mirror of
https://github.com/wassname/attentive-neural-processes.git
synced 2026-08-03 12:40:14 +08:00
fix logging
This commit is contained in:
wassname
+39
-273
@@ -14,8 +14,8 @@
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@@ -76,8 +76,8 @@
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@@ -130,15 +130,15 @@
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@@ -288,8 +288,8 @@
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@@ -416,7 +416,7 @@
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@@ -425,7 +425,7 @@
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"{'number': 0, 'params': {'learning_rate': 2.7156306033353145e-05, 'hidden_dim': 64, 'latent_dim': 1024, 'attention_layers': 2, 'n_latent_encoder_layers': 8, 'n_det_encoder_layers': 1, 'n_decoder_layers': 1, 'dropout': 0.10823088635411837, 'attention_dropout': 0.019358765571233644, 'latent_enc_self_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'uniform', 'det_enc_cross_attn_type': 'uniform', 'use_lvar': True, 'use_deterministic_path': True, 'min_std': 0.005, 'grad_clip': 40, 'num_context': 48, 'num_extra_target': 24, 'max_nb_epochs': 2, 'num_workers': 4, 'batch_size': 16, 'num_heads': 8, 'x_dim': 16, 'y_dim': 1, 'vis_i': 670}}\n"
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@@ -436,19 +436,19 @@
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"INFO:root:VISIBLE GPUS: 0\n",
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"INFO:root:\n",
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" Name Type Params\n",
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"0 model LatentModel 1 M\n",
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"1 model._latent_encoder LatentEncoder 214 K\n",
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"0 model LatentModel 9 M\n",
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"2 model._latent_encoder._input_layer NPBlockRelu2d 10 K\n",
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"4 model._latent_encoder._input_layer.act ReLU 0 \n",
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".. ... ... ...\n",
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"108 model._decoder._decoder.0.act ReLU 0 \n",
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"109 model._decoder._decoder.0.dropout Dropout2d 0 \n",
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"110 model._decoder._decoder.0.norm BatchNorm2d 2 K\n",
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"111 model._decoder._mean Linear 1 K\n",
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"154 model._decoder._decoder.3.act ReLU 0 \n",
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"155 model._decoder._decoder.3.dropout Dropout2d 0 \n",
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"156 model._decoder._decoder.3.norm BatchNorm2d 1 K\n",
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"157 model._decoder._mean Linear 577 \n",
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@@ -469,248 +469,14 @@
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"[I 2020-01-27 15:41:34,744] Finished trial#0 resulted in value: 1684.0211181640625. Current best value is 1684.0211181640625 with parameters: {'learning_rate': 2.7156306033353145e-05, 'hidden_dim': 64, 'latent_dim': 1024, 'attention_layers': 2, 'n_latent_encoder_layers': 8, 'n_det_encoder_layers': 1, 'n_decoder_layers': 1, 'dropout': 0.10823088635411837, 'attention_dropout': 0.019358765571233644, 'latent_enc_self_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'uniform', 'det_enc_cross_attn_type': 'uniform', 'use_lvar': True, 'use_deterministic_path': True, 'min_std': 0.005, 'grad_clip': 40, 'num_context': 48, 'num_extra_target': 24, 'max_nb_epochs': 2, 'num_workers': 4, 'batch_size': 16, 'num_heads': 8, 'x_dim': 16, 'y_dim': 1, 'vis_i': 670}.\n"
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"[I 2020-01-27 15:41:53,354] Finished trial#1 resulted in value: 6.0421600341796875. Current best value is 6.0421600341796875 with parameters: {'learning_rate': 0.009181076644010914, 'hidden_dim': 64, 'latent_dim': 256, 'attention_layers': 4, 'n_latent_encoder_layers': 4, 'n_det_encoder_layers': 16, 'n_decoder_layers': 2, 'dropout': 0.1668729202222619, 'attention_dropout': 0.44446097264782214, 'latent_enc_self_attn_type': 'dot', 'det_enc_self_attn_type': 'multihead', 'det_enc_cross_attn_type': 'multihead', 'use_lvar': True, 'use_deterministic_path': False, 'min_std': 0.005, 'grad_clip': 40, 'num_context': 48, 'num_extra_target': 24, 'max_nb_epochs': 2, 'num_workers': 4, 'batch_size': 16, 'num_heads': 8, 'x_dim': 16, 'y_dim': 1, 'vis_i': 670}.\n"
|
||||
]
|
||||
},
|
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|
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"name": "stdout",
|
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|
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"text": [
|
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"{'number': 2, 'params': {'learning_rate': 0.0010775352720169707, 'hidden_dim': 32, 'latent_dim': 256, 'attention_layers': 3, 'n_latent_encoder_layers': 1, 'n_det_encoder_layers': 8, 'n_decoder_layers': 8, 'dropout': 0.4030815943535827, 'attention_dropout': 0.6302759155214426, 'latent_enc_self_attn_type': 'dot', 'det_enc_self_attn_type': 'uniform', 'det_enc_cross_attn_type': 'uniform', 'use_lvar': False, 'use_deterministic_path': True, 'min_std': 0.005, 'grad_clip': 40, 'num_context': 48, 'num_extra_target': 24, 'max_nb_epochs': 2, 'num_workers': 4, 'batch_size': 16, 'num_heads': 8, 'x_dim': 16, 'y_dim': 1, 'vis_i': 670}}\n"
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
" Name Type Params\n",
|
||||
"0 model LatentModel 858 K\n",
|
||||
"1 model._latent_encoder LatentEncoder 19 K\n",
|
||||
"2 model._latent_encoder._input_layer NPBlockRelu2d 640 \n",
|
||||
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|
||||
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|
||||
".. ... ... ...\n",
|
||||
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|
||||
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|
||||
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|
||||
"119 model._decoder._mean Linear 321 \n",
|
||||
"120 model._decoder._std Linear 321 \n",
|
||||
"\n",
|
||||
"[121 rows x 3 columns]\n"
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"text": [
|
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"step 0, {'val_loss': '0.8895980715751648', 'val/kl': '1.8936854758067057e-05', 'val/std': '0.722773551940918', 'val/mse': '0.3085307776927948'}\n",
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||||
@@ -810,7 +576,7 @@
|
||||
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|
||||
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@@ -827,7 +593,7 @@
|
||||
"execution_count": null,
|
||||
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|
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"start_time": "2020-01-27T07:43:03.900Z"
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||||
"scrolled": true
|
||||
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|
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@@ -846,7 +612,7 @@
|
||||
"execution_count": null,
|
||||
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|
||||
"ExecuteTime": {
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||||
"start_time": "2020-01-27T07:43:04.000Z"
|
||||
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|
||||
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|
||||
"outputs": [],
|
||||
@@ -859,7 +625,7 @@
|
||||
"execution_count": null,
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||||
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|
||||
"ExecuteTime": {
|
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"start_time": "2020-01-27T07:43:04.000Z"
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|
||||
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|
||||
"outputs": [],
|
||||
|
||||
+58
-255
@@ -14,8 +14,8 @@
|
||||
"execution_count": 1,
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||||
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||||
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||||
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|
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@@ -44,8 +44,8 @@
|
||||
"execution_count": 2,
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||||
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||||
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||||
@@ -60,8 +60,8 @@
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-01-27T07:40:38.205268Z",
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"start_time": "2020-01-27T07:40:38.166821Z"
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||||
"end_time": "2020-01-27T07:43:07.709662Z",
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"start_time": "2020-01-27T07:43:07.672259Z"
|
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|
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|
||||
"outputs": [],
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||||
@@ -76,8 +76,8 @@
|
||||
"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2020-01-27T07:40:38.208303Z"
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"end_time": "2020-01-27T07:43:07.814838Z",
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"start_time": "2020-01-27T07:43:07.712319Z"
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||||
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|
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||||
"outputs": [],
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||||
@@ -94,8 +94,8 @@
|
||||
"execution_count": 5,
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||||
"metadata": {
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||||
"ExecuteTime": {
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"end_time": "2020-01-27T07:40:38.365279Z",
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"start_time": "2020-01-27T07:40:38.314895Z"
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||||
"end_time": "2020-01-27T07:43:07.855003Z",
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||||
"start_time": "2020-01-27T07:43:07.816925Z"
|
||||
}
|
||||
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|
||||
"outputs": [],
|
||||
@@ -117,8 +117,8 @@
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
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||||
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||||
"end_time": "2020-01-27T07:43:16.639754Z",
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||||
"start_time": "2020-01-27T07:43:07.857131Z"
|
||||
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|
||||
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|
||||
"outputs": [],
|
||||
@@ -157,15 +157,15 @@
|
||||
"execution_count": 7,
|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
||||
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|
||||
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|
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||||
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|
||||
"<matplotlib.legend.Legend at 0x7f404c4607b8>"
|
||||
"<matplotlib.legend.Legend at 0x7f74c55a8d30>"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
@@ -217,8 +217,8 @@
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-01-27T07:40:48.161001Z",
|
||||
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|
||||
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|
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||||
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|
||||
"outputs": [],
|
||||
@@ -235,8 +235,8 @@
|
||||
"execution_count": 9,
|
||||
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|
||||
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||||
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||||
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|
||||
"outputs": [],
|
||||
@@ -275,11 +275,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
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"end_time": "2020-01-27T07:41:22.368105Z",
|
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"start_time": "2020-01-27T07:41:22.312012Z"
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"end_time": "2020-01-27T07:43:17.542735Z",
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"start_time": "2020-01-27T07:43:17.487704Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -316,7 +316,7 @@
|
||||
" # 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(\"tb_logs\", name=\"anp\", version=trial.number)\n",
|
||||
" logger = DictLogger(MODEL_DIR, name=\"lstm\", version=trial.number)\n",
|
||||
" print(\"log_dir\", logger.experiment.log_dir)\n",
|
||||
"\n",
|
||||
" trainer = pl.Trainer(\n",
|
||||
@@ -343,11 +343,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
"outputs": [
|
||||
@@ -355,7 +355,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0 {'learning_rate': 1.961387604469747e-05, 'lstm_dropout': 0.1891197441240466, 'hidden_size': 1, 'lstm_layers': 16, 'bidirectional': False, 'window_length': 48, 'target_length': 24, 'max_nb_epochs': 2, 'num_workers': 4, 'grad_clip': 40, 'vis_i': 670, 'input_size': 17, 'batch_size': 16}\n",
|
||||
"0 {'learning_rate': 0.0066558126890954965, 'lstm_dropout': 0.6404976003848417, 'hidden_size': 128, 'lstm_layers': 128, 'bidirectional': True, 'window_length': 48, 'target_length': 24, 'max_nb_epochs': 2, 'num_workers': 4, 'grad_clip': 40, 'vis_i': 670, 'input_size': 17, 'batch_size': 16}\n",
|
||||
"log_dir tb_logs/anp/version_0\n"
|
||||
]
|
||||
},
|
||||
@@ -367,8 +367,8 @@
|
||||
"INFO:root:VISIBLE GPUS: 0\n",
|
||||
"INFO:root:\n",
|
||||
" Name Type Params\n",
|
||||
"0 lstm1 LSTM 320 \n",
|
||||
"1 linear Linear 17 \n"
|
||||
"0 lstm1 LSTM 50 M\n",
|
||||
"1 linear Linear 32 K\n"
|
||||
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|
||||
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|
||||
{
|
||||
@@ -389,228 +389,14 @@
|
||||
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||||
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|
||||
"step 0, {'val_loss': '0.2277267426252365'}\n",
|
||||
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|
||||
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|
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|
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|
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"[{'val_loss': 0.23126697540283203, 'epoch': 1}]\n"
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"[I 2020-01-27 15:41:47,100] Finished trial#0 resulted in value: 0.23126697540283203. Current best value is 0.23126697540283203 with parameters: {'learning_rate': 1.961387604469747e-05, 'lstm_dropout': 0.1891197441240466, 'hidden_size': 1, 'lstm_layers': 16, 'bidirectional': False, 'window_length': 48, 'target_length': 24, 'max_nb_epochs': 2, 'num_workers': 4, 'grad_clip': 40, 'vis_i': 670, 'input_size': 17, 'batch_size': 16}.\n"
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|
||||
"log_dir tb_logs/anp/version_1\n"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
"INFO:root:VISIBLE GPUS: 0\n",
|
||||
"INFO:root:\n",
|
||||
" Name Type Params\n",
|
||||
"0 lstm1 LSTM 100 K\n",
|
||||
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|
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|
||||
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|
||||
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|
||||
"text": [
|
||||
"step 0, {'val_loss': '0.23207616806030273'}\n",
|
||||
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},
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"metadata": {},
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||||
"output_type": "display_data"
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},
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{
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||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"step 32, {'val_loss': '0.0821017175912857'}\n"
|
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]
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},
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{
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"data": {
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"text/plain": [
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"HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=9.0, style=Prog…"
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},
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"metadata": {},
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"output_type": "display_data"
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{
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"name": "stdout",
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"output_type": "stream",
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||||
"text": [
|
||||
"step 65, {'val_loss': '0.09262563288211823'}\n",
|
||||
"\n",
|
||||
"[{'val_loss': 0.09262563288211823, 'epoch': 1}]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
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||||
"output_type": "stream",
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||||
"text": [
|
||||
"[I 2020-01-27 15:42:14,176] Finished trial#1 resulted in value: 0.09262563288211823. Current best value is 0.09262563288211823 with parameters: {'learning_rate': 0.0001324964724521358, 'lstm_dropout': 0.6061575334689335, 'hidden_size': 16, 'lstm_layers': 16, 'bidirectional': True, 'window_length': 48, 'target_length': 24, 'max_nb_epochs': 2, 'num_workers': 4, 'grad_clip': 40, 'vis_i': 670, 'input_size': 17, 'batch_size': 16}.\n"
|
||||
]
|
||||
},
|
||||
{
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||||
"name": "stdout",
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"output_type": "stream",
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"text": [
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||||
"2 {'learning_rate': 0.000769512914491473, 'lstm_dropout': 0.4010391775925083, 'hidden_size': 4, 'lstm_layers': 64, 'bidirectional': True, 'window_length': 48, 'target_length': 24, 'max_nb_epochs': 2, 'num_workers': 4, 'grad_clip': 40, 'vis_i': 670, 'input_size': 17, 'batch_size': 16}\n",
|
||||
"log_dir tb_logs/anp/version_2\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO:root:gpu available: True, used: True\n",
|
||||
"INFO:root:VISIBLE GPUS: 0\n",
|
||||
"INFO:root:\n",
|
||||
" Name Type Params\n",
|
||||
"0 lstm1 LSTM 28 K\n",
|
||||
"1 linear Linear 513 \n"
|
||||
]
|
||||
},
|
||||
{
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"HBox(children=(FloatProgress(value=0.0, description='Validation sanity check', layout=Layout(flex='2'), max=5.…"
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"metadata": {},
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"output_type": "display_data"
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},
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{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"step 0, {'val_loss': '0.41099223494529724'}\n",
|
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"\r"
|
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]
|
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "439a6b749da849aba385fc562daf3c08",
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"version_major": 2,
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"version_minor": 0
|
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},
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@@ -625,11 +411,22 @@
|
||||
"name": "stderr",
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||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Exception ignored in: <function tqdm.__del__ at 0x7f4097607048>\n",
|
||||
"Exception in thread Thread-4:\n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/tqdm/std.py\", line 1061, in __del__\n",
|
||||
" def __del__(self):\n",
|
||||
"KeyboardInterrupt: \n"
|
||||
" File \"/home/wassname/.pyenv/versions/3.7.3/lib/python3.7/threading.py\", line 917, in _bootstrap_inner\n",
|
||||
" self.run()\n",
|
||||
" File \"/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/tensorboard/summary/writer/event_file_writer.py\", line 211, in run\n",
|
||||
" self._record_writer.write(data)\n",
|
||||
" File \"/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/tensorboard/summary/writer/record_writer.py\", line 39, in write\n",
|
||||
" self._writer.write(header + header_crc + data + footer_crc)\n",
|
||||
" File \"/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/io/gfile.py\", line 507, in write\n",
|
||||
" self.fs.append(self.filename, file_content, self.binary_mode)\n",
|
||||
" File \"/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/io/gfile.py\", line 150, in append\n",
|
||||
" self._write(filename, file_content, \"ab\" if binary_mode else \"a\")\n",
|
||||
" File \"/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/io/gfile.py\", line 154, in _write\n",
|
||||
" with io.open(filename, mode, encoding=encoding) as f:\n",
|
||||
"FileNotFoundError: [Errno 2] No such file or directory: b'tb_logs/anp/version_0/events.out.tfevents.1580110997.mjcdesktop.9368.0'\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -639,20 +436,26 @@
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
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"\u001b[0;32m<ipython-input-16-6b19a7555e5a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mstudy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_study\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'minimize'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpruner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpruner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6000\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 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Number of finished trials: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m<ipython-input-11-6b19a7555e5a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mstudy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_study\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'minimize'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpruner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpruner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6000\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 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Number of finished trials: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m self._optimize_sequential(func, n_trials, timeout, catch, callbacks,\n\u001b[0;32m--> 302\u001b[0;31m gc_after_trial, None)\n\u001b[0m\u001b[1;32m 303\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0mtime_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_optimize_sequential\u001b[0;34m(self, func, n_trials, timeout, catch, callbacks, gc_after_trial, time_start)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_trial_and_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgc_after_trial\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 539\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial_and_callbacks\u001b[0;34m(self, func, catch, callbacks, gc_after_trial)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0;31m# type: (...) -> None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgc_after_trial\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 551\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mfrozen_trial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trial_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(self, func, catch, gc_after_trial)\u001b[0m\n\u001b[1;32m 567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 568\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\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 570\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialPruned\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 571\u001b[0m message = 'Setting status of trial#{} as {}. {}'.format(trial_number,\n",
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"\u001b[0;32m<ipython-input-15-a1b77f69801f>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0;32mglobal\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLSTM_PL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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 47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m<ipython-input-10-a1b77f69801f>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0;32mglobal\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLSTM_PL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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 47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;31m# ON CPU\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py\u001b[0m in \u001b[0;36msingle_gpu_train\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_pretrain_routine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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 442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdp_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mrun_pretrain_routine\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 839\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 840\u001b[0m \u001b[0;31m# CORE TRAINING LOOP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 841\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\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 842\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 843\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[0;31m# RUN TNG EPOCH\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 332\u001b[0m \u001b[0;31m# -----------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 333\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_epoch\u001b[0m\u001b[0;34m(\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 334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[0;31m# update LR schedulers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0;31m# RUN TRAIN STEP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;31m# ---------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\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 389\u001b[0m \u001b[0mbatch_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_norm_dic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_step_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_batch\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 537\u001b[0m model.optimizer_step(self.current_epoch, batch_idx,\n\u001b[0;32m--> 538\u001b[0;31m optimizer, opt_idx, optimizer_closure)\n\u001b[0m\u001b[1;32m 539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[0;31m# calculate running loss for display\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/core/lightning.py\u001b[0m in \u001b[0;36moptimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure)\u001b[0m\n\u001b[1;32m 790\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msecond_order_closure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 792\u001b[0;31m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\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 793\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 794\u001b[0m \u001b[0;31m# clear gradients\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;31m# Decay the first and second moment running average coefficient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m \u001b[0mexp_avg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\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 96\u001b[0m \u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddcmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mamsgrad\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 417\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mshould_log_metrics\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfast_dev_run\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0;31m# logs user requested information to logger\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 419\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_step_metrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_norm_dic\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 420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 421\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_step\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/logging.py\u001b[0m in \u001b[0;36mlog_metrics\u001b[0;34m(self, metrics, grad_norm_dic, step)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproc_rank\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar_metrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\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 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0madd_tqdm_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/logging/base.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrank\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_fn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/logging/tensorboard.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperiment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\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 103\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;31m# you are using PT version (<v1.2) which does not have implemented flush\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/utils/tensorboard/writer.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 956\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 957\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mwriter\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_writers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 958\u001b[0;31m \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\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 959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 960\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/utils/tensorboard/writer.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[0mdisk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 132\u001b[0m \"\"\"\n\u001b[0;32m--> 133\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevent_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\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 134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/tensorboard/summary/writer/event_file_writer.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0mdisk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \"\"\"\n\u001b[0;32m--> 106\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_async_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\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 107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/tensorboard/summary/writer/event_file_writer.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_closed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Writer is closed'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_byte_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\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 156\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/3.7.3/lib/python3.7/queue.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_tasks_done\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munfinished_tasks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_tasks_done\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\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 90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mqsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/3.7.3/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\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 297\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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]
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}
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Reference in New Issue
Block a user