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#!/usr/bin/env python3
"""
Test script to verify all dependencies are working correctly
"""
import sys
import traceback
def test_imports():
"""Test importing all required dependencies"""
print("Testing imports...")
try:
import torch
print(f"✓ PyTorch {torch.__version__}")
print(f" CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f" CUDA version: {torch.version.cuda}")
print(f" GPU count: {torch.cuda.device_count()}")
except Exception as e:
print(f"✗ PyTorch import failed: {e}")
return False
# Check if torch-sparse is available or disabled
try:
import torch_sparse
print("✓ torch-sparse")
except ImportError:
# Check if torch-sparse was disabled
try:
with open("NeuralJacobianFields/PoissonSystem.py", 'r') as f:
content = f.read()
if "USE_TORCH_SPARSE = False" in content:
print("✓ torch-sparse (disabled, using built-in PyTorch sparse)")
else:
print("✗ torch-sparse (not available)")
return False
except:
print("✗ torch-sparse (not available)")
return False
except Exception as e:
print(f"✗ torch-sparse import failed: {e}")
return False
# Check if torch-scatter is available (not critical)
try:
import torch_scatter
print("✓ torch-scatter")
except ImportError:
print("⚠ torch-scatter (not available, may not be critical)")
except Exception as e:
print(f"⚠ torch-scatter import failed: {e} (may not be critical)")
try:
import nvdiffrast
print("✓ nvdiffrast")
except Exception as e:
print(f"✗ nvdiffrast import failed: {e}")
return False
try:
import pytorch3d
print("✓ PyTorch3D")
except Exception as e:
print(f"✗ PyTorch3D import failed: {e}")
return False
try:
import clip
print("✓ CLIP")
except Exception as e:
print(f"✗ CLIP import failed: {e}")
return False
return True
def test_basic_functionality():
"""Test basic functionality of key components"""
print("\nTesting basic functionality...")
try:
import torch
# Test basic tensor operations
x = torch.randn(10, 10)
y = torch.randn(10, 10)
z = x + y
print("✓ Basic tensor operations")
# Test sparse operations (using built-in PyTorch sparse)
indices = torch.randint(0, 10, (2, 20))
values = torch.randn(20)
sparse_tensor = torch.sparse_coo_tensor(indices, values, (10, 10))
print("✓ Sparse tensor creation")
# Test if torch-sparse is available (optional)
try:
import torch_sparse
print("✓ torch-sparse operations available")
except ImportError:
print("✓ Using built-in PyTorch sparse operations")
# Test if torch-scatter is available (optional)
try:
import torch_scatter
print("✓ torch-scatter operations available")
except ImportError:
print("✓ torch-scatter not available (not critical)")
except Exception as e:
print(f"✗ Basic functionality test failed: {e}")
traceback.print_exc()
return False
return True
def main():
"""Main test function"""
print("=== Garment3DGen Dependency Test ===\n")
# Test imports
if not test_imports():
print("\n❌ Import tests failed!")
sys.exit(1)
# Test basic functionality
if not test_basic_functionality():
print("\n❌ Functionality tests failed!")
sys.exit(1)
print("\n✅ All tests passed! Dependencies are working correctly.")
print("The Garment3DGen application should be ready to run.")
if __name__ == "__main__":
main() |