wehub-resource-sync
123 lines
4.5 KiB
Python
123 lines
4.5 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from matplotlib.ticker import MultipleLocator
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def spec_to_figure(spec, vmin=None, vmax=None, title=None):
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if isinstance(spec, torch.Tensor):
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spec = spec.cpu().numpy()
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fig = plt.figure(figsize=(12, 9))
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plt.pcolormesh(spec.T, vmin=vmin, vmax=vmax)
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if title is not None:
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plt.title(title, fontsize=15)
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plt.tight_layout()
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return fig
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def dur_to_figure(dur_gt, dur_pred, txt, title=None):
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if isinstance(dur_gt, torch.Tensor):
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dur_gt = dur_gt.cpu().numpy()
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if isinstance(dur_pred, torch.Tensor):
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dur_pred = dur_pred.cpu().numpy()
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dur_gt = dur_gt.astype(np.int64)
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dur_pred = dur_pred.astype(np.int64)
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dur_gt = np.cumsum(dur_gt)
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dur_pred = np.cumsum(dur_pred)
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width = max(12, min(48, len(txt) // 2))
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fig = plt.figure(figsize=(width, 8))
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plt.vlines(dur_pred, 12, 22, colors='r', label='pred')
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plt.vlines(dur_gt, 0, 10, colors='b', label='gt')
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for i in range(len(txt)):
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shift = (i % 8) + 1
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plt.text((dur_pred[i-1] + dur_pred[i]) / 2 if i > 0 else dur_pred[i] / 2, 12 + shift, txt[i],
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size=16, horizontalalignment='center')
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plt.text((dur_gt[i-1] + dur_gt[i]) / 2 if i > 0 else dur_gt[i] / 2, shift, txt[i],
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size=16, horizontalalignment='center')
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plt.plot([dur_pred[i], dur_gt[i]], [12, 10], color='black', linewidth=2, linestyle=':')
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plt.yticks([])
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plt.xlim(0, max(dur_pred[-1], dur_gt[-1]))
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plt.legend()
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if title is not None:
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plt.title(title, fontsize=15)
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plt.tight_layout()
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return fig
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def pitch_note_to_figure(pitch_gt, pitch_pred=None, note_midi=None, note_dur=None, note_rest=None, title=None):
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if isinstance(pitch_gt, torch.Tensor):
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pitch_gt = pitch_gt.cpu().numpy()
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if isinstance(pitch_pred, torch.Tensor):
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pitch_pred = pitch_pred.cpu().numpy()
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if isinstance(note_midi, torch.Tensor):
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note_midi = note_midi.cpu().numpy()
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if isinstance(note_dur, torch.Tensor):
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note_dur = note_dur.cpu().numpy()
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if isinstance(note_rest, torch.Tensor):
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note_rest = note_rest.cpu().numpy()
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fig = plt.figure()
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if note_midi is not None and note_dur is not None:
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note_dur_acc = np.cumsum(note_dur)
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if note_rest is None:
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note_rest = np.zeros_like(note_midi, dtype=np.bool_)
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for i in range(len(note_midi)):
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# if note_rest[i]:
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# continue
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plt.gca().add_patch(
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plt.Rectangle(
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xy=(note_dur_acc[i-1] if i > 0 else 0, note_midi[i] - 0.5),
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width=note_dur[i], height=1,
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edgecolor='grey', fill=False,
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linewidth=1.5, linestyle='--' if note_rest[i] else '-'
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)
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)
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plt.plot(pitch_gt, color='b', label='gt')
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if pitch_pred is not None:
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plt.plot(pitch_pred, color='r', label='pred')
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plt.gca().yaxis.set_major_locator(MultipleLocator(1))
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plt.grid(axis='y')
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plt.legend()
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if title is not None:
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plt.title(title, fontsize=15)
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plt.tight_layout()
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return fig
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def curve_to_figure(curve_gt, curve_pred=None, curve_base=None, grid=None, title=None):
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if isinstance(curve_gt, torch.Tensor):
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curve_gt = curve_gt.cpu().numpy()
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if isinstance(curve_pred, torch.Tensor):
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curve_pred = curve_pred.cpu().numpy()
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if isinstance(curve_base, torch.Tensor):
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curve_base = curve_base.cpu().numpy()
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fig = plt.figure()
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if curve_base is not None:
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plt.plot(curve_base, color='g', label='base')
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plt.plot(curve_gt, color='b', label='gt')
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if curve_pred is not None:
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plt.plot(curve_pred, color='r', label='pred')
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if grid is not None:
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plt.gca().yaxis.set_major_locator(MultipleLocator(grid))
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plt.grid(axis='y')
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plt.legend()
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if title is not None:
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plt.title(title, fontsize=15)
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plt.tight_layout()
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return fig
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def distribution_to_figure(title, x_label, y_label, items: list, values: list, zoom=0.8, rotate=False):
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fig = plt.figure(figsize=(int(len(items) * zoom), 10))
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plt.bar(x=items, height=values)
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plt.tick_params(labelsize=15)
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plt.xlim(-1, len(items))
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for a, b in zip(items, values):
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plt.text(a, b, b, ha='center', va='bottom', fontsize=15)
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plt.grid()
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plt.title(title, fontsize=30)
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plt.xlabel(x_label, fontsize=20)
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plt.ylabel(y_label, fontsize=20)
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if rotate:
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fig.autofmt_xdate(rotation=45)
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return fig
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