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shg8
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Apr 11, 2024
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import pandas as pd | ||
import matplotlib.pyplot as plt | ||
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# Ask for the path to the CSV file | ||
path = input('Enter the path to the CSV file: ') | ||
df = pd.read_csv(path) | ||
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# Convert nanoseconds to milliseconds | ||
for col in ['preprocess', 'prefix_sum', 'preprocess_sort', 'sort', 'tile_boundary', 'render']: | ||
df[col] = df[col] / 1e6 # Convert from ns to ms | ||
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# Sort DataFrame by num_instances | ||
df = df.sort_values(by='num_instances') | ||
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# Order the kernels according to the execution sequence | ||
kernel_sequence = ['preprocess', 'prefix_sum', 'preprocess_sort', 'sort', 'tile_boundary', 'render'] | ||
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# Calculate cumulative sums for the specified order | ||
df_cumulative = df.copy() | ||
for i, kernel in enumerate(kernel_sequence): | ||
if i == 0: | ||
df_cumulative[kernel + '_cumulative'] = df_cumulative[kernel] | ||
else: | ||
df_cumulative[kernel + '_cumulative'] = df_cumulative[kernel_sequence[i-1] + '_cumulative'] + df_cumulative[kernel] | ||
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# Plotting | ||
plt.figure(figsize=(10, 6)) | ||
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# Collect line plots to access their colors for the fill | ||
line_plots = [] | ||
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# Plot lines for all kernels to store their colors | ||
for kernel in kernel_sequence: | ||
cumulative_column = kernel + '_cumulative' | ||
line_plot, = plt.plot(df_cumulative['num_instances'], df_cumulative[cumulative_column], label=kernel) | ||
line_plots.append(line_plot) | ||
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plt.fill_between(df_cumulative['num_instances'], 0, df_cumulative[kernel_sequence[0] + '_cumulative'], color=line_plots[0].get_color(), alpha=0.3) | ||
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# Fill the area with the color of the line above | ||
for i in range(len(line_plots)-1): | ||
plt.fill_between(df_cumulative['num_instances'], df_cumulative[kernel_sequence[i] + '_cumulative'], df_cumulative[kernel_sequence[i+1] + '_cumulative'], color=line_plots[i+1].get_color(), alpha=0.3) | ||
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plt.xlabel('Number of Instances') | ||
plt.ylabel('Milliseconds') | ||
plt.title('Cumulative Duration of Compute Kernels') | ||
plt.legend() | ||
plt.grid(True) | ||
plt.show() | ||
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# Save the plot | ||
plt.savefig('cumulative_duration.png') |
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import os | ||
import subprocess | ||
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# Ask for the folder and executable if not in environment variables | ||
folder_path = os.getenv("MODEL_FOLDER_PATH") | ||
executable_path = os.getenv("3DGS_VIEWER_PATH") | ||
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resolutions = input("Please enter the resolutions (separated by comma): ") | ||
resolutions = resolutions.split(',') | ||
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if not folder_path: | ||
folder_path = input("Please enter the folder containing model folders: ") | ||
if not executable_path: | ||
executable_path = input("Please enter the 3dgs_viewer path: ") | ||
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# List the files in the folder | ||
files_in_folder = os.listdir(folder_path) | ||
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# Run the command for each file | ||
for file in files_in_folder: | ||
# skip non-folders | ||
if not os.path.isdir(os.path.join(folder_path, file)): | ||
continue | ||
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for resolution in resolutions: | ||
width, height = resolution.split('x') | ||
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file_path = os.path.join(folder_path, file) | ||
basename = os.path.basename(file_path) + "_" + resolution | ||
print(f"Running benchmark for {basename}") | ||
command = [executable_path, "--no-gui", "--width", width, "--height", height, "-i", "-v", "--validation", "-b", | ||
"../benchmark_output/" + basename + ".csv", "--", file_path] | ||
subprocess.run(command) |
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