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import gradio as gr
import torch
import copy
import time
import requests
import io
import numpy as np
import re
from einops import rearrange
import ipdb
from PIL import Image
from vilt.config import ex
from vilt.modules import ViLTransformerSS
from vilt.modules.objectives import cost_matrix_cosine, ipot
from vilt.transforms import pixelbert_transform
from vilt.datamodules.datamodule_base import get_pretrained_tokenizer
@ex.automain
def main(_config):
_config = copy.deepcopy(_config)
loss_names = {
"itm": 1,
"mlm": 0.5,
"mpp": 0,
"vqa": 0,
"imgcls": 0,
"nlvr2": 0,
"irtr": 1,
"arc": 0,
}
tokenizer = get_pretrained_tokenizer(_config["tokenizer"])
_config.update(
{
"loss_names": loss_names,
}
)
model = ViLTransformerSS(_config)
model.setup("test")
model.eval()
device = "cuda:0" if _config["num_gpus"] > 0 else "cpu"
model.to(device)
lst_imgs = [f"C:\\Users\\alimh\\PycharmProjects\\ViLT\\assets\\database\\{i}.jpg" for i in range(1,10)]
def infer( mp_text, hidx =0 ):
def get_image(path):
image = Image.open(path).convert("RGB")
img = pixelbert_transform(size=384)(image)
return img.unsqueeze(0).to(device)
imgs = [get_image(pth) for pth in lst_imgs]
batch = []
for img in imgs:
batch.append({"text": [mp_text], "image": [img]})
for dic in batch:
encoded = tokenizer(dic["text"])
dic["text_ids"] = torch.tensor(encoded["input_ids"]).to(device)
dic["text_labels"] = torch.tensor(encoded["input_ids"]).to(device)
dic["text_masks"] = torch.tensor(encoded["attention_mask"]).to(device)
scores = []
with torch.no_grad():
for dic in batch:
s = time.time()
infer = model(dic)
e = time.time()
print("time ", round(e - s, 2))
score = model.rank_output(infer["cls_feats"])
scores.append(score.item())
print(scores)
img_idx =np.argmax(scores)
print(np.argmax(scores) + 1 )
selected_image = Image.open(lst_imgs[img_idx]).convert("RGB")
selected_image = np.asarray(selected_image)
print(selected_image.shape)
selected_token =""
if hidx > 0 and hidx < len(encoded["input_ids"][0][:-1]):
image = Image.open(lst_imgs[img_idx]).convert("RGB")
selected_batch = batch[img_idx]
with torch.no_grad():
infer = model(selected_batch)
txt_emb, img_emb = infer["text_feats"], infer["image_feats"]
txt_mask, img_mask = (
infer["text_masks"].bool(),
infer["image_masks"].bool(),
)
for i, _len in enumerate(txt_mask.sum(dim=1)):
txt_mask[i, _len - 1] = False
txt_mask[:, 0] = False
img_mask[:, 0] = False
txt_pad, img_pad = ~txt_mask, ~img_mask
cost = cost_matrix_cosine(txt_emb.float(), img_emb.float())
joint_pad = txt_pad.unsqueeze(-1) | img_pad.unsqueeze(-2)
cost.masked_fill_(joint_pad, 0)
txt_len = (txt_pad.size(1) - txt_pad.sum(dim=1, keepdim=False)).to(
dtype=cost.dtype
)
img_len = (img_pad.size(1) - img_pad.sum(dim=1, keepdim=False)).to(
dtype=cost.dtype
)
T = ipot(
cost.detach(),
txt_len,
txt_pad,
img_len,
img_pad,
joint_pad,
0.1,
1000,
1,
)
plan = T[0]
plan_single = plan * len(txt_emb)
cost_ = plan_single.t()
cost_ = cost_[hidx][1:].cpu()
patch_index, (H, W) = infer["patch_index"]
heatmap = torch.zeros(H, W)
for i, pidx in enumerate(patch_index[0]):
h, w = pidx[0].item(), pidx[1].item()
heatmap[h, w] = cost_[i]
heatmap = (heatmap - heatmap.mean()) / heatmap.std()
heatmap = np.clip(heatmap, 1.0, 3.0)
heatmap = (heatmap - heatmap.min()) / (heatmap.max() - heatmap.min())
_w, _h = image.size
overlay = Image.fromarray(np.uint8(heatmap * 255), "L").resize(
(_w, _h), resample=Image.NEAREST
)
image_rgba = image.copy()
image_rgba.putalpha(overlay)
selected_image = image_rgba
selected_token = tokenizer.convert_ids_to_tokens(
encoded["input_ids"][0][hidx]
)
return [selected_image,hidx]
imgs = [Image.open(pth).convert("RGB") for pth in lst_imgs]
inputs = [
gr.inputs.Textbox(label="Caption with [MASK] tokens to be filled.", lines=5),
gr.inputs.Slider(
minimum=0,
maximum=38,
step=1,
label="Index of token for heatmap visualization (ignored if zero)",
),
]
outputs = [
gr.outputs.Image(label="Image"),
gr.outputs.Textbox(label="matching index "),
]
interface = gr.Interface(
fn=infer,
inputs=inputs,
outputs=outputs,
server_name="localhost",
server_port=8888,
)
interface.launch(debug=True,share=False) |