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Add Recall@K and AP panels to train_metrics.png

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train_metrics.png now has 6 panels (2x3):
  Row 1: Train Loss, Train R@1/5/10, Train AP
  Row 2: Temperature, Gates, Learning Rate

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
pikaliov
2026-04-24 13:01:51 +03:00
parent e0db8d2726
commit ce7892926f
1 changed files with 40 additions and 11 deletions
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51
src/training/plot_metrics.py
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@@ -25,26 +25,54 @@ sns.set_theme(style="whitegrid", palette="deep", font_scale=1.1)
def plot_train_metrics(train_df: pd.DataFrame, out_dir: Path) -> None:
"""Plot training metrics: loss, temperature, gates, lr."""
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
"""Plot training metrics: loss, recall, AP, temperature, gates, lr."""
fig, axes = plt.subplots(2, 3, figsize=(20, 10))
fig.suptitle("Training Metrics", fontsize=16, fontweight="bold")
# 1. Loss.
ax = axes[0, 0]
sns.lineplot(data=train_df, x="epoch", y="total", ax=ax, marker="o", linewidth=2)
ax.set_title("Loss")
col = "train_loss" if "train_loss" in train_df.columns else "total"
if col in train_df.columns:
sns.lineplot(data=train_df, x="epoch", y=col, ax=ax, marker="o", linewidth=2)
ax.set_title("Train Loss")
ax.set_xlabel("Epoch")
ax.set_ylabel("InfoNCE Loss")
# 2. Temperature (tau).
# 2. Recall@K (train).
ax = axes[0, 1]
sns.lineplot(data=train_df, x="epoch", y="temperature", ax=ax, marker="s", linewidth=2, color="orange")
for k in [1, 5, 10]:
col = f"r@{k}_q2g"
if col in train_df.columns:
df_valid = train_df.dropna(subset=[col])
if not df_valid.empty:
sns.lineplot(data=df_valid, x="epoch", y=col, ax=ax, marker="o", linewidth=2, label=f"R@{k}")
ax.set_title("Train Recall@K (drone → sat)")
ax.set_xlabel("Epoch")
ax.set_ylabel("Recall")
ax.set_ylim(0, 1)
ax.legend()
# 3. Average Precision (train).
ax = axes[0, 2]
if "ap_q2g" in train_df.columns:
df_valid = train_df.dropna(subset=["ap_q2g"])
if not df_valid.empty:
sns.lineplot(data=df_valid, x="epoch", y="ap_q2g", ax=ax, marker="s", linewidth=2, color="purple")
ax.set_title("Train AP (drone → sat)")
ax.set_xlabel("Epoch")
ax.set_ylabel("AP")
ax.set_ylim(0, 1)
# 4. Temperature (tau).
ax = axes[1, 0]
if "temperature" in train_df.columns:
sns.lineplot(data=train_df, x="epoch", y="temperature", ax=ax, marker="s", linewidth=2, color="orange")
ax.set_title("Temperature (τ)")
ax.set_xlabel("Epoch")
ax.set_ylabel("τ")
# 3. Gate values.
ax = axes[1, 0]
# 5. Gate values.
ax = axes[1, 1]
if "gate_q" in train_df.columns and "gate_g" in train_df.columns:
sns.lineplot(data=train_df, x="epoch", y="gate_q", ax=ax, marker="o", linewidth=2, label="gate_q (drone)")
sns.lineplot(data=train_df, x="epoch", y="gate_g", ax=ax, marker="s", linewidth=2, label="gate_g (sat)")
@@ -54,9 +82,10 @@ def plot_train_metrics(train_df: pd.DataFrame, out_dir: Path) -> None:
ax.set_ylabel("Image weight")
ax.set_ylim(0, 1)
# 4. Learning rate.
ax = axes[1, 1]
sns.lineplot(data=train_df, x="epoch", y="lr", ax=ax, marker="^", linewidth=2, color="green")
# 6. Learning rate.
ax = axes[1, 2]
if "lr" in train_df.columns:
sns.lineplot(data=train_df, x="epoch", y="lr", ax=ax, marker="^", linewidth=2, color="green")
ax.set_title("Learning Rate")
ax.set_xlabel("Epoch")
ax.set_ylabel("LR")