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fetch_sample_images.py

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+import io
+import os
+import cv2
+import tqdm
+import numpy
+import requests
+
+
+def get_bytes(x: str):
+    return numpy.frombuffer(requests.get(x).content, numpy.uint8)
+
+
+def get_image(x):
+    return cv2.imdecode(get_bytes(x), cv2.IMREAD_COLOR)
+
+
+os.chdir(os.path.dirname(os.path.abspath(__file__)))
+# classification
+# uids = """
+# a784af0713a643b19ffcf65194bc0fbf
+# 569a71ccf4d94c1585c9573521fb998f
+# 4e6d591f6e50493aa5e31355084fc4e8
+# """.split()
+
+# caption
+# uids = """
+# 283c845f2c2c4567971d42dc46831372
+# fc655111af5b49bf84722affc3ddba00
+# fa17099f18804409bc6d9e8e397b4681
+# d3c0e3495b5d40d087a7f82d1690b9cb
+# 4b27adcf92f644bdabf8ecc6c5bef399
+# f8c13a19e84343e7b644c19f7b9488d3
+# """.split()
+
+# sd
+uids = """
+b464ff8d732d44fab00b903652c8274e
+efae586a477b49cea1a0777487cc2df3
+f8272460c67d476a8af29e1f2e344bc0
+ff2875fb1a5b4771805a5fd35c8fe7bb
+b8db8dc5caad4fa5842a9ed6dbd2e9d6
+tpvzmLUXAURQ7ZxccJIBZvcIDlr
+""".split()
+
+
+uri_fmt = 'https://objaverse-thumbnail-images.s3.us-west-2.amazonaws.com/{}.jpg'
+for u in tqdm.tqdm(uids):
+    img = get_image(uri_fmt.format(u))
+    max_edge = max(img.shape)
+    if max_edge > 512:
+        s = 512 / max_edge
+        img = cv2.resize(img, [0, 0], fx=s, fy=s, interpolation=cv2.INTER_CUBIC)
+    cv2.imwrite("samples/sd/%s.jpg" % u, img)

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cu113
+torch
+-f https://data.dgl.ai/wheels/repo.html
+dgl-cu113
+numpy
+# open_clip_torch
+diffusers
+accelerate
+datasets
+transformers==4.29.2
+einops
+torch.redstone
+objaverse
+altair<5
+trimesh
+matplotlib
+plotly

samples_index.py

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+import os
+
+
+cap_base = 'samples/caption'
+cap = [
+    dict(cap_objaid=os.path.splitext(x)[0], dispi=os.path.join(cap_base, x))
+    for x in sorted(os.listdir(cap_base))
+]
+
+cls_base = 'samples/classification'
+classification = [
+    dict(cls_objaid=os.path.splitext(x)[0], dispi=os.path.join(cls_base, x))
+    for x in sorted(os.listdir(cls_base))
+]
+
+sd_base = 'samples/sd'
+sd_texts = {
+    'b8db8dc5caad4fa5842a9ed6dbd2e9d6': 'falcon',
+    'ff2875fb1a5b4771805a5fd35c8fe7bb': 'in the woods',
+    'tpvzmLUXAURQ7ZxccJIBZvcIDlr': 'above the fields'
+}
+sd = [
+    dict(
+        sd_objaid=os.path.splitext(x)[0],
+        dispi=os.path.join(sd_base, x),
+        sdtprompt=sd_texts.get(os.path.splitext(x)[0], '')
+    )
+    for x in sorted(os.listdir(sd_base))
+]
+
+retrieval_texts = """
+shark
+swordfish
+dolphin
+goldfish
+high heels
+boots
+slippers
+sneakers
+tiki mug
+viking mug
+animal-shaped mug
+travel mug
+white conical mug
+green cubic mug
+blue spherical mug
+orange cylinder mug
+""".splitlines()
+retrieval_texts = [x.strip() for x in retrieval_texts if x.strip()]
+
+pret_base = 'samples/retrieval-pc'
+pret = [
+    dict(retpc_objaid=os.path.splitext(x)[0], dispi=os.path.join(pret_base, x))
+    for x in sorted(os.listdir(pret_base))
+]
+
+iret_base = 'samples/retrieval-img'
+iret = [
+    dict(rimageinput=os.path.join(iret_base, x), dispi=os.path.join(iret_base, x))
+    for x in sorted(os.listdir(iret_base))
+]