diff --git a/.gitignore b/.gitignore
index 0ffc9575a2d120e1cfad342dedb833397cdb99bc..742b4519a237e22b6c4a6352873c58a4c8166e50 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,4 +3,5 @@
*/trained_ddpm
root
experiments
-trainer/__pycache__
\ No newline at end of file
+.ipynb_checkpoints
+trainer/__pycache__
diff --git a/.ipynb_checkpoints/experiment_creator-checkpoint.ipynb b/.ipynb_checkpoints/experiment_creator-checkpoint.ipynb
deleted file mode 100644
index 2e35c4ee2b0852f0c14234c07a8669da508e5f5b..0000000000000000000000000000000000000000
--- a/.ipynb_checkpoints/experiment_creator-checkpoint.ipynb
+++ /dev/null
@@ -1,189 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "from trainer.train import *\n",
- "from dataloader.load import *\n",
- "from models.Framework import *\n",
- "from models.unet_unconditional_diffusion import *\n",
- "import torch \n",
- "from torch import nn \n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Prepare experiment\n",
- "1. Adapt settings below (for data path, only use absolute paths!!)\n",
- "2. run both cells of the notebook, this creates a folder containing the json setting files \n",
- "2. put the folder on the HPC\n",
- "3. the following command starts the training `python main.py train \"<absolute path of folder in hpc>\"` add it to the batch file "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch \n",
- "\n",
- "#path to store, path to load data , path to checkpoint \n",
- "\n",
- "#basic settings:\n",
- "learning_rate = 0.0001\n",
- "batchsize = 8\n",
- "datapath = \"/work/lect0100/lhq_256\"\n",
- "checkpoint_path = None #when training from checkpoint\n",
- "experimentname = \"/Users/gonzalo/Desktop/testing/\" + \"test1\" #always change experiment name! \n",
- "epochs = 20\n",
- "diffusion_steps = 25\n",
- "image_size = 64\n",
- "channels = 3\n",
- "store_iter = 5\n",
- "optimizername = \"torch.optim.AdamW\"\n",
- "name_appendix = 'DM_bottleneck'# id for WANDB\n",
- "\n",
- "#advanced settings: change directly in dictionary \n",
- "meta_setting = dict(modelname = \"UNet_Unconditional_Diffusion_Bottleneck_Variant\",\n",
- " dataset = \"UnconditionalDataset\",\n",
- " framework = \"DDPM\",\n",
- " trainloop_function = \"ddpm_trainer\",\n",
- " batchsize = batchsize,\n",
- " )\n",
- "\n",
- "\n",
- "dataset_setting = dict(fpath = datapath,\n",
- " img_size = image_size,\n",
- " frac =0.8,\n",
- " skip_first_n = 0,\n",
- " ext = \".png\",\n",
- " transform=True\n",
- " )\n",
- "\n",
- "\n",
- "model_setting = dict( channels_in=channels, \n",
- " channels_out =channels , \n",
- " activation='relu', # activation function. Options: {'relu', 'leakyrelu', 'selu', 'gelu', 'silu'/'swish'}\n",
- " weight_init='he', # weight initialization. Options: {'he', 'torch'}\n",
- " projection_features=64, # number of image features after first convolution layer\n",
- " time_dim=batchsize, #dont chnage!!!\n",
- " time_channels=diffusion_steps, # number of time channels #TODO same as diffusion steps? \n",
- " num_stages=4, # number of stages in contracting/expansive path\n",
- " stage_list=None, # specify number of features produced by stages\n",
- " num_blocks=1, # number of ConvResBlock in each contracting/expansive path\n",
- " num_groupnorm_groups=32, # number of groups used in Group Normalization inside a ConvResBlock\n",
- " dropout=0.1, # drop-out to be applied inside a ConvResBlock\n",
- " attention_list=None, # specify MHA pattern across stages\n",
- " num_attention_heads=1,\n",
- " )\n",
- "\n",
- "\n",
- "framework_setting = dict(\n",
- " diffusion_steps = diffusion_steps, # dont change!!\n",
- " out_shape = (channels,image_size,image_size), # dont change!!\n",
- " noise_schedule = 'linear', \n",
- " beta_1 = 1e-4, \n",
- " beta_T = 0.02,\n",
- " alpha_bar_lower_bound = 0.9,\n",
- " var_schedule = 'same', \n",
- " kl_loss = 'simplified', \n",
- " recon_loss = 'none',\n",
- " \n",
- " )\n",
- "\n",
- "\n",
- "training_setting = dict(\n",
- " epochs = epochs,\n",
- " store_iter = store_iter,\n",
- " eval_iter = 3,\n",
- " optimizer_class=optimizername, \n",
- " optimizer_params=dict(lr=learning_rate), # don't change!\n",
- " scheduler_class= None, \n",
- " scheduler_params=None,\n",
- " last_epoch=-1,\n",
- " learning_rate = learning_rate,\n",
- " lr_schedule = False,\n",
- " verbose = True,\n",
- " name_appendix=name_appendix,\n",
- " checkpoint_path= checkpoint_path,\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "path already exists, pick a new name!\n",
- "break\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "import json\n",
- "f = experimentname\n",
- "if os.path.exists(f):\n",
- " print(\"path already exists, pick a new name!\")\n",
- " print(\"break\")\n",
- "else:\n",
- " print(\"create folder\")\n",
- " os.mkdir(f)\n",
- " print(\"folder created \")\n",
- " with open(f+\"/meta_setting.json\",\"w+\") as fp:\n",
- " json.dump(meta_setting,fp)\n",
- "\n",
- " with open(f+\"/dataset_setting.json\",\"w+\") as fp:\n",
- " json.dump(dataset_setting,fp)\n",
- " \n",
- " with open(f+\"/model_setting.json\",\"w+\") as fp:\n",
- " json.dump(model_setting,fp)\n",
- " \n",
- " with open(f+\"/framework_setting.json\",\"w+\") as fp:\n",
- " json.dump(framework_setting,fp)\n",
- "\n",
- " with open(f+\"/training_setting.json\",\"w+\") as fp:\n",
- " json.dump(training_setting,fp)\n",
- "\n",
- " print(\"stored json files in folder\")\n",
- " print(meta_setting)\n",
- " print(dataset_setting)\n",
- " print(model_setting)\n",
- " print(framework_setting)\n",
- " print(training_setting)\n",
- " "
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.9 (pytorch)",
- "language": "python",
- "name": "pytorch"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}