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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch.utils.data import DataLoader\n",
"from torch.optim import Optimizer\n",
"import os\n",
"from datetime import datetime\n",
"from train.learner import DiffproLearner\n",
"\n",
"class TrainConfig:\n",
"\n",
" model: torch.nn.Module\n",
" train_dl: DataLoader\n",
" val_dl: DataLoader\n",
" optimizer: Optimizer\n",
"\n",
" def __init__(self, params, param_scheduler, output_dir) -> None:\n",
" self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
" self.params = params\n",
" self.param_scheduler = param_scheduler\n",
" self.output_dir = output_dir\n",
"\n",
" def train(self):\n",
" # collect and display total parameters\n",
" total_parameters = sum(\n",
" p.numel() for p in self.model.parameters() if p.requires_grad\n",
" )\n",
" print(f\"Total parameters: {total_parameters}\")\n",
"\n",
" # dealing with the output storing\n",
" output_dir = self.output_dir\n",
" if os.path.exists(f\"{output_dir}/chkpts/weights.pt\"):\n",
" print(\"Checkpoint already exists.\")\n",
" if input(\"Resume training? (y/n)\") != \"y\":\n",
" return\n",
" else:\n",
" output_dir = f\"{output_dir}/{datetime.now().strftime('%m-%d_%H%M%S')}\"\n",
" print(f\"Creating new log folder as {output_dir}\")\n",
"\n",
" # prepare the learner structure and parameters\n",
" learner = DiffproLearner(\n",
" output_dir, self.model, self.train_dl, self.val_dl, self.optimizer,\n",
" self.params\n",
" )\n",
" learner.train(max_epoch=self.params.max_epoch)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from model import init_ldm_model, init_diff_pro_sdf\n",
"from data.dataset_loading import load_datasets, create_dataloader\n",
"\n",
"WITH_RHYTHM = \"onset\"\n",
"\n",
"class LdmTrainConfig(TrainConfig):\n",
"\n",
" def __init__(self, params, output_dir, debug_mode=False) -> None:\n",
" super().__init__(params, None, output_dir)\n",
" self.debug_mode = debug_mode\n",
" #self.use_autoreg_cond = use_autoreg_cond\n",
" #self.use_external_cond = use_external_cond\n",
" #self.mask_background = mask_background\n",
" #self.random_pitch_aug = random_pitch_aug\n",
"\n",
" # create model\n",
" self.ldm_model = init_ldm_model(params, debug_mode)\n",
" self.model = init_diff_pro_sdf(self.ldm_model, params, self.device)\n",
"\n",
" # Create dataloader\n",
" train_set = load_datasets(with_rhythm=WITH_RHYTHM)\n",
" self.train_dl = create_dataloader(params.batch_size, train_set)\n",
" self.val_dl = create_dataloader(params.batch_size, train_set) # we temporarily use train_set for validation\n",
"\n",
" # Create optimizer4\n",
" self.optimizer = torch.optim.Adam(\n",
" self.model.parameters(), lr=params.learning_rate\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/music/chord_trainer/train/learner.py:45: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
" self.autocast = torch.cuda.amp.autocast(enabled=params.fp16)\n",
"/home/music/chord_trainer/train/learner.py:46: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
" self.scaler = torch.cuda.amp.GradScaler(enabled=params.fp16)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total parameters: 36755330\n",
"Creating new log folder as results/test/09-13_171940\n",
"{\n",
" \"attention_levels\": [\n",
" 2,\n",
" 3\n",
" ],\n",
" \"batch_size\": 16,\n",
" \"channel_multipliers\": [\n",
" 1,\n",
" 2,\n",
" 4,\n",
" 4\n",
" ],\n",
" \"channels\": 64,\n",
" \"d_cond\": 2,\n",
" \"fp16\": true,\n",
" \"in_channels\": 4,\n",
" \"latent_scaling_factor\": 0.18215,\n",
" \"learning_rate\": 5e-05,\n",
" \"linear_end\": 0.012,\n",
" \"linear_start\": 0.00085,\n",
" \"max_epoch\": 10,\n",
" \"max_grad_norm\": 10,\n",
" \"n_heads\": 4,\n",
" \"n_res_blocks\": 2,\n",
" \"n_steps\": 1000,\n",
" \"out_channels\": 2,\n",
" \"tf_layers\": 1\n",
"}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 0: 100%|██████████| 1141/1141 [00:51<00:00, 22.08it/s]\n",
"Epoch 1: 100%|██████████| 1141/1141 [00:50<00:00, 22.43it/s]\n",
"Epoch 2: 100%|██████████| 1141/1141 [00:47<00:00, 24.02it/s]\n",
"Epoch 3: 100%|██████████| 1141/1141 [00:47<00:00, 24.07it/s]\n",
"Epoch 4: 100%|██████████| 1141/1141 [01:04<00:00, 17.70it/s]\n",
"Epoch 5: 100%|██████████| 1141/1141 [00:50<00:00, 22.42it/s]\n",
"Epoch 6: 100%|██████████| 1141/1141 [00:50<00:00, 22.38it/s]\n",
"Epoch 7: 100%|██████████| 1141/1141 [00:50<00:00, 22.38it/s]\n",
"Epoch 8: 100%|██████████| 1141/1141 [01:05<00:00, 17.38it/s]\n",
"Epoch 9: 100%|██████████| 1141/1141 [00:49<00:00, 22.83it/s]\n"
]
}
],
"source": [
"\n",
"# Import necessary libraries\n",
"from train.train_params import params_chord_cond, params_chord\n",
"import os\n",
"\n",
"# Set the argument values directly\n",
"args = {\n",
" 'output_dir': 'results',\n",
" 'uniform_pitch_shift': False,\n",
" # 'debug': False,\n",
" # 'data_source': \"lmd\",\n",
" # 'load_chkpt_from': None,\n",
" # 'dataset_path': \"data/lmd_sample/no_drum_sample\",\n",
"}\n",
"\n",
"# Determine random pitch augmentation\n",
"random_pitch_aug = not args['uniform_pitch_shift']\n",
"\n",
"# Generate the filename based on argument settings\n",
"fn = 'test'\n",
"\n",
"# Set the output directory\n",
"output_dir = os.path.join(args['output_dir'], fn)\n",
"\n",
"# Create the training configuration\n",
"config = LdmTrainConfig(params_chord_cond, output_dir)\n",
"\n",
"config.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "music_demo",
"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": "3.9.19"
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"nbformat": 4,
"nbformat_minor": 2
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