I’m a designer with no coding skills. I recently discovered that using CUDA is more than 10 times faster than using the CPU for image synthesis. However, when I run the image synthesis script made with PyCUDA using GPT, I encounter an error:
PyCUDA WARNING: a clean-up operation failed (dead context maybe?)
cuMemFree failed: an illegal memory access was encountered
PyCUDA WARNING: a clean-up operation failed (dead context maybe?)
cuMemFree failed: an illegal memory access was encountered
Traceback (most recent call last):
File "mapHE4b.py", line 102, in merge_diamond_images_pycuda
image_gpu = cuda.mem_alloc(image.nbytes)
pycuda._driver.LogicError: cuMemAlloc failed: an illegal memory access was encountered
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "mapHE4b.py", line 153, in <module>
merge_diamond_images_pycuda(map_id)
File "mapHE4b.py", line 135, in merge_diamond_images_pycuda
except cuda.CudaError as e:
AttributeError: module 'pycuda.driver' has no attribute 'CudaError'
PyCUDA WARNING: a clean-up operation failed (dead context maybe?)
cuModuleUnload failed: an illegal memory access was encountered
The error occurs only when synthesizing more than 3? images; it works fine with fewer images, and the hardware resources are far from being fully utilized.
Here is the script content:
import subprocess
import sys
import time
import os
import numpy as np
import cv2
import pycuda.driver as cuda
import pycuda.autoinit
from pycuda.compiler import SourceModule
def check_and_install_package(package_name, import_name=None):
if import_name is None:
import_name = package_name
try:
__import__(import_name)
except ImportError:
print(f"{package_name} not found, installing...")
subprocess.check_call([sys.executable, "-m", "pip", "install", package_name])
# Ensure necessary packages are installed
check_and_install_package('setuptools')
check_and_install_package('pycuda')
check_and_install_package('opencv-python')
# CUDA kernel for image merging
cuda_code = """
__global__ void merge_images(unsigned char* canvas, const unsigned char* image,
int canvas_width, int canvas_height,
int image_width, int image_height,
int x_offset, int y_offset) {
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x < image_width && y < image_height) {
int canvas_x = x + x_offset;
int canvas_y = y + y_offset;
if (canvas_x >= 0 && canvas_x < canvas_width && canvas_y >= 0 && canvas_y < canvas_height) {
int canvas_idx = (canvas_y * canvas_width + canvas_x) * 4;
int image_idx = (y * image_width + x) * 4;
float alpha = image[image_idx + 3] / 255.0f;
for (int c = 0; c < 3; c++) {
canvas[canvas_idx + c] = (unsigned char)(
alpha * image[image_idx + c] +
(1 - alpha) * canvas[canvas_idx + c]
);
}
canvas[canvas_idx + 3] = max(canvas[canvas_idx + 3], image[image_idx + 3]);
}
}
}
"""
# Compile the CUDA kernel
mod = SourceModule(cuda_code)
merge_images = mod.get_function("merge_images")
def merge_diamond_images_pycuda(map_id):
start_time = time.time()
# Get all png files in the current directory
png_files = [f for f in os.listdir('.') if f.endswith('.png') and f.startswith(str(map_id))]
# Filter out files that do not match the format
valid_files = [f for f in png_files if len(f.split('_')) == 3]
# Extract unique N and M values
n_values = sorted(set(int(f.split('_')[1]) for f in valid_files))
m_values = sorted(set(int(f.split('_')[2].split('.')[0]) for f in valid_files))
sN = len(n_values)
sM = len(m_values)
# Calculate overall canvas size
canvas_width = int((sN + sM) / 2 * 6144)
canvas_height = int((sN + sM) / 2 * 3072)
# If there are no valid image files, skip processing
if not valid_files:
print(f"No valid image files found, skipping processing")
return
try:
# Create canvas
canvas = cuda.mem_alloc(canvas_width * canvas_height * 4)
cuda.memset_d8(canvas, 0, canvas_width * canvas_height * 4)
# Draw diamond images
for n in reversed(n_values):
for m in reversed(m_values):
filename = f"{map_id}_{n}_{m}.png"
if filename in valid_files:
image = cv2.imread(filename, cv2.IMREAD_UNCHANGED)
if image.shape[2] == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA)
x = (n_values.index(n) + m_values.index(m)) * 3072 - 768
y = (sN - 1 - n_values.index(n) + m_values.index(m)) * 1536 - 792
# Allocate memory on GPU for the image
image_gpu = cuda.mem_alloc(image.nbytes)
cuda.memcpy_htod(image_gpu, image)
# Set up grid and block dimensions
block_dim = (32, 32, 1)
grid_dim = ((image.shape[1] + block_dim[0] - 1) // block_dim[0],
(image.shape[0] + block_dim[1] - 1) // block_dim[1])
# Call CUDA kernel
merge_images(canvas, image_gpu, np.int32(canvas_width), np.int32(canvas_height),
np.int32(image.shape[1]), np.int32(image.shape[0]),
np.int32(x), np.int32(y),
block=block_dim, grid=grid_dim)
# Free GPU memory for the image
image_gpu.free()
# Copy result back to CPU
result = np.zeros((canvas_height, canvas_width, 4), dtype=np.uint8)
cuda.memcpy_dtoh(result, canvas)
# Save the result
output_filename = f"{map_id}_all.png"
cv2.imwrite(output_filename, result)
end_time = time.time()
elapsed_time = end_time - start_time
print(f"Merge complete, saved as {output_filename}")
print(f"Processing time: {elapsed_time:.2f} seconds")
except cuda.MemoryError:
print("Insufficient GPU memory. Try using smaller images or increasing GPU memory.")
except cuda.CudaError as e:
print(f"CUDA error: {e}")
except Exception as e:
print(f"An error occurred: {e}")
finally:
# Free GPU memory
if 'canvas' in locals():
canvas.free()
# Get all unique map_ids
map_ids = sorted(set(f.split('_')[0] for f in os.listdir('.') if f.endswith('.png')))
# Process all map_ids or a specified map_id
map_id = None # Process images for all map_ids
if map_id:
merge_diamond_images_pycuda(map_id)
else:
for map_id in map_ids:
merge_diamond_images_pycuda(map_id)
I had GPT review all relevant error resolution methods, but it did not return the correct result.
Could someone please help me out?