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Quick Setup Test for Basketball Binary Classifier
🧪 This tests if everything is ready before full training
"""
def test_imports():
"""Test if all required modules can be imported"""
print("🧪 Testing imports...")
try:
import torch
import torchvision
print(f"✅ PyTorch: {torch.__version__}")
print(f"✅ TorchVision: {torchvision.__version__}")
if torch.cuda.is_available():
print(f"✅ CUDA: {torch.version.cuda}")
print(f"🚀 GPU: {torch.cuda.get_device_name(0)}")
else:
print("⚠️ CUDA: Not available (will use CPU - slower training)")
except ImportError as e:
print(f"❌ Import error: {e}")
return False
try:
from binary_dataset import get_binary_dataloaders
from binary_model import get_binary_model
print("✅ Custom modules imported successfully")
except ImportError as e:
print(f"❌ Custom module error: {e}")
return False
return True
def test_data_paths():
"""Test if data paths exist"""
print("\n📂 Testing data paths...")
import os
data_root = r"C:\ASH_PROJECT\data\UCF101"
annotation_path = r"C:\ASH_PROJECT\data\ucfTrainTestlist"
if os.path.exists(data_root):
print("✅ UCF101 data directory found")
# Count some directories
dirs = [d for d in os.listdir(data_root) if os.path.isdir(os.path.join(data_root, d))]
print(f" 📁 Found {len(dirs)} action classes")
# Check if Basketball exists
if "Basketball" in dirs:
print("✅ Basketball class found")
basket_path = os.path.join(data_root, "Basketball")
videos = [f for f in os.listdir(basket_path) if f.endswith(('.avi', '.mp4'))]
print(f" 🏀 Basketball videos: {len(videos)}")
else:
print("⚠️ Basketball class not found")
else:
print(f"❌ UCF101 data not found at: {data_root}")
return False
if os.path.exists(annotation_path):
print("✅ Annotation path found")
else:
print(f"❌ Annotations not found at: {annotation_path}")
return False
return True
def test_model_creation():
"""Test if we can create a binary model"""
print("\n🔧 Testing model creation...")
try:
from binary_model import get_binary_model
model = get_binary_model("simple")
print("✅ Binary model created successfully")
# Test with dummy input
import torch
dummy_input = torch.randn(1, 3, 16, 112, 112)
with torch.no_grad():
output = model(dummy_input)
print(f"✅ Model forward pass: {output.shape}")
if output.shape == (1, 2):
print("✅ Correct output shape for binary classification")
else:
print(f"⚠️ Expected (1, 2), got {output.shape}")
return True
except Exception as e:
print(f"❌ Model creation failed: {e}")
return False
def test_dataset_loading():
"""Test if we can load a small batch of data"""
print("\n📊 Testing dataset loading...")
try:
from binary_dataset import get_binary_dataloaders
# Test with very small batch
train_loader, test_loader, class_weights = get_binary_dataloaders(
r"C:\ASH_PROJECT\data\UCF101",
r"C:\ASH_PROJECT\data\ucfTrainTestlist",
target_action="Basketball",
batch_size=2, # Very small for testing
num_workers=0
)
print("✅ DataLoaders created successfully")
print(f" 📊 Training batches: {len(train_loader)}")
print(f" 📊 Test batches: {len(test_loader)}")
print(f" ⚖️ Class weights: {class_weights}")
# Try to load one batch
print("\n🔍 Testing batch loading...")
for clips, labels in train_loader:
print(f"✅ First batch: clips={clips.shape}, labels={labels}")
break
return True
except Exception as e:
print(f"❌ Dataset loading failed: {e}")
return False
def main():
"""Run all tests"""
print("🚀" * 30)
print("🚀 BASKETBALL BINARY CLASSIFIER SETUP TEST")
print("🚀" * 30)
all_passed = True
all_passed &= test_imports()
all_passed &= test_data_paths()
all_passed &= test_model_creation()
all_passed &= test_dataset_loading()
print("\n" + "=" * 50)
if all_passed:
print("🎉 ALL TESTS PASSED!")
print("✅ Ready for basketball binary classifier training!")
print("\n🚀 Next step: Run 'python train_basketball.py'")
else:
print("❌ SOME TESTS FAILED!")
print("💡 Fix the issues above before training")
print("=" * 50)
if __name__ == "__main__":
main() |