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batch_inference.py
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batch_inference.py
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from argparse import ArgumentParser
from pathlib import Path
from typing import Dict, List, Optional, TextIO, Tuple
import torch
from PIL import Image, UnidentifiedImageError
from torch import Tensor
from torch.nn import Module, Parameter
from torch.nn.functional import relu, sigmoid
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
import torch.nn.functional as F
import os
import json
from ram import get_transform
from ram.models import ram_plus, ram, tag2text
from ram.utils import build_openset_llm_label_embedding, build_openset_label_embedding, get_mAP, get_PR
device = "cuda" if torch.cuda.is_available() else "cpu"
class _Dataset(Dataset):
def __init__(self, imglist, input_size):
self.imglist = imglist
self.transform = get_transform(input_size)
def __len__(self):
return len(self.imglist)
def __getitem__(self, index):
try:
img = Image.open(self.imglist[index]+".jpg")
except (OSError, FileNotFoundError, UnidentifiedImageError):
img = Image.new('RGB', (10, 10), 0)
print("Error loading image:", self.imglist[index])
return self.transform(img)
def parse_args():
parser = ArgumentParser()
# model
parser.add_argument("--model-type",
type=str,
choices=("ram_plus", "ram", "tag2text"),
required=True)
parser.add_argument("--checkpoint",
type=str,
required=True)
parser.add_argument("--backbone",
type=str,
choices=("swin_l", "swin_b"),
default=None,
help="If `None`, will judge from `--model-type`")
parser.add_argument("--open-set",
action="store_true",
help=(
"Treat all categories in the taglist file as "
"unseen and perform open-set classification. Only "
"works with RAM."
))
# data
parser.add_argument("--dataset",
type=str,
choices=(
"openimages_common_214",
"openimages_rare_200"
),
required=True)
parser.add_argument("--input-size",
type=int,
default=384)
# threshold
group = parser.add_mutually_exclusive_group()
group.add_argument("--threshold",
type=float,
default=None,
help=(
"Use custom threshold for all classes. Mutually "
"exclusive with `--threshold-file`. If both "
"`--threshold` and `--threshold-file` is `None`, "
"will use a default threshold setting."
))
group.add_argument("--threshold-file",
type=str,
default=None,
help=(
"Use custom class-wise thresholds by providing a "
"text file. Each line is a float-type threshold, "
"following the order of the tags in taglist file. "
"See `ram/data/ram_tag_list_threshold.txt` as an "
"example. Mutually exclusive with `--threshold`. "
"If both `--threshold` and `--threshold-file` is "
"`None`, will use default threshold setting."
))
# miscellaneous
parser.add_argument("--output-dir", type=str, default="./outputs")
parser.add_argument("--batch-size", type=int, default=128)
parser.add_argument("--num-workers", type=int, default=4)
args = parser.parse_args()
# post process and validity check
args.model_type = args.model_type.lower()
assert not (args.model_type == "tag2text" and args.open_set)
if args.backbone is None:
args.backbone = "swin_l" if args.model_type == "ram_plus" or args.model_type == "ram" else "swin_b"
return args
def load_dataset(
dataset: str,
model_type: str,
input_size: int,
batch_size: int,
num_workers: int
) -> Tuple[DataLoader, Dict]:
dataset_root = str(Path(__file__).resolve().parent / "datasets" / dataset)
img_root = dataset_root + "/imgs"
# Label system of tag2text contains duplicate tag texts, like
# "train" (noun) and "train" (verb). Therefore, for tag2text, we use
# `tagid` instead of `tag`.
if model_type == "ram_plus" or model_type == "ram":
tag_file = dataset_root + f"/{dataset}_ram_taglist.txt"
annot_file = dataset_root + f"/{dataset}_ram_annots.txt"
else:
tag_file = dataset_root + f"/{dataset}_tag2text_tagidlist.txt"
annot_file = dataset_root + f"/{dataset}_{model_type}_idannots.txt"
with open(tag_file, "r", encoding="utf-8") as f:
taglist = [line.strip() for line in f]
with open(annot_file, "r", encoding="utf-8") as f:
imglist = [img_root + "/" + line.strip().split(",")[0] for line in f]
loader = DataLoader(
dataset=_Dataset(imglist,input_size),
shuffle=False,
drop_last=False,
pin_memory=True,
batch_size=batch_size,
num_workers=num_workers
)
open_tag_des = dataset_root + f"/{dataset}_llm_tag_descriptions.json"
if os.path.exists(open_tag_des):
with open(open_tag_des, 'rb') as fo:
tag_des = json.load(fo)
else:
tag_des = None
info = {
"taglist": taglist,
"imglist": imglist,
"annot_file": annot_file,
"img_root": img_root,
"tag_des": tag_des
}
return loader, info
def get_class_idxs(
model_type: str,
open_set: bool,
taglist: List[str]
) -> Optional[List[int]]:
"""Get indices of required categories in the label system."""
if model_type == "ram_plus" or model_type == "ram":
if not open_set:
model_taglist_file = "ram/data/ram_tag_list.txt"
with open(model_taglist_file, "r", encoding="utf-8") as f:
model_taglist = [line.strip() for line in f]
return [model_taglist.index(tag) for tag in taglist]
else:
return None
else: # for tag2text, we directly use tagid instead of text-form of tag.
# here tagid equals to tag index.
return [int(tag) for tag in taglist]
def load_thresholds(
threshold: Optional[float],
threshold_file: Optional[str],
model_type: str,
open_set: bool,
class_idxs: List[int],
num_classes: int,
) -> List[float]:
"""Decide what threshold(s) to use."""
if not threshold_file and not threshold: # use default
if model_type == "ram_plus" or model_type == "ram":
if not open_set: # use class-wise tuned thresholds
ram_threshold_file = "ram/data/ram_tag_list_threshold.txt"
with open(ram_threshold_file, "r", encoding="utf-8") as f:
idx2thre = {
idx: float(line.strip()) for idx, line in enumerate(f)
}
return [idx2thre[idx] for idx in class_idxs]
else:
return [0.5] * num_classes
else:
return [0.68] * num_classes
elif threshold_file:
with open(threshold_file, "r", encoding="utf-8") as f:
thresholds = [float(line.strip()) for line in f]
assert len(thresholds) == num_classes
return thresholds
else:
return [threshold] * num_classes
def gen_pred_file(
imglist: List[str],
tags: List[List[str]],
img_root: str,
pred_file: str
) -> None:
"""Generate text file of tag prediction results."""
with open(pred_file, "w", encoding="utf-8") as f:
for image, tag in zip(imglist, tags):
# should be relative to img_root to match the gt file.
s = str(Path(image).relative_to(img_root))
if tag:
s = s + "," + ",".join(tag)
f.write(s + "\n")
def load_ram_plus(
backbone: str,
checkpoint: str,
input_size: int,
taglist: List[str],
tag_des: List[str],
open_set: bool,
class_idxs: List[int],
) -> Module:
model = ram_plus(pretrained=checkpoint, image_size=input_size, vit=backbone)
# trim taglist for faster inference
if open_set:
print("Building tag embeddings ...")
label_embed, _ = build_openset_llm_label_embedding(tag_des)
model.label_embed = Parameter(label_embed.float())
model.num_class = len(tag_des)
else:
model.label_embed = Parameter(model.label_embed.data.reshape(model.num_class,51,512)[class_idxs, :, :].reshape(len(class_idxs)*51, 512))
model.num_class = len(class_idxs)
return model.to(device).eval()
def load_ram(
backbone: str,
checkpoint: str,
input_size: int,
taglist: List[str],
open_set: bool,
class_idxs: List[int],
) -> Module:
model = ram(pretrained=checkpoint, image_size=input_size, vit=backbone)
# trim taglist for faster inference
if open_set:
print("Building tag embeddings ...")
label_embed, _ = build_openset_label_embedding(taglist)
model.label_embed = Parameter(label_embed.float())
else:
model.label_embed = Parameter(model.label_embed[class_idxs, :])
return model.to(device).eval()
def load_tag2text(
backbone: str,
checkpoint: str,
input_size: int
) -> Module:
model = tag2text(
pretrained=checkpoint,
image_size=input_size,
vit=backbone
)
return model.to(device).eval()
@torch.no_grad()
def forward_ram_plus(model: Module, imgs: Tensor) -> Tensor:
image_embeds = model.image_proj(model.visual_encoder(imgs.to(device)))
image_atts = torch.ones(
image_embeds.size()[:-1], dtype=torch.long).to(device)
image_cls_embeds = image_embeds[:, 0, :]
image_spatial_embeds = image_embeds[:, 1:, :]
bs = image_spatial_embeds.shape[0]
des_per_class = int(model.label_embed.shape[0] / model.num_class)
image_cls_embeds = image_cls_embeds / image_cls_embeds.norm(dim=-1, keepdim=True)
reweight_scale = model.reweight_scale.exp()
logits_per_image = (reweight_scale * image_cls_embeds @ model.label_embed.t())
logits_per_image = logits_per_image.view(bs, -1,des_per_class)
weight_normalized = F.softmax(logits_per_image, dim=2)
label_embed_reweight = torch.empty(bs, model.num_class, 512).cuda()
weight_normalized = F.softmax(logits_per_image, dim=2)
label_embed_reweight = torch.empty(bs, model.num_class, 512).cuda()
for i in range(bs):
reshaped_value = model.label_embed.view(-1, des_per_class, 512)
product = weight_normalized[i].unsqueeze(-1) * reshaped_value
label_embed_reweight[i] = product.sum(dim=1)
label_embed = relu(model.wordvec_proj(label_embed_reweight))
tagging_embed, _ = model.tagging_head(
encoder_embeds=label_embed,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=False,
mode='tagging',
)
return sigmoid(model.fc(tagging_embed).squeeze(-1))
@torch.no_grad()
def forward_ram(model: Module, imgs: Tensor) -> Tensor:
image_embeds = model.image_proj(model.visual_encoder(imgs.to(device)))
image_atts = torch.ones(
image_embeds.size()[:-1], dtype=torch.long).to(device)
label_embed = relu(model.wordvec_proj(model.label_embed)).unsqueeze(0)\
.repeat(imgs.shape[0], 1, 1)
tagging_embed, _ = model.tagging_head(
encoder_embeds=label_embed,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=False,
mode='tagging',
)
return sigmoid(model.fc(tagging_embed).squeeze(-1))
@torch.no_grad()
def forward_tag2text(
model: Module,
class_idxs: List[int],
imgs: Tensor
) -> Tensor:
image_embeds = model.visual_encoder(imgs.to(device))
image_atts = torch.ones(
image_embeds.size()[:-1], dtype=torch.long).to(device)
label_embed = model.label_embed.weight.unsqueeze(0)\
.repeat(imgs.shape[0], 1, 1)
tagging_embed, _ = model.tagging_head(
encoder_embeds=label_embed,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=False,
mode='tagging',
)
return sigmoid(model.fc(tagging_embed))[:, class_idxs]
def print_write(f: TextIO, s: str):
print(s)
f.write(s + "\n")
if __name__ == "__main__":
args = parse_args()
# set up output paths
output_dir = args.output_dir
Path(output_dir).mkdir(parents=True, exist_ok=True)
pred_file, pr_file, ap_file, summary_file, logit_file = [
output_dir + "/" + name for name in
("pred.txt", "pr.txt", "ap.txt", "summary.txt", "logits.pth")
]
with open(summary_file, "w", encoding="utf-8") as f:
print_write(f, "****************")
for key in (
"model_type", "backbone", "checkpoint", "open_set",
"dataset", "input_size",
"threshold", "threshold_file",
"output_dir", "batch_size", "num_workers"
):
print_write(f, f"{key}: {getattr(args, key)}")
print_write(f, "****************")
# prepare data
loader, info = load_dataset(
dataset=args.dataset,
model_type=args.model_type,
input_size=args.input_size,
batch_size=args.batch_size,
num_workers=args.num_workers
)
taglist, imglist, annot_file, img_root, tag_des = \
info["taglist"], info["imglist"], info["annot_file"], info["img_root"], info["tag_des"]
# get class idxs
class_idxs = get_class_idxs(
model_type=args.model_type,
open_set=args.open_set,
taglist=taglist
)
# set up threshold(s)
thresholds = load_thresholds(
threshold=args.threshold,
threshold_file=args.threshold_file,
model_type=args.model_type,
open_set=args.open_set,
class_idxs=class_idxs,
num_classes=len(taglist)
)
# inference
if Path(logit_file).is_file():
logits = torch.load(logit_file)
else:
# load model
if args.model_type == "ram_plus":
model = load_ram_plus(
backbone=args.backbone,
checkpoint=args.checkpoint,
input_size=args.input_size,
taglist=taglist,
tag_des = tag_des,
open_set=args.open_set,
class_idxs=class_idxs
)
elif args.model_type == "ram":
model = load_ram(
backbone=args.backbone,
checkpoint=args.checkpoint,
input_size=args.input_size,
taglist=taglist,
open_set=args.open_set,
class_idxs=class_idxs
)
elif args.model_type == "tag2text":
model = load_tag2text(
backbone=args.backbone,
checkpoint=args.checkpoint,
input_size=args.input_size
)
# inference
logits = torch.empty(len(imglist), len(taglist))
pos = 0
for imgs in tqdm(loader, desc="inference"):
if args.model_type == "ram_plus":
out = forward_ram_plus(model, imgs)
elif args.model_type == "ram":
out = forward_ram(model, imgs)
else:
out = forward_tag2text(model, class_idxs, imgs)
bs = imgs.shape[0]
logits[pos:pos+bs, :] = out.cpu()
pos += bs
# save logits, making threshold-tuning super fast
torch.save(logits, logit_file)
# filter with thresholds
pred_tags = []
for scores in logits.tolist():
pred_tags.append([
taglist[i] for i, s in enumerate(scores) if s >= thresholds[i]
])
# generate result file
gen_pred_file(imglist, pred_tags, img_root, pred_file)
# evaluate and record
mAP, APs = get_mAP(logits.numpy(), annot_file, taglist)
CP, CR, Ps, Rs = get_PR(pred_file, annot_file, taglist)
with open(ap_file, "w", encoding="utf-8") as f:
f.write("Tag,AP\n")
for tag, AP in zip(taglist, APs):
f.write(f"{tag},{AP*100.0:.2f}\n")
with open(pr_file, "w", encoding="utf-8") as f:
f.write("Tag,Precision,Recall\n")
for tag, P, R in zip(taglist, Ps, Rs):
f.write(f"{tag},{P*100.0:.2f},{R*100.0:.2f}\n")
with open(summary_file, "w", encoding="utf-8") as f:
print_write(f, f"mAP: {mAP*100.0}")
print_write(f, f"CP: {CP*100.0}")
print_write(f, f"CR: {CR*100.0}")