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| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import torch | |
| from matplotlib.ticker import MultipleLocator | |
| def spec_to_figure(spec, vmin=None, vmax=None, title=None): | |
| if isinstance(spec, torch.Tensor): | |
| spec = spec.cpu().numpy() | |
| fig = plt.figure(figsize=(12, 9)) | |
| plt.pcolor(spec.T, vmin=vmin, vmax=vmax) | |
| if title is not None: | |
| plt.title(title, fontsize=15) | |
| plt.tight_layout() | |
| return fig | |
| def dur_to_figure(dur_gt, dur_pred, txt, title=None): | |
| if isinstance(dur_gt, torch.Tensor): | |
| dur_gt = dur_gt.cpu().numpy() | |
| if isinstance(dur_pred, torch.Tensor): | |
| dur_pred = dur_pred.cpu().numpy() | |
| dur_gt = dur_gt.astype(np.int64) | |
| dur_pred = dur_pred.astype(np.int64) | |
| dur_gt = np.cumsum(dur_gt) | |
| dur_pred = np.cumsum(dur_pred) | |
| width = max(12, min(48, len(txt) // 2)) | |
| fig = plt.figure(figsize=(width, 8)) | |
| plt.vlines(dur_pred, 12, 22, colors='r', label='pred') | |
| plt.vlines(dur_gt, 0, 10, colors='b', label='gt') | |
| for i in range(len(txt)): | |
| shift = (i % 8) + 1 | |
| plt.text((dur_pred[i-1] + dur_pred[i]) / 2 if i > 0 else dur_pred[i] / 2, 12 + shift, txt[i], | |
| size=16, horizontalalignment='center') | |
| plt.text((dur_gt[i-1] + dur_gt[i]) / 2 if i > 0 else dur_gt[i] / 2, shift, txt[i], | |
| size=16, horizontalalignment='center') | |
| plt.plot([dur_pred[i], dur_gt[i]], [12, 10], color='black', linewidth=2, linestyle=':') | |
| plt.yticks([]) | |
| plt.xlim(0, max(dur_pred[-1], dur_gt[-1])) | |
| plt.legend() | |
| if title is not None: | |
| plt.title(title, fontsize=15) | |
| plt.tight_layout() | |
| return fig | |
| def pitch_note_to_figure(pitch_gt, pitch_pred=None, note_midi=None, note_dur=None, note_rest=None, title=None): | |
| if isinstance(pitch_gt, torch.Tensor): | |
| pitch_gt = pitch_gt.cpu().numpy() | |
| if isinstance(pitch_pred, torch.Tensor): | |
| pitch_pred = pitch_pred.cpu().numpy() | |
| if isinstance(note_midi, torch.Tensor): | |
| note_midi = note_midi.cpu().numpy() | |
| if isinstance(note_dur, torch.Tensor): | |
| note_dur = note_dur.cpu().numpy() | |
| if isinstance(note_rest, torch.Tensor): | |
| note_rest = note_rest.cpu().numpy() | |
| fig = plt.figure() | |
| if note_midi is not None and note_dur is not None: | |
| note_dur_acc = np.cumsum(note_dur) | |
| if note_rest is None: | |
| note_rest = np.zeros_like(note_midi, dtype=np.bool_) | |
| for i in range(len(note_midi)): | |
| # if note_rest[i]: | |
| # continue | |
| plt.gca().add_patch( | |
| plt.Rectangle( | |
| xy=(note_dur_acc[i-1] if i > 0 else 0, note_midi[i] - 0.5), | |
| width=note_dur[i], height=1, | |
| edgecolor='grey', fill=False, | |
| linewidth=1.5, linestyle='--' if note_rest[i] else '-' | |
| ) | |
| ) | |
| plt.plot(pitch_gt, color='b', label='gt') | |
| if pitch_pred is not None: | |
| plt.plot(pitch_pred, color='r', label='pred') | |
| plt.gca().yaxis.set_major_locator(MultipleLocator(1)) | |
| plt.grid(axis='y') | |
| plt.legend() | |
| if title is not None: | |
| plt.title(title, fontsize=15) | |
| plt.tight_layout() | |
| return fig | |
| def curve_to_figure(curve_gt, curve_pred=None, curve_base=None, grid=None, title=None): | |
| if isinstance(curve_gt, torch.Tensor): | |
| curve_gt = curve_gt.cpu().numpy() | |
| if isinstance(curve_pred, torch.Tensor): | |
| curve_pred = curve_pred.cpu().numpy() | |
| if isinstance(curve_base, torch.Tensor): | |
| curve_base = curve_base.cpu().numpy() | |
| fig = plt.figure() | |
| if curve_base is not None: | |
| plt.plot(curve_base, color='g', label='base') | |
| plt.plot(curve_gt, color='b', label='gt') | |
| if curve_pred is not None: | |
| plt.plot(curve_pred, color='r', label='pred') | |
| if grid is not None: | |
| plt.gca().yaxis.set_major_locator(MultipleLocator(grid)) | |
| plt.grid(axis='y') | |
| plt.legend() | |
| if title is not None: | |
| plt.title(title, fontsize=15) | |
| plt.tight_layout() | |
| return fig | |
| def distribution_to_figure(title, x_label, y_label, items: list, values: list, zoom=0.8): | |
| fig = plt.figure(figsize=(int(len(items) * zoom), 10)) | |
| plt.bar(x=items, height=values) | |
| plt.tick_params(labelsize=15) | |
| plt.xlim(-1, len(items)) | |
| for a, b in zip(items, values): | |
| plt.text(a, b, b, ha='center', va='bottom', fontsize=15) | |
| plt.grid() | |
| plt.title(title, fontsize=30) | |
| plt.xlabel(x_label, fontsize=20) | |
| plt.ylabel(y_label, fontsize=20) | |
| return fig | |