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https://github.com/wassname/attentive-neural-processes.git
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test set, inputnorm, lstm before encoder
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@@ -20,8 +20,8 @@
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@@ -36,8 +36,8 @@
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@@ -63,8 +63,8 @@
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@@ -79,17 +79,30 @@
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"text": [
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"/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/pytorch_lightning/core/decorators.py:13: UserWarning:\n",
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"\n",
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"data_loader decorator deprecated in 0.7.0. Will remove 0.9.0\n",
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"\n"
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]
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}
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],
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"source": [
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"from src.models.model import LatentModel\n",
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"from src.data.smart_meter import collate_fns, SmartMeterDataSet, get_smartmeter_df\n",
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"from src.plot import plot_from_loader\n",
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"from src.models.lstm_seqseq import LSTMSeq2Seq_PL\n",
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"from src.dict_logger import DictLogger"
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"from src.dict_logger import DictLogger\n",
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"from src.utils import PyTorchLightningPruningCallback\n",
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"from src.train import main, objective, add_number, run_trial"
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]
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},
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{
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@@ -97,8 +110,8 @@
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@@ -113,8 +126,8 @@
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@@ -136,13 +149,13 @@
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"execution_count": 7,
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}
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},
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"outputs": [],
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"source": [
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"df_train, df_test = get_smartmeter_df()"
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"df_train, df_val, df_test = get_smartmeter_df()"
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]
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},
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{
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@@ -150,15 +163,15 @@
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"outputs": [
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{
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"data": {
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"execution_count": 8,
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@@ -167,7 +180,7 @@
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||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
@@ -181,140 +194,12 @@
|
||||
"source": [
|
||||
"# Show split\n",
|
||||
"df_train['energy(kWh/hh)'].plot(label='train')\n",
|
||||
"df_val['energy(kWh/hh)'].plot(label='val')\n",
|
||||
"df_test['energy(kWh/hh)'].plot(label='test')\n",
|
||||
"plt.title('energy(kWh/hh)')\n",
|
||||
"plt.legend()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-16T00:24:15.905017Z",
|
||||
"start_time": "2020-02-16T00:24:15.747859Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Train helpers"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:41:41.584788Z",
|
||||
"start_time": "2020-03-01T03:41:41.503778Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def main(trial, train=True):\n",
|
||||
" # PyTorch Lightning will try to restore model parameters from previous trials if checkpoint\n",
|
||||
" # filenames match. Therefore, the filenames for each trial must be made unique.\n",
|
||||
" \n",
|
||||
" checkpoint_callback = pl.callbacks.ModelCheckpoint(\n",
|
||||
" os.path.join(MODEL_DIR, name, 'version_{}'.format(trial.number), \"chk\"), monitor='val_loss', mode='min')\n",
|
||||
"\n",
|
||||
" # The default logger in PyTorch Lightning writes to event files to be consumed by\n",
|
||||
" # TensorBoard. We create a simple logger instead that holds the log in memory so that the\n",
|
||||
" # final accuracy can be obtained after optimization. When using the default logger, the\n",
|
||||
" # final accuracy could be stored in an attribute of the `Trainer` instead.\n",
|
||||
" logger = DictLogger(MODEL_DIR, name=name, version=trial.number)\n",
|
||||
"# print(\"log_dir\", logger.experiment.log_dir)\n",
|
||||
" hparams = dict(**trial.params, **trial.user_attrs)\n",
|
||||
"\n",
|
||||
" trainer = pl.Trainer(\n",
|
||||
" logger=logger,\n",
|
||||
" val_percent_check=PERCENT_TEST_EXAMPLES,\n",
|
||||
" checkpoint_callback=checkpoint_callback,\n",
|
||||
" max_epochs=hparams['max_nb_epochs'],\n",
|
||||
" gpus=-1 if torch.cuda.is_available() else None,\n",
|
||||
" early_stop_callback=PyTorchLightningPruningCallback(trial, monitor='val_loss')\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" model = LSTMSeq2Seq_PL(hparams)\n",
|
||||
" if train:\n",
|
||||
" trainer.fit(model)\n",
|
||||
" return model, trainer"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:41:41.652356Z",
|
||||
"start_time": "2020-03-01T03:41:41.588888Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def add_suggest(trial):\n",
|
||||
" trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-2)\n",
|
||||
" trial.suggest_uniform(\"lstm_dropout\", 0, 0.75)\n",
|
||||
" trial.suggest_categorical(\"hidden_size\", [1, 2, 4, 8, 16, 32, 64, 128]) \n",
|
||||
" trial.suggest_categorical(\"lstm_layers\", [1, 2, 4, 8]) \n",
|
||||
" trial.suggest_categorical(\"bidirectional\", [False, True]) \n",
|
||||
" \n",
|
||||
"\n",
|
||||
" trial._user_attrs = {\n",
|
||||
" 'batch_size': 16,\n",
|
||||
" 'grad_clip': 40,\n",
|
||||
" 'max_nb_epochs': 200,\n",
|
||||
" 'num_workers': 4,\n",
|
||||
" 'num_extra_target': 24*4,\n",
|
||||
" 'vis_i': '670',\n",
|
||||
" 'num_context': 24*4,\n",
|
||||
" 'input_size': 18,\n",
|
||||
" 'input_size_decoder': 17,\n",
|
||||
" 'context_in_target': True,\n",
|
||||
" 'output_size': 1\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" # For manual experiment we will start at -1 and deincr by 1\n",
|
||||
" versions = [int(s.stem.split('_')[-1]) for s in (MODEL_DIR / name).glob('version_*')] + [-1]\n",
|
||||
" trial.number = min(versions)-1\n",
|
||||
" print('trial.number', trial.number)\n",
|
||||
" return trial"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:41:41.723534Z",
|
||||
"start_time": "2020-03-01T03:41:41.655316Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"def objective(trial):\n",
|
||||
" # see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py\n",
|
||||
" trial = add_suggest(trial)\n",
|
||||
"\n",
|
||||
" \n",
|
||||
" print('trial', trial.number, 'params', trial.params)\n",
|
||||
" \n",
|
||||
" model, trainer = main(trial)\n",
|
||||
" \n",
|
||||
" # also report to tensorboard & print\n",
|
||||
" print('logger.metrics', model.logger.metrics[-1:])\n",
|
||||
" model.logger.experiment.add_hparams(trial.params, logger.metrics[-1])\n",
|
||||
" model.logger.save()\n",
|
||||
" \n",
|
||||
" return model.logger.metrics[-1]['val_loss']\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -322,47 +207,6 @@
|
||||
"# Train"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:41:41.777701Z",
|
||||
"start_time": "2020-03-01T03:41:41.726164Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"PERCENT_TEST_EXAMPLES = 0.3\n",
|
||||
"EPOCHS = 2\n",
|
||||
"DIR = Path(os.getcwd())\n",
|
||||
"MODEL_DIR = DIR/ 'optuna_result'/ 'lstm'\n",
|
||||
"name = \"lstm1\"\n",
|
||||
"MODEL_DIR.mkdir(parents=True, exist_ok=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:41:41.829950Z",
|
||||
"start_time": "2020-03-01T03:41:41.779861Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"now run `tensorboard --logdir /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/optuna_result/lstm\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(f\"now run `tensorboard --logdir {MODEL_DIR}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -372,64 +216,317 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-01T12:52:22.492191Z",
|
||||
"start_time": "2020-02-01T12:52:22.416653Z"
|
||||
"end_time": "2020-03-15T01:51:46.861012Z",
|
||||
"start_time": "2020-03-15T01:04:31.130838Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-16T01:47:59.598921Z",
|
||||
"start_time": "2020-02-16T01:47:02.100Z"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"Note that the LSTM has access to the y values for the first half of the plot (the context) to match the NP setup."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2020-03-01T03:47:34.500Z"
|
||||
},
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"trial.number -21\n"
|
||||
"now run `tensorboard --logdir lightning_logs`\n",
|
||||
"trial.number -8\n",
|
||||
"INFO:root:GPU available: True, used: True\n",
|
||||
"INFO:root:VISIBLE GPUS: 0\n",
|
||||
"INFO:root:\n",
|
||||
" | Name | Type | Params\n",
|
||||
"-----------------------------------------------------------------\n",
|
||||
"0 | model | Seq2SeqNet | 8 M \n",
|
||||
"1 | model.norm_input | BatchNormSequence | 36 \n",
|
||||
"2 | model.norm_input.norm | BatchNorm1d | 36 \n",
|
||||
"3 | model.encoder | LSTM | 3 M \n",
|
||||
"4 | model.multihead_attn | MultiheadAttention | 263 K \n",
|
||||
"5 | model.multihead_attn.out_proj | Linear | 65 K \n",
|
||||
"6 | model.norm_target | BatchNormSequence | 34 \n",
|
||||
"7 | model.norm_target.norm | BatchNorm1d | 34 \n",
|
||||
"8 | model.decoder | LSTM | 3 M \n",
|
||||
"9 | model.mean | Linear | 257 \n",
|
||||
"10 | model.std | Linear | 257 \n"
|
||||
]
|
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"INFO:root:gpu available: True, used: True\n",
|
||||
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"step 0, {'val_loss': '0.010536636225879192', 'val/loss_mse': '0.0017317170277237892', 'val/loss_p': '0.010536636225879192', 'val/sigma': '1.0592342615127563'}\n",
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|
||||
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|
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|
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|
||||
"step 5480, {'val_loss': '0.027184773236513138', 'val/loss_mse': '0.0013622333062812686', 'val/loss_p': '0.027184773236513138', 'val/sigma': '0.13322952389717102'}\n"
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|
||||
"Epoch 3: reducing learning rate of group 0 to 2.0000e-05.\n"
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|
||||
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|
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|
||||
"text": [
|
||||
"step 9134, {'val_loss': '0.024827884510159492', 'val/loss_mse': '0.0014371582074090838', 'val/loss_p': '0.024827884510159492', 'val/sigma': '0.14072538912296295'}\n",
|
||||
"INFO:root:Epoch 00005: early stopping\n",
|
||||
"\n",
|
||||
"Loading checkpoint lightning_logs/seq2seq/version_-8/_ckpt_epoch_0.ckpt\n"
|
||||
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"step 9135, {'val_loss': '-0.003565264632925391', 'val/loss_mse': '0.0002942961873486638', 'val/loss_p': '-0.003565264632925391', 'val/sigma': '0.17361457645893097'}\n",
|
||||
"----------------------------------------------------------------------------------------------------\n",
|
||||
"TEST RESULTS\n",
|
||||
"{}\n",
|
||||
"----------------------------------------------------------------------------------------------------\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'plot_from_loader' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-9-aa3a84af01a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;34m'patience'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m },\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mPL_MODEL_CLS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mLSTMSeq2Seq_PL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 25\u001b[0m )\n",
|
||||
"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/attentive-neural-processes/src/train.py\u001b[0m in \u001b[0;36mrun_trial\u001b[0;34m(name, PL_MODEL_CLS, params, user_attrs, MODEL_DIR)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0mdset_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[0mlabel_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdset_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m \u001b[0mplot_from_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m670\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 110\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mNameError\u001b[0m: name 'plot_from_loader' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"trial = optuna.trial.FixedTrial(\n",
|
||||
"trial, trainer, model = run_trial(\n",
|
||||
" name=\"seq2seq\",\n",
|
||||
" params={\n",
|
||||
" 'bidirectional': False,\n",
|
||||
" 'hidden_size': 128,\n",
|
||||
" 'learning_rate': 2e-5,\n",
|
||||
" 'lstm_dropout': 0.0,\n",
|
||||
" 'lstm_layers': 2,\n",
|
||||
" })\n",
|
||||
"trial._user_attrs = {\n",
|
||||
" 'hidden_size': 256,\n",
|
||||
" 'learning_rate': 2e-4,\n",
|
||||
" 'lstm_dropout': 0.4,\n",
|
||||
" 'lstm_layers': 8,\n",
|
||||
" },\n",
|
||||
" user_attrs = {\n",
|
||||
" 'batch_size': 16,\n",
|
||||
" 'grad_clip': 40,\n",
|
||||
" 'max_nb_epochs': 200,\n",
|
||||
@@ -440,11 +537,11 @@
|
||||
" 'input_size': 18,\n",
|
||||
" 'input_size_decoder': 17,\n",
|
||||
" 'context_in_target': True,\n",
|
||||
" 'output_size': 1\n",
|
||||
"}\n",
|
||||
"trial = add_suggest(trial)\n",
|
||||
"model, trainer = main(trial, train=False)\n",
|
||||
"trainer.fit(model)"
|
||||
" 'output_size': 1,\n",
|
||||
" 'patience':2,\n",
|
||||
" },\n",
|
||||
" PL_MODEL_CLS=LSTMSeq2Seq_PL\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -452,35 +549,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:31:43.522361Z",
|
||||
"start_time": "2020-03-01T03:31:15.800Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.307396Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# test\n",
|
||||
"trainer.test(model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.308984Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.863848Z",
|
||||
"start_time": "2020-03-15T01:04:18.100Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -496,8 +566,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.310698Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.865188Z",
|
||||
"start_time": "2020-03-15T01:04:18.100Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -531,8 +601,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.312313Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.866502Z",
|
||||
"start_time": "2020-03-15T01:04:18.100Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -569,8 +639,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.313923Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.867929Z",
|
||||
"start_time": "2020-03-15T01:04:18.100Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -594,8 +664,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.315413Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.869282Z",
|
||||
"start_time": "2020-03-15T01:04:18.100Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -608,8 +678,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.317152Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.870600Z",
|
||||
"start_time": "2020-03-15T01:04:18.100Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -630,8 +700,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.318533Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.872993Z",
|
||||
"start_time": "2020-03-15T01:04:18.100Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -644,8 +714,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.319880Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.874727Z",
|
||||
"start_time": "2020-03-15T01:04:18.100Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -660,8 +730,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.321408Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.876536Z",
|
||||
"start_time": "2020-03-15T01:04:18.100Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -676,8 +746,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.322849Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.878187Z",
|
||||
"start_time": "2020-03-15T01:04:18.100Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -690,8 +760,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.324307Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.879862Z",
|
||||
"start_time": "2020-03-15T01:04:18.200Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -709,8 +779,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.325661Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.881522Z",
|
||||
"start_time": "2020-03-15T01:04:18.200Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -723,8 +793,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-03-01T03:47:33.327059Z",
|
||||
"start_time": "2020-03-01T03:41:28.300Z"
|
||||
"end_time": "2020-03-15T01:51:46.883268Z",
|
||||
"start_time": "2020-03-15T01:04:18.200Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -747,6 +817,18 @@
|
||||
"language": "python",
|
||||
"name": "jup3.7.3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.3"
|
||||
},
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"npconvert_exporter": "python",
|
||||
|
||||
File diff suppressed because it is too large.
Load diff
@@ -155,7 +155,7 @@ def get_smartmeter_df(indir=Path('./data/smart-meters-in-london'), use_logy=Fals
|
||||
df['dayofweek'] = time.dt.dayofweek / 7.0
|
||||
|
||||
# Drop nan and 0's
|
||||
df = df[df['energy(kWh/hh)']!=0]
|
||||
df = df[df['energy(kWh/hh)'] != 0]
|
||||
df = df.dropna()
|
||||
|
||||
if use_logy:
|
||||
@@ -163,7 +163,9 @@ def get_smartmeter_df(indir=Path('./data/smart-meters-in-london'), use_logy=Fals
|
||||
df = df.sort_values('tstp')
|
||||
|
||||
# split data
|
||||
n_split = -int(len(df)*0.1)
|
||||
df_train = df[:n_split]
|
||||
df_test = df[n_split:]
|
||||
return df_train, df_test
|
||||
test_split= -int(len(df) * 0.1)
|
||||
val_split= int(len(df) * 0.15)
|
||||
df_test = df[:val_split]
|
||||
df_train = df[val_split:test_split]
|
||||
df_val = df[test_split:]
|
||||
return df_train, df_val, df_test
|
||||
+45
-48
@@ -69,7 +69,7 @@ class LatentModelPL(pl.LightningModule):
|
||||
|
||||
# agg and print self.train_logs HACK https://github.com/PyTorchLightning/pytorch-lightning/issues/100
|
||||
train_logs = self.agg_logs(self.train_logs)
|
||||
train_logs_str = {k: f"{v.mean()}" for k, v in train_logs.items()}
|
||||
train_logs_str = {k: f"{v}" for k, v in train_logs.items()}
|
||||
self.train_logs = []
|
||||
print(f"step val {self.trainer.global_step}, {tensorboard_logs_str} {train_logs}")
|
||||
return logs
|
||||
@@ -95,10 +95,10 @@ class LatentModelPL(pl.LightningModule):
|
||||
if isinstance(outputs[0][j], dict):
|
||||
# Take mean of sub dicts
|
||||
keys = outputs[0][j].keys()
|
||||
aggs[j] = {k: torch.stack([x[j][k] for x in outputs if k in x[j]]).mean() for k in keys}
|
||||
aggs[j] = {k: torch.stack([x[j][k] for x in outputs if k in x[j]]).mean().item() for k in keys}
|
||||
else:
|
||||
# Take mean of numbers
|
||||
aggs[j] = torch.stack([x[j] for x in outputs if j in x]).mean()
|
||||
aggs[j] = torch.stack([x[j] for x in outputs if j in x]).mean().item()
|
||||
return aggs
|
||||
|
||||
# # Log hparams with metric, doesn't work
|
||||
@@ -117,15 +117,14 @@ class LatentModelPL(pl.LightningModule):
|
||||
return self.validation_end(*args, **kwargs)
|
||||
|
||||
def configure_optimizers(self):
|
||||
optim = torch.optim.Adam(self.parameters(), lr=self.hparams["learning_rate"], weight_decay=0)
|
||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optim, patience=1, verbose=True, min_lr=1e-7) # note early stopping has patience 3
|
||||
optim = torch.optim.AdamW(self.parameters(), lr=self.hparams["learning_rate"], weight_decay=0)
|
||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optim, patience=self.hparams["patience"], verbose=True, min_lr=1e-7) # note early stopping has patience 3
|
||||
return [optim], [scheduler]
|
||||
|
||||
def _get_cache_dfs(self):
|
||||
if self._dfs is None:
|
||||
df_train, df_test = get_smartmeter_df()
|
||||
# self._dfs = dict(df_train=df_train[:600], df_test=df_test[:600])
|
||||
self._dfs = dict(df_train=df_train, df_test=df_test)
|
||||
df_train, df_val, df_test = get_smartmeter_df()
|
||||
self._dfs = dict(df_train=df_train, df_val=df_val, df_test=df_test)
|
||||
return self._dfs
|
||||
|
||||
def train_dataloader(self):
|
||||
@@ -144,7 +143,7 @@ class LatentModelPL(pl.LightningModule):
|
||||
)
|
||||
|
||||
def val_dataloader(self):
|
||||
df_test = self._get_cache_dfs()['df_test']
|
||||
df_test = self._get_cache_dfs()['df_val']
|
||||
data_test = SmartMeterDataSet(
|
||||
df_test, self.hparams["num_context"], self.hparams["num_extra_target"]
|
||||
)
|
||||
@@ -172,49 +171,47 @@ class LatentModelPL(pl.LightningModule):
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def add_model_specific_args(parent_parser):
|
||||
"""
|
||||
Specify the hyperparams for this LightningModule
|
||||
"""
|
||||
# MODEL specific
|
||||
parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser], add_help=False)
|
||||
parser.opt_range("--learning_rate", default=1e-3, type=float, tunable=True, high=1e-2, low=1e-5, log_base=10)
|
||||
def add_suggest(trial):
|
||||
trial.suggest_loguniform("learning_rate", 1e-5, 1e-2)
|
||||
|
||||
trial.suggest_categorical("hidden_dim", [8*2**i for i in range(6)])
|
||||
trial.suggest_categorical("latent_dim", [8*2**i for i in range(6)])
|
||||
|
||||
parser.opt_list("--hidden_dim", default=128, type=int, tunable=True, options=[8*2**i for i in range(8)])
|
||||
parser.opt_list("--latent_dim", default=128, type=int, tunable=True, options=[8*2**i for i in range(8)])
|
||||
parser.add_argument("--num_heads", default=8, type=int)
|
||||
parser.add_argument("--attention_layers", default=1, type=int)
|
||||
parser.opt_list("--n_latent_encoder_layers", default=4, type=int, tunable=True, options=[1, 2, 4, 8, 16])
|
||||
parser.opt_list("--n_det_encoder_layers", default=4, type=int, tunable=True, options=[1, 2, 4, 8, 16])
|
||||
parser.opt_list("--n_decoder_layers", default=2, type=int, tunable=True, options=[1, 2, 4, 8, 16])
|
||||
trial.suggest_int("attention_layers", 1, 4)
|
||||
trial.suggest_categorical("n_latent_encoder_layers", [1, 2, 4, 8])
|
||||
trial.suggest_categorical("n_det_encoder_layers", [1, 2, 4, 8])
|
||||
trial.suggest_categorical("n_decoder_layers", [1, 2, 4, 8])
|
||||
trial.suggest_int("num_heads", 8, 8)
|
||||
|
||||
parser.opt_range("--dropout", default=0, type=float, tunable=True, low=0, high=0.75)
|
||||
parser.opt_range("--attention_dropout", default=0, type=float, tunable=True, low=0, high=0.75)
|
||||
parser.add_argument("--min_std", default=0.005, type=float)
|
||||
trial.suggest_uniform("dropout", 0, 0.9)
|
||||
trial.suggest_uniform("attention_dropout", 0, 0.9)
|
||||
|
||||
parser.opt_list(
|
||||
"--latent_enc_self_attn_type", default="multihead", type=str, tunable=True, options=['uniform', 'dot', 'multihead', 'ptmultihead']
|
||||
trial.suggest_categorical(
|
||||
"latent_enc_self_attn_type", ['uniform', 'multihead', 'ptmultihead']
|
||||
)
|
||||
parser.opt_list("--det_enc_self_attn_type", default="multihead", type=str, tunable=True, options=['uniform', 'dot', 'multihead', 'ptmultihead'])
|
||||
parser.opt_list("--det_enc_cross_attn_type", default="multihead", type=str, tunable=True, options=['uniform', 'dot', 'multihead', 'ptmultihead'])
|
||||
trial.suggest_categorical("det_enc_self_attn_type", ['uniform', 'multihead', 'ptmultihead'])
|
||||
trial.suggest_categorical("det_enc_cross_attn_type", ['uniform', 'multihead', 'ptmultihead'])
|
||||
|
||||
parser.opt_list("--use_lvar", default=False, type=bool, tunable=True, options=[False, True])
|
||||
parser.opt_list("--use_rnn", default=False, type=bool, tunable=True, options=[False, True])
|
||||
parser.opt_list("--use_deterministic_path", default=True, tunable=True, type=bool, options=[False, True])
|
||||
parser.opt_list("--use_self_attn", default=True, tunable=True, type=bool, options=[False, True])
|
||||
parser.opt_list("--batchnorm", default=True, tunable=True, type=bool, options=[False, True])
|
||||
|
||||
# training specific (for this model)
|
||||
parser.add_argument("--context_in_target", default=True, type=bool)
|
||||
parser.add_argument("--grad_clip", default=0, type=float)
|
||||
parser.add_argument("--num_context", type=int, default=24 * 2)
|
||||
parser.add_argument("--num_extra_target", type=int, default=24)
|
||||
parser.add_argument("--max_nb_epochs", default=20, type=int)
|
||||
parser.add_argument("--num_workers", default=4, type=int)
|
||||
trial.suggest_categorical("batchnorm", [False, True])
|
||||
trial.suggest_categorical("use_self_attn", [False, True])
|
||||
trial.suggest_categorical("use_lvar", [False, True])
|
||||
trial.suggest_categorical("use_deterministic_path", [False, True])
|
||||
trial.suggest_categorical("use_rnn", [True, False])
|
||||
|
||||
trial._user_attrs = {
|
||||
'batch_size': 16,
|
||||
'grad_clip': 40,
|
||||
'max_nb_epochs': 200,
|
||||
'num_workers': 4,
|
||||
'num_context': 24* 4,
|
||||
'vis_i': '670',
|
||||
'num_extra_target': 24*4,
|
||||
'x_dim': 18,
|
||||
'context_in_target': True,
|
||||
'y_dim': 1,
|
||||
'patience': 3,
|
||||
'min_std': 0.005,
|
||||
}
|
||||
return trial
|
||||
|
||||
parser.add_argument("--batch_size", default=16, type=int)
|
||||
parser.add_argument("--x_dim", default=16, type=int)
|
||||
parser.add_argument("--y_dim", default=1, type=int)
|
||||
parser.add_argument("--vis_i", default=670, type=int)
|
||||
return parser
|
||||
|
||||
+37
-24
@@ -132,7 +132,7 @@ class LSTM_PL(pl.LightningModule):
|
||||
def validation_end(self, outputs):
|
||||
# TODO send an image to tensroboard, like in the lighting_anp.py file
|
||||
if int(self.hparams["vis_i"]) > 0:
|
||||
loader = self.val_dataloader()[0]
|
||||
loader = self.val_dataloader()
|
||||
vis_i = min(int(self.hparams["vis_i"]), len(loader.dataset))
|
||||
if isinstance(self.hparams["vis_i"], str):
|
||||
image = plot_from_loader(loader, self, vis_i=vis_i, window_len=self.hparams["window_length"])
|
||||
@@ -163,15 +163,14 @@ class LSTM_PL(pl.LightningModule):
|
||||
def configure_optimizers(self):
|
||||
optim = torch.optim.Adam(self.parameters(), lr=self.hparams["learning_rate"])
|
||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
|
||||
optim, patience=2, verbose=True, min_lr=1e-5
|
||||
optim, patience=self.hparams["patience"], verbose=True, min_lr=1e-5
|
||||
) # note early stopping has patient 3
|
||||
return [optim], [scheduler]
|
||||
|
||||
def _get_cache_dfs(self):
|
||||
if self._dfs is None:
|
||||
df_train, df_test = get_smartmeter_df()
|
||||
# self._dfs = dict(df_train=df_train[:600], df_test=df_test[:600])
|
||||
self._dfs = dict(df_train=df_train, df_test=df_test)
|
||||
df_train, df_val, df_test = get_smartmeter_df()
|
||||
self._dfs = dict(df_train=df_train, df_val=df_val, df_test=df_test)
|
||||
return self._dfs
|
||||
|
||||
@pl.data_loader
|
||||
@@ -193,7 +192,7 @@ class LSTM_PL(pl.LightningModule):
|
||||
|
||||
@pl.data_loader
|
||||
def val_dataloader(self):
|
||||
df_test = self._get_cache_dfs()["df_test"]
|
||||
df_test = self._get_cache_dfs()["df_val"]
|
||||
dset_test = SequenceDfDataSet(
|
||||
df_test,
|
||||
self.hparams,
|
||||
@@ -216,27 +215,41 @@ class LSTM_PL(pl.LightningModule):
|
||||
return DataLoader(dset_test, batch_size=self.hparams.batch_size, shuffle=False)
|
||||
|
||||
@staticmethod
|
||||
def add_model_specific_args(parent_parser):
|
||||
def add_suggest(trial: optuna.Trial):
|
||||
"""
|
||||
Specify the hyperparams for this LightningModule
|
||||
Add hyperparam ranges to an optuna trial and typical user attrs.
|
||||
|
||||
Usage:
|
||||
trial = optuna.trial.FixedTrial(
|
||||
params={
|
||||
'hidden_size': 128,
|
||||
}
|
||||
)
|
||||
trial = add_suggest(trial)
|
||||
trainer = pl.Trainer()
|
||||
model = LSTM_PL(dict(**trial.params, **trial.user_attrs), dataset_train,
|
||||
dataset_test, cache_base_path, norm)
|
||||
trainer.fit(model)
|
||||
"""
|
||||
# MODEL specific
|
||||
parser = HyperOptArgumentParser(parents=[parent_parser])
|
||||
parser.add_argument("--learning_rate", default=0.002, type=float)
|
||||
parser.add_argument("--batch_size", default=16, type=int)
|
||||
parser.add_argument("--lstm_dropout", default=0.5, type=float)
|
||||
parser.add_argument("--hidden_size", default=16, type=int)
|
||||
parser.add_argument("--input_size", default=8, type=int)
|
||||
parser.add_argument("--lstm_layers", default=8, type=int)
|
||||
parser.add_argument("--bidirectional", default=False, type=bool)
|
||||
trial.suggest_loguniform("learning_rate", 1e-6, 1e-2)
|
||||
trial.suggest_uniform("lstm_dropout", 0, 0.75)
|
||||
trial.suggest_categorical(
|
||||
"hidden_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
|
||||
)
|
||||
trial.suggest_categorical("lstm_layers", [1, 2, 3, 4, 6, 8])
|
||||
trial.suggest_categorical("bidirectional", [False, True])
|
||||
|
||||
# training specific (for this model)
|
||||
parser.add_argument("--window_length", type=int, default=12)
|
||||
parser.add_argument("--target_length", type=int, default=2)
|
||||
parser.add_argument("--max_nb_epochs", default=10, type=int)
|
||||
parser.add_argument("--num_workers", default=4, type=int)
|
||||
|
||||
return parser
|
||||
trial._user_attrs = {
|
||||
"batch_size": 16,
|
||||
"grad_clip": 40,
|
||||
"max_nb_epochs": 200,
|
||||
"num_workers": 4,
|
||||
"vis_i": 670,
|
||||
"input_size": 6,
|
||||
"output_size": 1,
|
||||
"patience": 2,
|
||||
}
|
||||
return trial
|
||||
|
||||
|
||||
def plot_from_loader(loader, model, vis_i=670, n=1, window_len=0):
|
||||
|
||||
+47
-28
@@ -20,7 +20,7 @@ import torch
|
||||
import io
|
||||
import PIL
|
||||
from torchvision.transforms import ToTensor
|
||||
|
||||
from src.models.modules import BatchNormSequence
|
||||
from src.data.smart_meter import get_smartmeter_df
|
||||
|
||||
from src.utils import ObjectDict
|
||||
@@ -41,6 +41,9 @@ class Seq2SeqNet(nn.Module):
|
||||
self.hparams = hparams
|
||||
self._min_std = _min_std
|
||||
|
||||
|
||||
|
||||
self.norm_input = BatchNormSequence(self.hparams.input_size)
|
||||
self.encoder = nn.LSTM(
|
||||
input_size=self.hparams.input_size,
|
||||
hidden_size=self.hparams.hidden_size,
|
||||
@@ -49,6 +52,9 @@ class Seq2SeqNet(nn.Module):
|
||||
bidirectional=self.hparams.bidirectional,
|
||||
dropout=self.hparams.lstm_dropout,
|
||||
)
|
||||
self.multihead_attn = nn.MultiheadAttention(self.hparams.hidden_size, num_heads=8)
|
||||
|
||||
self.norm_target = BatchNormSequence(self.hparams.input_size_decoder)
|
||||
self.decoder = nn.LSTM(
|
||||
input_size=self.hparams.input_size_decoder,
|
||||
hidden_size=self.hparams.hidden_size,
|
||||
@@ -66,9 +72,23 @@ class Seq2SeqNet(nn.Module):
|
||||
|
||||
def forward(self, context_x, context_y, target_x, target_y=None):
|
||||
x = torch.cat([context_x, context_y], -1)
|
||||
|
||||
# Sometimes input normalisation can be important, an initial batch norm is a nice way to ensure this
|
||||
x = self.norm_input(x)
|
||||
target_x = self.norm_target(target_x)
|
||||
|
||||
_, (h_out, cell) = self.encoder(x)
|
||||
# hidden = [batch size, n layers * n directions, hid dim]
|
||||
# cell = [batch size, n layers * n directions, hid dim]
|
||||
|
||||
# context_x, d_encoded, target_x = k, v, q
|
||||
|
||||
# query, key, value = target_x, context_x, d_encoded
|
||||
attn_output, _ = self.multihead_attn(h_out.permute(1, 0, 2), h_out.permute(1, 0, 2), h_out.permute(1, 0, 2))
|
||||
h_out = attn_output.permute(1, 0, 2).contiguous()
|
||||
attn_output, _ = self.multihead_attn(cell.permute(1, 0, 2), cell.permute(1, 0, 2), cell.permute(1, 0, 2))
|
||||
cell = attn_output.permute(1, 0, 2).contiguous()
|
||||
|
||||
outputs, (_, _) = self.decoder(target_x, (h_out, cell))
|
||||
# output = [batch size, seq len, hid dim * n directions]
|
||||
|
||||
@@ -155,7 +175,7 @@ class LSTMSeq2Seq_PL(pl.LightningModule):
|
||||
|
||||
def show_image(self):
|
||||
# https://github.com/PytorchLightning/pytorch-lightning/blob/f8d9f8f/pytorch_lightning/core/lightning.py#L293
|
||||
loader = self.val_dataloader()[0]
|
||||
loader = self.val_dataloader()
|
||||
vis_i = min(int(self.hparams["vis_i"]), len(loader.dataset))
|
||||
# print('vis_i', vis_i)
|
||||
if isinstance(self.hparams["vis_i"], str):
|
||||
@@ -174,15 +194,14 @@ class LSTMSeq2Seq_PL(pl.LightningModule):
|
||||
def configure_optimizers(self):
|
||||
optim = torch.optim.Adam(self.parameters(), lr=self.hparams["learning_rate"])
|
||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
|
||||
optim, patience=2, verbose=True, min_lr=1e-5
|
||||
optim, patience=self.hparams["patience"], verbose=True, min_lr=1e-5
|
||||
) # note early stopping has patient 3
|
||||
return [optim], [scheduler]
|
||||
|
||||
def _get_cache_dfs(self):
|
||||
if self._dfs is None:
|
||||
df_train, df_test = get_smartmeter_df()
|
||||
# self._dfs = dict(df_train=df_train[:600], df_test=df_test[:600])
|
||||
self._dfs = dict(df_train=df_train, df_test=df_test)
|
||||
df_train, df_val, df_test = get_smartmeter_df()
|
||||
self._dfs = dict(df_train=df_train, df_val=df_val, df_test=df_test)
|
||||
return self._dfs
|
||||
|
||||
@pl.data_loader
|
||||
@@ -203,7 +222,7 @@ class LSTMSeq2Seq_PL(pl.LightningModule):
|
||||
|
||||
@pl.data_loader
|
||||
def val_dataloader(self):
|
||||
df_test = self._get_cache_dfs()['df_test']
|
||||
df_test = self._get_cache_dfs()['df_val']
|
||||
data_test = SmartMeterDataSet(
|
||||
df_test, self.hparams["num_context"], self.hparams["num_extra_target"]
|
||||
)
|
||||
@@ -232,25 +251,25 @@ class LSTMSeq2Seq_PL(pl.LightningModule):
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def add_model_specific_args(parent_parser):
|
||||
"""
|
||||
Specify the hyperparams for this LightningModule
|
||||
"""
|
||||
# MODEL specific
|
||||
parser = HyperOptArgumentParser(parents=[parent_parser])
|
||||
parser.add_argument("--learning_rate", default=0.002, type=float)
|
||||
parser.add_argument("--batch_size", default=16, type=int)
|
||||
parser.add_argument("--lstm_dropout", default=0.5, type=float)
|
||||
parser.add_argument("--hidden_size", default=16, type=int)
|
||||
parser.add_argument("--input_size", default=8, type=int)
|
||||
parser.add_argument("--input_size_decoder", default=8, type=int)
|
||||
parser.add_argument("--lstm_layers", default=8, type=int)
|
||||
parser.add_argument("--bidirectional", default=False, type=bool)
|
||||
def add_suggest(trial):
|
||||
trial.suggest_loguniform("learning_rate", 1e-5, 1e-2)
|
||||
trial.suggest_uniform("lstm_dropout", 0, 0.75)
|
||||
trial.suggest_categorical("hidden_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512])
|
||||
trial.suggest_categorical("lstm_layers", [1, 2, 4, 8])
|
||||
trial.suggest_categorical("bidirectional", [False, True])
|
||||
|
||||
|
||||
# training specific (for this model)
|
||||
parser.add_argument("--num_context", type=int, default=12)
|
||||
parser.add_argument("--num_extra_target", type=int, default=2)
|
||||
parser.add_argument("--max_nb_epochs", default=10, type=int)
|
||||
parser.add_argument("--num_workers", default=4, type=int)
|
||||
|
||||
return parser
|
||||
trial._user_attrs = {
|
||||
'batch_size': 16,
|
||||
'grad_clip': 40,
|
||||
'max_nb_epochs': 200,
|
||||
'num_workers': 4,
|
||||
'num_extra_target': 24*4,
|
||||
'vis_i': '670',
|
||||
'num_context': 24*4,
|
||||
'input_size': 18,
|
||||
'input_size_decoder': 17,
|
||||
'context_in_target': True,
|
||||
'output_size': 1
|
||||
}
|
||||
return trial
|
||||
+28
-27
@@ -165,7 +165,7 @@ class LSTM_PL(pl.LightningModule):
|
||||
def validation_end(self, outputs):
|
||||
# TODO send an image to tensroboard, like in the lighting_anp.py file
|
||||
if int(self.hparams["vis_i"]) > 0:
|
||||
loader = self.val_dataloader()[0]
|
||||
loader = self.val_dataloader()
|
||||
vis_i = min(int(self.hparams["vis_i"]), len(loader.dataset))
|
||||
if isinstance(self.hparams["vis_i"], str):
|
||||
image = plot_from_loader(loader, self, vis_i=vis_i)
|
||||
@@ -196,15 +196,14 @@ class LSTM_PL(pl.LightningModule):
|
||||
def configure_optimizers(self):
|
||||
optim = torch.optim.Adam(self.parameters(), lr=self.hparams["learning_rate"])
|
||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
|
||||
optim, patience=2, verbose=True, min_lr=1e-5
|
||||
optim, patience=self.hparams["patience"], verbose=True, min_lr=1e-5
|
||||
) # note early stopping has patient 3
|
||||
return [optim], [scheduler]
|
||||
|
||||
def _get_cache_dfs(self):
|
||||
if self._dfs is None:
|
||||
df_train, df_test = get_smartmeter_df()
|
||||
# self._dfs = dict(df_train=df_train[:600], df_test=df_test[:600])
|
||||
self._dfs = dict(df_train=df_train, df_test=df_test)
|
||||
df_train, df_val, df_test = get_smartmeter_df()
|
||||
self._dfs = dict(df_train=df_train, df_val=df_val, df_test=df_test)
|
||||
return self._dfs
|
||||
|
||||
@pl.data_loader
|
||||
@@ -226,7 +225,7 @@ class LSTM_PL(pl.LightningModule):
|
||||
|
||||
@pl.data_loader
|
||||
def val_dataloader(self):
|
||||
df_test = self._get_cache_dfs()["df_test"]
|
||||
df_test = self._get_cache_dfs()["df_val"]
|
||||
dset_test = SequenceDfDataSet(
|
||||
df_test,
|
||||
self.hparams,
|
||||
@@ -249,27 +248,29 @@ class LSTM_PL(pl.LightningModule):
|
||||
return DataLoader(dset_test, batch_size=self.hparams.batch_size, shuffle=False)
|
||||
|
||||
@staticmethod
|
||||
def add_model_specific_args(parent_parser):
|
||||
"""
|
||||
Specify the hyperparams for this LightningModule
|
||||
"""
|
||||
# MODEL specific
|
||||
parser = HyperOptArgumentParser(parents=[parent_parser])
|
||||
parser.add_argument("--learning_rate", default=0.002, type=float)
|
||||
parser.add_argument("--batch_size", default=16, type=int)
|
||||
parser.add_argument("--lstm_dropout", default=0.5, type=float)
|
||||
parser.add_argument("--hidden_size", default=16, type=int)
|
||||
parser.add_argument("--input_size", default=8, type=int)
|
||||
parser.add_argument("--lstm_layers", default=8, type=int)
|
||||
parser.add_argument("--bidirectional", default=False, type=bool)
|
||||
|
||||
# training specific (for this model)
|
||||
parser.add_argument("--window_length", type=int, default=12)
|
||||
parser.add_argument("--target_length", type=int, default=2)
|
||||
parser.add_argument("--max_nb_epochs", default=10, type=int)
|
||||
parser.add_argument("--num_workers", default=4, type=int)
|
||||
|
||||
return parser
|
||||
def add_suggest(trial):
|
||||
trial.suggest_loguniform("learning_rate", 1e-5, 1e-2)
|
||||
trial.suggest_uniform("lstm_dropout", 0, 0.75)
|
||||
trial.suggest_categorical("hidden_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512])
|
||||
trial.suggest_categorical("lstm_layers", [1, 2, 4, 8])
|
||||
trial.suggest_categorical("bidirectional", [False, True])
|
||||
|
||||
# constants
|
||||
trial._user_attrs = {
|
||||
'batch_size': 16,
|
||||
'grad_clip': 40,
|
||||
'max_nb_epochs': 200,
|
||||
'num_workers': 4,
|
||||
'num_extra_target': 24*4,
|
||||
'vis_i': '670',
|
||||
'num_context': 24*4,
|
||||
'input_size': 18,
|
||||
'input_size_decoder': 17,
|
||||
'context_in_target': True,
|
||||
'output_size': 1,
|
||||
'patience': 3,
|
||||
}
|
||||
return trial
|
||||
|
||||
|
||||
def plot_from_loader(loader, model, vis_i=670, n=1, window_len=0):
|
||||
|
||||
+31
-7
@@ -5,7 +5,7 @@ from torch.utils.data import TensorDataset, DataLoader
|
||||
import math
|
||||
|
||||
from src.models.modules import LatentEncoder, DeterministicEncoder, Decoder
|
||||
|
||||
from src.models.modules import BatchNormSequence
|
||||
|
||||
def log_prob_sigma(value, loc, log_scale):
|
||||
"""A slightly more stable (not confirmed yet) log prob taking in log_var instead of scale.
|
||||
@@ -66,18 +66,32 @@ class LatentModel(nn.Module):
|
||||
self._use_rnn = use_rnn
|
||||
self.context_in_target = context_in_target
|
||||
|
||||
# Sometimes input normalisation can be important, an initial batch norm is a nice way to ensure this
|
||||
self.norm_x = BatchNormSequence(x_dim)
|
||||
self.norm_y = BatchNormSequence(y_dim)
|
||||
|
||||
if self._use_rnn:
|
||||
self._lstm = nn.LSTM(
|
||||
self._lstm_x = nn.LSTM(
|
||||
input_size=x_dim,
|
||||
hidden_size=hidden_dim,
|
||||
num_layers=attention_layers,
|
||||
dropout=dropout,
|
||||
batch_first=True
|
||||
)
|
||||
self._lstm_y = nn.LSTM(
|
||||
input_size=y_dim,
|
||||
hidden_size=hidden_dim,
|
||||
num_layers=attention_layers,
|
||||
dropout=dropout,
|
||||
batch_first=True
|
||||
)
|
||||
x_dim = hidden_dim
|
||||
y_dim2 = hidden_dim
|
||||
else:
|
||||
y_dim2 = y_dim
|
||||
|
||||
self._latent_encoder = LatentEncoder(
|
||||
x_dim + y_dim,
|
||||
x_dim + y_dim2,
|
||||
hidden_dim=hidden_dim,
|
||||
latent_dim=latent_dim,
|
||||
self_attention_type=latent_enc_self_attn_type,
|
||||
@@ -93,7 +107,7 @@ class LatentModel(nn.Module):
|
||||
)
|
||||
|
||||
self._deterministic_encoder = DeterministicEncoder(
|
||||
input_dim=x_dim + y_dim,
|
||||
input_dim=x_dim + y_dim2,
|
||||
x_dim=x_dim,
|
||||
hidden_dim=hidden_dim,
|
||||
self_attention_type=det_enc_self_attn_type,
|
||||
@@ -126,16 +140,26 @@ class LatentModel(nn.Module):
|
||||
|
||||
def forward(self, context_x, context_y, target_x, target_y=None):
|
||||
|
||||
# https://stackoverflow.com/a/46772183/221742
|
||||
target_x = self.norm_x(target_x)
|
||||
context_x = self.norm_x(context_x)
|
||||
context_y = self.norm_y(context_y)
|
||||
|
||||
if self._use_rnn:
|
||||
# see https://arxiv.org/abs/1910.09323 where x is substituted with h = RNN(x)
|
||||
# x need to be provided as [B, T, H]
|
||||
target_x, _ = self._lstm(target_x)
|
||||
context_x, _ = self._lstm(context_x)
|
||||
target_x, _ = self._lstm_x(target_x)
|
||||
context_x, _ = self._lstm_x(context_x)
|
||||
context_y, _ = self._lstm_y(context_y)
|
||||
|
||||
|
||||
dist_prior, log_var_prior = self._latent_encoder(context_x, context_y)
|
||||
|
||||
if target_y is not None:
|
||||
dist_post, log_var_post = self._latent_encoder(target_x, target_y)
|
||||
target_y2 = self.norm_y(target_y)
|
||||
if self._use_rnn:
|
||||
target_y2, _ = self._lstm_y(target_y2)
|
||||
dist_post, log_var_post = self._latent_encoder(target_x, target_y2)
|
||||
z = dist_post.loc
|
||||
else:
|
||||
z = dist_prior.loc
|
||||
|
||||
@@ -24,6 +24,22 @@ class LSTMBlock(nn.Module):
|
||||
return self._lstm(x)[0]
|
||||
|
||||
|
||||
class BatchNormSequence(nn.Module):
|
||||
"""Applies batch norm on features of a batch first sequence."""
|
||||
def __init__(
|
||||
self, out_channels
|
||||
):
|
||||
super().__init__()
|
||||
self.norm = nn.BatchNorm1d(out_channels)
|
||||
|
||||
def forward(self, x):
|
||||
# x.shape is (Batch, Sequence, Channels)
|
||||
# Now we want to apply batchnorm and dropout to the channels. So we put it in shape
|
||||
# (Batch, Channels, Sequence) so we can use BatchNorm1d
|
||||
x = x.permute(0, 2, 1)
|
||||
x = self.norm(x)
|
||||
return x.permute(0, 2, 1)
|
||||
|
||||
class NPBlockRelu2d(nn.Module):
|
||||
"""Block for Neural Processes."""
|
||||
|
||||
|
||||
@@ -19,9 +19,11 @@ from matplotlib import pyplot as plt
|
||||
import torch
|
||||
import io
|
||||
import PIL
|
||||
import optuna
|
||||
from torchvision.transforms import ToTensor
|
||||
|
||||
from src.data.smart_meter import get_smartmeter_df
|
||||
from src.models.modules import BatchNormSequence
|
||||
|
||||
from src.utils import ObjectDict
|
||||
|
||||
@@ -41,7 +43,6 @@ class TransformerSeq2SeqNet(nn.Module):
|
||||
self.hparams = hparams
|
||||
self._min_std = _min_std
|
||||
|
||||
# TODO project to 8*nhead
|
||||
hidden_out_size = self.hparams.hidden_out_size
|
||||
self.enc_emb = nn.Linear(self.hparams.input_size, hidden_out_size)
|
||||
layer_enc = nn.TransformerEncoderLayer(
|
||||
@@ -92,7 +93,7 @@ class TransformerSeq2SeqNet(nn.Module):
|
||||
log_sigma = torch.clamp(log_sigma, math.log(self._min_std), -math.log(self._min_std))
|
||||
|
||||
sigma = torch.exp(log_sigma)
|
||||
y_dist=torch.distributions.Normal(mean, sigma)
|
||||
y_dist = torch.distributions.Normal(mean, sigma)
|
||||
|
||||
# Loss
|
||||
loss_mse = loss_p = None
|
||||
@@ -188,15 +189,14 @@ class TransformerSeq2Seq_PL(pl.LightningModule):
|
||||
def configure_optimizers(self):
|
||||
optim = torch.optim.Adam(self.parameters(), lr=self.hparams["learning_rate"])
|
||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
|
||||
optim, patience=2, verbose=True, min_lr=1e-5
|
||||
) # note early stopping has patient 3
|
||||
optim, patience=self.hparams["patience"], verbose=True, min_lr=1e-7
|
||||
) # note early stopping has patience 3
|
||||
return [optim], [scheduler]
|
||||
|
||||
def _get_cache_dfs(self):
|
||||
if self._dfs is None:
|
||||
df_train, df_test = get_smartmeter_df()
|
||||
# self._dfs = dict(df_train=df_train[:600], df_test=df_test[:600])
|
||||
self._dfs = dict(df_train=df_train, df_test=df_test)
|
||||
df_train, df_val, df_test = get_smartmeter_df()
|
||||
self._dfs = dict(df_train=df_train, df_val=df_val, df_test=df_test)
|
||||
return self._dfs
|
||||
|
||||
@pl.data_loader
|
||||
@@ -217,7 +217,7 @@ class TransformerSeq2Seq_PL(pl.LightningModule):
|
||||
|
||||
@pl.data_loader
|
||||
def val_dataloader(self):
|
||||
df_test = self._get_cache_dfs()['df_test']
|
||||
df_test = self._get_cache_dfs()['df_val']
|
||||
data_test = SmartMeterDataSet(
|
||||
df_test, self.hparams["num_context"], self.hparams["num_extra_target"]
|
||||
)
|
||||
@@ -246,27 +246,41 @@ class TransformerSeq2Seq_PL(pl.LightningModule):
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def add_model_specific_args(parent_parser):
|
||||
def add_suggest(trial: optuna.Trial):
|
||||
"""
|
||||
Specify the hyperparams for this LightningModule
|
||||
Add hyperparam ranges to an optuna trial and typical user attrs.
|
||||
|
||||
Usage:
|
||||
trial = optuna.trial.FixedTrial(
|
||||
params={
|
||||
'hidden_size': 128,
|
||||
}
|
||||
)
|
||||
trial = add_suggest(trial)
|
||||
trainer = pl.Trainer()
|
||||
model = LSTM_PL(dict(**trial.params, **trial.user_attrs), dataset_train,
|
||||
dataset_test, cache_base_path, norm)
|
||||
trainer.fit(model)
|
||||
"""
|
||||
# MODEL specific
|
||||
parser = HyperOptArgumentParser(parents=[parent_parser])
|
||||
parser.add_argument("--learning_rate", default=0.002, type=float)
|
||||
parser.add_argument("--batch_size", default=16, type=int)
|
||||
parser.add_argument("--attention_dropout", default=0.5, type=float)
|
||||
parser.add_argument("--hidden_size", default=16, type=int)
|
||||
parser.add_argument("--hidden_out_size", default=16, type=int)
|
||||
parser.add_argument("--input_size", default=8, type=int)
|
||||
parser.add_argument("--nhead", default=8, type=int)
|
||||
parser.add_argument("--input_size_decoder", default=8, type=int)
|
||||
parser.add_argument("--nlayers", default=8, type=int)
|
||||
# parser.add_argument("--bidirectional", default=False, type=bool)
|
||||
trial.suggest_loguniform("learning_rate", 1e-6, 1e-2)
|
||||
trial.suggest_uniform("attention_dropout", 0, 0.75)
|
||||
trial.suggest_categorical("hidden_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048])
|
||||
trial.suggest_categorical("hidden_out_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048])
|
||||
trial.suggest_categorical("nlayers", [1, 2, 4, 8])
|
||||
trial.suggest_categorical("nhead", [1, 2, 8, 16])
|
||||
|
||||
# training specific (for this model)
|
||||
parser.add_argument("--num_context", type=int, default=12)
|
||||
parser.add_argument("--num_extra_target", type=int, default=2)
|
||||
parser.add_argument("--max_nb_epochs", default=10, type=int)
|
||||
parser.add_argument("--num_workers", default=4, type=int)
|
||||
|
||||
return parser
|
||||
trial._user_attrs = {
|
||||
'batch_size': 16,
|
||||
'grad_clip': 40,
|
||||
'max_nb_epochs': 200,
|
||||
'num_workers': 4,
|
||||
'num_extra_target': 24*4,
|
||||
'vis_i': '670',
|
||||
'num_context': 24*4,
|
||||
'input_size': 18,
|
||||
'input_size_decoder': 17,
|
||||
'context_in_target': True,
|
||||
'output_size': 1,
|
||||
'patience': 3,
|
||||
}
|
||||
return trial
|
||||
+114
@@ -0,0 +1,114 @@
|
||||
from pytorch_lightning.callbacks import EarlyStopping
|
||||
from optuna.integration.pytorch_lightning import _check_pytorch_lightning_availability
|
||||
from pathlib import Path
|
||||
import optuna
|
||||
import pytorch_lightning as pl
|
||||
import torch
|
||||
from .dict_logger import DictLogger
|
||||
from .utils import PyTorchLightningPruningCallback
|
||||
from .plot import plot_from_loader
|
||||
|
||||
|
||||
def main(
|
||||
trial: optuna.Trial,
|
||||
PL_MODEL_CLS: pl.LightningModule,
|
||||
name: str,
|
||||
MODEL_DIR: Path = Path("./lightning_logs"),
|
||||
train=True,
|
||||
prune=True,
|
||||
PERCENT_TEST_EXAMPLES=0.5,
|
||||
):
|
||||
# PyTorch Lightning will try to restore model parameters from previous trials if checkpoint
|
||||
# filenames match. Therefore, the filenames for each trial must be made unique.
|
||||
|
||||
checkpoint_callback = pl.callbacks.ModelCheckpoint(
|
||||
MODEL_DIR / name / "version_{}".format(trial.number) / "chk",
|
||||
monitor="val_loss",
|
||||
mode="min",
|
||||
)
|
||||
|
||||
# The default logger in PyTorch Lightning writes to event files to be consumed by
|
||||
# TensorBoard. We create a simple logger instead that holds the log in memory so that the
|
||||
# final accuracy can be obtained after optimization. When using the default logger, the
|
||||
# final accuracy could be stored in an attribute of the `Trainer` instead.
|
||||
logger = DictLogger(MODEL_DIR, name=name, version=trial.number)
|
||||
# print("log_dir", logger.experiment.log_dir)
|
||||
hparams = dict(**trial.params, **trial.user_attrs)
|
||||
|
||||
trainer = pl.Trainer(
|
||||
logger=logger,
|
||||
val_percent_check=PERCENT_TEST_EXAMPLES,
|
||||
checkpoint_callback=checkpoint_callback,
|
||||
max_epochs=hparams["max_nb_epochs"],
|
||||
gpus=-1 if torch.cuda.is_available() else None,
|
||||
early_stop_callback=PyTorchLightningPruningCallback(trial, monitor="val_loss")
|
||||
if prune
|
||||
else EarlyStopping(
|
||||
patience=hparams["patience"] * 2, monitor="val_loss", verbose=True
|
||||
),
|
||||
)
|
||||
|
||||
model = PL_MODEL_CLS(hparams)
|
||||
if train:
|
||||
trainer.fit(model)
|
||||
return model, trainer
|
||||
|
||||
|
||||
def objective(trial, PL_MODEL_CLS):
|
||||
# see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py
|
||||
trial = PL_MODEL_CLS.add_suggest(trial)
|
||||
|
||||
print("trial", trial.number, "params", trial.params)
|
||||
|
||||
model, trainer = main(trial)
|
||||
|
||||
# also report to tensorboard & print
|
||||
print("logger.metrics", model.logger.metrics[-1:])
|
||||
model.logger.experiment.add_hparams(trial.params, logger.metrics[-1])
|
||||
model.logger.save()
|
||||
|
||||
return model.logger.metrics[-1]["val_loss"]
|
||||
|
||||
|
||||
def add_number(trial: optuna.Trial, model_dir: Path):
|
||||
# For manual experiment we will start at -1 and deincr by 1
|
||||
versions = [int(s.stem.split("_")[-1]) for s in model_dir.glob("version_*")] + [-1]
|
||||
trial.number = min(versions) - 1
|
||||
print("trial.number", trial.number)
|
||||
return trial
|
||||
|
||||
|
||||
def run_trial(
|
||||
name: str,
|
||||
PL_MODEL_CLS: pl.LightningModule,
|
||||
params: dict = {},
|
||||
user_attrs: dict = {},
|
||||
MODEL_DIR: Path = Path("./lightning_logs"),
|
||||
):
|
||||
print(f"now run `tensorboard --logdir {MODEL_DIR}`")
|
||||
(MODEL_DIR / name).mkdir(parents=True, exist_ok=True)
|
||||
trial = optuna.trial.FixedTrial(params=params)
|
||||
trial = PL_MODEL_CLS.add_suggest(trial)
|
||||
trial = add_number(trial, MODEL_DIR / name)
|
||||
trial._user_attrs.update(user_attrs)
|
||||
model, trainer = main(
|
||||
trial, PL_MODEL_CLS, name=name, MODEL_DIR=MODEL_DIR, train=False, prune=False
|
||||
)
|
||||
trainer.fit(model)
|
||||
|
||||
# Load checkpoint
|
||||
checkpoint = sorted(Path(trainer.checkpoint_callback.dirpath).glob("*.ckpt"))[-1]
|
||||
device = next(model.parameters()).device
|
||||
print(f"Loading checkpoint {checkpoint}")
|
||||
model = model.load_from_checkpoint(checkpoint).to(device)
|
||||
|
||||
trainer.test(model)
|
||||
|
||||
# Plot
|
||||
loader = model.val_dataloader()
|
||||
dset_test = loader.dataset
|
||||
label_names = dset_test.label_names
|
||||
plot_from_loader(model.val_dataloader(), model, i=670, title='val 670')
|
||||
plot_from_loader(model.train_dataloader(), model, i=670, title='train 670')
|
||||
plot_from_loader(model.test_dataloader(), model, i=670, title='test 670')
|
||||
return trial, trainer, model
|
||||
+21
-7
@@ -1,5 +1,18 @@
|
||||
from pytorch_lightning.callbacks import EarlyStopping
|
||||
from optuna.integration.pytorch_lightning import _check_pytorch_lightning_availability
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import torch
|
||||
import optuna
|
||||
|
||||
|
||||
def init_random_seed(seed):
|
||||
# https://pytorch.org/docs/stable/notes/randomness.html
|
||||
np.random.seed(seed)
|
||||
torch.random.manual_seed(seed)
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
|
||||
|
||||
class PyTorchLightningPruningCallback(EarlyStopping):
|
||||
"""Optuna PyTorch Lightning callback to prune unpromising trials.
|
||||
@@ -20,10 +33,10 @@ class PyTorchLightningPruningCallback(EarlyStopping):
|
||||
how this dictionary is formatted.
|
||||
"""
|
||||
|
||||
def __init__(self, trial, monitor):
|
||||
def __init__(self, trial, monitor, **kwargs):
|
||||
# type: (optuna.trial.Trial, str) -> None
|
||||
|
||||
super(PyTorchLightningPruningCallback, self).__init__(monitor)
|
||||
super().__init__(monitor, **kwargs)
|
||||
|
||||
_check_pytorch_lightning_availability()
|
||||
|
||||
@@ -41,25 +54,26 @@ class PyTorchLightningPruningCallback(EarlyStopping):
|
||||
message = "Trial was pruned at epoch {}.".format(epoch)
|
||||
raise optuna.exceptions.TrialPruned(message)
|
||||
|
||||
|
||||
class ObjectDict(dict):
|
||||
"""
|
||||
Interface similar to an argparser
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
|
||||
def __setattr__(self, attr, value):
|
||||
self[attr] = value
|
||||
return self[attr]
|
||||
|
||||
|
||||
def __getattr__(self, attr):
|
||||
if attr.startswith('_'):
|
||||
if attr.startswith("_"):
|
||||
# https://stackoverflow.com/questions/10364332/how-to-pickle-python-object-derived-from-dict
|
||||
raise AttributeError
|
||||
return dict(self)[attr]
|
||||
|
||||
|
||||
@property
|
||||
def __dict__(self):
|
||||
return dict(self)
|
||||
|
||||
|
||||
Reference in new issue
Block a user