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import torch
import torch.nn
import torchvision.models as models
from copy import deepcopy
import cv2
import cv2
import numpy as np
import sys
import itertools
import os
import IPython
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import pandas as pd
import openai
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA, KernelPCA
import seaborn as sns
import time
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import colorsys
from torchvision import datasets
import argparse
import matplotlib.patheffects as PathEffects
from sklearn.cluster import KMeans
sns.set_style("white")
sns.set_palette("muted")
font = {
"size": 22,
}
matplotlib.rc("font", **font)
sns.set_context("paper", font_scale=3.0)
plt_param = {'legend.fontsize': 60,
'axes.labelsize': 80,
'axes.titlesize':80,
'font.size' : 80 ,
'xtick.labelsize':80,
'ytick.labelsize':80,
'lines.linewidth': 10,
'lines.color': (0,0,0)}
plt.rcParams.update(plt_param)
openai.api_key ="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"
GPT_MODEL = "gpt4"
EMBEDDING_MODEL = "text-embedding-ada-002"
def normalize_numpy_array(arr):
return arr / (arr.max(axis=-1, keepdims=True) - arr.min(axis=-1, keepdims=True))
def fashion_scatter(
x, class_labels, fig_name, class_names, add_text=True
):
# choose a color palette with seaborn.
x = np.array(x)
class_labels = np.array(class_labels)
num_classes = np.max(class_labels) + 1
# create a scatter plot.
fig_size1, fig_size2 = 140 * 0.8, 80 * 0.6
plt.clf()
plt.cla()
f = plt.figure(figsize=(fig_size1, fig_size2))
ax = plt.subplot()
# divide by a scale
# x = normalize_numpy_array(x)
for x_i in range(num_classes):
mask = class_labels == x_i
if mask.sum() > 0:
sc = ax.scatter(
x[mask, 0],
x[mask, 1],
lw=0,
s=1500,
label=class_names[x_i]
# c=rgb_color[mask],
) # 40
if add_text:
txts = []
for i in range(len(class_names)):
xtext, ytext = x[i, :] # np.median(x[i, :], axis=0)
txt = ax.text(xtext, ytext, str(class_names[i]), fontsize=40) # 24
txt.set_path_effects(
[PathEffects.Stroke(linewidth=5, foreground="w"), PathEffects.Normal()]
)
txts.append(txt)
# ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
ax.axis("on")
# ax.axis("tight")
plt.savefig(fig_name +".pdf")
plt.clf()
print("save figure to ", fig_name)
def compute_embedding(response):
while True:
try:
print('ping openai api')
response_embedding = openai.Embedding.create(
model=EMBEDDING_MODEL,
input=response,
)
response_embedding = np.array(response_embedding["data"][0]['embedding'])
return response_embedding
except Exception as e:
print(e)
def draw_latent_plot(
max_num=80,
method="pca+tsne",
fig_name="",
):
# query: (response, embeddings)
latents = []
class_labels = []
label_sets = []
# chatgpt embedding
total_tasks = [os.path.join("cliport/tasks", x) for x in os.listdir("cliport/tasks")] + [os.path.join("cliport/generated_tasks", x) for x in os.listdir("cliport/generated_tasks")]
total_tasks = [t for t in total_tasks if 'pycache' not in t and 'init' not in t \
and 'README' not in t and 'extended' not in t and 'gripper' not in t and 'primitive' not in t\
and 'task.py' not in t and 'camera' not in t and 'seq' not in t]
cache_embedding_path = "output/output_embedding/task_cache_embedding.npz"
cache_embedding = {}
if os.path.exists(cache_embedding_path):
cache_embedding = dict(np.load(cache_embedding_path))
print(total_tasks)
for idx, task_name in enumerate(total_tasks):
if task_name in cache_embedding:
code_embedding = cache_embedding[task_name]
else:
code = open(task_name).read()
code_embedding = compute_embedding(code)
latents.append(code_embedding)
label_sets.append(task_name.split("/")[-1][:-3])
cache_embedding[task_name] = code_embedding
class_labels.append(idx)
latents = np.array(latents)
print("latents shape:", latents.shape)
np.savez(cache_embedding_path, **cache_embedding)
n_clusters = 6
kmeans = KMeans(n_clusters=n_clusters, init="k-means++", random_state=42)
kmeans.fit(latents)
cluster_labels = kmeans.labels_
if method == "pca+tsne":
# reduce dimension to the number of datapoints
pca = PCA(random_state=123, n_components=min(50, max_num)) # kernel PCA
X_embedded = pca.fit_transform(latents)
print(
"Variance explained per principal component: {}".format(
pca.explained_variance_ratio_[:5]
)
)
print("PCA data shape:", X_embedded.shape)
X_embedded = TSNE(random_state=123, perplexity=20).fit_transform(X_embedded)
if method == "pca":
pca = KernelPCA(random_state=123, n_components=2) # kernel PCA
X_embedded = pca.fit_transform(latents[:, :5])
if method == "tsne":
X_embedded = TSNE(random_state=123).fit_transform(latents) # perplexity
fashion_scatter(X_embedded, class_labels, fig_name, label_sets)
fashion_scatter(X_embedded, cluster_labels, fig_name + "_cluster", label_sets)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate chat-gpt embeddings")
"""
load task descriptions from the tasks folder and embed
"""
parser.add_argument("--file", type=str, default="task_embedding")
args = parser.parse_args()
draw_latent_plot(fig_name=f'output/output_embedding/{args.file}') |