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from openTSNE import TSNE
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import random
def visualize(
x,
y,
ax=None,
title=None,
draw_legend=True,
draw_centers=False,
draw_cluster_labels=False,
colors=None,
legend_kwargs=None,
label_order=None,
**kwargs
):
if ax is None:
_, ax = matplotlib.pyplot.subplots(figsize=(10, 8))
if title is not None:
ax.set_title(title)
plot_params = {"alpha": kwargs.get("alpha", 0.6), "s": kwargs.get("s", 1)}
# Create main plot
if label_order is not None:
assert all(np.isin(np.unique(y), label_order))
classes = [l for l in label_order if l in np.unique(y)]
else:
classes = np.unique(y)
if colors is None:
default_colors = matplotlib.rcParams["axes.prop_cycle"]
colors = {k: v["color"] for k, v in zip(classes, default_colors())}
point_colors = list(map(colors.get, y))
ax.scatter(x[:, 0], x[:, 1], c=point_colors, rasterized=True, **plot_params)
# Plot mediods
if draw_centers:
centers = []
for yi in classes:
mask = yi == y
centers.append(np.median(x[mask, :2], axis=0))
centers = np.array(centers)
center_colors = list(map(colors.get, classes))
ax.scatter(
centers[:, 0], centers[:, 1], c=center_colors, s=48, alpha=1, edgecolor="k"
)
# Draw mediod labels
if draw_cluster_labels:
for idx, label in enumerate(classes):
ax.text(
centers[idx, 0],
centers[idx, 1] + 2.2,
label,
fontsize=kwargs.get("fontsize", 6),
horizontalalignment="center",
)
# Hide ticks and axis
ax.set_xticks([]), ax.set_yticks([]), ax.axis("off")
if draw_legend:
legend_handles = [
matplotlib.lines.Line2D(
[],
[],
marker="s",
color="w",
markerfacecolor=colors[yi],
ms=10,
alpha=1,
linewidth=0,
label=yi,
markeredgecolor="k",
)
for yi in classes
]
legend_kwargs_ = dict(loc="best", bbox_to_anchor=(0.05, 0.5), frameon=False, )
if legend_kwargs is not None:
legend_kwargs_.update(legend_kwargs)
ax.legend(handles=legend_handles, **legend_kwargs_)
tsne = TSNE(
perplexity=30,
metric="euclidean",
n_jobs=8,
random_state=42,
verbose=True,
)
idexp_lm3d_pred_lrs3 = np.load("infer_out/tmp_npys/lrs3_pred_all.npy")
idx = np.random.choice(np.arange(len(idexp_lm3d_pred_lrs3)), 10000)
idexp_lm3d_pred_lrs3 = idexp_lm3d_pred_lrs3[idx]
person_ds = np.load("data/binary/videos/May/trainval_dataset.npy", allow_pickle=True).tolist()
person_idexp_mean = person_ds['idexp_lm3d_mean'].reshape([1,204])
person_idexp_std = person_ds['idexp_lm3d_std'].reshape([1,204])
person_idexp_lm3d_train = np.stack([s['idexp_lm3d_normalized'].reshape([204,]) for s in person_ds['train_samples']])
person_idexp_lm3d_val = np.stack([s['idexp_lm3d_normalized'].reshape([204,]) for s in person_ds['val_samples']])
lrs3_stats = np.load('/home/yezhenhui/datasets/binary/lrs3_0702/stats.npy',allow_pickle=True).tolist()
lrs3_idexp_mean = lrs3_stats['idexp_lm3d_mean'].reshape([1,204])
lrs3_idexp_std = lrs3_stats['idexp_lm3d_std'].reshape([1,204])
person_idexp_lm3d_train = person_idexp_lm3d_train * person_idexp_std + person_idexp_mean
# person_idexp_lm3d_train = (person_idexp_lm3d_train - lrs3_idexp_mean) / lrs3_idexp_std
person_idexp_lm3d_val = person_idexp_lm3d_val * person_idexp_std + person_idexp_mean
# person_idexp_lm3d_val = (person_idexp_lm3d_val - lrs3_idexp_mean) / lrs3_idexp_std
idexp_lm3d_pred_lrs3 = idexp_lm3d_pred_lrs3 * lrs3_idexp_std + lrs3_idexp_mean
idexp_lm3d_pred_vae = np.load("infer_out/tmp_npys/pred_exp_0_vae.npy").reshape([-1,204])
idexp_lm3d_pred_postnet = np.load("infer_out/tmp_npys/pred_exp_0_postnet_hubert.npy").reshape([-1,204])
# idexp_lm3d_pred_postnet = idexp_lm3d_pred_postnet * lrs3_idexp_std + lrs3_idexp_mean
idexp_lm3d_all = np.concatenate([idexp_lm3d_pred_lrs3, person_idexp_lm3d_train,idexp_lm3d_pred_vae, idexp_lm3d_pred_postnet])
idexp_lm3d_all_emb = tsne.fit(idexp_lm3d_all) # array(float64) [B,50]==>[B, 2]
# z_p_emb = tsne.fit(z_p) # array(float64) [B,50]==>[B, 2]
y1 = ["pred_lrs3" for _ in range(len(idexp_lm3d_pred_lrs3))]
y2 = ["person_train" for _ in range(len(person_idexp_lm3d_train))]
y3 = ["vae" for _ in range(len(idexp_lm3d_pred_vae))]
y4 = ["postnet" for _ in range(len(idexp_lm3d_pred_postnet))]
visualize(idexp_lm3d_all_emb, y1+y2+y3+y4)
plt.savefig("infer_out/tmp_npys/lrs3_pred_all_0k.png") |