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from typing import Optional
import matplotlib.pyplot as plt
import numpy as np
try:
from torch.utils.tensorboard import SummaryWriter
except ModuleNotFoundError:
SummaryWriter = None
try:
import wandb
except ModuleNotFoundError:
wandb = None
plt.ioff()
PLOT_FILE_NAME = "metrics_plot.png"
def safe_index(arr, idx):
return arr[idx] if 0 <= idx < len(arr) else None
class MetricsPlotSink:
"""
The MetricsPlotSink class records training metrics and saves them to a plot.
Args:
output_dir (str): Directory where the plot will be saved.
"""
def __init__(self, output_dir: str):
self.output_dir = output_dir
self.history = []
def update(self, values: dict):
self.history.append(values)
def save(self):
if not self.history:
print("No data to plot.")
return
def get_array(key):
return np.array([h[key] for h in self.history if key in h])
epochs = get_array('epoch')
train_loss = get_array('train_loss')
test_loss = get_array('test_loss')
test_coco_eval = [h['test_coco_eval_bbox'] for h in self.history if 'test_coco_eval_bbox' in h]
ap50_90 = np.array([safe_index(x, 0) for x in test_coco_eval if x is not None], dtype=np.float32)
ap50 = np.array([safe_index(x, 1) for x in test_coco_eval if x is not None], dtype=np.float32)
ar50_90 = np.array([safe_index(x, 8) for x in test_coco_eval if x is not None], dtype=np.float32)
ema_coco_eval = [h['ema_test_coco_eval_bbox'] for h in self.history if 'ema_test_coco_eval_bbox' in h]
ema_ap50_90 = np.array([safe_index(x, 0) for x in ema_coco_eval if x is not None], dtype=np.float32)
ema_ap50 = np.array([safe_index(x, 1) for x in ema_coco_eval if x is not None], dtype=np.float32)
ema_ar50_90 = np.array([safe_index(x, 8) for x in ema_coco_eval if x is not None], dtype=np.float32)
fig, axes = plt.subplots(2, 2, figsize=(18, 12))
# Subplot (0,0): Training and Validation Loss
if len(epochs) > 0:
if len(train_loss):
axes[0][0].plot(epochs, train_loss, label='Training Loss', marker='o', linestyle='-')
if len(test_loss):
axes[0][0].plot(epochs, test_loss, label='Validation Loss', marker='o', linestyle='--')
axes[0][0].set_title('Training and Validation Loss')
axes[0][0].set_xlabel('Epoch Number')
axes[0][0].set_ylabel('Loss Value')
axes[0][0].legend()
axes[0][0].grid(True)
# Subplot (0,1): Average Precision @0.50
if ap50.size > 0 or ema_ap50.size > 0:
if ap50.size > 0:
axes[0][1].plot(epochs[:len(ap50)], ap50, marker='o', linestyle='-', label='Base Model')
if ema_ap50.size > 0:
axes[0][1].plot(epochs[:len(ema_ap50)], ema_ap50, marker='o', linestyle='--', label='EMA Model')
axes[0][1].set_title('Average Precision @0.50')
axes[0][1].set_xlabel('Epoch Number')
axes[0][1].set_ylabel('AP50')
axes[0][1].legend()
axes[0][1].grid(True)
# Subplot (1,0): Average Precision @0.50:0.95
if ap50_90.size > 0 or ema_ap50_90.size > 0:
if ap50_90.size > 0:
axes[1][0].plot(epochs[:len(ap50_90)], ap50_90, marker='o', linestyle='-', label='Base Model')
if ema_ap50_90.size > 0:
axes[1][0].plot(epochs[:len(ema_ap50_90)], ema_ap50_90, marker='o', linestyle='--', label='EMA Model')
axes[1][0].set_title('Average Precision @0.50:0.95')
axes[1][0].set_xlabel('Epoch Number')
axes[1][0].set_ylabel('AP')
axes[1][0].legend()
axes[1][0].grid(True)
# Subplot (1,1): Average Recall @0.50:0.95
if ar50_90.size > 0 or ema_ar50_90.size > 0:
if ar50_90.size > 0:
axes[1][1].plot(epochs[:len(ar50_90)], ar50_90, marker='o', linestyle='-', label='Base Model')
if ema_ar50_90.size > 0:
axes[1][1].plot(epochs[:len(ema_ar50_90)], ema_ar50_90, marker='o', linestyle='--', label='EMA Model')
axes[1][1].set_title('Average Recall @0.50:0.95')
axes[1][1].set_xlabel('Epoch Number')
axes[1][1].set_ylabel('AR')
axes[1][1].legend()
axes[1][1].grid(True)
plt.tight_layout()
plt.savefig(f"{self.output_dir}/{PLOT_FILE_NAME}")
plt.close(fig)
print(f"Results saved to {self.output_dir}/{PLOT_FILE_NAME}")
class MetricsTensorBoardSink:
"""
Training metrics via TensorBoard.
Args:
output_dir (str): Directory where TensorBoard logs will be written.
"""
def __init__(self, output_dir: str):
if SummaryWriter:
self.writer = SummaryWriter(log_dir=output_dir)
print(f"TensorBoard logging initialized. To monitor logs, use 'tensorboard --logdir {output_dir}' and open http://localhost:6006/ in browser.")
else:
self.writer = None
print("Unable to initialize TensorBoard. Logging is turned off for this session. Run 'pip install tensorboard' to enable logging.")
def update(self, values: dict):
if not self.writer:
return
epoch = values['epoch']
if 'train_loss' in values:
self.writer.add_scalar("Loss/Train", values['train_loss'], epoch)
if 'test_loss' in values:
self.writer.add_scalar("Loss/Test", values['test_loss'], epoch)
if 'test_coco_eval_bbox' in values:
coco_eval = values['test_coco_eval_bbox']
ap50_90 = safe_index(coco_eval, 0)
ap50 = safe_index(coco_eval, 1)
ar50_90 = safe_index(coco_eval, 8)
if ap50_90 is not None:
self.writer.add_scalar("Metrics/Base/AP50_90", ap50_90, epoch)
if ap50 is not None:
self.writer.add_scalar("Metrics/Base/AP50", ap50, epoch)
if ar50_90 is not None:
self.writer.add_scalar("Metrics/Base/AR50_90", ar50_90, epoch)
if 'ema_test_coco_eval_bbox' in values:
ema_coco_eval = values['ema_test_coco_eval_bbox']
ema_ap50_90 = safe_index(ema_coco_eval, 0)
ema_ap50 = safe_index(ema_coco_eval, 1)
ema_ar50_90 = safe_index(ema_coco_eval, 8)
if ema_ap50_90 is not None:
self.writer.add_scalar("Metrics/EMA/AP50_90", ema_ap50_90, epoch)
if ema_ap50 is not None:
self.writer.add_scalar("Metrics/EMA/AP50", ema_ap50, epoch)
if ema_ar50_90 is not None:
self.writer.add_scalar("Metrics/EMA/AR50_90", ema_ar50_90, epoch)
self.writer.flush()
def close(self):
if not self.writer:
return
self.writer.close()
class MetricsWandBSink:
"""
Training metrics via W&B.
Args:
output_dir (str): Directory where W&B logs will be written locally.
project (str, optional): Associate this training run with a W&B project. If None, W&B will generate a name based on the git repo name.
run (str, optional): W&B run name. If None, W&B will generate a random name.
config (dict, optional): Input parameters, like hyperparameters or data preprocessing settings for the run for later comparison.
"""
def __init__(self, output_dir: str, project: Optional[str] = None, run: Optional[str] = None, config: Optional[dict] = None):
self.output_dir = output_dir
if wandb:
self.run = wandb.init(
project=project,
name=run,
config=config,
dir=output_dir
)
print(f"W&B logging initialized. To monitor logs, open {wandb.run.url}.")
else:
self.run = None
print("Unable to initialize W&B. Logging is turned off for this session. Run 'pip install wandb' to enable logging.")
def update(self, values: dict):
if not wandb or not self.run:
return
epoch = values['epoch']
log_dict = {"epoch": epoch}
if 'train_loss' in values:
log_dict["Loss/Train"] = values['train_loss']
if 'test_loss' in values:
log_dict["Loss/Test"] = values['test_loss']
if 'test_coco_eval_bbox' in values:
coco_eval = values['test_coco_eval_bbox']
ap50_90 = safe_index(coco_eval, 0)
ap50 = safe_index(coco_eval, 1)
ar50_90 = safe_index(coco_eval, 8)
if ap50_90 is not None:
log_dict["Metrics/Base/AP50_90"] = ap50_90
if ap50 is not None:
log_dict["Metrics/Base/AP50"] = ap50
if ar50_90 is not None:
log_dict["Metrics/Base/AR50_90"] = ar50_90
if 'ema_test_coco_eval_bbox' in values:
ema_coco_eval = values['ema_test_coco_eval_bbox']
ema_ap50_90 = safe_index(ema_coco_eval, 0)
ema_ap50 = safe_index(ema_coco_eval, 1)
ema_ar50_90 = safe_index(ema_coco_eval, 8)
if ema_ap50_90 is not None:
log_dict["Metrics/EMA/AP50_90"] = ema_ap50_90
if ema_ap50 is not None:
log_dict["Metrics/EMA/AP50"] = ema_ap50
if ema_ar50_90 is not None:
log_dict["Metrics/EMA/AR50_90"] = ema_ar50_90
wandb.log(log_dict)
def close(self):
if not wandb or not self.run:
return
self.run.finish() |