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| # %% | |
| import numpy as np | |
| import torch | |
| from pathlib import Path | |
| import os, sys | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| HEAD = Path(os.getcwd()).parent.parent | |
| head = HEAD / 'logs' | |
| sys.path.append(HEAD) | |
| from dataloaders.JSRT import JSRTDataset | |
| from dataloaders.NIH import NIHDataset | |
| from dataloaders.Montgomery import MonDataset | |
| NIHPATH = "<PATH_TO_DATA>/NIH/" | |
| NIHFILE = "correspondence_with_chestXray8.csv" | |
| MONPATH = "<PATH_TO_DATA>/MontgomerySet/" | |
| MONFILE = "patient_data.csv" | |
| JSRTPATH = "<PATH_TO_DATA>/JSRT" | |
| if __name__=="__main__": | |
| predictions = {'baseline':{'JSRT':{}, 'NIH':{}, 'Montgomery':{}}, | |
| 'LEDM':{'JSRT':{}, 'NIH':{}, 'Montgomery':{}}, | |
| 'TEDM':{'JSRT':{}, 'NIH':{}, 'Montgomery':{}},} | |
| files_needed = ["JSRT_val_predictions.pt", "JSRT_test_predictions.pt", "NIH_predictions.pt", "Montgomery_predictions.pt",] | |
| for exp in ['baseline', 'LEDM', "TEDM"]: | |
| for datasize in [1,3,6,12,24,49,98,197]: | |
| if len(set(files_needed) - set(os.listdir(head / exp / str(datasize) ))) == 0: | |
| for file in files_needed[1:]: | |
| output = torch.load(head / exp / str(datasize) / file) | |
| metrics_datasize = 197 if datasize == "None" else int(datasize) | |
| predictions[exp][file.rsplit("_")[0]][metrics_datasize]= output['y_hat'] | |
| else: | |
| print(f"Experiment {exp} is missing files") | |
| # %% | |
| img_size = 128 | |
| NIH_dataset = NIHDataset(NIHPATH, NIHPATH, NIHFILE, img_size) | |
| JSRT_dataset = JSRTDataset(JSRTPATH, HEAD/ "data/", "JSRT_test_split.csv", img_size) | |
| MON_dataset = MonDataset(MONPATH, MONPATH, MONFILE, img_size) | |
| # %% | |
| loaders = {'JSRT': JSRT_dataset, 'NIH': NIH_dataset, 'Montgomery': MON_dataset} | |
| m ="dice" | |
| sz=4 | |
| ftsize= 40 | |
| fig, all_axs = plt.subplots(6, 21, figsize=(21*sz, 6*sz)) | |
| all_patients = [17, 13, 0, 1, 72, 78] | |
| # JSRT | |
| dataset ="JSRT" | |
| patient = np.random.randint(0, len(loaders[dataset])) | |
| patient = all_patients[0] | |
| print("JSRT1 - ", patient) | |
| out = loaders[dataset][patient] | |
| axs = all_axs[:3, :7] | |
| for rowax, exp in zip(axs, ['baseline', 'LEDM', 'TEDM']): | |
| rowax[0].imshow(out[0][0].numpy(), cmap='gray') | |
| rowax[1].imshow(out[1][0].numpy(), interpolation='none', cmap='gray') | |
| for ax, dssize in zip(rowax[2:], [1, 3, 6, 12, 197]): | |
| ax.imshow(predictions[exp][dataset][dssize][patient].numpy()[0]>.5, interpolation='none') | |
| axs[0, 0].set_title("JSRT - Image", fontsize=ftsize) | |
| axs[0, 1].set_title("JSRT - GT", fontsize=ftsize) | |
| axs[0, 2].set_title("1 (1%)" , fontsize=ftsize) | |
| axs[0, 3].set_title("3 (2%)", fontsize=ftsize) | |
| axs[0, 4].set_title("6 (3%)", fontsize=ftsize) | |
| axs[0, 5].set_title("12 (6%)", fontsize=ftsize) | |
| axs[0, 6].set_title("197 (100%)", fontsize=ftsize) | |
| axs[0,0].set_ylabel("Baseline", fontsize=ftsize) | |
| axs[1,0].set_ylabel("LEDM", fontsize=ftsize) | |
| axs[2,0].set_ylabel("TEDM", fontsize=ftsize) | |
| # | |
| axs = all_axs[3:, :7] | |
| dataset ="JSRT" | |
| patient = np.random.randint(0, len(loaders[dataset])) | |
| patient = all_patients[1] | |
| print("JSRT2 - ", patient) | |
| out = loaders[dataset][patient] | |
| for rowax, exp in zip(axs, ['baseline', 'LEDM', 'TEDM']): | |
| rowax[0].imshow(out[0][0].numpy(), cmap='gray') | |
| rowax[1].imshow(out[1][0].numpy(), interpolation='none', cmap='gray') | |
| for ax, dssize in zip(rowax[2:], [1, 3, 6, 12, 197]): | |
| ax.imshow(predictions[exp][dataset][dssize][patient].numpy()[0]>.5, interpolation='none') | |
| axs[0,0].set_ylabel("Baseline", fontsize=ftsize) | |
| axs[1,0].set_ylabel("LEDM", fontsize=ftsize) | |
| axs[2,0].set_ylabel("TEDM", fontsize=ftsize) | |
| # | |
| axs = all_axs[:3, 7:14] | |
| dataset ="NIH" | |
| patient = np.random.randint(0, len(loaders[dataset])) | |
| patient = all_patients[2] | |
| print("NIH1 - ", patient) | |
| out = loaders[dataset][patient] | |
| for rowax, exp in zip(axs, ['baseline', 'LEDM', 'TEDM']): | |
| rowax[0].imshow(out[0][0].numpy(), cmap='gray') | |
| rowax[1].imshow(out[1][0].numpy(), interpolation='none', cmap='gray') | |
| for ax, dssize in zip(rowax[2:], [1, 3, 6, 12, 197]): | |
| ax.imshow(predictions[exp][dataset][dssize][patient].numpy()[0]>.5, interpolation='none') | |
| axs[0, 0].set_title("NIH - Image", fontsize=ftsize) | |
| axs[0, 1].set_title("NIH - GT", fontsize=ftsize) | |
| axs[0, 2].set_title("1 (1%)" , fontsize=ftsize) | |
| axs[0, 3].set_title("3 (2%)", fontsize=ftsize) | |
| axs[0, 4].set_title("6 (3%)", fontsize=ftsize) | |
| axs[0, 5].set_title("12 (6%)", fontsize=ftsize) | |
| axs[0, 6].set_title("197 (100%)", fontsize=ftsize) | |
| # | |
| # | |
| axs = all_axs[3:, 7:14] | |
| dataset ="NIH" | |
| patient = np.random.randint(0, len(loaders[dataset])) | |
| patient = all_patients[3] | |
| print("NIH2 - ", patient) | |
| out = loaders[dataset][patient] | |
| for rowax, exp in zip(axs, ['baseline', 'LEDM', 'TEDM']): | |
| rowax[0].imshow(out[0][0].numpy(), cmap='gray') | |
| rowax[1].imshow(out[1][0].numpy(), interpolation='none', cmap='gray') | |
| for ax, dssize in zip(rowax[2:], [1, 3, 6, 12, 197]): | |
| ax.imshow(predictions[exp][dataset][dssize][patient].numpy()[0]>.5, interpolation='none') | |
| # | |
| # | |
| axs = all_axs[:3, 14:] | |
| dataset ="Montgomery" | |
| patient = np.random.randint(0, len(loaders[dataset])) | |
| patient = all_patients[4] | |
| print("MON1 - ",patient) | |
| out = loaders[dataset][patient] | |
| for rowax, exp in zip(axs, ['baseline', 'LEDM', 'TEDM']): | |
| rowax[0].imshow(out[0][0].numpy(), cmap='gray') | |
| rowax[1].imshow(out[1][0].numpy(), interpolation='none', cmap='gray') | |
| for ax, dssize in zip(rowax[2:], [1, 3, 6, 12, 197]): | |
| ax.imshow(predictions[exp][dataset][dssize][patient].numpy()[0]>.5, interpolation='none') | |
| axs[0, 0].set_title("Mont. - Image", fontsize=ftsize) | |
| axs[0, 1].set_title("Mont. - GT", fontsize=ftsize) | |
| axs[0, 2].set_title("1 (1%)", fontsize=ftsize) | |
| axs[0, 3].set_title("3 (2%)", fontsize=ftsize) | |
| axs[0, 4].set_title("6 (3%)", fontsize=ftsize) | |
| axs[0, 5].set_title("12 (6%)", fontsize=ftsize) | |
| axs[0, 6].set_title("197 (100%)", fontsize=ftsize) | |
| # | |
| axs = all_axs[3:, 14:] | |
| dataset ="Montgomery" | |
| patient = np.random.randint(0, len(loaders[dataset])) | |
| patient = all_patients[5] | |
| print("MON2 - ",patient) | |
| out = loaders[dataset][patient] | |
| for rowax, exp in zip(axs, ['baseline', 'LEDM', 'TEDM']): | |
| rowax[0].imshow(out[0][0].numpy(), cmap='gray') | |
| rowax[1].imshow(out[1][0].numpy(), interpolation='none', cmap='gray') | |
| for ax, dssize in zip(rowax[2:], [1, 3, 6, 12, 197]): | |
| ax.imshow(predictions[exp][dataset][dssize][patient].numpy()[0]>.5, interpolation='none') | |
| # remove ticks | |
| for ax in all_axs.flatten(): | |
| ax.set_xticks([]) | |
| ax.set_yticks([]) | |
| sns.despine(ax=ax, left=True, bottom=True) | |
| plt.subplots_adjust(wspace=0.00, | |
| hspace=0.00) | |
| plt.tight_layout() | |
| plt.savefig("visualisations2.pdf", bbox_inches='tight') | |
| plt.show() | |