imgn = img[0].detach().cpu().numpy()\n", "f, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)\n", @@ -393,9 +391,7 @@ "outputs": [], "source": [ "num_train_timesteps = 1000\n", - "scheduler = DDPMScheduler(\n", - " num_train_timesteps=num_train_timesteps # , schedule=\"scaled_linear_beta\", beta_start=0.0005, beta_end=0.0195, clip_sample=False\n", - ")" + "scheduler = DDPMScheduler(num_train_timesteps=num_train_timesteps)" ] }, { From 97ac9fb7866fd9f318262f043cc818874dd08e84 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Wed, 6 Nov 2024 17:16:13 +0000 Subject: [PATCH 07/12] Update Signed-off-by: Eric Kerfoot --- generation/3d_ddpm/3d_ddpm_tutorial.ipynb | 119 ++++++++++------------ 1 file changed, 53 insertions(+), 66 deletions(-) diff --git a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb index a0c83ad60..243a0f179 100644 --- a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -1,29 +1,21 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "fa57bdf5", - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright (c) MONAI Consortium\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "# http://www.apache.org/licenses/LICENSE-2.0\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, { "cell_type": "markdown", "id": "6286986e", "metadata": {}, "source": [ + "Copyright (c) MONAI Consortium \n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); \n", + "you may not use this file except in compliance with the License. \n", + "You may obtain a copy of the License at \n", + "    http://www.apache.org/licenses/LICENSE-2.0 \n", + "Unless required by applicable law or agreed to in writing, software \n", + "distributed under the License is distributed on an \"AS IS\" BASIS, \n", + "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n", + "See the License for the specific language governing permissions and \n", + "limitations under the License.\n", + "\n", "# Denoising Diffusion Probabilistic Model on 3D data\n", "\n", "This tutorial illustrates how to use MONAI for training a denoising diffusion probabilistic model (DDPM)[1] to create synthetic 3D images.\n", @@ -56,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "cdea37d5", "metadata": {}, "outputs": [ @@ -149,13 +141,7 @@ "execution_count": null, "id": "c38b4c33", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [] - } - ], + "outputs": [], "source": [ "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", "root_dir = tempfile.mkdtemp() if directory is None else directory" @@ -230,8 +216,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Loading dataset: 100%|██████████| 388/388 [01:53<00:00, 3.41it/s]\n", - "Loading dataset: 100%|██████████| 96/96 [00:28<00:00, 3.35it/s]\n" + "Loading dataset: 100%|██████████| 388/388 [01:40<00:00, 3.86it/s]\n", + "Loading dataset: 100%|██████████| 96/96 [00:25<00:00, 3.72it/s]\n" ] } ], @@ -274,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "id": "bffb4abc", "metadata": {}, "outputs": [ @@ -301,22 +287,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "efc86997", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [] - }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -355,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "d499f7b1", "metadata": { "lines_to_next_cell": 2 @@ -391,12 +372,18 @@ "outputs": [], "source": [ "num_train_timesteps = 1000\n", - "scheduler = DDPMScheduler(num_train_timesteps=num_train_timesteps)" + "scheduler = DDPMScheduler(\n", + " num_train_timesteps=num_train_timesteps,\n", + " schedule=\"scaled_linear_beta\",\n", + " beta_start=0.0005,\n", + " beta_end=0.0195,\n", + " clip_sample=False,\n", + ")" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "id": "36d3e99a", "metadata": {}, "outputs": [ @@ -406,13 +393,13 @@ "Text(0, 0.5, 'alpha cumprod')" ] }, - "execution_count": 14, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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IiIg6n1NZWQmpVL9kmXbaMNdDsV7tXV2xsX9/yCS351AtP3UKmy5eFLcoIiIyW6J2S8XGxmLt2rXYsGEDzpw5g2nTpqGiogJTp04FAEyaNAnz5s3THT969GisXr0aGzduRHZ2Nnbv3o0FCxZg9OjRupBD1mlIy5Z4PzRUd/+55GQcLy4WsSIiIjJXdmK++bhx41BYWIiFCxciLy8PISEh2LFjh26QcU5Ojt6Vmvnz50MikWD+/Pm4cuUKWrRogdGjR2Pp0qVifQRqQjMCA5FWXIwNFy7gllqNJ/btw5Hhw9HC0VHs0oiIyIxIBBvrzykrK4O7uztKS0s5/sYCVanVGLx7Nw4XFQEABvj4YFd0NOylFjU2noiIDGTI9ze/EciiOGo32Wyp3YMqsaAAc44eFbkqIiIyJww3ZHH8nJ3x48CBcNBerVmdlYW1586JXBUREZkLhhuySOHe3lgdHq67PzMlBSnarioiIrJtDDdksaa0b4/pgYEAbm/R8PT+/SjiDvFERDaP4YYs2vu9eiHC2xsAkFNRwRWMiYiI4YYsm4NMhk0DBsBHOx1897VrWJyeLnJVREQkJoYbsnitnJ3xTb9+kGpXMH47PR1bLl8WuSoiIhILww1ZhUG+vogLCdHdn3zwIC6Ul4tXEBERiYbhhqzGy1274nGlEgBQolLhycREVNbWilwVERE1NYYbshoSiQTrIyLQydUVAHDixg1MP3KEm6oSEdkYhhuyKm4ODvhh4EA4azdS/c+FC/j8jz9EroqIiJoSww1ZnW4eHlgbEaG7P/voUZy8cUPEioiIqCkx3JBVeiYgANM6dQJwe7PNcYmJKK+pEbkqIiJqCgw3ZLXeDw1FT09PAEBWeTmmHT7M8TdERDaA4YaslqNMho0DBsDV3h4A8O3Fi1jH8TdERFaP4YasWgdXV3zWt6/u/qyjR5HO8TdERFaN4Yas3tP+/vrjb/bvx02OvyEisloMN2QT7h5/k1lWhv/j+jdERFaL4YZswp/H33ydnY3158+LXBUREZkCww3ZjA6urvh3eLju/syUFI6/ISKyQgw3ZFPGBQTgxbvG34w/cID7TxERWRmGG7I5H4SGIkQ7/uZMaSleTk0VuSIiIjImhhuyOY4yGb7u10+3/9Rn587hp5wckasiIiJjYbghm9TZ3R0fhYXp7v/90CHkVlSIWBERERkLww3ZrOc6dMBf2rQBANxQqTApKQlqjUbkqoiI6GEx3JDNkkgk+Hd4ONo0awYASCwoQFxGhshVERHRw2K4IZvmKZfjq6goSCUSAMDi9HQkFRSIXBURET0MhhuyeVE+PlgQHAwAUAsC/paUhBKVSuSqiIiosRhuiAC8HhSEfi1aAAByKirw4uHD3J6BiMhCMdwQAbCTSvHffv3g4eAAAPj+0iV8we0ZiIgsEsMNkVabZs30tmeYlZKCrLIyESsiIqLGYLghusuT/v54vkMHAEClWo1JSUmo4fRwIiKLwnBD9CcfhoWhk6srACDl+nW8nZ4uckVERGQIhhuiP2lmZ4f/REXBTjs9fFlGBpILC0WuioiIGorhhqgOvb29sbB7dwCARhAwKSkJ5TU1IldFREQNwXBDdB+vdeuGSO308As3byL26FGRKyIiooZguCG6DzupFBsiI+FiZwcAWH/+PH7m7uFERGaP4YaoHu1cXbGid2/d/X8cPoxrlZUiVkRERA/CcEP0AFPatcPjSiUA4Hp1NZ47dIirFxMRmTGGG6IHkEgkWBMeDl9HRwDAzqtXsTorS+SqiIjofhhuiBrA29ER6yMjdfdfPXYMZ0pLRayIiIjuh+GGqIFi/PzwUmAgAKBKu3qxSq0WuSoiIvozhhsiAyzv2RNd3N0BAMeKi7GYqxcTEZkdhhsiAzjZ2eE/kZGwl97+q/POqVNcvZiIyMzYNeSgjz/+uMEvOHPmzEYXQ2QJejVvjje7d8cbaWnQCAKmHjyI1JEj0cyuQX+diIjIxCRCA+a0tm3bVu9+YWEhKisr4eHhAQAoKSmBs7MzfHx8cOHCBZMUaixlZWVwd3dHaWkp3NzcxC6HLFStRoOBu3bhUFERAGB6YCA+vms9HCIiMi5Dvr8b1C2VnZ2tuy1duhQhISE4c+YMiouLUVxcjDNnzqBXr15YsmSJUT4Akbmzk0rxRWQknGQyAMCqzEzEX7smclVERAQ08MrN3dq3b48ffvgBPXv21GtPTU3Fk08+iezsbKMWaGy8ckPGtPLsWczS7jmldHbGiVGj4O7gIHJVRETWx+hXbu527do11NbW3tOuVquRn59v6MsRWbT/CwzEYF9fAEBuZSXmcHNNIiLRGRxuhgwZgn/84x84duyYri01NRXTpk1DdHS0UYsjMndSiQTrIiLgZm8PANhw4QJ+zc0VuSoiIttmcLhZv349fH19ERYWBrlcDrlcjj59+kChUODzzz83RY1EZq1Ns2b4KCxMd/8fhw+jqKpKxIqIiGybwWNu7sjKysKZM2cgkUjQuXNndOrUydi1mQTH3JApCIKAsQkJ2HLlCgDgL23aYFP//pBIJCJXRkRkHQz5/m50uAGg2xnZkv4BZ7ghU8m7dQvdt2zB9epqAMB/o6Lw1z8to0BERI1j0gHFAPCf//wHwcHBcHJygpOTE7p3747//ve/jSqWyFr4Ojnh0z59dPdnpKTgSmWliBUREdkmg8PNhx9+iGnTpmHEiBH47rvv8N1332HYsGF48cUX8dFHH5miRiKL8aS/P54JCAAAlKhUeOHQITzExVEiImoEg7ul2rZti7feeguTJk3Sa9+wYQPefPNNrnNDNq+4uhrdt2zBtVu3AACrw8Px944dRa6KiMiymXydm8jIyHvaIyMjcY0rtBLBSy7H2r59dfdfSU3FhfJyESsiIrItBoebDh064LvvvrunfdOmTejYiN9OV61ahYCAADg6OiI8PBxHjhyp9/iSkhJMnz4dLVu2hFwuR6dOnbBt2zaD35fIlIa3aoUXOnQAAFTU1uLZ5GSoNRqRqyIisg0Gb2P81ltvYdy4cUhMTERUVBQAICkpCfHx8XWGnvps2rQJsbGxWLNmDcLDw7FixQrExMQgMzMTPj4+9xyvUqnw6KOPwsfHBz/88ANatWqFS5cu6TbwJDIn74WG4ve8PGTfvIn9BQX4JDMTs7t0EbssIiKr16ip4MeOHcOHH36IM2fOAAC6dOmCl19++Z79ph4kPDwcvXv3xsqVKwEAGo0GSqUSM2bMwNy5c+85fs2aNXjvvfdw9uxZ2GtXhH2Q6upqVGun5gK3++yUSiXH3FCTSMzPx6DduwEAjjIZjo8ciU78/46IyGAmG3NTU1ODZ599Fp6envjqq6+QmpqK1NRUfPXVVwYHG5VKhdTUVL0tG6RSKaKjo5GcnFznc3799VdERERg+vTpUCgUCAoKwrJly6BWq+/7PnFxcXB3d9fdlEqlQXUSPYwBCgVmBAYCAKrUajzH7ikiIpMzKNzY29vjxx9/NMobFxUVQa1WQ6FQ6LUrFArk5eXV+ZwLFy7ghx9+gFqtxrZt27BgwQJ88MEHePvtt+/7PvPmzUNpaanulst9f6iJLe3ZE+1dXAAABwsL8fHZsyJXRERk3QweUDx27Fhs3rzZBKU8mEajgY+PDz777DOEhoZi3LhxeOONN7BmzZr7Pkcul8PNzU3vRtSUmtnZYV1kJO6s4z3/xAlklpaKWhMRkTUzeEBxx44dsXjxYiQlJSE0NBTNmjXTe3zmzJkNeh1vb2/IZDLk5+frtefn58PX17fO57Rs2RL29vaQyWS6ti5duiAvLw8qlQoODg4GfhqiptHfxwczO3fGv86e1XVP7Rs6FDJpoxYJJyKiehgcbtatWwcPDw/deJu7SSSSBocbBwcHhIaGIj4+HmPHjgVw+8pMfHw8XnrppTqfExUVhW+++QYajQZS7ZdCVlYWWrZsyWBDZu/tkBBsu3IF58rLkVxUhBVnz+Llrl3FLouIyOoY/Gtjdnb2fW8XLlww6LViY2Oxdu1abNiwAWfOnMG0adNQUVGBqVOnAgAmTZqEefPm6Y6fNm0aiouLMWvWLGRlZWHr1q1YtmwZpk+fbujHIGpyznZ2WBcRoeueWpCWhrPsniIiMjqDr9zc7WF3BR83bhwKCwuxcOFC5OXlISQkBDt27NANMs7JydFdoQEApVKJnTt3Ys6cOejevTtatWqFWbNm4bXXXnuYj0HUZKJ8fDCrc2esOHsW1RoNnk1Oxn52TxERGVWj1rlZt24dPvroI5w7dw7A7XE4s2fPxvPPP2/0Ao2Ne0uR2CpraxG6dSuytFsyLO/ZE6926yZyVURE5s2ke0stXLgQs2bNwujRo/H999/j+++/x+jRozFnzhwsXLiw0UUT2QrnP82eWnTiBM6we4qIyGgMvnLTokULfPzxxxg/frxe+7fffosZM2agqKjIqAUaG6/ckLl4NTUVH2pX+e7TvDn2x8TAjt1TRER1MumVm5qaGoSFhd3THhoaitraWkNfjshmLe7RA4Hav6BHrl/XBR0iIno4BoebiRMnYvXq1fe0f/bZZ5gwYYJRiiKyBU52dlgfEQGpdkD+ohMncKqkRNyiiIisQKNmS61btw67du1C3759AQCHDx9GTk4OJk2ahNjYWN1xH374oXGqJLJSfVu0QGyXLnj/9GmoNBo8e/AgkoYNY/cUEdFDMHjMzaBBgxr2whIJ9uzZ06iiTIljbsjcVKnVCN26FWfLygAAS0NCMDcoSOSqiIjMiyHf342aCm7JGG7IHB0uKkK/nTuhEQQ4SKVIGTECQR4eYpdFRGQ2TDqgmIiML9zbG69ot2K40z1Vo9GIXBURkWUyeMxNVVUVPvnkE+zduxcFBQXQ/Okf4GPHjhmtOCJbsqh7d2y5fBmnS0uRWlyM906dwuvBwWKXRURkcQwON8899xx27dqFJ598En369Gn01gtEpM9RJsO6iAhEabunFqenY3Tr1gj29BS7NCIii2LwmBt3d3ds27YNUVFRpqrJpDjmhszd68eP451TpwAAvby8cHDYMNhz9hQR2TiTjrlp1aoVXF1dG10cEdVvUffu6ObuDgA4VlyMdzIyRK6IiMiyGBxuPvjgA7z22mu4dOmSKeohsnlymQzrIyMh03b5vp2RgZM3bohcFRGR5TA43ISFhaGqqgrt2rWDq6srvLy89G5E9PDCmjfHP7U7hddw9hQRkUEMHlA8fvx4XLlyBcuWLYNCoeCAYiITWRAcjN8uX0ZGSQmO37iB5RkZWNC9u9hlERGZPYMHFDs7OyM5ORk9evQwVU0mxQHFZElSr19HxI4dUAsC7CQSHB4+HCG8QkpENsikA4o7d+6MW7duNbo4Imq40ObNMVfbPVUrCHg2ORkqtVrkqoiIzJvB4Wb58uV4+eWXkZCQgOvXr6OsrEzvRkTGNT84GMHarRhO3LiBOM6eIiKql8HdUlLteht/HmsjCAIkEgnUZv5bJbulyBIdu34dfdk9RUQ2zJDvb4MHFO/du7fRhRFR4/Rq3hzzgoLwdno6agUBUw8exOHhw+Egk4ldGhGR2eGu4EQWQqVWI3z7dpwsKQFwezbVmxY6sJ+IyFAmvXKTmJhY7+MDBgww9CWJqAEctIv79d2+HbWCgLiMDIxp3Rq9mjcXuzQiIrPS6DE3ei9y1/gbjrkhMq23TpzA4vR0AECwhweOsHuKiGyASaeC37hxQ+9WUFCAHTt2oHfv3ti1a1ejiyaihpkXFIQe2p3C00tK8LY26BAR0W1GG3Ozb98+xMbGIjU11RgvZzK8ckPWIK24GOHa7imZRILkYcMQyu4pIrJiJr1ycz8KhQKZmZnGejkiqkeIlxfeCA4GAKi1i/tVm3mXMBFRUzF4QPHJkyf17guCgGvXrmH58uUICQkxVl1E9ADzgoLwS24u0m7cQIa2e2oJ/w4SERkebkJCQiCRSPDn3qy+ffti/fr1RiuMiOpnL5Xii8hI9Nm+HTUaDd45dQqPKZUIY/cUEdk4g8NNdna23n2pVIoWLVrA0dHRaEURUcN09/TE/KAgLDp58nb31MGDSBkxAnLOniIiG2ZwuPH39zdFHUTUSK8FBeGXy5dxrLgYp0pLsfjkSSzt2VPssoiIRGPwgOKZM2fi448/vqd95cqVmD17tjFqIiID2EulWB8RAXvtGlTvnj6NlKIikasiIhKPweHmxx9/RFRU1D3tkZGR+OGHH4xSFBEZJtjTEwu1s6c02tlTVZw9RUQ2yuBwc/36dbi7u9/T7ubmhiL+tkgkmn9264ZQ7U7hp0tL8dafZjYSEdkKg8NNhw4dsGPHjnvat2/fjnbt2hmlKCIynJ1UivWRkXDQdk+9f/o0DvMXDiKyQQYPKI6NjcVLL72EwsJCDB48GAAQHx+PDz74ACtWrDB2fURkgCAPDyzs3h3z09Jud08dPIjUkSPhyNlTRGRDGrX9wurVq7F06VJcvXoVABAQEIA333wTkyZNMnqBxsbtF8ja1Wo0iNqxA0eLiwEAr3btiuW9eolcFRHRwzHk+/uh9pYqLCyEk5MTXFxcGvsSTY7hhmzBqZIShG3bBpVGA6lEgv1Dh6JvixZil0VE1GhNtrdUixYtLCrYENmKbh4eeLN7dwD/mz11q7ZW5KqIiJqG0TbOJCLz8nLXruij3Yohs6wMi06cELkiIqKmwXBDZKXspFKsi4jQzZ768MwZJBcWilwVEZHpMdwQWbGuHh54q0cPAIAA4NmDB9k9RURWj+GGyMrFdumCcG9vAEBWeTkWsnuKiKycwevcAEBFRQX27duHnJwcqFQqvcdmzpxplMKIyDjstHtP9dq6FdUaDT46cwaPt2mDSM6eIiIrZfBU8OPHj2PEiBGorKxERUUFvLy8UFRUBGdnZ/j4+ODChQumqtUoOBWcbNX7p07htePHAQAdXV1xbORIONs16vcbIqImZ9Kp4HPmzMHo0aNx48YNODk54dChQ7h06RJCQ0Px/vvvN7poIjKtOV26oK+2e+pceTnmp6WJWxARkYkYHG7S0tLw8ssvQyqVQiaTobq6GkqlEu+++y5ef/11U9RIREYg086ekmtnT3189iwS8vJEroqIyPgMDjf29vaQav9x9PHxQU5ODgDA3d0dubm5xq2OiIyqs7s7lvXsCUA7eyo5GWV/GjdHRGTpDA43PXv2REpKCgBg4MCBWLhwIb7++mvMnj0bQUFBRi+QiIxrZufOGOjjAwC4VFGB2NRUkSsiIjIug8PNsmXL0LJlSwDA0qVL4enpiWnTpqGwsBCfffaZ0QskIuOSSiRYHxkJV3t7AMAX58/jV151JSIr8lAbZ1oizpYiuu2L8+fxfHIyAMDH0REnR41CC0dHkasiIqpbk22cSUSWa0q7dhjdujUAoKCqCi8ePgwb+12HiKyUweEmPz8fEydOhJ+fH+zs7CCTyfRuRGQZJBIJ/h0eDm+5HACwOTcXX2Vni1wVEdHDM3gFrylTpiAnJwcLFixAy5YtIZFITFEXETUBhZMT1oSH48nERADAzJQUPKJQQNmsmciVERE1nsFjblxdXbF//36EhISYqCTT4pgbontNOXgQ/9WuLj7E1xc7hgyBlL+4EJEZMemYG6VSyX55IiuzIiwMSmdnAEB8Xh4+zcwUuSIiosYzONysWLECc+fOxcWLF01QDhGJwcPBAesiInT3Xzt+HGdLS0WsiIio8RoUbjw9PeHl5QUvLy8888wzSEhIQPv27eHq6qprv3NrjFWrViEgIACOjo4IDw/HkSNHGvS8jRs3QiKRYOzYsY16XyL6nyEtW+KlwEAAQJVajSkHD6JWoxG5KiIiwzVoQPGKFStMVsCmTZsQGxuLNWvWIDw8HCtWrEBMTAwyMzPho11FtS4XL17EK6+8gv79+5usNiJbE9ezJ3ZdvYqs8nKkXL+O5RkZmN+9u9hlEREZRPRF/MLDw9G7d2+sXLkSAKDRaKBUKjFjxgzMnTu3zueo1WoMGDAAzz77LPbv34+SkhJs3ry5Qe/HAcVE9TtSVIR+O3dCLQiwk0hwcNgwhDZvLnZZRGTjTL6In1qtxg8//IAlS5ZgyZIl+PHHH1FbW2vw66hUKqSmpiI6Ovp/BUmliI6ORrJ25dS6LF68GD4+Pnjuuece+B7V1dUoKyvTuxHR/fXx9sY87T5xtYKAyQcP4lYj/n4TEYnF4HBz6tQpdOrUCZMnT8bPP/+Mn3/+GZMnT0bHjh2RkZFh0GsVFRVBrVZDoVDotSsUCuTl5dX5nAMHDmDdunVYu3Ztg94jLi4O7u7uuptSqTSoRiJb9EZQEHppx9CdKS3F3OPHRa6IiKjhDA43zz//PLp164bLly/j2LFjOHbsGHJzc9G9e3f8/e9/N0WNOuXl5Zg4cSLWrl0Lb2/vBj1n3rx5KC0t1d1yuUEg0QM5yGTYEBkJR+2q4yszM7H9yhWRqyIiahiDVyhOS0vD0aNH4enpqWvz9PTE0qVL0bt3b4Ney9vbGzKZDPn5+Xrt+fn58PX1vef48+fP4+LFixg9erSuTaOdzWFnZ4fMzEy0b99e7zlyuRxy7fLyRNRwXT088F6vXpiRkgIAeC45GWmjRsGHm2sSkZkz+MpNp06d7gkjAFBQUIAOHToY9FoODg4IDQ1FfHy8rk2j0SA+Ph4Rd625cUfnzp2Rnp6OtLQ03W3MmDEYNGgQ0tLS2OVEZGTTOnXCcD8/AEB+VRVeSE7mIp5EZPYMvnITFxeHmTNn4s0330Tfvn0BAIcOHcLixYvxzjvv6A3YbchspNjYWEyePBlhYWHo06cPVqxYgYqKCkydOhUAMGnSJLRq1QpxcXFwdHREkHag4x0eHh4AcE87ET08iUSCdRER6LFlCwqrq7HlyhV8du4c/tGpk9ilERHdl8HhZtSoUQCAp59+Wrdp5p3f5O50FwmCAIlEArVa/cDXGzduHAoLC7Fw4ULk5eUhJCQEO3bs0A0yzsnJgVTaqEldRGQECicnfB4RgccSEgAAL6emYqBCgc7u7uIWRkR0Hwavc7Nv374GHztw4ECDCzI1rnND1DjTjxzBmqwsAEAvLy8kxcTAQTvgmIjI1Az5/hZ9Eb+mxnBD1DiVtbXovW0bzmq7nl/r1g3LevYUuSoishWGfH83qFvq5MmTDX7z7lyqncgqOdvZ4at+/RCxYwdqNBq8e+oUhrZsiUfqmNlIRCSmBl25kUqlkEgkD5wl0dBxNmLilRuih/PeqVO6Rf2Uzs44PnIkPLncAhGZmNGv3GRnZxulMCKyfC937YqdV69ib34+cisr8X9HjuCbfv10EwyIiMTWoHDj7+9v6jqIyEJIJRJ8ERmJkK1bUaJS4btLlzC8VStMatdO7NKIiAA0Yir4HadPn0ZOTg5UKpVe+5gxYx66KCIyb8pmzbA6PBzj9+8HAMw4cgQR3t7oyK5eIjIDBoebCxcu4PHHH0d6erreOJw7l6TNfcwNERnH0/7+2HHlCjZcuICbtbX464EDOBATAzmnhxORyAxeHW/WrFlo27YtCgoK4OzsjFOnTiExMRFhYWFI0C7yRUS24ePevRGovVpzrLgYr3P3cCIyAwaHm+TkZCxevBje3t6QSqWQSqXo16+fblsGIrIdLvb2+KZfPzhoVxFfcfYstnH3cCISmcHhRq1Ww9XVFcDtXb2vXr0K4Pag48zMTONWR0RmL8TLC+/16qW7P/XgQVytrBSxIiKydQaHm6CgIJw4cQIAEB4ejnfffRdJSUlYvHgx2nG2BJFNmh4YiNGtWwMAiqqrMSkpCWqNRuSqiMhWGRxu5s+fD432H63FixcjOzsb/fv3x7Zt2/Dxxx8bvUAiMn93dg9v5ewMANibn493Tp0SuSoislVG2VuquLgYnp6eFrGIF1coJjKdxPx8DPn9d2gEATKJBHsffRRRPj5il0VEVsCQ72+Dr9zUxcvLyyKCDRGZ1gCFAguCgwEAakHA35KSUFxdLXJVRGRrjBJuiIjueCMoCAO0V2tyKirwwqFDD9yXjojImBhuiMioZFIp/hsVBS8HBwDA5txcfJqVJXJVRGRLGG6IyOhaN2uGdRERuvsvp6YipahIxIqIyJYw3BCRSYxRKjGnSxcAQI1Gg3H793P8DRE1CYYbIjKZuJ49EeHtDQC4VFGBKQcPQsPxN0RkYgw3RGQy9lIpvu3fH83lcgDA1itX8MHp0yJXRUTWjuGGiExK2awZ/hMVhTuLRbyRlob9BQWi1kRE1o3hhohMbpifH14PCgJwe/2bv+7fj4KqKpGrIiJrxXBDRE1iUffuGKRQAACu3rqFvx04wP2niMgkGG6IqEnIpFJ81a8ffB0dAQDxeXl4Oz1d5KqIyBox3BBRk/F1csI3/ftDqt2uZUl6OnZdvSpyVURkbRhuiKhJDVQosKRHDwCAAGDCgQO4ePOmuEURkVVhuCGiJvfPbt0wslUrAECxSoUn9+3DrdpakasiImvBcENETU4qkeA/UVHo4OoKADh+4wZePHyYG2wSkVEw3BCRKDwcHPDjwIFoZmcHAPgqO5sbbBKRUTDcEJFogjw88PldG2zGHj3KBf6I6KEx3BCRqJ7298crXbsCAGoFAeMSE3GlslLkqojIkjHcEJHoloaEYIivLwAgv6oKTycmolqtFrkqIrJUDDdEJDo7qRRf9+uHNs2aAQAOFRVhztGjIldFRJaK4YaIzEILR0f8MGAA5NLb/yz9+9w5rD13TuSqiMgSMdwQkdkIbd4cn4aH6+6/dOQIEvPzRayIiCwRww0RmZUp7dtjRmAggNsDjJ9KTEQ2VzAmIgMw3BCR2Xk/NBTRLVsCAIqqqzE2IQHlNTUiV0VEloLhhojMjp1Uio39+qGTdgXjjJISTEpKgoYrGBNRAzDcEJFZ8pTLsfmRR+Bubw8A+PXyZSw8cULkqojIEjDcEJHZCnR3x8b+/SGVSAAAcRkZ+CY7W+SqiMjcMdwQkVkb6ueHD0JDdfefT07GkaIiESsiInPHcENEZm9GYCCebd8eAFCt0WBsQgIucQYVEd0Hww0RmT2JRIJVffqgv48PgNtbNIzauxclKpXIlRGROWK4ISKL4CCT4ceBA9FRO4PqdGkpnkpMhIp7UBHRnzDcEJHFaC6XY8vgwfCWywEAe/Ly8OLhwxA4RZyI7sJwQ0QWpYOrK35+5BHdHlQbLlzAsowMkasiInPCcENEFieyRQtsiIrS3V944gS+5hRxItJiuCEii/SUvz+W9+ypu/98cjI32SQiAAw3RGTBXunaFS906AAAUGk0eGLfPpwqKRG3KCISHcMNEVksiUSClX36IMbPDwBwQ6XC8D17kFNRIXJlRCQmhhsismh2Uik29e+PMC8vAMCVykoMj4/H9epqkSsjIrEw3BCRxXO1t8dvgwfr1sA5W1aG0Xv3oqK2VuTKiEgMDDdEZBV8HB2xfcgQ+Do6AgAOFxXh6cRE1Gg0IldGRE2N4YaIrEZbFxdsHzIEbvb2AIAdV6/i+eRkaLjIH5FNYbghIqvS3dMTm+9a5O+r7Gz889gxrmJMZEMYbojI6gxUKPB1v36QSiQAgI/OnMGS9HSRqyKipmIW4WbVqlUICAiAo6MjwsPDceTIkfseu3btWvTv3x+enp7w9PREdHR0vccTkW16vE0brO7TR3f/rZMn8cHp0yJWRERNRfRws2nTJsTGxmLRokU4duwYevTogZiYGBQUFNR5fEJCAsaPH4+9e/ciOTkZSqUSQ4cOxZUrV5q4ciIyd8937IgPQ0N19/957BhWZ2WJWBERNQWJIHJHdHh4OHr37o2VK1cCADQaDZRKJWbMmIG5c+c+8PlqtRqenp5YuXIlJk2a9MDjy8rK4O7ujtLSUri5uT10/URk/palp2PBiRO6++sjIjC5fXsRKyIiQxny/S3qlRuVSoXU1FRER0fr2qRSKaKjo5GcnNyg16isrERNTQ28tAt4/Vl1dTXKysr0bkRkW+YFBeG1bt10958/dAjfX7okYkVEZEqihpuioiKo1WooFAq9doVCgby8vAa9xmuvvQY/Pz+9gHS3uLg4uLu7625KpfKh6yYiyyKRSLA0JAQvBQYCADSCgL8dOIDfLl8WuTIiMgXRx9w8jOXLl2Pjxo34+eef4ahduOvP5s2bh9LSUt0tNze3iaskInMgkUjwUVgYpmq7o2oFAU8lJjLgEFkhUcONt7c3ZDIZ8vPz9drz8/Ph6+tb73Pff/99LF++HLt27UL37t3ve5xcLoebm5vejYhsk1Qiwb/Dw/FMQAAAoEajwVOJifiFv/QQWRVRw42DgwNCQ0MRHx+va9NoNIiPj0dERMR9n/fuu+9iyZIl2LFjB8LCwpqiVCKyEjKpFBsiIzH+roDzdGIifs7JEbcwIjIa0bulYmNjsXbtWmzYsAFnzpzBtGnTUFFRgalTpwIAJk2ahHnz5umOf+edd7BgwQKsX78eAQEByMvLQ15eHm7evCnWRyAiC2OnDTgT2rYFcLuL6pn9+/ETAw6RVbATu4Bx48ahsLAQCxcuRF5eHkJCQrBjxw7dIOOcnBxIpf/LYKtXr4ZKpcKTTz6p9zqLFi3Cm2++2ZSlE5EFk0ml+CIiAhLc3qLhTsD5pl8/POnvL3Z5RPQQRF/npqlxnRsiuptao8Hzhw7hPxcuAABkEgn+ExWlG5dDRObBYta5ISISm0wqxed9+2KKdhaVWjtN/LNz50SujIgai+GGiGyeTCrF2r598UKHDgAAAcC0w4fx7qlT4hZGRI3CcENEhNvTxFeHh+OVrl11bfOOH8frx4/DxnrviSweww0RkZZEIsHynj2xNCRE1/bOqVOYfuQI1BqNeIURkUEYboiI7iKRSDA3KAgr+/SBRNv273PnMDEpCSq1WtTaiKhhGG6IiOowrVMn/CcqCjLJ7Yiz6dIljNq7F2UqlciVEdGDMNwQEd3HX9u2xU8DB8JRJgMAxOflYeCuXbhSWSlyZURUH4YbIqJ6jGrdGruGDIGXgwMA4GRJCSJ37EBGSYm4hRHRfTHcEBE9QJSPDw7ExKCtiwsA4HJlJQbs3Im9eXkiV0ZEdWG4ISJqgEB3dyTFxCDMywsAUFpTg+F79uAr7crGRGQ+GG6IiBpI4eSEPUOHYkSrVgBu7yg++eBBvHH8ODRcC4fIbDDcEBEZoJmdHX4eOBD/6NhR17b81Cn8Zd8+lNfUiFgZEd3BcENEZCA7qRSr+vTBirAwSLVTxX+9fBn9d+7ExZs3Ra6OiBhuiIgaQSKRYEbnztg6aBDc7e0BAOklJQjfvh37CwpEro7ItjHcEBE9hKF+fkgeNgydXF0BAEXV1Xj099+xKjOTe1IRiYThhojoIQW6u+PgsGGIbtkSwO2BxjNTUjAxKQkVtbUiV0dkexhuiIiMwFMux9ZBgxDbpYuu7duLFxGxfTuyyspErIzI9jDcEBEZiZ1UivdCQ7Gpf3+4asfhnCotRZ/t2/FTTo7I1RHZDoYbIiIje9LfH4eHDUNXd3cAQHlNDZ5KTETs0aOo5s7iRCbHcENEZAKB7u5IHjYMzwQE6Nr+dfYsonbsQGZpqXiFEdkAhhsiIhNxsbfHV1FR+KR3bzhIb/9ze/zGDYRt24b1f/zB2VREJsJwQ0RkQhKJBP8XGIhDw4ejs5sbAKBSrcYLhw5h/IEDKFGpRK6QyPow3BARNYEenp5IGTECL3TooGv7/tIl9Ny6FfHXrolYGZH1YbghImoiznZ2WNO3Lzb17w8PBwcAQE5FBYbGx+OlI0dwk3tTERkFww0RURN70t8fx0eOxCMKha5tdVYWQrZuxb78fBErI7IODDdERCJo06wZdkdH4+PeveEskwEAsm/exODduzE7JYUrGxM9BIYbIiKRSCUSTA8MxPFRo9CvRQtd+yeZmQj+7TdsvXxZxOqILBfDDRGRyDq4umLPo4/iw9BQOGqv4lyqqMCYhAQ8nZiIq5WVIldIZFkYboiIzIBMKsWsLl2QNnIkhvj66tp/zMlB199+w6eZmVBrNCJWSGQ5GG6IiMxIRzc37BwyBBsiI+EtlwO4vX3DjJQUROzYgYOFhSJXSGT+GG6IiMyMRCLB39q1w+kxY/Bs+/a69tTiYvTfuRMTDhxAbkWFiBUSmTeGGyIiM9VcLsfaiAgkDB2KYA8PXfvGixfR5ddfseTkSVRyVhXRPRhuiIjMXH8fHxwdMQKr+vRBc21X1S21Gm+ePImuv/6KL8+f53gcorsw3BARWQA7qRQvduqEzDFjMKtzZ9hJJACA3MpKPJecjJCtW7E5N5ebcRKB4YaIyKJ4yuX4MCwMJ0aNwnA/P1376dJS/GXfPkTt3Im9eXkiVkgkPoYbIiIL1NndHVsGD8beRx9F5F0LAB4uKkL0778jevdu7M3L45UcskkSwcb+zy8rK4O7uztKS0vh5uYmdjlERA9NEARsuXIF89PSkFFSovdYVIsWeD04GDEtW0Ki7coiskSGfH8z3BARWQm1RoNvLl7E2+np+KO8XO+xUC8vzAsKwpjWrSGT8qI9WR6Gm3ow3BCRtavVaPDdpUuIy8jA6dJSvcfau7hgZufOmNK+PVzs7UWqkMhwDDf1YLghIluhEQRszs3FsvR0HL9xQ+8xd3t7vNCxI14KDISyWTORKiRqOIabejDcEJGtEQQBO65exYqzZ/H7tWt6j8kkEjyuVOLvHTtikK8vpByXQ2aK4aYeDDdEZMvSb9zAv86exdfZ2VD9aeG/9i4ueL5jR0xu1w4KJyeRKiSqG8NNPRhuiIiA/Fu3sCYrC2vOnUNBVZXeY3YSCR5TKjG1fXs82rIl7DgAmcwAw009GG6IiP5HpVbj18uX8dm5c4ivY/E/H0dHjPP3x4S2bRHWvDmnk5NoGG7qwXBDRFS38+XlWPfHH/ji/Pl7ruYAQCdXV0xo2xZP+fsj0N1dhArJljHc1IPhhoiofiq1GtuuXsU32dnYcvkyquvYlLOruzvGKpV4XKlETy8vXtEhk2O4qQfDDRFRw5WoVPgxJwffZGdjX34+6vrC8G/WDGOVSoxp3RqRLVrAQSZr8jrJ+jHc1IPhhoiocXIrKvDDpUvYnJuLpMLCOoOOq709Bvv6IqZlS8T4+SHAxaXJ6yTrxHBTD4YbIqKHl3frFn7JzcXPubnYm5eH2vt8lXR2c8OjLVtioEKBAQoFmsvlTVwpWQuGm3ow3BARGdeN6mpsvXIFO69exa5r11BUXX3fY4M8PDDAxwcDFAoM8PHhejrUYAw39WC4ISIyHY0g4FhxMXZevYqdV68iuagImnq+Zjq6uqKPtzf6NG+OPt7e6OHpCTnH7FAdGG7qwXBDRNR0SlQqJObnI7GgAIn5+Th+40a9YcdBKkWIpyd6e3ujp5cXenh4oKuHBxwZeGwew009GG6IiMRTqlIhqbAQifn52F9QgGPFxfdsA/FnMokEndzc0N3DA909PRHs4YEgDw8omzXjXlg2hOGmHgw3RETmo1qtxskbN5By/ToOFxUh5fp1ZJaVNei5jjIZOrq6opObGwLd3NBJewt0c4OHg4OJK6emxnBTD4YbIiLzVqJS4ej160i/cQMnSkqQfuMGTpeWPvAKz928HBwQ4OIC/2bNEODigoBmzeB/589mzeDG8GNxGG7qwXBDRGR5ajQaZJWV4cSNG0gvKcHZ0lJklZXhj/Ly+05Dr4+rvT38nJzQUnvT/ezsrLvv4+gIN3t7rr5sJgz5/rZroprqtWrVKrz33nvIy8tDjx498Mknn6BPnz73Pf7777/HggULcPHiRXTs2BHvvPMORowY0YQVExFRU7KXStHNwwPdPDz02ms1GmTfvInMsjJklZUhs6wM58rKcLGiArmVlfcdvFxeU4PMmpoHdoHZSSTwlsvRXC6//aejI7zv/Ky9eTg4wM3eHu729nDX/uxmbw977qYuGtHDzaZNmxAbG4s1a9YgPDwcK1asQExMDDIzM+Hj43PP8QcPHsT48eMRFxeHUaNG4ZtvvsHYsWNx7NgxBAUFifAJiIhILHZSKTq6uaFjHb/J12g0uFJZiYs3b+JiRQUu3fXn1Vu3cPXWLVTU1tb7+rWCgLyqKuTVsZHogzjJZHDXBp07ocfF3h7OMhmc7ezu+dPpT23N7OzgbGcHR5kMDlIp5No/HaRSyKVSOGjvc1D1vUTvlgoPD0fv3r2xcuVKAIBGo4FSqcSMGTMwd+7ce44fN24cKioqsGXLFl1b3759ERISgjVr1jzw/dgtRUREd5TX1ODarVu4WlmJa7du6W5Xb91CUXU1rldV3f6zuhqVarXY5dbJTiK5HXpkstuh504A0oYfO4kEdlIpZBIJZNqfpXd+1v4p+/Mx9bTJpFJIAUglktu3u36W3PXz3zt2NOrAbovpllKpVEhNTcW8efN0bVKpFNHR0UhOTq7zOcnJyYiNjdVri4mJwebNm+s8vrq6GtV3rZZZ1sBR+EREZP1c7e3ham+PTg34ZbeythbXq6t1YefOn6UqFUpralBWU6P7ubymRq+trKamzr24jKFWEFCrVptd+Hra31+0WWuihpuioiKo1WooFAq9doVCgbNnz9b5nLy8vDqPz8vLq/P4uLg4vPXWW8YpmIiIbJaztptI2ayZwc/VCAJu1tSgorYWlWo1Ku/+809tt/7UdkuthkqjgUqjQfVdP6vubr/rfvWdx7XH1wpCvQsnmopMxO4y0cfcmNq8efP0rvSUlZVBqVSKWBEREdkaqUQCNwcH0aagC4IA9V23Wo3m9p91tD2wXaOBgNuBTXPnzzs37XtpAHiLuEmqqOHG29sbMpkM+fn5eu35+fnw9fWt8zm+vr4GHS+XyyHnLrRERGTDJNoxM1Z/RUNL1HlqDg4OCA0NRXx8vK5No9EgPj4eERERdT4nIiJC73gA2L17932PJyIiItsieoiLjY3F5MmTERYWhj59+mDFihWoqKjA1KlTAQCTJk1Cq1atEBcXBwCYNWsWBg4ciA8++AAjR47Exo0bcfToUXz22WdifgwiIiIyE6KHm3HjxqGwsBALFy5EXl4eQkJCsGPHDt2g4ZycHEjvWggpMjIS33zzDebPn4/XX38dHTt2xObNm7nGDREREQEwg3VumhrXuSEiIrI8hnx/c21oIiIisioMN0RERGRVGG6IiIjIqjDcEBERkVVhuCEiIiKrwnBDREREVoXhhoiIiKwKww0RERFZFYYbIiIisiqib7/Q1O4syFxWViZyJURERNRQd763G7Kxgs2Fm/LycgCAUqkUuRIiIiIyVHl5Odzd3es9xub2ltJoNLh69SpcXV0hkUiM+tplZWVQKpXIzc3lvlUmxPPcNHiemw7PddPgeW4apjrPgiCgvLwcfn5+ehtq18XmrtxIpVK0bt3apO/h5ubGvzhNgOe5afA8Nx2e66bB89w0THGeH3TF5g4OKCYiIiKrwnBDREREVoXhxojkcjkWLVoEuVwudilWjee5afA8Nx2e66bB89w0zOE829yAYiIiIrJuvHJDREREVoXhhoiIiKwKww0RERFZFYYbIiIisioMN0ayatUqBAQEwNHREeHh4Thy5IjYJVmUuLg49O7dG66urvDx8cHYsWORmZmpd0xVVRWmT5+O5s2bw8XFBX/5y1+Qn5+vd0xOTg5GjhwJZ2dn+Pj44NVXX0VtbW1TfhSLsnz5ckgkEsyePVvXxvNsHFeuXMHf/vY3NG/eHE5OTggODsbRo0d1jwuCgIULF6Jly5ZwcnJCdHQ0zp07p/caxcXFmDBhAtzc3ODh4YHnnnsON2/ebOqPYtbUajUWLFiAtm3bwsnJCe3bt8eSJUv09h/iuTZcYmIiRo8eDT8/P0gkEmzevFnvcWOd05MnT6J///5wdHSEUqnEu+++a5wPINBD27hxo+Dg4CCsX79eOHXqlPDCCy8IHh4eQn5+vtilWYyYmBjhiy++EDIyMoS0tDRhxIgRQps2bYSbN2/qjnnxxRcFpVIpxMfHC0ePHhX69u0rREZG6h6vra0VgoKChOjoaOH48ePCtm3bBG9vb2HevHlifCSzd+TIESEgIEDo3r27MGvWLF07z/PDKy4uFvz9/YUpU6YIhw8fFi5cuCDs3LlT+OOPP3THLF++XHB3dxc2b94snDhxQhgzZozQtm1b4datW7pjhg0bJvTo0UM4dOiQsH//fqFDhw7C+PHjxfhIZmvp0qVC8+bNhS1btgjZ2dnC999/L7i4uAj/+te/dMfwXBtu27ZtwhtvvCH89NNPAgDh559/1nvcGOe0tLRUUCgUwoQJE4SMjAzh22+/FZycnIR///vfD10/w40R9OnTR5g+fbruvlqtFvz8/IS4uDgRq7JsBQUFAgBh3759giAIQklJiWBvby98//33umPOnDkjABCSk5MFQbj9l1EqlQp5eXm6Y1avXi24ubkJ1dXVTfsBzFx5ebnQsWNHYffu3cLAgQN14Ybn2Thee+01oV+/fvd9XKPRCL6+vsJ7772nayspKRHkcrnw7bffCoIgCKdPnxYACCkpKbpjtm/fLkgkEuHKlSumK97CjBw5Unj22Wf12p544glhwoQJgiDwXBvDn8ONsc7pp59+Knh6eur9u/Haa68JgYGBD10zu6UekkqlQmpqKqKjo3VtUqkU0dHRSE5OFrEyy1ZaWgoA8PLyAgCkpqaipqZG7zx37twZbdq00Z3n5ORkBAcHQ6FQ6I6JiYlBWVkZTp061YTVm7/p06dj5MiReucT4Hk2ll9//RVhYWF46qmn4OPjg549e2Lt2rW6x7Ozs5GXl6d3nt3d3REeHq53nj08PBAWFqY7Jjo6GlKpFIcPH266D2PmIiMjER8fj6ysLADAiRMncODAAQwfPhwAz7UpGOucJicnY8CAAXBwcNAdExMTg8zMTNy4ceOharS5jTONraioCGq1Wu8fegBQKBQ4e/asSFVZNo1Gg9mzZyMqKgpBQUEAgLy8PDg4OMDDw0PvWIVCgby8PN0xdf13uPMY3bZx40YcO3YMKSkp9zzG82wcFy5cwOrVqxEbG4vXX38dKSkpmDlzJhwcHDB58mTdearrPN59nn18fPQet7Ozg5eXF8/zXebOnYuysjJ07twZMpkMarUaS5cuxYQJEwCA59oEjHVO8/Ly0LZt23te485jnp6eja6R4YbMzvTp05GRkYEDBw6IXYrVyc3NxaxZs7B79244OjqKXY7V0mg0CAsLw7JlywAAPXv2REZGBtasWYPJkyeLXJ11+e677/D111/jm2++Qbdu3ZCWlobZs2fDz8+P59qGsVvqIXl7e0Mmk90zmyQ/Px++vr4iVWW5XnrpJWzZsgV79+5F69atde2+vr5QqVQoKSnRO/7u8+zr61vnf4c7j9HtbqeCggL06tULdnZ2sLOzw759+/Dxxx/Dzs4OCoWC59kIWrZsia5du+q1denSBTk5OQD+d57q+3fD19cXBQUFeo/X1taiuLiY5/kur776KubOnYtnnnkGwcHBmDhxIubMmYO4uDgAPNemYKxzasp/SxhuHpKDgwNCQ0MRHx+va9NoNIiPj0dERISIlVkWQRDw0ksv4eeff8aePXvuuVQZGhoKe3t7vfOcmZmJnJwc3XmOiIhAenq63l+o3bt3w83N7Z4vGls1ZMgQpKenIy0tTXcLCwvDhAkTdD/zPD+8qKioe5YyyMrKgr+/PwCgbdu28PX11TvPZWVlOHz4sN55LikpQWpqqu6YPXv2QKPRIDw8vAk+hWWorKyEVKr/VSaTyaDRaADwXJuCsc5pREQEEhMTUVNToztm9+7dCAwMfKguKQCcCm4MGzduFORyufDll18Kp0+fFv7+978LHh4eerNJqH7Tpk0T3N3dhYSEBOHatWu6W2Vlpe6YF198UWjTpo2wZ88e4ejRo0JERIQQERGhe/zOFOWhQ4cKaWlpwo4dO4QWLVpwivID3D1bShB4no3hyJEjgp2dnbB06VLh3Llzwtdffy04OzsLX331le6Y5cuXCx4eHsIvv/winDx5UnjsscfqnErbs2dP4fDhw8KBAweEjh072vT05LpMnjxZaNWqlW4q+E8//SR4e3sL//znP3XH8Fwbrry8XDh+/Lhw/PhxAYDw4YcfCsePHxcuXbokCIJxzmlJSYmgUCiEiRMnChkZGcLGjRsFZ2dnTgU3J5988onQpk0bwcHBQejTp49w6NAhsUuyKADqvH3xxRe6Y27duiX83//9n+Dp6Sk4OzsLjz/+uHDt2jW917l48aIwfPhwwcnJSfD29hZefvlloaampok/jWX5c7jheTaO3377TQgKChLkcrnQuXNn4bPPPtN7XKPRCAsWLBAUCoUgl8uFIUOGCJmZmXrHXL9+XRg/frzg4uIiuLm5CVOnThXKy8ub8mOYvbKyMmHWrFlCmzZtBEdHR6Fdu3bCG2+8oTe9mOfacHv37q3z3+TJkycLgmC8c3rixAmhX79+glwuF1q1aiUsX77cKPVLBOGuZRyJiIiILBzH3BAREZFVYbghIiIiq8JwQ0RERFaF4YaIiIisCsMNERERWRWGGyIiIrIqDDdERERkVRhuiIiIyKow3BCR0UyZMgVjx44Vu4wm8+WXX0IikUAikWD27Nn1HpuQkKA71pbOEZEY7MQugIgsg0QiqffxRYsW4V//+hfEXvR8ypQpKCkpwebNm5vk/dzc3JCZmYlmzZrp2gICAjB79my9wBMZGYlr165h1qxZqK6ubpLaiGwVww0RNci1a9d0P2/atAkLFy7U2/naxcUFLi4uYpQmKolEAl9f3wce5+DgAF9fXzg5OTHcEJkYu6WIqEF8fX11N3d3d92X+p2bi4vLPd1SjzzyCGbMmIHZs2fD09MTCoUCa9euRUVFBaZOnQpXV1d06NAB27dv13uvjIwMDB8+HC4uLlAoFJg4cSKKiop0j//www8IDg6Gk5MTmjdvjujoaFRUVODNN9/Ehg0b8Msvv+i6gBISEgAAubm5ePrpp+Hh4QEvLy889thjuHjxou4179T+1ltvoUWLFnBzc8OLL74IlUpl0Hl65JFHcOnSJcyZM0dXAxE1LYYbIjKpDRs2wNvbG0eOHMGMGTMwbdo0PPXUU4iMjMSxY8cwdOhQTJw4EZWVlQCAkpISDB48GD179sTRo0exY8cO5Ofn4+mnnwZw+wrS+PHj8eyzz+LMmTNISEjAE088AUEQ8Morr+Dpp5/GsGHDcO3aNVy7dg2RkZGoqalBTEwMXF1dsX//fiQlJcHFxQXDhg3TCy/x8fG61/z222/x008/4a233jLo8/70009o3bo1Fi9erKuBiJoWu6WIyKR69OiB+fPnAwDmzZuH5cuXw9vbGy+88AIAYOHChVi9ejVOnjyJvn37YuXKlejZsyeWLVume43169dDqVQiKysLN2/eRG1tLZ544gn4+/sDAIKDg3XH3un2ubur6KuvvoJGo8Hnn3+uu5LyxRdfwMPDAwkJCRg6dCiA211H69evh7OzM7p164bFixfj1VdfxZIlSyCVNux3QS8vL8hkMri6ujaou4qIjI/hhohMqnv37rqfZTIZmjdvrhdGFAoFAKCgoAAAcOLECezdu7fO8Tvnz5/H0KFDMWTIEAQHByMmJgZDhw7Fk08+CU9Pz/vWcOLECfzxxx9wdXXVa6+qqsL58+d193v06AFnZ2fd/YiICNy8eRO5ubm6IEVE5o/hhohMyt7eXu++RCLRa7tzJUWj0QAAbt68idGjR+Odd96557VatmwJmUyG3bt34+DBg9i1axc++eQTvPHGGzh8+DDatm1bZw03b95EaGgovv7663sea9GiRaM/GxGZJ4YbIjIrvXr1wo8//oiAgADY2dX9T5REIkFUVBSioqKwcOFC+Pv74+eff0ZsbCwcHBygVqvvec1NmzbBx8cHbm5u933vEydO4NatW3BycgIAHDp0CC4uLlAqlQZ9hrpqIKKmwwHFRGRWpk+fjuLiYowfPx4pKSk4f/48du7cialTp0KtVuPw4cNYtmwZjh49ipycHPz0008oLCxEly5dANxeY+bkyZPIzMxEUVERampqMGHCBHh7e+Oxxx7D/v37kZ2djYSEBMycOROXL1/WvbdKpcJzzz2H06dPY9u2bVi0aBFeeumlBo+3uSMgIACJiYm4cuWK3iwvImoaDDdEZFb8/PyQlJQEtVqNoUOHIjg4GLNnz4aHhwekUinc3NyQmJiIESNGoFOnTpg/fz4++OADDB8+HADwwgsvIDAwEGFhYWjRogWSkpLg7OyMxMREtGnTBk888QS6dOmC5557DlVVVXpXcoYMGYKOHTtiwIABGDduHMaMGYM333zT4M+wePFiXLx4Ee3bt2e3F5EIJILYy4kSEZmBxqxs/OWXX2L27NkoKSkx6fsQkWF45YaI6CGUlpbCxcUFr732Wr3H7d+/Hy4uLnUOaiYi4+KAYiKiRvrLX/6Cfv36AQA8PDzqPTYsLAxpaWkAYJPbVBA1JXZLERERkVVhtxQRERFZFYYbIiIisioMN0RERGRVGG6IiIjIqjDcEBERkVVhuCEiIiKrwnBDREREVoXhhoiIiKzK/wPwaELlRv2c+AAAAABJRU5ErkJggg==", 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" ] @@ -437,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "id": "8685da6e", "metadata": {}, "outputs": [], @@ -458,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "bd10b595", "metadata": { "lines_to_next_cell": 0 @@ -468,21 +455,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 00: 100%|██████████| 25/25 [00:35<00:00, 1.40s/it, loss=0.8353]\n", - "Epoch 01: 100%|██████████| 25/25 [00:36<00:00, 1.44s/it, loss=0.5049]\n", - "Epoch 02: 100%|██████████| 25/25 [00:36<00:00, 1.48s/it, loss=0.2776]\n", - "Epoch 03: 100%|██████████| 25/25 [00:36<00:00, 1.48s/it, loss=0.1520]\n", - "Epoch 04: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0833]\n", - "Epoch 05: 100%|██████████| 25/25 [00:37<00:00, 1.48s/it, loss=0.0503]\n", - "Epoch 06: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0308]\n", - "Epoch 07: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0231]\n", - "100%|██████████| 1000/1000 [00:34<00:00, 28.60it/s]\n", + "Epoch 00: 100%|██████████| 25/25 [00:35<00:00, 1.42s/it, loss=0.8352]\n", + "Epoch 01: 100%|██████████| 25/25 [00:36<00:00, 1.47s/it, loss=0.5043]\n", + "Epoch 02: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.2762]\n", + "Epoch 03: 100%|██████████| 25/25 [00:37<00:00, 1.48s/it, loss=0.1481]\n", + "Epoch 04: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0797]\n", + "Epoch 05: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0483]\n", + "Epoch 06: 100%|██████████| 25/25 [00:37<00:00, 1.51s/it, loss=0.0288]\n", + "Epoch 07: 100%|██████████| 25/25 [00:37<00:00, 1.51s/it, loss=0.0218]\n", + "100%|██████████| 1000/1000 [00:35<00:00, 28.29it/s]\n", "...\n" ] }, { "data": { - "image/png": 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+ "image/png": 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" ] @@ -494,7 +481,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "train completed, total time: 1676.2901484966278.\n" + "train completed, total time: 1691.9202527999878.\n" ] } ], @@ -588,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "id": "c7520419", "metadata": { "lines_to_next_cell": 2 @@ -596,7 +583,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -636,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "id": "092eb6a0", "metadata": { "lines_to_next_cell": 2 @@ -647,7 +634,7 @@ "output_type": "stream", "text": [ "You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - "100%|██████████| 1000/1000 [00:30<00:00, 32.99it/s]\n" + "100%|██████████| 1000/1000 [00:31<00:00, 31.56it/s]\n" ] } ], @@ -662,13 +649,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "id": "5dc3e69d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -699,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "id": "e3e43b95", "metadata": {}, "outputs": [ @@ -707,7 +694,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 250/250 [00:07<00:00, 32.82it/s]\n" + "100%|██████████| 250/250 [00:08<00:00, 29.97it/s]\n" ] } ], @@ -731,13 +718,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "id": "89f93ab8", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] From a1b550f26071f9e90523a981bad81801f75d7924 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Thu, 7 Nov 2024 11:56:29 +0000 Subject: [PATCH 08/12] Val interval fix Signed-off-by: Eric Kerfoot --- generation/3d_ddpm/3d_ddpm_tutorial.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb index 243a0f179..1ebdc8aa5 100644 --- a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -445,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "bd10b595", "metadata": { "lines_to_next_cell": 0 @@ -487,7 +487,7 @@ ], "source": [ "max_epochs = 40\n", - "val_interval = max_epochs // 5\n", + "val_interval = max(1, max_epochs // 5)\n", "use_amp = False\n", "epoch_loss_list = []\n", "val_epoch_loss_list = []\n", From def362507917f36c970625d99215c352cced5fd4 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Thu, 7 Nov 2024 13:59:55 +0000 Subject: [PATCH 09/12] Val interval fix Signed-off-by: Eric Kerfoot --- generation/3d_ddpm/3d_ddpm_tutorial.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb index 1ebdc8aa5..52d976bd7 100644 --- a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -623,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "092eb6a0", "metadata": { "lines_to_next_cell": 2 @@ -640,7 +640,6 @@ ], "source": [ "model.eval()\n", - "model.load_state_dict(torch.load(\"model_0039.pth\"))\n", "noise = torch.randn((1, 1, 32, 40, 32))\n", "noise = noise.to(device)\n", "scheduler.set_timesteps(num_inference_steps=1000)\n", From d055e7d29f56abc7c2ee2f338d5822347981c85a Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Fri, 8 Nov 2024 09:08:14 +0000 Subject: [PATCH 10/12] Update from comments Signed-off-by: Eric Kerfoot --- generation/3d_ddpm/3d_ddpm_tutorial.ipynb | 25 +++++++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb index 52d976bd7..309d3d740 100644 --- a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "cdea37d5", "metadata": {}, "outputs": [ @@ -93,6 +93,7 @@ "import os\n", "import tempfile\n", "import time\n", + "import shutil\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -633,7 +634,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", "100%|██████████| 1000/1000 [00:31<00:00, 31.56it/s]\n" ] } @@ -741,6 +741,27 @@ "plt.axis(\"off\")\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "id": "b39a47bb", + "metadata": {}, + "source": [ + "### Cleanup data directory\n", + "\n", + "Remove directory if a temporary was used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "957a45ff", + "metadata": {}, + "outputs": [], + "source": [ + "if directory is None:\n", + " shutil.rmtree(root_dir)" + ] } ], "metadata": { From 459b74a04a183916a243d463078fb69bf1f90e2f Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Fri, 8 Nov 2024 10:25:50 +0000 Subject: [PATCH 11/12] Using previous transforms, seems to work this time? Signed-off-by: Eric Kerfoot --- generation/3d_ddpm/3d_ddpm_tutorial.ipynb | 67 ++++++++++++----------- 1 file changed, 36 insertions(+), 31 deletions(-) diff --git a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb index 309d3d740..b6157f799 100644 --- a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "cdea37d5", "metadata": {}, "outputs": [ @@ -182,7 +182,7 @@ "5. `CenterSpatialCropd`: Crop the background of the images using a roi of size `[160, 200, 155]`.\n", "6. `Resized`: Resize the images to a volume with size `[32, 40, 32]`.\n", "\n", - "For the data loader, we are using mini-batches of 8 images, which consumes about 21GB of GPU memory during training. Please, reduce this value to run on smaller GPUs." + "For the data loader, we are using mini-batches of 16 images, which consumes about 11GB of GPU memory during training. Please, reduce this value to run on smaller GPUs." ] }, { @@ -197,10 +197,9 @@ " LoadImaged(keys=[\"image\"]),\n", " Lambdad(keys=\"image\", func=lambda x: x[:, :, :, 1]),\n", " EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n", + " ScaleIntensityd(keys=[\"image\"]),\n", " CenterSpatialCropd(keys=[\"image\"], roi_size=[160, 200, 155]),\n", " Resized(keys=[\"image\"], spatial_size=(32, 40, 32)),\n", - " ScaleIntensityd(keys=[\"image\"]),\n", - " ScaleIntensityRangePercentilesd(keys=[\"image\"], lower=3, upper=97, b_min=0, b_max=1),\n", " ]\n", ")" ] @@ -217,8 +216,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Loading dataset: 100%|██████████| 388/388 [01:40<00:00, 3.86it/s]\n", - "Loading dataset: 100%|██████████| 96/96 [00:25<00:00, 3.72it/s]\n" + "Loading dataset: 100%|██████████| 388/388 [01:57<00:00, 3.29it/s]\n", + "Loading dataset: 100%|██████████| 96/96 [00:30<00:00, 3.13it/s]\n" ] } ], @@ -267,7 +266,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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03E42gpPl2/Y+ZLjGrmyTGkAy78f+/iS2EqwMG8ny35GREcd2X/va18zYbly3UZD5d0wcDYfDiEQi8Pl82Lx5M4aHhxEMBjE6OmoqV6S0gm2Q+Hw+M5a/Z5kPSA8IsDgfxePxJZ5yGiLAokemXq/D6/WahWqxWDTnaiAQQLlcRigUQi6XQ7lcNvNBsVg0nvZsNmvmlm6sltmQxgdPAL/fj1gsBq/Xi1gshng8Dr/fj23btmFoaAjRaBRbt241MVxarT6fz4RgmL0sK1porNBgoDeDoRBa1cDZCxDLa6nvQcsZODu50tDI5XKoVCrIZrNIJpMm85n7ZaVMPp/H9PQ0isUiMpkMksmkw5uynhw7dgxTU1O46aabzH3xeBx79+7FM88809L4sJUN11oSWlGUjYfMw+BikuH2wcFBhEIh7Nq1C+Pj4wgEAmaxyGs05wMagTJPRFY4ytfjf85DzMuo1+sIhUKOXA+Zwyc93bxdr9dRKpWQyWTQaDQwOjpqjBd6xOXzZ2ZmcPr0aRQKBbz22muYnJxErVZzeEq6hQ1nfMgvndar3+83QlUUh2Ecb3BwEJFIxKHxQVcaq1S4IrLLZ21PAxNEmT8CwFjasq6bdeF83OfzoVqtoq+vD6VSCS6Xy+R6MHNa4vV6USgU4PF4jIXLkxxY9ATY/9eCqakpAEv7o4yNjZnHmtHtyoaKovQesryVeXsMtUSjUYRCIfT39xujg9WNTBLlc23Ptgy1NEM+j4tTGkDcj/RGyIoZ3uaf2+02IRqmDgAw++V75L6KxSKy2SxCoRD8fr/RDqGnpVuMkA1jfMgKlng8bmJlW7ZsQSAQMAaH3+/H2NgYBgYGTNwuEAg44nXSVcYTSFazNBOg4WtzG7pj6QkBYCpqfD6f8Z4wF4QnO70ko6Ojxh1HbwaPJZ/PY25uDsViERMTEzh+/LjJAwFgDBJ6U6gZ0o2uN9JK2fDll1+Gz+dbUn1gu7SJnVUuDbeBgQHHY/Y+L5Tx8XEzPnnypOMxGSaRbnb74iVd/82qq5o9z246J58nQzJ2ubJ8TBqLssEaAEf7cy27VXoBhsllxWIkEsHmzZsRCAQwPDxsQi3j4+MYGhoyCqX0lNDz0Up8Uup1sNrFDrtwO/s+aXzIogV6Wuj1oHedApX0uPB15fE0Gg3j3SkUCujv78f8/DxyuRzOnDmDfD6PTCaDRCJhjJn1nA82lPFBy3ZkZARDQ0MYHBzEJZdcgkgkgmg0aipb4vE4wuGwkcnlycEJQlqUtvVKS5RINxvzQKrV6pLcDrfbbV5LxvWYvMq8FFueXb4OvTB0w5XLZRw9ehSxWAzVatUYN/l8HlNTUyY8UygUzPF0OizDUsbp6WmHdsb09DSuueaals/rBmVDpbu57LLLHLc/+MEPmrEsk22X42HTKtenXX6FbcRKpLS7ncsh80Ha5fO0ymGyDet2ejFyn9KwtH9jzMsCgMnJSTM+ceIEehlWjfh8PoyOjhov9+7duxGJRDA8PIyxsTHjBeECVBoMzcIqRMo20FCQImV2tYvs+yXlFmyDwufzmWs1DQSJndxqC5YNDg5ifHwc1WoVu3fvRi6XQyKRwG9+8xssLCzg9OnTpspmvaXce9744JfAGB3LpEKhkEkqikQiDv19xvTsTGXb5dXK4rXh83gCSoPEPk6OZWhIVrhIVTxb5pcJTizb8ng85v3VajWjTQLA1Igze3qtZHp37tyJTZs24amnnjLGRjqdxs9//nPcddddHXtdRVEUGS7nfBAKhRCLxRCLxcz1MhQKGS9Hs5wO2SJD0i65lP+l8cHFqvSUy7mgmfEhn08ND7u6RS6MpZeE21erVYTDYRPCHxwcBABkMhmEw2FTtWmH6teSnjY+5IkzODiI4eFhBAIBk1Da39+P7du3G8s2GAwaKXUplyvrs/mlSmEvadUCcORXSNl1WqHFYtGcXDxGWXorT1QeE91tfG3CMI3tvmNuyOjoKK688kpT9UIPSKlUMlU2uVzOJKdms9mmmiQXyq9//Wts374d27dvx0c/+lH87d/+LXbv3m1KbcfHx4288ko4efIkvF7vklWvXH3KC4K9spOZ/lLRE8Calv3KC5n83O3vwM7vafZ8+3n26qjV/ptdTIn0AFx++eWOx6Q+yy9/+cuW+1CU9YSeb1Ysjo6OIhwOY9euXdi0aRMikQhGR0eNQSIVSOX5L/NEJPZikr8nmRPIfbXLt5MGjnwtv9/fthus7IrO12M+SKFQcBgS3K5Wq2FkZASxWAy1Wg0vv/wywuEw0uk0Tp48aRJSm3lZOk1PGx80JLxer3E3hUIhbNmyBQMDA4jFYhgdHTWiLZzE7ZwN+z6eTFI9zlYt5RclDQ+pTkdDg4aOrEaROv8s5wJgni8V6/iDktYyS4IbjYaRbK9UKkilUsjn83C73SYJtVQqob+/35Rq8T9dfqvFW9/6Vtxxxx14+OGH8Rd/8RfI5XL40Ic+hGQyiT/4gz/Ak08+qRofynnBDP877rjDcb8stZVlzuuJNPBsY08qo7YKwQDOHJ52oRX5PPu3JTvvyufJYwCc4ap3vOMdZvyVr3yl5TF1K6xOZI7Y6OgoYrEYLrnkEmzdutVR/ShDIfQmA0sTSoGlRgf/23OIND4k0ishj1V6ufv6+pZIAdjwcS4cabywmEEqnnL/XLwODg6acTabNWJnsmfMWtOTxocspY1Go/D5fKZ0ilYtww2EX75dwSLdZjJ8wv92Z0E71iZdb/wCpQIqH+eET08K9yW3sY0g+7jkNnYWt8vlMrXoXq8XlUrFuNdKpRIKhQIqlYqpAZfej9UwQlKplPEsuFwufPrTn8anP/3pC96voihKO+i9DgaDiMfjCAQCGBsbM96OaDRqFql2gzg7xGKHNuwQuvzf7E8ir+8Mr8gWHu3yitphe0cDgQAajQYKhYJR4a7VaggEAshkMg49q1QqZcqKJyYmMDs7i1KphFQqZUp81yoE03PGhwybDA0NmbDKjh07sHPnTkc5FWN6wFlDolKpmOc3a/wlvRYU7rKrRGi88MSRhgpjaPl83qxspEurXC6jXq+b0lpar/R42LFAYLFUVz4uS3nliodCQ9VqFVu3bkW1WsXs7CzGx8eRzWYRDAaNHglwtvpBCtwoiqL0Eiwa8Hq9GBsbw2WXXYZoNIpdu3Zh165dZoEaCAQc3gYZ9pDlq7YkOse2cdHMCJGVLM2MF1ZidoJgMGg85KRZYvTevXvh9XoxNzcH4Oy8lUqlcOzYMaMXtVbzQc8ZH/wSaT3GYjGTyTw0NOTQ6GDyDuD0fDSL50lkJrL0UtgluBxze35xLJ/lscqkIPlnW93Nqmg4tmOEfA+s/ZZGUa1WQyQSMe43lt6eOXPGeEN4XPyhdGsJ7tzcHPr6+jAxMeG4f8uWLWYs3cp2zFT+2M/l1rxQ+IMGlrrS5WpFfrf2D10KrclQQrNE51aPyX3art5Wz5NuV1tNdr1zPv7oj/4IPp9vSW8g6dnslrBLO+Q5IPOPZOdhAEvyD1ohz3v7PJKr6lZjANi+fbsZy7yqvXv3Orb72c9+1vI41hNe93w+H8LhMAYHBxGPxzE2Nobx8fGmuX3NPMjNqguXm3xqzwet9nG+no7l0u5ckbBb7+joKAYGBkwUYa3ng54xPvjBslQ2FAphbGwMW7duNcYHrVvb+LD/mrnTAKfLTZbX8sLM/AyZ21Eul5fUTPOH4HK5EAwG4ff7jceBVSoyf4MeGQDGUJHJqTLnRFa62HXetOBlFnQoFMLAwICREU6n08jlcqaRHRNR11sZVVEUZbnwGkdhsFAohJGREWzevBnxeBzRaNQYHtJ7LK+ztkECOEMlzcIqskqS/6UQJOcBqXLdbYyMjGBkZASvvfYaXn75ZbhcLsRiMZTL5TWdD3rK+HC73SaeNzAwgG3btuGqq65ylE5JI4OGAm+3K2O1S6PoVaDSaDgcNsp3fD6zjPP5vCNrmolPjENSZY5hDtkjhmEd/pDkfukd4cnNjOhGo2FObGkM8fXtahZqg1AVNZVKmb4xlHdX40NRlF5AejtCoRBGR0cRj8fxute9Drt37zbecF5nOQcAznC2TPy354NmCacej8cxDyzX09CtXHPNNfjtb3+LQCCA2dlZAFjT+aBnjA9pTFAuneVSFJORRoft6bDvk9h5Fs1ifLxfWsl8jtQFsROZGAqhUSGPhffZLi6px2E/Jt8PjRYaKTJrm4/z8wJgJIVrtZrp6lssFh2xym5jamoKbrcbp0+fdtz/e7/3e2YslUpt93MnhctstU95XtlhAHnOSHe5fS7K1dL5lr7JfciLSDvXvHT1j46OOraTIS5F6QZoVPh8PtM641zzgYTPbRaC4f5tj4jUiNoIUGwzn8+bnJG1nA96wvhwuVxGNCwWi2FkZATDw8MYGBgwHxqw2KhHhh+k603qZch98wRjfbZ8HvM5mMTa19dnqlkajYbxbNArIWN/DP2w1JaqptJI4L7snA4pOiN7DdCrIt+vnaEtk1sBmNekol8ymUQmk0EwGMTMzIzprFsoFBwdNRWlG9izZw+CwSC2bt3quF8aVmutUXChyMnQnsxaGYX2Ymi5Wi/yee0+J2kwy1wQoLtyPtzus11p4/E4+vv7cckll2BsbAybN2/GwMCA8YLTE07tD84B9DL3Qp5QJ4lGo7jyyisxNDSEVCoFv9+PmZkZ481nh9xO0RPGh9t9Vnp8cHAQ0WjU6PL39/c7mufI8lbb5SbFumx3ml1lIjOf2a+FBo2c3JnTIY0CO2nUbkokwzosj7K7GMqEWCr1yRgilU2l10XunzkivDjTA0KxmYWFBSwsLJgfYiKRMF0TeSyKoijdBq/fbAg3MjKCnTt3YuvWrRgYGEB/fz8CgQACgYC5bobDYSO90ErE72Ll8ssvRzwex/T0tJl3EokEvF4vGo1GR7vhdv03wQ+EsunRaNTIplMuXUrj2mGXZiGYVgmnMtmIxgG9H9KI4ON0xdGT0cxV1yrMw3AJjRsaDQBMXxkAxtvSqn2zXbHAE4VlwlJXxC4ZppEWCARQrVaRz+e7NvyiKMrFDReTLDrYtGkThoaGMDAwYEQluRilASLzNJSljIyMoFwumwarlKJ3u8/Kr6fT6YvT+OCJ5vf7sWnTJlx66aWIxWLYvXs3RkZGTGkVKz9k7oPMEaFxIOXJ7fJVYoc9ACzR5+excfLma8jSXpmk1K5Mi9LrfB0AjmqXlTRco+HAhFWW/DLRla40drxlNcyWLVuM+FgymTTH0A3Mzc3B5XJhenracT+1SoD2Lux2csUXiv0ZyTDAuUq5iV1mKXVblpvQZq/mWilk2hcRuX9Z4mu/rtyHrZAplTo7BRcYTIoj8nfRy+q59sS43MZy7fQY5GNyLH83NiMjI2b85je/2fHYk08+acYLCwst99FJAoEAotEoIpEI9uzZg+uuuw7xeByXXXYZBgcH4ff7jdDixR5SWS5+vx+XXHIJTp48iWKxaDRTcrkcfvvb3zo64K42XW18cHKngimzmsfGxjA8PAyv14tQKNQ0CZTPp0HQytCQt6Xh0O7CzzCLLH+VXhbpXWmW7MRjlCGg1YDGB8t6S6USyuWykc8tlUqmiy4nSkoRB4NB43qzdU0URVHWG3oxotEoXve61+Hqq69GOBzGtm3bEIvFer76ZD0ZHh5GPB43nv58Po/Tp08baYdOzAddbXww3BIKhRCNRtHf328ydGmhcVVkT+R2FnMzqNMhJ2wJE5Nkx1mXy+XIpZClsrLiRR6TDHXI41ttZMWLx+NBtVo1xo9MpPX5fIhGo2g0GshkMibcQmOuUqmgWCyq8qmiKF2Dz+dDJBJBLBZzeL0o7Ci7wyorIxqNIh6PmypJ9sFh871OzAdd/U35/X4MDAwgEolgfHwcO3bsMJ0JY7GYY5KXxoGs+uCqX+ZUADBlrvl8HpVKBfl8Hslk0pEf4fP5MDw8bLKnmcQquweWSiVjfNDrwZijLFmSZbjyGFYbloPJcA4TcXmfy+XCpk2bEI/HMTs7i1QqZbRMhoaGUCqVjLttveExPP/88477b7rpJjMeGxsz40Qi4djukksu6dix2d/fcl29tpEskeEaObbdnvLiazcck9vKsX1hlsa2fMwOXbVTSV0LmLtkv095nPJzfO211xzbdfIc6AStyqPtxZH8XuzzSIah2n1/8jbbrgPOxnSAs/x6vcIuoVAImzZtMnMCr62FQsHMAfRKL6cctlQqOfLiWLZ7LorFoiPXcCOwadMmJBIJ5HI59Pf3I5fL4fjx4xgeHkY+n+/IfNDVnx5PIlvXg5OrzMOQHhBO8jypaGgAi/kbHFerVVSrVRSLRSOuwr9AIIBIJOLopWJ7UmzvB40TuxLFppMuQoaAZBKszFuhNL3b7UY6nXZ8hhQxW81wkKIoyoXCXA727KLxIReWvNYtx/jIZrOOhPxarbYs46NUKqFSqZh8vk5qCa0Vfr/f0TUXgJGRqFarHZkPutr4oHEgrVOWv0rV0Hq9jlKpZDwhspEQjQFZ0gosisgwrFIsFo3LiVQqFWQyGaOZEY1GjfHBUiR6WOwKF9vwWEl+R7lcNpZmNBpFNBpd0ZdPzRF6XuyqF9bJ+/1+JJNJY6hIy389VriKoiit8Pl8iMViiMVixovLsttIJIJKpWI80dVq1QiCtVroDQ0NoVarIZPJmHkkn887Goc2g/2iisXihlGGTiQSZj6dm5szLTg453XC29XVxgcnS2mAAHCUh+ZyOdMbhaJesrSKBosMv8gEVHpRCoWCSa6Rr08VS5fLheHhYUdOBQBTaSONjXbVLcthfn4ev/3tb1EsFrFjxw7s2LHDlI0tF+qPUO+DTe/YF4Y/3oWFBYcFH4lEllT7dAP2yf+5z33OjG+//XYzvuWWW9bsmOxGdfJ7t88BeVu6L9tVqtgVVxJ5235M7qOV2BTgdO9LMSHb5S4b5qkInbIeuFwuBAIB9Pf3o7+/33ho+/r6EIlEzGJrYWEB5XIZuVzOLMJsxV4JdUAYgslms3C5XI7Kn3bHxAUqF6S95gVJJBKmn0u9Xkcul8PU1JQxPpjI2wlV1+6aYSzsChXZbdZW9eR9Ut+CjxNbtlx6KWhQ2B4Lbs9yIwp4NTMmbO2QdhNEM6SxZXtoVhqmsTvw0mNEjwY9PLb+iazYUZRuYHJyEoFAALt27XLcL39T0pCy837C4bAZ2waj3IfMqRgeHr6wg14Bdj5PqzLZdoafXfLcavHQ7jokPxuZRwWc9RKsJ1ICwZYvkPpLhJ8pw+rtFlPyes3P51zPIXYuYa8ZH/l83lH9yPmH3v5WGlOrQVcbH16vF5FIxIQdSqUSSqUScrmckSoPh8Mm8ZOS58lkEoVCwXhB3G63QxJddoTlhxoOh7F582aThFooFIxVy31T9x5wluXK280eA2C8L60qcPhaVJTbuXMnXC4XRkdH27aCp6sQgPHKUIFV5qJ4vV4TK5Xy61LEjO7GVtokiqIo64F9jQIWE5FlP6twOAyv12uMjmq1ilQqZSpllrNoBM4aeramjQ0Xi72+UJPzBueTXC5nVLw7RVcbH0yMDAQCcLlcRruiUCiYeJ6sKqFXJJvNIpvNGgVUnox8nN4MaTgwkaleryOdTsPr9TraC1erVdPhlha4XcHSrLxXCofJ1+UPho+XSiWkUink83lEo1GMj48vSw64WCwaN7nsI2NX4QAwnxnrtpk0JUXYZOKuoihKN2ALRzLXDnAaDZwPWBpar9dNWKRYLC47nFIul5HJZExYvhlycddLukh8b0wklcUIUg+q0/ksXW18+Hw+U1YVCoXMKh1Y2oFWGhWElrEMrXDyb6a9wduyE2wsFjNGDsv9qKEhvSDNDA665BiuYVxRvi6/dLpXZQOkdoaHLD/kPrkPmWAqVwaykqdSqZjwDo+fr90Lxsfk5KQZf+1rXzPjSy+91LHd7t27zXg5F56VYF+UpPvc9hy1ChHYFyzptpXff7ty2nbS0dIdb+9Dvrbcx8mTJx3byTCGVEJdK37+85+bMnfJFVdcYcYyJj0/P+/YTjZJ66Ti7fnSLj9I5uLYpbbyPLIfk991qxwgwHn9k0nmR44ccWzXDfLkrH60hRsZHuC4HXLRKZ9vbwOcW2xSFh0st7y3G7ArfGQ+oK2ue9GGXYaGhnDllVciHo9jeHjYUcUCOHNCarWaCVnw5KxWq2bCd7nONoHjh8v7bO0NAEbXo16vo7+/37jvKFfOL48VIlLl1DaE+PqFQsF0DJTy6Wxtz0xuiqk1MzxowPB9UceERoRUopOSuLIcjfdzP+Vy2RhbMq+l2xJOFUW5eOF1PRAImBJQJtQXCgVEIhHHJGproMjcwHaJ19KbQs0QG3nN7LUcDwAmnEKJCbbjoGcfWFQHD4fDHZsPunqGCQQCGBoaQn9/PyKRiIn3NVMylcmoPLk4yfM2cz1kQqeEz5WWHsV6stms+aIYxpH5I3ZyKQ0Q5ppQxIzVOVyZsFdBMBhEPB43hkgzGHKSYRUaCzyZ5GdBA0gmzdLSlc+nEcYQleZ8KIrSbUjvrPR+8LpfqVQcIpHyec2KAIhcpMn/zRLv0+k0crmcuW73CgylcD4AFudHphTQi85FPb05nZoPutr4kDD/Q3oa6PGQZbPclkmWwKKbiScpJ2FgqSuaFq/P53OctAyFAGfdmJlMxoRx6AVhCEP+AJisSslabk9DgTojDA21cxtKwTDmdtg5LIyF0uMi3ze9JJVKBel02uSZcH/0Ki0307tbkNUMH/7wh9ftONr9QOVFTH627eKq8ty09y3PE1t5UD6v1evaj8lKCrvZnX1bUdYL23jgAo/XWZmHIeUQOB/I3xG3ZYsN6THmb4Pluj6fzzzWizLuhUIBxWLRoYVC7atqtYpsNotyuWx+6x6Px6Q7XJSltnIy9vl8CIVC8Pl85mTgap+TPichWVJKL4CsdpHxLRnnohHDZnUyxkj1O4/Hg2w2a+rIuT+ZByI9DtL4YDiHxwUsJqLKmGWrxCXZmZdGBg0ZWxwtm82aHyNfn03mCoUCJicnkclkzI+RDfz4o7uQk61YLOJjH/sYHn30UZRKJdx88834/Oc/v6R8T1GWA/O27KovaXTJXBQpEw60Nwq7AXt1LY83FouZcTqddmwn83nsa4bMe5EGrl2uK8MKMr/E1tWxX3s9kHpK8hrLKkR5rZNdxvlfCo6xXby83rK/F//ncjnjbZFCjPzrBVikwcVmLpdDOp02ISouRlOpFEql0pI+X8FgEIFAwLHAWy26+1f5/9MsAUh6EKS0rvyTE7SdYMMxvwAZL+T+pTeBf9LVJytYzvXHL5TGE6t4bE+OfL82fF+yOsW+cDXTO7HfuzTGeAGXrrZWnXjbIYWp7r33Xnz3u9/F448/jkOHDmFiYgK33nrrivanKIpCWoXJ5QJM5nvI7Zvl9jHfjQs0OSdwf7xGyj85J3Q79ufS7Nov75deH3qOfD5fW5XYC6GrPR+0QIvFIlKpFOr1s81/KpWKSQil54L5IPSG0Fsh8x040UqrGVg8OW3DRX7g0o0nE1/D4bAp75K5Isy9iEQi5nXsslweCw0ePr9SqTgaQxGq+QGLPzxZycIfovwc5CqBJ1ypVILf70ehUEA+nzeqgDQ4qAGyEp5//nls3rwZqVQKX/nKV/DII4/gxhtvBAA89NBDuPLKK/Hss8/i+uuvX9F+l0OvNQ6Tq03byJPuXLmyt7+PdmEXudJt91pydSuNR3uVOz093eRdKMrawhCI1+s1oWNWnLAakb8FJoOy8MD2VFDLSRocgHOh22wBdq7QeDchDQ9gsUt7f3+/Y/Fdq9UcnykLK5heQLXT1aYnjA/mJpRKJSMLzuRPulvZA0WeNDIkISd5YNGLQoOAq35bUVQaIbyQy74xfr/fGA22UiiwGG88l2Q569FpjWezWfT19bVsdMTXiEajS7w4NMakcSWrXWq1Gvr7+1EulzE1NYV0Ou1I2OKJtxIGBgYAAIcPH0alUnF0nb3iiiuwfft2PPPMMx0xPpSNDZMMf/KTnzjuv+GGG8xYlljbZadskQCsrXLp+dLK62gnOMoFii2JLx+TISk7x0hOpDIk88Y3vtGx3bFjx8zYLsNdi5wghtYZ8uBEydw3Xu9kZWMwGDTiWZwHuLDitZJhm0qlYsIq0vMrKyuBxTB5LyCv95yf6HEH4KhuDAaDZtHOxQdFPbPZ7MVnfADOrrF2qANYPDlsVVH7ZLHdcDKMwonclhaXYRDZD0YaKraomHyevW07GJNkstRykR4XPlfuQx4bj58VO41Gw4SBWHZVKpVMAtZKuOqqqwAAU1NT8Pl86O/vdzw+NjaGqampls/niU66IcasKEp3IEMszAGS4XRZqQjAsZCUz5dhcF6/md8mF57trvFyH90MjXYAjmu6nA9omMjkXFZzyvngojM++AHQGpWCJzL5R4qhAM7yW1lOKg0NnmhMKJUltlLgS55ospOiHV6xsU/i5RAKhRAKhRw6IMt9Hq1YPk++V2mcyFWA2+1GLBbDpk2bkM/ncezYMUxOTiKdTjuSz9aCgwcP4lOf+tSavqaiKL0BveD0CLMKQ3qVZQsJuyKFFWByQcjHpREjE1YZvpbecWCxVUa3a3xIKXq3+2x7Ejl3SQl6mdTNxn0LCwuYnp42UYfVpquNDztUQM8HjQufz4doNOrI7wCWekHsJCGZWCndbHLClvuRVrXtVbCPl9gJqiuBP6bluvf4XqTbVKq68kdEY0x6WYLBIPr7+41hlUwmkclklpQgL5dNmzahXC4jmUw6vB/T09PYtGlTy+fdd999OHDggLmdTqexbdu2Zb2mVLDsNezzQ4YMZMWRbYzKx9q5vds1I5OvdebMGTOWYQpg/b1QIyMj8Pl8S86HViGUdoqhdgO2btRqkN9nu1Jp+b7siVCGV2S+g71Qkt+13AfDqGR8fNyM10PJk3l7xWLR5H3wmsXrrJwPZOidBoVcgNLjK6/llUrF6DBJpWc7FM95SbJSj/Vaw1CL9AaxGzzng0ajAb/fbwy7ev1sq5ELmQ/a0dXGB08GaalS1MsWmAGWdpO175fGBE8embRph0+kJ0GGZiSc0KW3xX7d86GZwM258Pl8S0qIpYUv37OdWGVLsJ9vXPPaa6+F1+vFU089hdtuuw0AcPToUZw8eRL79u1r+bxeVQtUFGXtkBIIgUDA0XIDWFSulnNAs8pFYhtjcuHJa6MU2qJhY+fidbPhAcDkesi8l1bzCxe/5zMHrYSuNj7y+TxmZmZMti1PIBofjcZZ+XLG7eQka1ezSC+EzPClRSjda5y8KdlerVYRCASMVS1PWDlJ83VWS4BmpV88ZYe5SpDHyc/K7ivCz47bU4xmpcZHoVBALBZDPB7HnXfeiQMHDmBwcBCxWAz33HMP9u3bp8mmiqKcN7IENBwOo7+/36HnwXAIDRA778+eB+T8QOhNkYuzYDBotJ96HSadykRUYHERzccBGI95p4yQrjY+mInc19dnkl6kESJPKnuylCcQ4GxAR+NCGhLNEooY8qlUKsbV2MxqZrxM3i89IWuJdCPagjz2ikA+x86RWelxf+Mb38Bdd90FAPjMZz4Dt9uN2267zSEy1im+/vWvm/GXv/zljr1OJ7A/Z+nSlueU7XGT1Q3tvisZdmnmtSMzMzNmPDc359huvcMuiiLhAk82mZOGBrexPeHy+c3uB5zecRmiXg3DQzb5XG+aaUQBTk94pxNqu9r4YNil0WiYGKSdu8ETj+4kwCm0xdvyufxgm528wNkLtn3Rlq8lJ3QZoqBUuiyvlRN+M8Olk18wM8MZ85QSxFJqnpUmxWIR+Xz+vNop33777WYcCATw4IMP4sEHH1ztt6RchBw/fhwej2fZOUB2ctzOnTs7cVirhl0aLF36coK04+5yIrO9ra3KcO3ftcwHkUnmyWTSsZ2cfDOZzNI3sQZUq1Xk83kEg0HjuZYTpTQcbMPCLhSQoeZm1ZDBYBCRSGRVr8/dVB3jcrlMvpOcpzhfcD4oFAqmn9hqsyJz7uDBg9izZw+i0ShGR0fxrne9C0ePHnVsUywWsX//fgwNDSESieC22247b5GicrmMdDpthE9ksgywaKWx7IqTqNT4b5bf4PV6jUR7s460+XweiUTCyO9KBVJuI1VRmRfCJCjuV7r3mPjEL7ZQKCCXyyGbzZo/hjyot38huRf8AbKpXT6fN58NlfpoRPEky+VyZttOlFYpirJxWI7g1mrOB9Q/ymQyxvjgtZbXWx6TrOSgt9z29kqVaFutejUMD1tlupuMDxIOh03iLY0PuRDNZrMdmw9WZHwcOnQI+/fvx7PPPovvf//7qFQqePvb394xaW2eRLS6mnkpJM0s3mbQYJE13PL5svRKbt/KTdXME2MfUzOJc3mf/G+HS84ndCP3IV+z2WtIad3zCbkoinLx0UrrolPzASdGNkaT13F6nGVhgpRnkNswP1D+sSzVbnVxvjQaDSOQyfmrG40PO9fRnv86OSesKOzy5JNPOm4//PDDGB0dxeHDh3HDDTesurQ2Ez55csRiMYTDYbhcLoe3QXo0gOa9YBxv2joxqYInS7J4UjJLmKVIdN/RXcn90wqXXyQAk6tiJ4E2K8u1VUlliAdYdA/aSUD2fwAOKXW6zdg0SWp9sMESG9HJErNeQeYkSKVLAHj11VfX+nDOSasySMBZUiu9fLbbU96249F2ojGxv1Ppqpclp3aOh50Dsta87W1vQyAQWCKjH4/HzViGILo9zAIsv5ux/P7s58jH7NCN3FaO7ZBUqxJKW+fn5MmTZiy9Hc0mpU61WshkMjh16hRyuRx27doFj8cDv9/v0PbgfCAXWPzz+XyIRCLnzLu40ImWrycrM7v5eir1TqS0hb0IX/XXvZAns0acXSTPR1q7nbKlbILDFr+BQAAul2tJ8k6zfAxpfMj/MkOa3hTpGQAWld4YRmFyKi1uW5iMTXhsC7JSqTgkfW0tEmm9s+xX1ozTGKLxYp8I0tvC15cnUblcNsYHP0tg8QfGToc0Plo1rFOUc/G73/0O1157rbmt3Y0vTi6k1UK7+SCfz2N6etpc03h9jkQips2FrUEi8/KW24mW1z4+j2Fwe9Enr7uyizqv0bY0e7diH7v0hHeS807hrdfr+OhHP4q3vOUteP3rXw/g/KS1Dx48iHg8bv5kUpmsOGHVidT1sC1bmSMhy7Kkp8OWzCUyf4SGBA0Uu5LFDmXI++w4nzw++WXaqwf5x+Np9iefbyuWygRbua9moSD5XCYV2Z+voqyEW265RbsbKxfUaqHdfMCiArZ+z2QyyGQyKBaL5n67U6tciJ4Pcq6xr+3ct52HyHnGDmN063XV7XYb4TYAxltOr31XhF0k+/fvxwsvvICf/vSnF3QA7ZQtOTG6XC7kcjlzYZMJRJxwaaQwJMLVOz0l0kNg53wAi/kkjcbZpmzhcNjsS1rC/E+PhEwutQVu6PmwDSa7LIxeD74fWYVjJ0nJ9ypV+1juS6+QNMRo2dJ6l59dLpfD1NQUkskkFhYWTLim01Zvpzhx4oTj9mc+8xkzvvfee9f6cJrSzu1rX8hIO3VL20Uuzxkp3Gare0pDYXZ21ozn5+dbHl87Tp061ZEQLL1ydqdn+TnKFa/9PruRdueAXPm3C3HYi5FW+5BhFztxUIZd5D5sL0E7pdzVpN18wAaj9XodJ0+exJEjRxCNRk2jTIbeGcLmmItPeY2UhQJ2QUKzPD6ZzMqQyrmEuJjwz/1xYduNDA0NoVKpYGFhATMzM0gkEpibm+toAcJ5fRJ33303nnjiCfz4xz/G1q1bzf3nI63dTtlSVoiwza/P51vyg5SJqXK1L2u0ZSlWsxwJecLJC4PM4bANHXmyyte1rWU7aVSe7FL8xjY05AlvHyMNDG5jv5Z8HrdplghbLpcdK4hut9KV7qZTIVil91jt+YA5gH19fUilUpibm0OlUsHQ0JCp2OBkT7FFqUrN63aj0TCre3md6+vrc8gl2L3CAOc1Xc4pzWCovJmnvdsIBoPG4GQYnh6lTnnDV2R8NBoN3HPPPfjmN7+JH/3oR0sSu85XWrvd6zFXIZ1OY3Z2FoVCAZFIBPV6HYFAALFYbInqKGkWarDvP1cyULNqEx4TABMTZPKTrDXnttJokYYHX58rk2ZuOrsEzDYy+Ljb7TYtke28Evs9SGOtXC6bUlv+p7dGUVbC9ddff8EhWG0uuHFY7fmA16u+vj7Mz89jcnISxWIRY2NjJumUxoM0Ouj9kHBukbmBfX19Zj/N5giGWOr1+rLaQfSS97hQKCCTySCVSiGZTCKZTCKdTiOXy3VHV9v9+/fjkUcewbe//W1Eo1FzEYnH4wgGg6surS0n7tnZWbz22muIxWLweDzI5XJmzNCI9DrYhgewVE7Xjtc1y5GwTyBas/QS0ADw+/2OTGrboJHVLraXhS5Be1uGUGR/Gfm5yF4tdOnZ3hw+bueg0KPEky6dTiOVShm3Zi/9cCS20fTxj3/cjP/lX/7F8dhLL720Jse0EuT3185d3q6KRVY+yO/RvgDLBmZ2uOp8+Ld/+7cLev6FNBdUuodOtVqgyFi1WsWZM2fg8XgwPDyMkZERU5kYjUYd53mrapNgMOgwKBiqabYQlZLjvI4up7merFJcDYXUTpJKpTAzM4OpqSlMTExgfn4e09PTSCQSjkKM1WRFxscXvvAFAGdL3yQPPfQQ3v/+9wNYfWltWqjFYhG5XA59fX3I5/MIBALw+/2mM5/sZGg/3x7TALATM9u5llolucr29HKCkKEdWfolwyzyGOywizSYmpXfNnvc/t/uvdD4kGGtCxU2Uy5utmzZYsar6XI/fvw4vF4v3vCGNzjub6Xw2Wvnr8y9AZzGo3yP9sTYKoEdcH4e8rpkbycnFSmx/8ILLzi2e+6551q/AYtOtVqQBQjFYhHZbBbBYNB4ben1ta/RrTzbshR2ueWwzZqLngupMdKt0ANOgTGGXCg/0QlWHHY5F52Q1q7Xz7b2nZiYQCaTQSAQMIqggUAAwWAQsVgMfr/fTPQy0UfmbNAFZ8vy2ieqPMFsz4nH4zESv8Vi0SiG8suSyMTRZsaB3THRjik2q2CRlT8y7FIsFs02tFbpQZG5K8ViEadPn0YikcCJEycwNzeHVCplnq8oF8pqu9yV3qFTrRZkKDuTyWB6ehq1Wg0nT55ErVbD5s2bMTo6inA4bOYFJps2o1PGAD3cLFhYbonvejI9PY2FhQUkEgnMz89jfn5+SU7MatO9ppigXq8jmUwil8shHA7D6/UayfVoNIpoNAq/328ynu2yUwCO+J4UDQOaNxpqFjaRSqs+n89RAszcD9ub0UyV1T4+6ns0Mz6aJTpRv0OW+dpGFh+zE6Pq9bPCYidOnMDp06cxPT1tspo7fbIpG5vp6Wl4vd6OhGAVBVi8DvP6Xy6Xcfz4cZRKJfh8Pni9XkQiEUcn2rVO9Eyn0+Z30ElvB99/IBBY1utQL8uei2SIhcZHIpFAPp9X4wNYTJJknoLP5zNZuS6XyyRK2idbs5wOmazJihHpjZA5GM0ME3lb5lq0ymhu9gXaoR8aJPJ17X3K0I2dgMr3IA0S22CRmv0sXebn1svlte2QnqgjR444HhsdHTVjKY713//9350/sBbIc0W6y+2cD/t8lcgLUbtYsyyvfd3rXmfG3/nOd1ZwxItcdtllHQnBPv/883C73XjnO9/puF/+1uT3bKt9rhf2ccjvVn5HMvfGfkyObXVSuX87Ji8fs0uxJTJ3QTaMk4qmzW6vN1JEkZofvJ4Vi0WzuOS1ca0MkGbX6E69ttQzWY7xkc/nHSqwXDxns1mkUinzGXY63EJ6xvjgpFooFDAzM4NUKoV8Po9KpYJIJIJisWhKa0OhkBEIk5Lr0gsCwCR02t4KhiqkJghjdgyFcB/BYBDBYNCR1EnsapVmia4MEfG2bGBHz4k9udi6IdyPlHKXxhVPplOnTmFqagqJRAKvvfYapqamkEqlzCpCK1yUCyGVSiEWi5nb2t1Y6QTMWeO19vjx45ifn4fb7caWLVswNDSE8fFxjI+PO3q5LCdJ9EKRCtIUPZNdilcD5mcw9C5bIzSjVCphfn7eEZZn+L1cLmNmZga/+c1vcPz4cUxMTGBhYWFNmov2jPEhS6NSqRQ8Ho+ZLMPhMGKxGIaHh42oGLDYw6WZQimwqLMhH5PI/BCevNL4kIJmDLvY+5M/Evm60viQYRj7C6eImTRIWvV4kM/lfTKRaH5+HqdPn8bCwoIJt+RyORSLRe1iqyhKzyDVpefm5pDNZtHf348zZ86gVCohGAyiv7/fkW/BRWSnj4uLWTvvTiLVtlcSmqnVakgmkw7Piu3dPH36tMMTyNxEHgvnJCpbz8zM4MSJE3jttdeM4WF72TpBzxgfErmiZ55CIpHA9PS08UJUq1WjuyHzOwBnCa/sYyI9E60qUJpph0ixMJm8KvfVDtsjY9OsckaGV2xkE7lcLod0Oo1CoYDZ2VnMz8+b2zJZ9mJEhh26RdBKustbhWAApwveTnKWKzxZSWFXVcgL8eHDh834zJkzKz1sRVkXmOgPAMlkEidPnkQmkzGTeyAQwMDAAEKhELxeLwKBgGPi5/OpkUSdkHO9pm1M0NCQzUkBOBaLzcLy9Xp9RcYHF7jSo57L5RzeD1vYktVBlUrFSNNXq1UTapmdnUUikTDCYmsVfu8544PhBTkJ0yORy+UQCoWwY8cODA8PIxqNmhpt6QFhozepgCotULqzZAmWXa3CL1cKcklvRjtVOPsk5L55XNwHPSutjB5avbbRVC6XkUgkUCwWMTMzg1OnTiGfz+PkyZM4c+YMCoUCpqamkMlkHCEkRelGjh07BgB48cUXHff/v//3/8y4lUz4epJMJh23pVG43AmnlWYL0L6EViIfs41YNgcFFj9nAPjhD3+4rONbb1j5wpy3bDaLUCiEyy67DJdffjkikQh27dpltEBkuwzbYyCrAunptmUceF1n7gQAh1S7NDp4bea1GoBjX/zPQgUAprUGPe4kmUya3DweAw2d2dlZpNNpuFwuI5sg54hisYiFhQUUCgWkUikTgjl58iQmJyeRyWTwyiuvIJFION5Lp+k54wNY/DEx+bRSqSCZTJoOhwMDA8bgqFQqxjiRIl3sitvKyqX1KmNkAJb8l0YA0LyDYTNLuZUIWbMqmGbJr80SDqVBRLXSTCaD+fl55HI5JBIJpNNp5PN5E+9TFEXpVXjdBM56Lyn4GA6HEYlEEI/HMTg4aOYDGhRE5vXJOYIdc7kolY3ieL2XxoUt4yCNEPm4vYgEnItV+TwaKvRY2HmDfF6lUkE2mzXzBY2iZsUGuVwOyWTShFtOnz5tjBJWt6yVJ7wnjQ/C8Eq9Xkc+n0cymTQlV+l0GgMDA6jVagiHwwiFQkaBVAq+yBOKJ7HMWLb/ZGIony+fI++TSav2l9qs9FbGB2V+h31cNLp4PNw/LeNisWg8H4lEAplMBvl8Hul0GslksqkeycWOXEWvJfaKVX4v8nyxG3u1y1VqFZKZnJx0bPerX/3KjJ999tmVHLaidB21Wg2lUgm1Wg2zs7NG8dTr9SKVSjlu+/1+BAKBJY1IZXNO9mWxvd4AHMYHkYn+PB67kpKGi5wrWhkk9GCwqochGtkoVM4dfA6rljiHFAoF0yAunU5jZmbGhOHn5uZMR+C19hj2vPHBEEw6nTZ1zKlUCn6/HwMDAyYeNjQ0hLGxMfh8PsTj8SUSvDKUIscypCJdbtINJy1Mnly0PnlCyxhdswRYnmCsfGGSqXSB0c1GL0a5XDY/AhpgrP1Op9OoVquYm5tDIpFAoVDA/Pw8ZmdnHQmuitIrfP3rX3fcltLrssGlnb/zf//3f2Z89dVXr+oxybAFsLRsViI9je0UWVstUtqVW9vltK1UUm1DVXYw/tGPfmTGr776avM30cVUq1Xkcjm43W5MTEwgm80iHA4jk8lgaGgI0WgUmzdvNsmoQ0ND8Hg8qNfrJhwjdTDsHDvAaXxIg0V6QaQ0gtwP77PnGbsSh9fnXC5nwvr8PqkCzLlJzj0MO6VSKTQaDWNUlUolpNNpFItFzM/P48yZM8hkMjh9+jQmJiYc+1lLetr4ABatTZnF29fXZxJOM5kMGo0GAoGASbQJBALmi5HKoq3qseXJIpNQ7WTUZuGZZvtp9TxpFMisaV50eILRUi2VSsaTw5JdxvwYVqFULhOOaCApiqJsNGT+HL2FmUzGSKlHo1GTJ8h5QHqopTFxrlBJs8adzQoNmnk3pPFhLwZpWND7IhuZyoWv9MjTgGDOBl9Tfhb0gLDKpVQqrWvBQc8bH6RaraJQKBgPBSdkl+usounCwgKSyST8fj9GRkYQj8cRCARMPJAxPpmMJJOFpHHD/QPN2y3bx8XnMG4nk1FpRPAE42vwpMtms8hmswAWk5FkYzufz2eaHrGenbXbyWQS8/PzmJqaQqVSMRaxoijKRoVJ95lMxhggCwsLCIfDSKfTCAQCiEQiphR3bGwM/f398Pv9GBoagt/vd3ipuaAFnAmlbOEBLFZN2sYBaeb5kIaMXGDS8JBaHrI8tplnhgYHPeeygGFhYQFHjhxBIpHA1NQUTpw4gVKptO7zwYYyPmQnWCbqlMtleDweZDIZZLNZBAIB5HI5DA4OIhKJGLcbJWrtWJ50ndGgkYaNdNPZMT/A2YFW9mMhpVLJlMXycdnafnJyEpOTk3C5XIhEIg4dE7fbjVAoZI6bxok0OhKJBCYnJ9XjcQ7Gx8fN2E7EXe3eDPJ7sEMEMl9judURdtK03OfCwoIZy3JawOlmP3Xq1DmOev05fvy447YUL/vgBz9oxps3b3ZsJz8D2eQOcH7GNOKBpWXvMlQqH2tWMUHaVafIMIl8XXv/8ru187TaVSXIc1gek2weBzirWn7+85+33F+vwfCz2+1GqVSCx+NBKBRCNpuFz+dDKBQyPWDy+TxGR0cRi8XM9VVWp0jPuKx+5H00AJjLIZNTJfbCk98Lv0caD7Jdh63NIatRmP8hjQ8ZDuJnQFHJycnJrpoPNozxQeTkL780ZvpWq1WEQiFTRRKJRFCpVBAKhcyJI4W9iMzZ4OvQyyI9JM2SjuQJxNyOXC5njovWOb0ksncLDQx6cBjv43EwzsnXajQamJ2dNUmmTCRaqxOtWCziYx/7GB599FGHpPbY2NiavL6iKArhhE9PBa+HUlqBi9J6vY5oNGpC8+Fw2BQncD6QCtTcv/R00OtgGx/2fCAfl2EV+bj0tMvkUho9suJFltdyH6xsZCnueswH7dhwxgew6G3gF8F29/l8Hh6PB8lkEuFwGMFgENPT0wiFQhgYGMDo6KhJVI3FYo7ET+mGkx4P6nDwC5cnC8M1PLkymQzK5TJSqZTJMpbGBwVu2JTI7XYjGo1ieHjYUcbFfgbVahXJZBKJRAKlUglTU1Omnntubs54T9YiufSll17C3r17ce+99+J73/seHn/8ccTjcdx999249dZb8bOf/azjx6AoiiKR12Ben/v6+hCJRFCtVuHxeFCtVjEzM4NQKIS5uTmEw2EMDg5i8+bNxkvCyhh6l6UhIg0QWc1iV0JysclwCOXO8/m8o1SW3q5mXha7UpMekmw2i0qlgkQigdnZWdO5nPPAWs8Hy2FDGh/S+yA9E5VKxeRM5HI5BAIBY+UyeZP5H6wLB5YKx0iRGLn/ZvkhsvMtk3wWFhZw+vRplEolI29Ot6DX60U4HDbZyvF43BgffG90x9G1lkgkkMvlcOzYMUxOTjpihGvFc889hyuuuAJf+cpX8Mgjj+DGG28EADz00EO48sor8eyzz3Z1N1Pp0v+nf/onx2Of+MQnLnj/0pUuQyvtmsLJ1Ym9nWwCZkshS/f8888/b8a2AShLbRVlo8LrIOcFXsOZI0ehMnq/Q6EQ6vX6kjA3F5xyESq948084Hx9mffHni80BnK5HBYWFsxcRC0S/pfSC36/v6lHnuH7hYUFTExMIJfL4dVXX123+WA5bEjjQ2In9TBph0aBx+MxCnNutxt+vx/FYhHJZNJRAy69IDKuRktYvhZzTaRBImvQqUXi8XgQCARMAiuNDyrx9fX1GYuWx01vCfU6aOnSkFmvsqnrrrsOhw8fRqVSwU033WTuv+KKK7B9+3Y888wzbY0P/ihJt8idK92L7FL893//92Zsd79905veZMbPPfec4zFZKjs8PGzGsuMx4Oz6K3Mt7NwQ+ZjdyExe/FuNAafxKI1O28iU+SbtDNDXXnvNjA8dOuTY7hvf+IYZb/TfnKwwYei7r6/PLP6KxSJcLheCwaD5Xn0+n9GJcrvdCAaDJteChgDDMnwNKZ/QLA9Q4vF4EA6HTTjdPl4+l1UpbCdSq9XMfFAoFIz3u1vmg+Ww4Y0PwOl6AxblbH0+nzEEkskk5ubm4PF4EAwGTQjEbuwGwBEvpGUqWxVLiVtZxkVvisfjQTQaRV9fn+O1eGLzh1GpVDA/P28EwyiJXigUsLCwYHoS0KhiTgnf81pyxRVX4IknnoDP51uS1Dc2Noapqam2zz948CA+9alPdfAIFUW5mOE84HK5kM1mkc/nHdd1v99vKiInJiZw6tQpEwJnQUI4HIbP5zPd0+1cPC4UK5UK8vm8w+hwu92m0MHn82FgYAB+vx+Dg4Po7++Hy3W2RUg+nzd9uaRaNcPsyWTSzAepVAq5XA5zc3PGo9IN88FyuCiMDwBL3GBEJuAw4ZSWsMfjMc3pZAWMjLvx5JWWMBNG5eNerxf1et2czNyHz+czt2mcyBghVeny+TwSiQQWFhaMVn83qZTKVej5cN999+HAgQPmdjqddohIKYqirAYyR09Sr59VEJVVifROc2HIxarf7zeVlOVy2SSsyjA7iwoI5xMuZqVoJXNKpNecx8SEUi5KOR9wYUq10l5rl3HRGB/NkK6rUqlkdEKY9CNDIbLZTzAYXNJ0SKrb8SQDnIp4tIwBGOleCuDIUlupYJrNZo1aaSqVMhZwtzWD+8IXvoA///M/R7lcRjKZdHg/pqensWnTprbPp3LfeiGblv3xH//xBe+vnRplu3LMVoqW1HohskNtIpFwPCZLJr/zne+Y8UaWUJdKnbYS6ksvvWTGu3btcjw2MDBgxm9729vM2Dbsp6enzbhdCbSUwbfzdGTJtiy1tdVJ5T7l2O5KLLH38cILL5jxj3/8YzP+n//5H8d29rlzMSO1NVgZw9wLesDptQiHwxgYGDCLSWp+yPyQWCxmDB1e75loKsP+c3NzmJiYAAAjCSGbg3I+oEGTzWYd8wErI3uNi9r44IlA7OQhhkfoZpNhE4ZSaJhIw8Lv95sWxzLLmkYDQzJ8nAlDs7Oz5uSam5szcT5pBdtKet1CuVzGtddeC6/Xi6eeegq33XYbAODo0aM4efIk9u3bt85HqCiK0hrm1RHp1eZ1n2EXLq7oAaGhIvP2WLHIfDZey5m3kUwmHSW49XrdhIPYkZyebzkftPrrNS5q4wNYKpFuQ4uW+RtMTCoWi0YrRHouCL0hNDioOCeTiGgVy3IpxvxoHfcK7373uxGPx3HnnXfiwIEDGBwcRCwWwz333IN9+/Z1daWLoigK0Ho+kMUEAIxHQi4sOR94vV54vV5zn0yml+WzUtOD+8nlcqYqMpvNGin0XpsPlsNFb3y0g1YqrVqGWFKpFKampoxVLLcH4GgUJN1hrH4BlvYAkC6/bgyrnAuW1n7mM5+B2+3Gbbfd5hAZ63Z+97vfmbGtaPrrX//ajN/whje03IfMewmHw47HZEhJeths9768LS82dsMyWd3wm9/8xvHYT37yEzO2VU0vBuzKj1/+8pdmbOcmxWIxM5bVI7JRHeCsmJEhRbsflIy724uZUCjU9BjtkJoMBckKFPuaINVK7TLqX/ziF2Ysz19Zoq0sD5mDRyGxQqGATCbjCNFL/SdZHSk7k0vjwy4Blt5xzgfMH+m1+WA5qPHRBpZkKcsnEAjgwQcfdEhfK4qi9Cq2GilwNsfG7misrAz3uTdZW3oxdqXo96Yoyuqj15XeZDnfW9d5PtQt2JtkMhnE4/H1Pozz5pVXXjHjZ555xvGYDJlIj87999/v2G52dtaMbVe6DMNIF6r9I23ltp+bm3Ns9/TTT5uxDLMAzjDDRosTKxcXOh/0JsuZD7rO+BgfH8epU6fQaDSwfft2nDp1yhGX7WWoXbGR3lOj0UAmk3F0hVWUbsU2CqWx961vfcuM3/GOdzi2u/XWW81Ydsm1J8dkMmnGdp5OK0VSu9xaVuDJ47PLaVmeCQDf+973HI+dPHkSG4GNPB8AG29OWMl80HXGh9vtxtatW02iVSwW2xBfimSjvade9ngoitK9XAzzAbCx3tdy54Ouy/lQFEVRFGVj03WeD0VZb7761a86bst8C5mH8Z73vMex3Yc+9CEztt2OMh9EqlZKFz7gDAtI1VXbNf/Nb37TjGVHXmVlyLDG5OSkGT/11FOO7WQ4Zfv27WZsq/IGg0EzthVJZRhGfs/2dyvPlVOnTpnxmTNnHNvZ546i9BJd6/nw+/24//7711Vye7XZiO9JURSl02zUa+dGfV/LoWs9H36/H3/zN3+z3oexqmzE96QoitJpNuq1c6O+r+XQtcaHoqwXdnnqt7/9bTNmzx4AuOqqqxzb/eAHPzDjLVu2OB6T7njJyMiI4/axY8fM+IknnjBjWQoMLFW7VBRF6SXU+FAURYEz98LOo9G8GkVZXbo250NRFEVRlI2Jq6H6tUoXkk6nVT+kx0ilUquqVZBKpRxN3JTeIJlM6m9XOSdd6fl48MEHsWPHDgQCAezdu9fRobEXOHjwIPbs2YNoNIrR0VG8613vwtGjRx3bFItF7N+/H0NDQ4hEIrjtttswPT29TkesKN2HSmv3Jqv9vel8sDHpOs/HY489hve973344he/iL179+KBBx7A448/jqNHj2J0dHS9D29ZvOMd78B73/te7NmzB9VqFX/5l3+JF154AS+++KLp8XHXXXfhe9/7Hh5++GHE43HcfffdcLvdS1pjX6yo56P3WG3PR71ex8TExIaV1j4fulmOW0prSyn5C0Hngw08HzS6jOuuu66xf/9+c7tWqzXGx8cbBw8eXMejujBmZmYaABqHDh1qNBqNRjKZbHi93sbjjz9utnnppZcaABrPPPPMeh1mV5FKpRoA9K+H/lKpVEfPhU7tv5e42D4LnQ827nzQVWGXcrmMw4cP46abbjL3ud1u3HTTTUs6jfYSqVQKADA4OAgAOHz4MCqViuN9XnHFFdi+fXtPv09FUZTVQueDjT0fdJXxMTc3h1qthrGxMcf9Y2NjmJqaWqejujDq9To++tGP4i1veQte//rXAwCmpqbg8/mWJNP18vtUFEVZTXQ+6N33uRxU56PD7N+/Hy+88AJ++tOfrvehKEpPcjFLUNvoZ9Hb6HywSFd5PoaHh9HX17cky3d6ehqbNm1ap6M6f+6++2488cQT+OEPf4itW7ea+zdt2oRyuexoVgX07vtUlE5CCWqdcC+uz0Lng958n8ulq4wPn8+Ha6+91tFRsl6v46mnnsK+ffvW8chWRqPRwN13341vfvObePrpp7Fz507H49deey28Xq/jfR49ehQnT57sqfepKIrSKXQ+2ODzwXpnvNo8+uijDb/f33j44YcbL774YuNDH/pQo7+/vzE1NbXeh7Zs7rrrrkY8Hm/86Ec/akxOTpq/fD5vtvnwhz/c2L59e+Ppp59u/PKXv2zs27evsW/fvnU86u5Cq1167+9iqcBQ1g6dDzYuXWd8NBqNxj//8z83tm/f3vD5fI3rrruu8eyzz673Ia2IVhfnhx56yGxTKBQaH/nIRxoDAwONUCjUuOWWWxqTk5Prd9BdhhofvfenxofSCXQ+2Jh0nciYogAqMtaLrLbImKIoG5euyvlQFEWR9Lq09vmgctzKxYAaH0pXog653mO1v7PHHnsMBw4cwP33349f/epXuPrqq3HzzTdjZmZmVV+n2zh06BD279+PZ599Ft///vdRqVTw9re/Hblczmxz77334rvf/S4ef/xxHDp0CBMTE7j11lvX8agVZWVo2EXpSk6fPo1t27at92EoK+DUqVOOEsILZe/evdizZw8+97nPAThb6bBt2zbcc889+MQnPrFqr9PtzM7OYnR0FIcOHcINN9yAVCqFkZERPPLII/jTP/1TAMCRI0dw5ZVX4plnnsH111+/zkesKOdGRcaUrmR8fBwvvvgirrrqqq5sorUedGtTsYZoKLZaUFr7vvvuM/dtBGnt82GlctxqfCi9gBofSlfidruxZcsWAEAsFuuqyXa96cbPY7WTg9tJax85cmRVX6ubUTluZaOixoeiKEqXonLcykZFE04VRek6Npq09vmgctzKRkaND6Vr0SZaTi6mz2OjSGufDw2V41YuArTaRVGUruSxxx7DHXfcgS996Uu47rrr8MADD+DrX/86jhw5siQXZCPxkY98BI888gi+/e1v4/LLLzf3x+NxBINBAMBdd92F//qv/8LDDz+MWCyGe+65BwDwv//7v+tyzIqyUtT4UBSla/nc5z6Hf/zHf8TU1BSuueYafPazn8XevXvX+7A6isvlanr/Qw89hPe///0AzoqMfexjH8N//ud/olQq4eabb8bnP/95DbsoPYMaH4qiKIqirCma86EoiqIoypqixoeiKIqiKGuKGh9K16JNxbSpmKIoGxM1PpSuRJuKaVMxRVE2LppwqnQl2lTsLNpUTFGUjYh6PpSug03FZOMsbSq2vKZiiqIovYAaH0rX0a6p2MXUOEubiimKslHRxnKK0qVoUzFFUTYq6vlQug5tKqZNxRRF2dio8aF0HdpUTJuKKYqysdGwi9KVHDhwAHfccQfe9KY3maZiuVwOH/jAB9b70DrK/v37TVOxaDRq8jjYVCwej+POO+/EgQMHMDg4aJqK7du3TytdFEXpGbTUVulatKnYItpUTFGUjYQaH4qiKIqirCma86EoiqIoypqixoeiKIqiKGuKGh+KoiiKoqwpanwoiqIoirKmqPGhKIqiKMqaosaHoiiKoihrihofiqIoiqKsKWp8KIqiKIqypqjxoSiKoijKmqLGh6IoiqIoa4oaH4qiKIqirClqfCiKoiiKsqb8fzCf3r6yKSgXAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -367,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "6c1de5ad", "metadata": {}, "outputs": [], @@ -446,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "bd10b595", "metadata": { "lines_to_next_cell": 0 @@ -456,21 +455,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 00: 100%|██████████| 25/25 [00:35<00:00, 1.42s/it, loss=0.8352]\n", - "Epoch 01: 100%|██████████| 25/25 [00:36<00:00, 1.47s/it, loss=0.5043]\n", - "Epoch 02: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.2762]\n", - "Epoch 03: 100%|██████████| 25/25 [00:37<00:00, 1.48s/it, loss=0.1481]\n", - "Epoch 04: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0797]\n", - "Epoch 05: 100%|██████████| 25/25 [00:37<00:00, 1.50s/it, loss=0.0483]\n", - "Epoch 06: 100%|██████████| 25/25 [00:37<00:00, 1.51s/it, loss=0.0288]\n", - "Epoch 07: 100%|██████████| 25/25 [00:37<00:00, 1.51s/it, loss=0.0218]\n", - "100%|██████████| 1000/1000 [00:35<00:00, 28.29it/s]\n", + "Epoch 00: 100%|██████████| 25/25 [00:35<00:00, 1.41s/it, loss=0.8318]\n", + "Epoch 01: 100%|██████████| 25/25 [00:35<00:00, 1.43s/it, loss=0.4999]\n", + "Epoch 02: 100%|██████████| 25/25 [00:37<00:00, 1.48s/it, loss=0.2719]\n", + "Epoch 03: 100%|██████████| 25/25 [00:36<00:00, 1.47s/it, loss=0.1401]\n", + "Epoch 04: 100%|██████████| 25/25 [00:36<00:00, 1.47s/it, loss=0.0709]\n", + "Epoch 05: 100%|██████████| 25/25 [00:37<00:00, 1.48s/it, loss=0.0394]\n", + "Epoch 06: 100%|██████████| 25/25 [00:36<00:00, 1.48s/it, loss=0.0207]\n", + "Epoch 07: 100%|██████████| 25/25 [00:37<00:00, 1.49s/it, loss=0.0145]\n", "...\n" ] }, { "data": { - "image/png": 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" ] @@ -482,7 +480,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "train completed, total time: 1691.9202527999878.\n" + "train completed, total time: 1676.1360158920288.\n" ] } ], @@ -584,7 +582,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -624,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "092eb6a0", "metadata": { "lines_to_next_cell": 2 @@ -634,7 +632,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1000/1000 [00:31<00:00, 31.56it/s]\n" + " 0%| | 0/1000 [00:00" ] @@ -685,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "e3e43b95", "metadata": {}, "outputs": [ @@ -693,7 +698,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 250/250 [00:08<00:00, 29.97it/s]\n" + "100%|██████████| 250/250 [00:07<00:00, 32.79it/s]\n" ] } ], @@ -717,13 +722,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "89f93ab8", "metadata": {}, "outputs": [ { "data": { - "image/png": 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GqVKl3Fwt+THTz4OZ2eeff+7m0citeh89/PDDsueCCy6QNbVIKlpY8s0338iaeo9Fr636eYpGEd955x1Ze/fdd928ZMmSsmfJkiWydujQITffv3+/7Nm+fbubn3baabJHLbeJ+qKRyIzgN3AAABLEAQ4AQII4wAEASBAHOAAACeIABwAgQRm+hf7SSy/JmrolrJYymGVt+chzzz0na2rxR+SFF16QNbV0IFooUblyZTd/7LHHZE+0LKFVq1ZuHt1y7devn6yp5QJbt26VPWoqILpp/v7778va9OnT3VzddjczO+ecc2Rt9uzZsqZ89tlnbn7jjTfKnmjRiXL8+HFZi26Uq9uxy5cvlz0VK1Z08+j2cDRRoRYUqYkTM33L28ysXLlybh69turG+wMPPCB7cufOLWtqCdCzzz4re6LpCPX4ooVMffr0cXO1GMXMbNOmTbJ20UUXuXn0edi9e3dZe/PNN908enxqAUr58uVlT/T+V0s8zjzzTNnz448/ylrXrl3dfOzYsbJHLYX63e9+J3ui57V3796y9q/gN3AAABLEAQ4AQII4wAEASBAHOAAACeIABwAgQRzgAAAkKNtv0faL/yb6T9zVMoBomYkaMShQoIDsUcsDzPR/dB99vWg8o2HDhm4+a9Ys2aMWaESjYn/4wx9kberUqW4ejTTt2rVL1tQCErU8w8zs1ltvdfP33ntP9kTLEtTiAzW2YaZH+szMmjZt6uarVq2SPQULFnTzX3/9VfaMHDlS1tq2bevm0YKFq6++WtbGjRvn5oUKFZI9mzdvdvPFixfLnmXLlsnaxx9/LGuKGqM0M7vvvvvcXP1dzcx69uzp5mrEzczs1VdflbXatWu7efPmzWVPtAymbNmybh4tVPn666/dXL2Pox4zPdZXr1492aM+V8zMHn30UTePRm7VQqalS5fKnujvmyNHDjfPly+f7GnQoIGsqSVOnTt3lj3qfRm9X6OxUTWyGS26WrBggaz9f1/3n/4JAADwPw4HOAAACeIABwAgQRzgAAAkiAMcAIAEcYADAJCgDG8jW79+vaypjUJ58uSRPWqjVrTBpkmTJrKmthBFm7GuueYaWVPbbdQoipkeOWnRooXsiRw4cMDNb7nlFtnz0EMPyZoaBTzllFNkz4wZM9z8ySeflD2XXHKJrCk1atSQNbVpK3L66afLmnpeDx48KHuuvfZaWStevLibV6hQQfZEz1+XLl3cvFatWrJn4sSJbh6NcqqtT2Z6JCz6HIhq0XtWURu1otGuaGOVGqmLPgfUaKiZ/txT2+7MzHLlyuXm0d+pb9++sqa2pa1YsUL2RBvq1AbJhQsXyh71Xok+K6OtjmoMT21yMzO7/PLLZe3IkSNuHo1pqeehdOnSsifa0HjHHXe4eaVKlWRPRvAbOAAACeIABwAgQRzgAAAkiAMcAIAEcYADAJCgDC8zUbfGI4899pisXXHFFW6+ZcsW2TN37lxZUzcN7777btlz//33y5q6AVuqVCnZo/5j+kaNGsmel156SdbUkhh1i9os/g/1u3bt6ubTpk2TPervu337dtnz1ltvydqzzz7r5nv37pU9EXXTddiwYbJH3QSuX79+lh7D/v373TxaCHLXXXfJmrqRr5bRmJmpH+N3331X9kTLW8qVK+fmo0aNkj1qCUX0vdSyHDP9WXDDDTfIntGjR8tazZo13Txa5pM/f35ZmzRpkpurJSdmeuJDLfkxi2+Uq/fYoEGDZM+bb74pa1WqVHFz9flqZjZv3jw3v/POO2WPmpow0ze9x4wZI3uin/eLL77YzaNb4+rnaeXKlbInmpK67rrr3FxNnJjFkyr/wG/gAAAkiAMcAIAEcYADAJAgDnAAABLEAQ4AQII4wAEASNAJGSNT42Kvvfaa7Jk+fbqbRyMY6j/uN9OjDL1795Y99957r6ypcYro6xUoUMDNo3GAaHSpYMGCbn7qqafKnmbNmsmaWrrxzTffyJ6WLVu6ebQAIlpUoF7fatWqyZ62bdvK2tatW908GnNTz180IhWNiKhRrYYNG8qev/71r7KmRixvv/122aMeX7Tk4aOPPpI1tahDjcyZxctRolEo5fHHH3fzHTt2yJ5oIc2DDz7o5mvWrJE9ZcqUkTW1rGnKlCmyZ8+ePW7es2dP2TN79mxZU58t99xzj+yZOnWqrLVu3drNzzvvPNkzefJkNy9atKjsiRZdqZHSqlWryp6cOfVerueee87No89KVdu0aZPsUUtYzPRCmuhnJiNHM7+BAwCQIA5wAAASxAEOAECCOMABAEgQBzgAAAniAAcAIEEZHiOLxinUCFA0RqZGrsaNGyd7Dh06JGvq8anRKbN4PEmNpc2fP1/2qC1J0QiN2jxlZvb888+7ebTlZ+fOnbL29ttvu/mVV14pe9RGKPX8mJllz67/Xai2m6lNUWbx+GDu3LndXI0pmpldeumlmcr/meHDh7t5tGmocOHCstavXz83j35U1ZYrNeJjpreymZl9+umnbt6uXTvZEz3naiNUtFlPbUS77LLLZM/YsWNlTW25atq0qeyJNrap7YjRc6T+vvXq1ZM90Ya1M888082jz8poQ516/jZv3ix71HMUjdx26NBB1tRmNjUOZhaP4c2aNcvNK1asKHtWrVrl5seOHZM9u3fvljX1WX7BBRfInvHjx8vaP/AbOAAACeIABwAgQRzgAAAkiAMcAIAEcYADAJAg/T/A/x+iG3arV69282hhw7nnnuvmffr0yehD+t/ccccdbv7CCy9k6esdPnzYzRcsWJDpr/Xjjz/K2saNG2WtWLFibv7TTz/Jnuh2s1pUkD9/ftlTt25dNy9SpIjsmTNnjqx99913bq5uipqZ3X///bKmboVGSzyi76WoG/xmZi+++KKbR7fxoxvqatlKyZIlZU+OHDncvH379rInWuZz2223uXm0NCK68f7tt9+6ebSYRN3cHTJkiOxRC0vM9Ovx1FNPyZ5oice6devcPLoJr5ZaRAtLGjVqJGtqQkN9JpvFz5GapImmb9RSnDfeeEP2qFve0feKzobodnj58uXdfMaMGbJHLUnq3Lmz7Ik+R6dNm5apPKP4DRwAgARxgAMAkCAOcAAAEsQBDgBAgjjAAQBIEAc4AAAJyvAY2ZEjR2StaNGibh4tKqhSpYqbR6MHahmBmR6X+eabb2TPwYMHZe3o0aNuHo3G/fzzz24eLXV55JFHZE2Nsj322GOyRy21MNMjENEClJtvvtnNo8UfXbt2lbU333zTzT/55BPZky9fPllr3Lixm6sRHzM9Elm7dm3Zo14LM/28durUSfZECyXUWM5NN90ke7Zt2+bma9askT3nnHOOrKnRuGisqn79+rK2aNEiWVPUaFA0eqbGHs30KJv6WTfTy3LM9BKgvHnzyh71ukdjmSVKlJA1tWzo73//u+xZuHChrLVo0cLNL7zwQtmjRtbUohUzs6VLl8qaWmYyYcIE2RO9l19++WU3v+KKK2SPWnT11VdfyZ4///nPsqZGQKMzMiP4DRwAgARxgAMAkCAOcAAAEsQBDgBAgjjAAQBIULbffvvtt4z8wei29J/+9Cc3v/3222XP+eef7+bR7dzoVmPZsmXdfM+ePbJH3XY0M3v11VfdvF27drJH3ULs2LGj7ClYsKCsjR492s1r1qwpe9QyAjN9k1rdDDcza9WqlZtH/3F/tMwhW7Zsbh49r2oBipnZaaed5ubR+6h79+5uHt20VYtgzMyefPJJN49+tC666CJZ+/TTT9187dq1ske9X1esWCF7IgUKFHDz/fv3Z+nrZYX62YimOn744QdZK1SokJt/+eWXsqdUqVKZrqnvY6ZvvKsJlqjHzKxMmTJuHk3sRMt8rr32WjdXn69mZq+88oqbR5Mb0YIiVWvQoIHsOf3002Vt6tSpbh6dJ+qzvE6dOrJnzJgxsqY+c9S0h1n8+fEP/AYOAECCOMABAEgQBzgAAAniAAcAIEEc4AAAJIgDHACABGV4jEyN/0TuvfdeWdu3b5+bR6MH0ajAQw895ObReMY777wja2qcIleuXLKnd+/ebt6oUaMsPQa1FKRy5cqyp1ixYrKmlixEi0SaN2/u5jlz6j040ahRjhw53DxauhG999RYR/R3Uq/t9OnTZU+0dKBv375uPmrUKNmjnlcz/VxUr15d9qjxqf79+8ueiPpYUAtBzMwaNmwoa1GfMnHiRDePFiutWrVK1tTnhxqZMzOrUKGCrKn3cjTCqN57mzdvlj3RWFrx4sXdPFp8ox63mVnTpk3d/Ndff5U9NWrUcPPos/fDDz+UtWeffdbNK1asKHvuvPNOWVNnw/vvvy971OhqtFhGjX+amc2bN8/No8/raMnUP/AbOAAACeIABwAgQRzgAAAkiAMcAIAEcYADAJAgDnAAABJ0QsbIhg0b5uZqS42Z2ezZs908ejh/+MMfZE1tklLbeszi0Qi18WvlypWy5+2333ZzNb5lFo+I3HbbbW7+1VdfyR61PczMbMuWLW4+YcIE2aPG+ooWLSp7og1OGzdudPNly5bJnui9p8Z8evbsKXteeOGFTD8G9dqa6W1W0ddTr0VUe+ONN2SPeh7q1q0re8aPHy9rSjQSqV5bs4yNxPyfKlWq5OaPPvqo7KlataqstWzZ0s3Xr18ve6LXXb3P1YismdmGDRvcvEWLFrLnggsukLVDhw65+V//+lfZE33GFilSxM337t0rez766CM3j8Yoo42F6uv95S9/kT2rV6+WtQEDBri52mRoZta4cWM3HzFihOyJ3v/qdYqwjQwAgJMUBzgAAAniAAcAIEEc4AAAJIgDHACABGX4Fnq7du1kTX2JqVOnZvoBffHFF7L22WefyZq6EXrPPffIno8//ljW1C3r6D+zX7p0qZtfc801skfduDQzy5Mnj5v/8ssvsmfbtm2ylpXXqWzZsm7erVs32XPqqafK2pw5c9x88eLFsudPf/qTrA0ZMsTNo1uz6ka0up1uZjZ//nxZU0sbzj77bNkT3fxv0KCBm/fr10/2qNuxx48flz1qysHMbMyYMW4evbbR94qmNzIrWlSjplEi0TRKtPhDvYbNmjWTPeqWd3RLP7pRrt4T48aNkz3Rz1P+/PndvHbt2rKnSZMmbr5w4ULZE90a79Gjh5tHExXRAhn1sztw4EDZoyar3nvvPdkTLaRRUyLR51S0DOYf+A0cAIAEcYADAJAgDnAAABLEAQ4AQII4wAEASBAHOAAACcqZ0T8YXfvv1KmTm0djBAUKFHDz6D/uj8a+pkyZ4ubRiFT9+vVlTY2LRf8Jf+vWrd08GhWI/sP/adOmuXm0qOOnn36StZw5/Zc7Gj3r2LFjpr6WmdngwYNlrXv37m5er1492RN9rxo1arj5qlWrZI9aVPD999/Lntdee03Wnn76aTePRi/vv/9+WVPjKNEo21133eXmkydPlj0vvviirCnRWGb0up9zzjluPn36dNmjfj6j8bfHHntM1r799ls3HzlypOxp27atrKnlFeqzyMysdOnSbh69Trt375Y1NXK1YsUK2aNGOc3047vyyitlz5IlS9y8fPnysuebb76RteLFi7t59F45duyYrKmxzIkTJ8qeOnXquHk0aqpeC7P4c/5fwW/gAAAkiAMcAIAEcYADAJAgDnAAABLEAQ4AQII4wAEASFCGx8jatGkja/PmzXPzbNmyyR41gqHGy8zi8Sk1ehNtqRk/frysdenSxc0rVqwoe5Roe9JZZ50la2os4dprr5U9JUuWlDU11qE2EJnpxx5thlMjGGZ6lCcaFZs9e7aszZw5083PPfdc2aPGG9euXSt7ovf/3Llz3VyNV5qZrV+/XtYefvhhN1fb7szMihUr5uaXXnqp7Ik2wKn3+eOPPy57oi156nFEo0Fq3O/111+XPVWrVpU1Ncqjtv6Zmd1+++2ypn4+1WebmR6NK1OmjOxR46RmZu+8846bL1q0SPZE44Nr1qxx8+uuu0725MuXz83bt28ve6INjernRv1dzcwOHDgga0899ZSbR+/X6PEp0bjwvwu/gQMAkCAOcAAAEsQBDgBAgjjAAQBIEAc4AAAJyvbbb7/9lqE/GNwoV6LFJOoG4BlnnCF7opvPDz30kJsPGTJE9jz44IOytnPnTjdX/9m/mdn555/v5tHtyZtuuknWrr/+ejevUqWK7OnWrZusvfrqq24eLVJQix6+/PJL2fPhhx/Kmno91E1Ws3iJjbqxqm5ym+nXvVy5crLn6quvlrX77rvPzaMFQNHrfvToUTePbpS///77spYV1apVc/Pt27fLnugm9dlnn+3m0fRBwYIF3XzYsGGyZ8+ePbKm3mPR444mE9SSjOg5UtMy0eKPaJph3Lhxbv7jjz/KnkivXr3cXE0amennr27durInmoDYtGmTm6vJIDOzWbNmydqMGTPcXC2fMjNr1aqVm0dTGBH1mR29vzJyNPMbOAAACeIABwAgQRzgAAAkiAMcAIAEcYADAJAgDnAAABKU4TGysmXLytqWLVtO2AOKZM+u/72hruM3b95c9vTv31/W2rZt6+bR8oUaNWq4+dChQ2WPGkEy02MYw4cPlz0LFiyQtYYNG7p55cqVZU/Lli3dPBoricbm1JKFM888U/ZEI1dTp05186+//lr29O7d281fe+012RMtvFBjXxMnTpQ90fIFtcRDjTaaxcsrlOLFi8ua+l7RopolS5Zk+jHUq1dP1tRImBpxMzPbsWOHrKmxr+jnKRqFVWOCF110kewZPHiwm5coUUL2RAta1KKf6Gc6K6N20eiqet2jx12kSBFZmzJlSqYfQ/S679u3z807dOgge6JFVyeSGtszM3v77bf/aT+/gQMAkCAOcAAAEsQBDgBAgjjAAQBIEAc4AAAJyvAt9OhW3oUXXujmx48fz9qjygJ1i/mKK66QPdGt2f3792f66xUqVMjN27dvL3umTZsma+oGeLTUJbp1/8EHH7h5nz59ZI+6bb5hwwbZM3bsWFlbvny5m992222y584775Q1dfv0lFNOkT01a9Z08/nz58ueJk2ayNrMmTNlTYlutR8+fDjTX++SSy5xc3VL38xs7969mf4+0UTFLbfckumvF1GfK7Vq1ZI9zzzzTKa/z8CBA7P09dTP2oQJE2SPWpgTLR+JFhS1aNHCze+44w7ZU6xYMVnbtWuXrCn58+d382jSIisqVqwoa0eOHJG1rVu3uvmNN94oe9R5p5bRmJnlypVL1g4dOuTm0efUzz//LGv/wG/gAAAkiAMcAIAEcYADAJAgDnAAABLEAQ4AQII4wAEASFCGx8iyZcsma+oqfMeOHWXPqFGjMvJt/ze1a9eWNTUa8c0338ie6O+UlRGI8847z82vv/562XPw4EFZU+Moo0ePlj2TJk2StVdeecXN33vvPdnTr18/N69evbrsGTRokKzt3r3bzVu1aiV71qxZI2tqaUO03GP9+vVuHo3TRaNiatwjb968sid6L3/11VduHo2eRWN9SunSpWVNLVQpVaqU7Il+ptXPUzTSpH4+o6Uuzz//vKxF44iKGvsy06Nf0RIPNe737LPPZu6B/ZdOnTq5+aeffip7otdw27Ztbq4et5leHBS9TpEePXq4efS5F8ngEfcvi34+1c+aGj02M/vuu+/+6ffkN3AAABLEAQ4AQII4wAEASBAHOAAACeIABwAgQRzgAAAkKMNjZA0bNpS1hQsXnqjHE6pWrZqsrV692s179+4te954441/+TFlhBrFMovHvtQGrDJlysie7du3y5oasSlYsKDsUY+9f//+sqdt27aypsaJorfhlClTZE2pV6+erKnRoGjjXlbcddddsjZ48GBZU6Ml0cYltcEsKyNDZmbnnHOOm69du1b2RJua/lOysmkr2iJ19OjRTD+GAQMGyNojjzzi5mp0yiwez7v88svdXG39MzOrUKGCrI0bN07WlOzZ/d8DT/Q2yujzP3pftmnTxs2jDYPqeciZM6fs+f7772WtRIkSbh5tjxwzZoys/QO/gQMAkCAOcAAAEsQBDgBAgjjAAQBIEAc4AAAJyvAt9J49e8ratGnT3Fz9Z/+Rq666KtPfx0z/Z/HRY4hqNWrUcPPodqfqiW59RjcX1eOLbmN27txZ1mrWrOnm0aKOrNzUj5ZkbNmyxc0LFCgge7KyWCZStGhRNy9cuLDsOfXUU2UtR44cbj5v3jzZE00S7N271827d+8ue0aOHOnm0RKKaJmPuoW7b98+2RO55ppr3PzNN9/M0tfLil69ern5iBEjZI+6wWymbxCr5R5mZkuWLHHz6L0XTR9UqVLFzVetWiV7duzYIWtK9N579913M/311AIsM7M9e/Zk+uup5TtmenlR9DOtJnOiiZ3p06fLWlZk5GjmN3AAABLEAQ4AQII4wAEASBAHOAAACeIABwAgQRzgAAAkKMNjZNHISaVKldw8uqb/66+/unk02vXTTz/JWt68ed28du3asica87n//vvdPHfu3LLn1VdfdfOKFSvKnrlz58qaGgn76KOPZM8FF1wgaxs3bnTzZcuWyR417pGVUQ8zvSRjxYoVsicae8nK42vVqpWbf/XVV7LnjDPOkLXo+VP69u0ra5999pmbb9iwIdPfJ1qWMHv27Ex/PQD/GYyRAQBwkuIABwAgQRzgAAAkiAMcAIAEcYADAJAgDnAAABKU80R8EbU9KStjL1l17NgxN2/QoIHsicbIpk6d6ubRGNnWrVvdPBp/izaLLVq0SNaUCRMmyNrvf/97N4/GoAYNGuTmN910U+Ye2H850Rt71LjYSy+9JHvUCFf0Whw6dChzD8ziTWAzZsyQtaz83KgNcPny5cv01wKQBn4DBwAgQRzgAAAkiAMcAIAEcYADAJAgDnAAABJ0Qm6hR7esld/97nduHt3Ojahb6FWrVs3S17vooovc/NFHH83016pcubKsVa9eXdbUEpShQ4fKnpIlS8pa48aN3Xz48OGyR9U6dOgge7Zs2SJrCxcudPNoWkD1mOnXKVqOomzbtk3W9u3bJ2vnnXeem3/44YeZfgxmenFKrly5ZI+aWFCTEQDSx2/gAAAkiAMcAIAEcYADAJAgDnAAABLEAQ4AQII4wAEASFC233777beM/MF+/frJ2oABA9z81ltvlT1Dhgxx83POOUf2rFq1StbU0oZojCZ//vyytnv3bje//PLLZc8777wja0o0jlW4cGE3//nnn2VP9HKqr7dp0ybZc+TIETc///zzZc+oUaNkbf369W4eLYlRjyFyzz33yNro0aPdvFChQrJn+fLlsnbFFVe4eTRGdvjwYVlr2rSpm0fjiO+++66sKa1atZI19bORlRFBAJmXkaOZ38ABAEgQBzgAAAniAAcAIEEc4AAAJIgDHACABGX4Fnq2bNlkTd2kXrlypexRt1kPHDgge9SSBzOzZcuWuXmJEiVkz4033ihrAwcOdHO1hMXMLEeOHG6+du1a2RPdRn7ooYcy9X3MzK6//npZy57d//da3759Zc8tt9zi5mvWrJE90Y1ttQyme/fusid6/mrVquXmn332mexRN97PPfdc2TNixAhZUy6++GJZ+/jjj2WtXr16bn7o0CHZo2rRxMLNN98sa19++aWbR1MTWVn0A8DHLXQAAE5SHOAAACSIAxwAgARxgAMAkCAOcAAAEsQBDgBAgnKeiC+SJ08eN1cLRsz0uFjZsmVlT7TwYujQoW4+btw42TNz5kxZU6M8vXv3lj1qAcTEiRNlzx//+EdZ+9vf/ubmzZo1kz3Rcz527Fg379q1q+w588wzZU05duyYrD3xxBNuXq1aNdkzZcoUWVOjdo0bN5Y9arxx9uzZsqdPnz6y9t1337n59OnTZU+lSpVkbfHixW7evn172bN69Wo3j94rzzzzjKwVL17czb/99lvZA+A/i9/AAQBIEAc4AAAJ4gAHACBBHOAAACSIAxwAgARxgAMAkKAMj5GNHDlS1nr27Jnpb6y2fakxIzOzzZs3y5raPlWyZEnZs337dllTY27RhpgZM2a4ebQhTI0gRX3r1q2TPd26dZO1cuXKuXk0GqRG4E4//XTZU7duXVkrXLiwm0fPa7t27WRt2rRpbh69V4YMGeLmOXPqH4fSpUvLmhpHrFmzZpa+XqFChdx80qRJskeJNrlVrlxZ1goUKODmP/30U6YfA4B/D34DBwAgQRzgAAAkiAMcAIAEcYADAJAgDnAAABKU7bfo+u9//4PZsmX6i1eoUEHWcuTIkancLL4lfPHFF7v53//+d9nz2muvyZq6mX3DDTfInvLly7v54MGDZc+ePXtkrVWrVm7+448/yp6zzz5b1qZOnermPXr0kD3z5s1z8/vvv1/2dOrUSdbU7etVq1bJniJFisiaehw//PCD7Fm+fLmbL1q0SPasWbNG1sqUKePmhw8flj1z5syRtVKlSrm5etxmZgsXLnRz9XNhZrZ161ZZa9iwoZur95CZXqgCIPMycjTzGzgAAAniAAcAIEEc4AAAJIgDHACABHGAAwCQIA5wAAASlOFlJpdddpmsHTt2zM0//PBD2dO6dWs3//LLL2VP586dZW3Dhg1uvmTJEtkTfa/HH39c1hT1HEVLLXr16iVrp5xyipsfOnRI9lSpUkXWhg0b5uZ9+/aVPbVq1XLzCy64QPY88MADsrZixQo3j8YHo7/vtm3b3Hzy5MmyJ3/+/G5etmzZTPeYmXXo0MHN58+fL3vGjBkja1988YWbt2zZUvY0b97czdVSEjOzuXPnZqkG4H8GfgMHACBBHOAAACSIAxwAgARxgAMAkCAOcAAAEsQBDgBAgk7INjI1AvTrr79m7VEJjRs3lrVHHnnEzcePHy97otG4n3/+2c0XL14se9Tf98Ybb5Q90baoNm3auHnevHllT79+/WQtT548bn7HHXfInm7durn53r17Zc/SpUtl7brrrnPzaHtYtAGrbt26bv7888/LnosuusjNDx48KHtKliwpa7NmzXLzffv2yZ62bdvKmhpvrF69uux5+eWX3Tx6XiPVqlVzc7UpzcxsxowZWfpeAP5vbCMDAOAkxQEOAECCOMABAEgQBzgAAAniAAcAIEEn5BZ6rly53Pzo0aOZfkAlSpSQtS5dusjaK6+84ubRreyPPvpI1vr06ePm5cuXlz3q9rW6pWxmNnz4cFkrUqSIm3/++eeyRy33MDMrXLiwm2fPrv8dp3qOHz8ue44cOSJrBw4ccPN69erJnmg5ihLd7l+2bJmbRz8K0dIZ9bOhltGYmQ0cOFDWunbt6ubRa9ujRw9ZU4oWLSpr6tb98uXLM/19AGQet9ABADhJcYADAJAgDnAAABLEAQ4AQII4wAEASBAHOAAACTohY2RZkT9/fjd/+OGHZU+HDh1k7a9//aubn3nmmbInX758srZ161Y379Spk+xRyzDWr18ve7Zs2SJrNWrUcPNoSYwa6TMzO/fcc91cLc8wMytbtqybf/fdd7KnZs2asrZixQo3j95faqGKmdnIkSPd/JJLLpE9EydOdPOFCxfKnkqVKsmaGjG75pprZE/Hjh1lbfbs2W4eLfOZO3eum0evxb333itrWRkBBXDiMEYGAMBJigMcAIAEcYADAJAgDnAAABLEAQ4AQIJOyC10deP3ww8/lD0tW7Z0c7VExMzs8OHDsrZq1So3b9asmewpV66crK1Zs8bNS5UqJXt27drl5tFN87PPPlvWZs6c6eZq2YWZ2Z49e2Tt2LFjbj5+/HjZc/PNN7v5U089JXui98qpp54qa0r0nCsFChSQtVGjRrl5ixYtZM8ZZ5wha+vWrXPzaPlOtPBlyZIlbq5up5uZValSxc2vvfZa2ZOVm+bRrXYWnQAnDrfQAQA4SXGAAwCQIA5wAAASxAEOAECCOMABAEgQBzgAAAnKmeE/mFP/0ZIlS7p5tCzkggsucPONGzfKHrXcw8ysTZs2bq4WeJiZjRgxQtZ+//vfu/no0aNljxqjiRaqNGzYUNYWLVrk5mrBiJnZvn37ZO3QoUNunjdvXtnz+uuvu7l6/czMFi9eLGtqIU2xYsVkT+HChWXthx9+cPMNGzbInoEDB7p50aJFZU/0+IoXL+7ml112mew55ZRTZE2Ni9WvX1/2dO7c2c2vuuoq2ROpU6eOm6sRNwD/efwGDgBAgjjAAQBIEAc4AAAJ4gAHACBBHOAAACSIAxwAgARleBtZNPayd+9eN4+2SFWqVMnN//jHP8qeaFvU/Pnz3fzAgQOy55577pG1zz77zM3Xr18ve9QIXLQZKxpdKl26tJtXrVpV9kQbq8qUKePm0ciV2rB28OBB2RONGqnxpNq1a8ueadOmZfrrqZE5M/08RFvUGjduLGvLli1z86+++kr23HbbbbI2depUN482111++eVunidPHtmzYMECWVPvieuvv172vPzyy7IGIHPYRgYAwEmKAxwAgARxgAMAkCAOcAAAEsQBDgBAgjK8zEQt9zAze+2119w8ujU7aNAgN1c3r83MChUqJGvqpnKFChVkT3Rb+ssvv3Tz/fv3y57evXu7ebTco169erL2+OOPu7m6cWxmVrlyZVm777773LxBgwayRz1/Xbp0kT3RwpfWrVu7+c6dO2VP9uz635lbt25187Vr18oeNRUQ3YSPlvmo6YPotYhu/qv3slpYYmZ2++23u3n03L333nuypowZMybTPQD+PfgNHACABHGAAwCQIA5wAAASxAEOAECCOMABAEgQBzgAAAnK8BjZkCFDMv3F69evL2vHjx9383nz5smeuXPnypoa8+nRo4fsufvuu2VNLVWJFrTMmTPHzRs1aiR7XnjhBVlTSzcmTJgge9Tzamb27LPPunm0LET9fdWyF7N4Kchpp53m5mohjplZ8+bNZU2Nfg0bNkz25MqVy82jhT3RYhI1qvXxxx/LnurVq8uaGh8sW7as7FGLD959913Z07JlS1krV66cm48aNUr2APjP4jdwAAASxAEOAECCOMABAEgQBzgAAAniAAcAIEEc4AAAJCjDY2TRKFTu3LndvGrVqrJHjcq89NJLsmfRokWytn79ejfPkSOH7KlYsaKsFSlSxM2jMRo1sjZz5kzZs2vXLlnbsWOHm0cbzMqXLy9r69atc/NoY9XUqVPdvEOHDrKnadOmsjZ//nw3//7772XPq6++KmsjR4508yNHjsge9X6Nxumi7Wtqg16vXr1kT7QJb8uWLW5+9dVXyx618a5OnTqyp0WLFrKmqG1yZnqDH4B/D34DBwAgQRzgAAAkiAMcAIAEcYADAJAgDnAAABKU7Te1BeH/ULduXVl78skn3TxavrBq1So3P/PMM2XPyy+/LGt33nmnm0+cOFH2qKUWZnohR9RTrFgxN9+6davs2bNnj6yp5RoHDhyQPfv375e1nj17uvmDDz4oex544IFM92zfvl3Wpk+f7uY//fST7FmzZo2sbdq0yc1nz54tey6++GI3z5s3r+xZvny5rD3zzDNuPmDAANkTPUc1a9Z0c7Wwx8xs4cKFbj506FDZs3nzZlnbuXOnm7/xxhuyp3fv3rIGIHMycjTzGzgAAAniAAcAIEEc4AAAJIgDHACABHGAAwCQIA5wAAASlOExsmuvvVbW1LhTq1atZI9aatG/f3/Zc+utt8pa8+bN3Xzp0qWyp0qVKrK2ceNGN3/ooYdkT758+WRNmTNnjqypxS5vvvmm7Dl27JisqTGkaCxNjSHddNNNsida4nHaaae5ebRYpnLlyrK2cuVKN4/er2pJzNixY2XPvn37ZK1bt25uHi2+Wb16taz96U9/cnO1uMVML9+JRjlPPfVUWVOjdpMmTZI97du3lzUAmcMYGQAAJykOcAAAEsQBDgBAgjjAAQBIEAc4AAAJyvAtdLXcw8ysU6dObn7NNdfIngYNGri5uiFsZjZ8+HBZ69ixo5sfP35c9kSLU9QCknXr1smeyy67zM2jRRjR3+nSSy9188WLF8ueaKGEWrpRoUIF2aPeHtFijQULFmS6VrJkSdmjlrCYmX3zzTduvn79etlTr149N7/hhhtkTzRhoCYqomUm0U19daO8cOHCsmf37t1uPnjwYNkzefJkWWvUqJGbRxMLy5YtkzUAmcMtdAAATlIc4AAAJIgDHACABHGAAwCQIA5wAAASxAEOAECC9CxQJnz99ddufvnll8ueqVOnunm1atVkTzRGo5ZhVKpUSfbMmjVL1goVKuTmrVu3lj1Llixx8+3bt8ueJ554Qtb69Onj5h988IHsWbhwoazt37/fzaMxsgIFCrj56NGjZU/BggVlrVatWm5+3333yZ41a9bI2i+//OLmd955p+wZN26cm6v3sZnZW2+9JWtqaU+XLl1kT44cOWStbdu2bq5GG83M1q5d6+YHDx6UPWeddZaszZ07182PHj0qewD8Z/EbOAAACeIABwAgQRzgAAAkiAMcAIAEcYADAJAgDnAAABKU4W1kF198saxt3rzZzXPlyiV7XnzxRTdX26XMzD755BNZe/nll9082pCUPbv+98vevXvdvE2bNrJHjexMmzZN9kTP0cyZM928WbNmske9FlGtSpUqsmfOnDluXrVqVdnTsGFDWVNbs1RuFo8uqU1vq1atkj07d+5080OHDsmeaCTy+eefd/PGjRvLnvz588ta7ty53Tzaaqe2+xUvXlz2fPHFF7Km3pdLly6VPQBOHLaRAQBwkuIABwAgQRzgAAAkiAMcAIAEcYADAJCgDN9Cz5Yt27/7sfxT0QKNlStXuvlVV10leypXrixrajnE1VdfLXvUgoropnmJEiVkTd0svvLKK2WPuj1vZrZ69Wo3P378uOxRN5XVwg0zsyuuuELWWrRo4eYTJkyQPWeccYasHTt2zM3r1q0re95//31ZU7p37y5r6jlSt9PNzF599VVZU0tsBg4cKHvUDXr1mpuZFStWTNYaNWrk5s2bN5c9AwYMkDUAmcMtdAAATlIc4AAAJIgDHACABHGAAwCQIA5wAAASxAEOAECCcmb0D3bo0EHWpkyZ4ubRwosff/zRzfft2yd7brjhBllTfdFiDbUsxMysUKFCbh6NE5UvX97N582bJ3t27dola5deeqmbr1mzRvYsXLhQ1q6//no379y5s+y55ZZb3HzFihWyp3379rK2ZcsWN48Wf7Ru3VrWhg0b5uYtW7aUPXnz5nXzESNGyB71fjAz27Fjh5v36tVL9pQsWVLWFixY4OZLliyRPepnrVu3brInGs9Ty2Cixw3gP4vfwAEASBAHOAAACeIABwAgQRzgAAAkiAMcAIAEcYADAJCgf+s2snLlysnaaaed5ubTp0+XPY8++qis1ahRI+MP7L9Eo0GtWrVy888//1z2ZM/u/3uoYsWKsueZZ56RtTJlyrh5tAksev4KFCjg5l27dpU9agTuww8/lD3vvfeerBUsWNDNf/nlF9lTqlQpWatQoYKb//TTT7KnSZMmbn7JJZfInui9t3v3bjevVq2a7NmwYYOsqfHBqOfss8928/nz58ued955R9aUCy+8UNbGjRuX6a8HwMc2MgAATlIc4AAAJIgDHACABHGAAwCQIA5wAAASlOFb6HfffbesqRvEOXPqXSkvvfSSmxcpUkT2RDeLH3/8cTfv16+f7In+TmqZQ3QbWS3keOihh2SPukVtZjZr1iw3j5bERI/vueeec3O13MPMrG7dum4eLQv5n6BTp06ypm73R+8vddPczGzv3r1uvnr1atlz/PhxWVOLedRiGTOzoUOHytqJVLZsWVnbvHnzf+QxAP9/wC10AABOUhzgAAAkiAMcAIAEcYADAJAgDnAAABLEAQ4AQIL+rctM8uTJI2uHDx928/r168ueRYsWydqAAQPcPFo+ctNNN8maGgEaP3687Bk0aJCb79mzR/asX79e1nLkyOHmpUuXlj1qWYiZ2Y8//ujmDzzwgOwpXLiwmx86dEj21KlTR9bUaOHYsWNlT1Zk5X0ULR9p2LChrI0ZM8bNq1SpInui5TvqPRaN+0Wvh9KsWTNZmzNnjpuff/75smfixImZfgwAfIyRAQBwkuIABwAgQRzgAAAkiAMcAIAEcYADAJCgDN9Cj5YYqCUQWbkZe8MNN8jaggULZO3YsWNuvnDhQtmjblibmbVr187N77//ftmzc+dONy9atKjsGTlypKwVKFDAzZ944gnZ07t3b1mrXbu2m0e3+9W0gLp5/c+o91HlypVlj1oWYmbWo0cPN48e32mnnebm0furSZMmslarVi0379+/v+y55557ZE0t0vn4449lj5o+UIuGzMyKFy8ua2qCZNu2bbJH/QwCyDxuoQMAcJLiAAcAIEEc4AAAJIgDHACABHGAAwCQIA5wAAASlOExsmiM5ttvv3XzYsWKyZ5du3a5+SOPPCJ71MISM7OmTZu6+dy5c2VPpGTJkm5epEgR2XPzzTe7+eDBg2VP7ty5ZW3SpElu/uc//1n2vPjii7LWqlUrNy9fvrzsGTFihKwp6rkzM9u+fXumv1407nfw4EE3P3r0aKa/T6lSpWQt+nq7d+928yuuuEL2jB49OuMP7N/knHPOkbVNmza5ebR8B8CJwxgZAAAnKQ5wAAASxAEOAECCOMABAEgQBzgAAAniAAcAIEE5M/oHq1SpImtqjEyNikWiUbHs2fW/N5o1a+bmjRo1kj1r1qyRtYYNG7r5vn37ZM+UKVPcPBq96du3r6wNGzbMzdX2q3+mRIkSbv7JJ59k+mvVr19f1mrUqCFr7777rptHG9uyZcsma2pTmXo/mJnlzOm/7dXmNbP4OVejgBs2bJA9WZEjRw5Zq1ChgptHW96WLl0qa3nz5s3w4wLw/wa/gQMAkCAOcAAAEsQBDgBAgjjAAQBIEAc4AAAJyvAyk7p168pa1apV3XzBggWyZ+PGjW4eLa5o3ry5rKnFHxF1K9tM33zOyjKOrGrQoIGbL1y48IR+nw4dOsja+PHj3Tx67nbs2CFraipg5cqVsuess86StcmTJ7t5nTp1ZI+6Ha6WvZiZLVu2TNbWrl0ra0q08KVatWpuPnPmTNmj/r5LliyRPVdddZWsjR071s2jKQwAJw7LTAAAOElxgAMAkCAOcAAAEsQBDgBAgjjAAQBIEAc4AAAJyvAYGQAA+J+D38ABAEgQBzgAAAniAAcAIEEc4AAAJIgDHACABHGAAwCQIA5wAAASxAEOAECCOMABAEjQ/wLU4iSfOdErLwAAAABJRU5ErkJggg==", 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" ] From 82bb91b9fd0e6fc043778b42c6ef58558be44ea6 Mon Sep 17 00:00:00 2001 From: Eric Kerfoot Date: Fri, 8 Nov 2024 10:37:25 +0000 Subject: [PATCH 12/12] Fix Signed-off-by: Eric Kerfoot --- generation/3d_ddpm/3d_ddpm_tutorial.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb index b6157f799..c5067fc72 100644 --- a/generation/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/generation/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "cdea37d5", "metadata": {}, "outputs": [ @@ -113,7 +113,6 @@ " LoadImaged,\n", " Resized,\n", " ScaleIntensityd,\n", - " ScaleIntensityRangePercentilesd,\n", ")\n", "from monai.utils import first, set_determinism\n", "from monai.inferers import DiffusionInferer\n",