VictorMylle/Thesis
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Saving samples plot as png at end of training

Browse Source
This commit is contained in:
Victor Mylle
2024-04-19 14:05:20 +02:00
parent 4e713ef564
commit 46c7c6f7e5
4 changed files with 60 additions and 53 deletions
Download Patch File Download Diff File Expand all files Collapse all files

45
src/trainers/autoregressive_trainer.py
View File

@@ -45,7 +45,6 @@ class AutoRegressiveTrainer(Trainer):
else:
initial, _, predictions, target = auto_regressive_output
# keep one initial
initial = initial[0]
target = target[0]
@@ -55,20 +54,38 @@ class AutoRegressiveTrainer(Trainer):
initial, target, predictions, show_legend=(0 == 0)
)
task.get_logger().report_matplotlib_figure(
title="Training" if train else "Testing",
series=f"Sample {actual_idx}",
iteration=epoch,
figure=fig,
)
if epoch > 0:
task.get_logger().report_matplotlib_figure(
title="Training" if train else "Testing",
series=f"Sample {actual_idx}",
iteration=epoch,
figure=fig,
)
task.get_logger().report_matplotlib_figure(
title="Training Samples" if train else "Testing Samples",
series=f"Sample {actual_idx} samples",
iteration=epoch,
figure=fig2,
report_interactive=False,
)
task.get_logger().report_matplotlib_figure(
title="Training Samples" if train else "Testing Samples",
series=f"Sample {actual_idx} samples",
iteration=epoch,
figure=fig2,
report_interactive=False,
)
else:
fig.savefig(f"sample_{actual_idx}_plot.png")
task.get_logger().report_image(
title="Final Training Plot",
series=f"Sample {actual_idx}",
iteration=epoch,
local_path=f"sample_{actual_idx}_plot.png",
)
fig2.savefig(f"sample_{actual_idx}_samples_plot.png")
task.get_logger().report_image(
title="Final Training Samples Plot",
series=f"Sample {actual_idx} samples",
iteration=epoch,
local_path=f"sample_{actual_idx}_samples_plot.png",
)
plt.close()

36
src/trainers/quantile_trainer.py
View File

@@ -630,9 +630,7 @@ class NonAutoRegressiveQuantileRegression(Trainer):
name=metric_name, value=metric_value
)
def debug_plots(
self, task, train: bool, data_loader, sample_indices, epoch, final=False
):
def debug_plots(self, task, train: bool, data_loader, sample_indices, epoch):
for actual_idx, idx in sample_indices.items():
features, target, _ = data_loader.dataset[idx]
@@ -653,22 +651,24 @@ class NonAutoRegressiveQuantileRegression(Trainer):
features[:96], target, samples, show_legend=(0 == 0)
)
task.get_logger().report_matplotlib_figure(
title="Training" if train else "Testing",
series=f"Sample {actual_idx}",
iteration=epoch,
figure=fig,
)
if epoch != -1:
task.get_logger().report_matplotlib_figure(
title="Training" if train else "Testing",
series=f"Sample {actual_idx}",
iteration=epoch,
figure=fig,
)
task.get_logger().report_matplotlib_figure(
title="Training Samples" if train else "Testing Samples",
series=f"Sample {actual_idx} samples",
iteration=epoch,
figure=fig2,
report_interactive=False,
)
task.get_logger().report_matplotlib_figure(
title="Training Samples" if train else "Testing Samples",
series=f"Sample {actual_idx} samples",
iteration=epoch,
figure=fig2,
report_interactive=False,
)
if final:
else:
print("Saving figs")
# fig to PIL image
fig.savefig(f"sample_{actual_idx}_plot.png")
task.get_logger().report_image(
@@ -789,7 +789,7 @@ class NonAutoRegressiveQuantileRegression(Trainer):
for i in range(10):
ax2.plot(predictions_np[i], label=f"Sample {i}")
ax2.plot(next_day_np, label="Real NRV", linewidth=3)
ax2.plot(next_day_np, label="Real NRV", linewidth=4, color="orange")
ax2.legend()
ax2.set_ylim(-1500, 1500)

18
src/trainers/trainer.py
View File

@@ -198,6 +198,7 @@ class Trainer:
if task:
self.finish_training(task=task)
self.debug_plots(task, False, test_loader, test_samples, -1)
# task.close()
except Exception:
if task:
@@ -342,21 +343,8 @@ class Trainer:
features[:96], target, predictions, show_legend=(0 == 0)
)
task.get_logger().report_matplotlib_figure(
title="Training" if train else "Testing",
series=f"Sample {actual_idx}",
iteration=epoch,
figure=fig,
)
task.get_logger().report_matplotlib_figure(
title="Training Samples" if train else "Testing Samples",
series=f"Sample {actual_idx} samples",
iteration=epoch,
figure=fig2,
report_interactive=False,
)
)
plt.close()
def debug_scatter_plot(self, task, train: bool, samples, epoch):

14
src/training_scripts/autoregressive_quantiles.py
View File

@@ -2,7 +2,9 @@ from src.utils.clearml import ClearMLHelper
#### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(task_name="AQR: Linear Baseline + Quarter Trigonometric")
task = clearml_helper.get_task(
task_name="AQR: Linear Baseline + Load + PV + Wind + Net Position + Quarter"
)
task.execute_remotely(queue_name="default", exit_process=True)
from src.policies.PolicyEvaluator import PolicyEvaluator
@@ -70,16 +72,16 @@ model_parameters = {
"hidden_size": 256,
"num_layers": 2,
"dropout": 0.2,
"time_feature_embedding": 2,
"time_feature_embedding": 5,
}
model_parameters = task.connect(model_parameters, name="model_parameters")
# time_embedding = TimeEmbedding(
# data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"]
# )
time_embedding = TimeEmbedding(
data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"]
)
time_embedding = TrigonometricTimeEmbedding(data_processor.get_time_feature_size())
# time_embedding = TrigonometricTimeEmbedding(data_processor.get_time_feature_size())
# lstm_model = GRUModel(
# time_embedding.output_dim(inputDim),