chore: import upstream snapshot with attribution

utils/plot.py

@@ -0,0 +1,122 @@
+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.pcolormesh(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, rotate=False):
+    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)
+    if rotate:
+        fig.autofmt_xdate(rotation=45)
+    return fig