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File size: 1,979 Bytes
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clear
% Define a folder containing your text files
folder_path = 'D:\Projects\Diffusion-Models-pytorch\logs\results\logs\20231028_1753_Infer_monai_pix2pix\saved_logs';
% Create arrays to store the metric values
all_SSIM = [];
all_MAE = [];
all_PSNR = [];
% Get a list of all text files in the folder
file_list = dir(fullfile(folder_path, '*.txt'));
for i = 1:length(file_list)
file_name = file_list(i).name;
% Check if the file name contains "infer_log_step"
if contains(file_name, 'infer_log_step')
file_path = fullfile(folder_path, file_name);
% Read the file
file_contents = fileread(file_path);
% -?
% Define regular expressions to match the lines with "val set" and the metrics
pattern_val_set = 'val set \d+,\s+SSIM: metatensor\((-?[\d.]+)\),\s+MAE: metatensor\((-?[\d.]+)\),\s+PSNR: metatensor\((-?[\d.]+)\)';
% Use regular expressions to extract values from the file
val_set_matches = regexp(file_contents, pattern_val_set, 'tokens');
% Loop through val set matches and extract the values
for j = 1:numel(val_set_matches)
values = val_set_matches{j};
SSIM_val = str2double(values{1});
MAE_val = str2double(values{2});
PSNR_val = str2double(values{3});
% Append values to the arrays
all_SSIM = [all_SSIM, SSIM_val];
all_MAE = [all_MAE, MAE_val];
all_PSNR = [all_PSNR, PSNR_val];
end
end
end
% Calculate mean and standard deviation for each metric
mean_SSIM = mean(all_SSIM);
std_SSIM = std(all_SSIM);
mean_MAE = mean(all_MAE);
std_MAE = std(all_MAE);
mean_PSNR = mean(all_PSNR);
std_PSNR = std(all_PSNR);
% Display the results
fprintf('Mean SSIM: %f, Std SSIM: %f\n', mean_SSIM, std_SSIM);
fprintf('Mean MAE: %f, Std MAE: %f\n', mean_MAE, std_MAE);
fprintf('Mean PSNR: %f, Std PSNR: %f\n', mean_PSNR, std_PSNR); |