tinygrad/examples/vit.py

50 lines
1.5 KiB
Python

import ast
import io
import numpy as np
from PIL import Image
from tinygrad.tensor import Tensor
from tinygrad.helpers import getenv
from models.vit import ViT
from extra.utils import fetch
"""
fn = "gs://vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz"
import tensorflow as tf
with tf.io.gfile.GFile(fn, "rb") as f:
dat = f.read()
with open("cache/"+ fn.rsplit("/", 1)[1], "wb") as g:
g.write(dat)
"""
Tensor.training = False
if getenv("LARGE", 0) == 1:
m = ViT(embed_dim=768, num_heads=12)
else:
# tiny
m = ViT(embed_dim=192, num_heads=3)
m.load_from_pretrained()
# category labels
lbls = fetch("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt")
lbls = ast.literal_eval(lbls.decode('utf-8'))
#url = "https://upload.wikimedia.org/wikipedia/commons/4/41/Chicken.jpg"
url = "https://repository-images.githubusercontent.com/296744635/39ba6700-082d-11eb-98b8-cb29fb7369c0"
# junk
img = Image.open(io.BytesIO(fetch(url)))
aspect_ratio = img.size[0] / img.size[1]
img = img.resize((int(224*max(aspect_ratio,1.0)), int(224*max(1.0/aspect_ratio,1.0))))
img = np.array(img)
y0,x0=(np.asarray(img.shape)[:2]-224)//2
img = img[y0:y0+224, x0:x0+224]
img = np.moveaxis(img, [2,0,1], [0,1,2])
img = img.astype(np.float32)[:3].reshape(1,3,224,224)
img /= 255.0
img -= 0.5
img /= 0.5
out = m.forward(Tensor(img))
outnp = out.numpy().ravel()
choice = outnp.argmax()
print(out.shape, choice, outnp[choice], lbls[choice])