#based on https://github.com/CompVis/taming-transformers import matplotlib.pyplot as plt import seaborn as sns import os from pathlib import Path import torchvision import torch import numpy as np from PIL import Image import json import csv import pandas as pd from sklearn.metrics import ConfusionMatrixDisplay def dump_to_json(dict, ckpt_path, name='results', get_fig_path=True): if get_fig_path: root = get_fig_pth(ckpt_path) else: root = ckpt_path if not os.path.exists(root): os.mkdir(root) with open(os.path.join(root, name+".json"), "w") as outfile: json.dump(dict, outfile) def save_to_cvs(ckpt_path, postfix, file_name, list_of_created_sequence): if ckpt_path is not None: root = get_fig_pth(ckpt_path, postfix=postfix) else: root = postfix file = open(os.path.join(root, file_name), 'w') with file: write = csv.writer(file) write.writerows(list_of_created_sequence) def save_to_txt(arr, ckpt_path, name='results'): root = get_fig_pth(ckpt_path) with open(os.path.join(root, name+".txt"), "w") as outfile: outfile.write(str(arr)) def save_image_grid(torch_images, ckpt_path=None, subfolder=None, postfix="", nrow=10): if ckpt_path is not None: root = get_fig_pth(ckpt_path, postfix=subfolder) else: root = subfolder grid = torchvision.utils.make_grid(torch_images, nrow=nrow) grid = torch.clamp(grid, -1., 1.) grid = (grid+1.0)/2.0 # -1,1 -> 0,1; c,h,w grid = grid.transpose(0,1).transpose(1,2).squeeze(-1) grid = grid.cpu().numpy() grid = (grid*255).astype(np.uint8) filename = "code_changes_"+postfix+".png" path = os.path.join(root, filename) os.makedirs(os.path.split(path)[0], exist_ok=True) Image.fromarray(grid).save(path, bbox_inches='tight') def unprocess_image(torch_image): torch_image = torch.clamp(torch_image, -1., 1.) torch_image = (torch_image+1.0)/2.0 # -1,1 -> 0,1; c,h,w torch_image = torch_image.transpose(0,1).transpose(1,2).squeeze(-1) torch_image = torch_image.cpu().numpy() torch_image = (torch_image*255).astype(np.uint8) return torch_image def save_image(torch_image, image_name, ckpt_path=None, subfolder=None): if ckpt_path is not None: root = get_fig_pth(ckpt_path, postfix=subfolder) else: root = subfolder torch_image = unprocess_image(torch_image) filename = image_name+".png" path = os.path.join(root, filename) os.makedirs(os.path.split(path)[0], exist_ok=True) fig = plt.figure() plt.imshow(torch_image[0].squeeze()) fig.savefig(path,bbox_inches='tight',dpi=300) def get_fig_pth(ckpt_path, postfix=None): figs_postfix = 'figs' postfix = os.path.join(figs_postfix, postfix) if postfix is not None else figs_postfix parent_path = Path(ckpt_path).parent.parent.absolute() fig_path = Path(os.path.join(parent_path, postfix)) os.makedirs(fig_path, exist_ok=True) return fig_path def plot_heatmap(heatmap, ckpt_path=None, title='default', postfix=None): if ckpt_path is not None: path = get_fig_pth(ckpt_path, postfix=postfix) else: path = postfix # show fig = plt.figure() ax = plt.imshow(heatmap, cmap='hot', interpolation='nearest') plt.tick_params(left=False, bottom=False) # cbar = ax.collections[0].colorbar cbar = plt.colorbar(ax) cbar.ax.tick_params(labelsize=15) plt.axis('off') plt.show() fig.savefig(os.path.join(path, title+ " heat_map.png"),bbox_inches='tight',dpi=300) pd.DataFrame(heatmap.numpy()).to_csv(os.path.join(path, title+ " heat_map.csv")) def plot_heatmap_at_path(heatmap, save_path, ckpt_path=None, title='default', postfix=None): if ckpt_path is not None: path = get_fig_pth(ckpt_path, postfix=postfix) else: path = postfix # show fig = plt.figure() ax = plt.imshow(heatmap, cmap='hot', interpolation='nearest') plt.tick_params(left=False, bottom=False) # cbar = ax.collections[0].colorbar cbar = plt.colorbar(ax) cbar.ax.tick_params(labelsize=15) plt.axis('off') plt.show() fig.savefig(os.path.join(save_path, title+ "_heat_map.png"),bbox_inches='tight',dpi=300) pd.DataFrame(heatmap.numpy()).to_csv(os.path.join(save_path, title+ "_heat_map.csv")) def plot_confusionmatrix(preds, classes, classnames, ckpt_path, postfix=None, title="", get_fig_path=True): fig, ax = plt.subplots(figsize=(30,30)) preds_max = np.argmax(preds.cpu().numpy(), axis=-1) disp = ConfusionMatrixDisplay.from_predictions(classes.cpu().numpy(), preds_max, display_labels=classnames, normalize='true', xticks_rotation='vertical', ax=ax) disp.plot() if get_fig_path: fig_path = get_fig_pth(ckpt_path, postfix=postfix) else: fig_path = ckpt_path if not os.path.exists(fig_path): os.mkdir(fig_path) print(fig_path) fig.savefig(os.path.join(fig_path, title+ " heat_map.png")) def plot_confusionmatrix_colormap(preds, classes, classnames, ckpt_path, postfix=None, title="", get_fig_path=True): fig, ax = plt.subplots(figsize=(30,30)) preds_max = np.argmax(preds.cpu().numpy(), axis=-1) class_labels = list(range(len(classnames))) disp = ConfusionMatrixDisplay.from_predictions(classes.cpu().numpy(), preds_max, display_labels=class_labels, normalize='true', xticks_rotation='vertical', ax=ax, cmap='coolwarm') disp.plot() if get_fig_path: fig_path = get_fig_pth(ckpt_path, postfix=postfix) else: fig_path = ckpt_path if not os.path.exists(fig_path): os.mkdir(fig_path) print(fig_path) fig.savefig(os.path.join(fig_path, title+ " heat_map_coolwarm.png")) class Histogram_plotter: def __init__(self, codes_per_phylolevel, n_phylolevels, n_embed, converter, indx_to_label, ckpt_path, directory): self.codes_per_phylolevel = codes_per_phylolevel self.n_phylolevels = n_phylolevels self.n_embed = n_embed self.converter = converter self.ckpt_path = ckpt_path self.directory = directory self.indx_to_label = indx_to_label def plot_histograms(self, histograms, species_indx, is_nonattribute=False, prefix="species"): fig, axs = plt.subplots(self.codes_per_phylolevel, self.n_phylolevels, figsize = (5*self.n_phylolevels,30)) for i, ax in enumerate(axs.reshape(-1)): ax.hist(histograms[i], density=True, range=(0, self.n_embed-1), bins=self.n_embed) if not is_nonattribute: code_location, level = self.converter.get_code_reshaped_index(i) ax.set_title("code "+ str(code_location) + "/level " +str(level)) else: ax.set_title("code "+ str(i)) plt.show() sub_dir = 'attribute' if not is_nonattribute else 'non_attribute' fig.savefig(os.path.join(get_fig_pth(self.ckpt_path, postfix=self.directory+'/'+sub_dir), "{}_{}_{}_hostogram.png".format(prefix, species_indx, self.indx_to_label[species_indx])),bbox_inches='tight',dpi=300) plt.close(fig)