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soy cooked

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Stupdi Go
2026-01-19 22:40:05 -06:00
parent 9256050292
commit 16a4f1d2b9
2 changed files with 72 additions and 18 deletions
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26
actual_test.py Normal file
View File

@@ -0,0 +1,26 @@
import os
base_path = "asl_kaggle"
# Check if train.csv exists
print(f"train.csv exists: {os.path.exists(os.path.join(base_path, 'train.csv'))}")
# Check first few rows
import pandas as pd
train_df = pd.read_csv(os.path.join(base_path, "train.csv"))
print("\nFirst few rows:")
print(train_df.head())
# Check if parquet files exist
sample_path = os.path.join(base_path, train_df.iloc[0]['path'])
print(f"\nFirst parquet file exists: {os.path.exists(sample_path)}")
print(f"Full path: {sample_path}")# Test extraction on one file
test_path = os.path.join(base_path, train_df.iloc[0]['path'])
print(f"Testing: {test_path}")
df = pd.read_parquet(test_path)
print(f"\nDataFrame shape: {df.shape}")
print(f"Columns: {df.columns.tolist()}")
print(f"Types present: {df['type'].unique()}")
print(f"Frames: {len(df['frame'].unique())}")
print(f"\nFirst few rows:")
print(df.head(10))

64
training.py
View File

@@ -56,9 +56,9 @@ TOTAL_POINTS_PER_FRAME = NUM_HAND_POINTS + NUM_FACE_POINTS
# =============================== # ===============================
# ENHANCED DATA EXTRACTION # ENHANCED DATA EXTRACTION (FIXED)
# =============================== # ===============================
def extract_multi_landmarks(path, min_valid_frames=5): def extract_multi_landmarks(path, min_valid_frames=3):
""" """
Extract both hands + selected face landmarks with modality flags Extract both hands + selected face landmarks with modality flags
Returns: dict with 'landmarks', 'left_hand_valid', 'right_hand_valid', 'face_valid' Returns: dict with 'landmarks', 'left_hand_valid', 'right_hand_valid', 'face_valid'
@@ -71,12 +71,16 @@ def extract_multi_landmarks(path, min_valid_frames=5):
face_valid_frames = [] face_valid_frames = []
all_types = df["type"].unique() all_types = df["type"].unique()
if "left_hand" in all_types or "right_hand" in all_types or "face" in all_types: # Check if we have at least one of the required types
frames = sorted(df["frame"].unique()) has_data = any(t in all_types for t in ["left_hand", "right_hand", "face"])
else:
if not has_data:
return None return None
if frames is None or len(frames) < min_valid_frames: # Get all frames (might not start at 0)
frames = sorted(df["frame"].unique())
if len(frames) < min_valid_frames:
return None return None
for frame in frames: for frame in frames:
@@ -88,9 +92,9 @@ def extract_multi_landmarks(path, min_valid_frames=5):
right_valid = False right_valid = False
face_valid = False face_valid = False
# Left hand # Left hand (need at least 10 valid points)
left = frame_df[frame_df["type"] == "left_hand"] left = frame_df[frame_df["type"] == "left_hand"]
if len(left) >= 15: if len(left) > 0:
valid_count = 0 valid_count = 0
for i in range(21): for i in range(21):
row = left[left["landmark_index"] == i] row = left[left["landmark_index"] == i]
@@ -98,13 +102,13 @@ def extract_multi_landmarks(path, min_valid_frames=5):
frame_points[pos] = row[['x', 'y', 'z']].values[0] frame_points[pos] = row[['x', 'y', 'z']].values[0]
valid_count += 1 valid_count += 1
pos += 1 pos += 1
left_valid = (valid_count >= 15) left_valid = (valid_count >= 10) # Relaxed from 15
else: else:
pos += 21 pos += 21
# Right hand # Right hand (need at least 10 valid points)
right = frame_df[frame_df["type"] == "right_hand"] right = frame_df[frame_df["type"] == "right_hand"]
if len(right) >= 15: if len(right) > 0:
valid_count = 0 valid_count = 0
for i in range(21): for i in range(21):
row = right[right["landmark_index"] == i] row = right[right["landmark_index"] == i]
@@ -112,11 +116,11 @@ def extract_multi_landmarks(path, min_valid_frames=5):
frame_points[pos] = row[['x', 'y', 'z']].values[0] frame_points[pos] = row[['x', 'y', 'z']].values[0]
valid_count += 1 valid_count += 1
pos += 1 pos += 1
right_valid = (valid_count >= 15) right_valid = (valid_count >= 10) # Relaxed from 15
else: else:
pos += 21 pos += 21
# Face # Face (need at least 30% of selected landmarks)
face = frame_df[frame_df["type"] == "face"] face = frame_df[frame_df["type"] == "face"]
if len(face) > 0: if len(face) > 0:
valid_count = 0 valid_count = 0
@@ -126,10 +130,11 @@ def extract_multi_landmarks(path, min_valid_frames=5):
frame_points[pos] = row[['x', 'y', 'z']].values[0] frame_points[pos] = row[['x', 'y', 'z']].values[0]
valid_count += 1 valid_count += 1
pos += 1 pos += 1
face_valid = (valid_count >= len(IMPORTANT_FACE_INDICES) * 0.5) face_valid = (valid_count >= len(IMPORTANT_FACE_INDICES) * 0.3) # Relaxed from 0.5
# Accept frame if we have at least 20% valid data overall
valid_ratio = 1 - np.isnan(frame_points).mean() valid_ratio = 1 - np.isnan(frame_points).mean()
if valid_ratio >= 0.40: if valid_ratio >= 0.20: # Relaxed from 0.40
frame_points = np.nan_to_num(frame_points, nan=0.0) frame_points = np.nan_to_num(frame_points, nan=0.0)
seq.append(frame_points) seq.append(frame_points)
left_valid_frames.append(left_valid) left_valid_frames.append(left_valid)
@@ -146,7 +151,9 @@ def extract_multi_landmarks(path, min_valid_frames=5):
'face_valid': np.array(face_valid_frames) 'face_valid': np.array(face_valid_frames)
} }
except Exception: except Exception as e:
# Uncomment for debugging:
# print(f"Error processing {path}: {e}")
return None return None
@@ -338,10 +345,14 @@ def main():
print("\nLoading metadata...") print("\nLoading metadata...")
train_df, _ = load_kaggle_asl_data(base_path) train_df, _ = load_kaggle_asl_data(base_path)
print(f"Total samples in train.csv: {len(train_df)}")
# Convert to simple tuples for multiprocessing compatibility # Convert to simple tuples for multiprocessing compatibility
rows = [(row['path'], row['sign']) for _, row in train_df.iterrows()] rows = [(row['path'], row['sign']) for _, row in train_df.iterrows()]
print("\nProcessing sequences with BOTH hands + FACE (enhanced)...") print("\nProcessing sequences with BOTH hands + FACE (enhanced)...")
print("This may take a few minutes...")
with Pool(cpu_count()) as pool: with Pool(cpu_count()) as pool:
results = list(tqdm( results = list(tqdm(
pool.imap( pool.imap(
@@ -354,17 +365,28 @@ def main():
)) ))
X_list, frame_masks_list, modality_masks_list, y_list = [], [], [], [] X_list, frame_masks_list, modality_masks_list, y_list = [], [], [], []
failed_count = 0
for feat, frame_mask, modality_mask, sign in results: for feat, frame_mask, modality_mask, sign in results:
if feat is not None and frame_mask is not None: if feat is not None and frame_mask is not None:
X_list.append(feat) X_list.append(feat)
frame_masks_list.append(frame_mask) frame_masks_list.append(frame_mask)
modality_masks_list.append(modality_mask) modality_masks_list.append(modality_mask)
y_list.append(sign) y_list.append(sign)
else:
failed_count += 1
if not X_list: if not X_list:
print("No valid sequences extracted!") print(f"\n❌ No valid sequences extracted!")
print(f"Failed to process: {failed_count}/{len(results)} files")
print("\nTroubleshooting tips:")
print("1. Check that parquet files contain 'left_hand', 'right_hand', or 'face' types")
print("2. Verify files have at least 3 frames")
print("3. Ensure landmark data is not all NaN")
return return
print(f"\n✓ Successfully processed: {len(X_list)}/{len(results)} files")
print(f"✗ Failed: {failed_count}/{len(results)} files")
X = np.stack(X_list) X = np.stack(X_list)
frame_masks = np.stack(frame_masks_list) frame_masks = np.stack(frame_masks_list)
modality_masks = np.stack(modality_masks_list) modality_masks = np.stack(modality_masks_list)
@@ -455,6 +477,10 @@ def main():
best_acc = 0.0 best_acc = 0.0
epochs = 70 epochs = 70
print("\n" + "=" * 60)
print("TRAINING START")
print("=" * 60)
for epoch in range(epochs): for epoch in range(epochs):
model.train() model.train()
total_loss = correct = total = 0 total_loss = correct = total = 0
@@ -508,7 +534,9 @@ def main():
else: else:
print() print()
print(f"\nBest test accuracy: {best_acc:.2f}%") print("\n" + "=" * 60)
print(f"TRAINING COMPLETE - Best test accuracy: {best_acc:.2f}%")
print("=" * 60)
if __name__ == "__main__": if __name__ == "__main__":