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import argparse
import os
import sys
import time
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, DistributedSampler
from groundingdino.models import build_model
import groundingdino.datasets.transforms as T
from groundingdino.util import box_ops, get_tokenlizer
from groundingdino.util.misc import clean_state_dict, collate_fn
from groundingdino.util.slconfig import SLConfig
# from torchvision.datasets import CocoDetection
import torchvision
from groundingdino.util.vl_utils import build_captions_and_token_span, create_positive_map_from_span
from groundingdino.datasets.cocogrounding_eval import CocoGroundingEvaluator
def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda"):
args = SLConfig.fromfile(model_config_path)
args.device = device
model = build_model(args)
checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
model.load_state_dict(clean_state_dict(checkpoint["ema_model"]), strict=False)
model.eval()
return model
class CocoDetection(torchvision.datasets.CocoDetection):
def __init__(self, img_folder, ann_file, transforms):
super().__init__(img_folder, ann_file)
self._transforms = transforms
def __getitem__(self, idx):
img, target = super().__getitem__(idx) # target: list
# import ipdb; ipdb.set_trace()
w, h = img.size
boxes = [obj["bbox"] for obj in target]
boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4)
boxes[:, 2:] += boxes[:, :2] # xywh -> xyxy
boxes[:, 0::2].clamp_(min=0, max=w)
boxes[:, 1::2].clamp_(min=0, max=h)
# filt invalid boxes/masks/keypoints
keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
boxes = boxes[keep]
target_new = {}
image_id = self.ids[idx]
target_new["image_id"] = image_id
target_new["boxes"] = boxes
target_new["orig_size"] = torch.as_tensor([int(h), int(w)])
if self._transforms is not None:
img, target = self._transforms(img, target_new)
return img, target
class PostProcessCocoGrounding(nn.Module):
""" This module converts the model's output into the format expected by the coco api"""
def __init__(self, num_select=300, coco_api=None, tokenlizer=None) -> None:
super().__init__()
self.num_select = num_select
assert coco_api is not None
category_dict = coco_api.dataset['categories']
cat_list = [item['name'] for item in category_dict]
captions, cat2tokenspan = build_captions_and_token_span(cat_list, True)
tokenspanlist = [cat2tokenspan[cat] for cat in cat_list]
positive_map = create_positive_map_from_span(
tokenlizer(captions), tokenspanlist) # 80, 256. normed
id_map = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10, 10: 11, 11: 13, 12: 14, 13: 15, 14: 16, 15: 17, 16: 18, 17: 19, 18: 20, 19: 21, 20: 22, 21: 23, 22: 24, 23: 25, 24: 27, 25: 28, 26: 31, 27: 32, 28: 33, 29: 34, 30: 35, 31: 36, 32: 37, 33: 38, 34: 39, 35: 40, 36: 41, 37: 42, 38: 43, 39: 44, 40: 46,
41: 47, 42: 48, 43: 49, 44: 50, 45: 51, 46: 52, 47: 53, 48: 54, 49: 55, 50: 56, 51: 57, 52: 58, 53: 59, 54: 60, 55: 61, 56: 62, 57: 63, 58: 64, 59: 65, 60: 67, 61: 70, 62: 72, 63: 73, 64: 74, 65: 75, 66: 76, 67: 77, 68: 78, 69: 79, 70: 80, 71: 81, 72: 82, 73: 84, 74: 85, 75: 86, 76: 87, 77: 88, 78: 89, 79: 90}
# build a mapping from label_id to pos_map
new_pos_map = torch.zeros((91, 256))
for k, v in id_map.items():
new_pos_map[v] = positive_map[k]
self.positive_map = new_pos_map
@torch.no_grad()
def forward(self, outputs, target_sizes, not_to_xyxy=False):
""" Perform the computation
Parameters:
outputs: raw outputs of the model
target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch
For evaluation, this must be the original image size (before any data augmentation)
For visualization, this should be the image size after data augment, but before padding
"""
num_select = self.num_select
out_logits, out_bbox = outputs['pred_logits'], outputs['pred_boxes']
# pos map to logit
prob_to_token = out_logits.sigmoid() # bs, 100, 256
pos_maps = self.positive_map.to(prob_to_token.device)
# (bs, 100, 256) @ (91, 256).T -> (bs, 100, 91)
prob_to_label = prob_to_token @ pos_maps.T
# if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
# import ipdb; ipdb.set_trace()
assert len(out_logits) == len(target_sizes)
assert target_sizes.shape[1] == 2
prob = prob_to_label
topk_values, topk_indexes = torch.topk(
prob.view(out_logits.shape[0], -1), num_select, dim=1)
scores = topk_values
topk_boxes = topk_indexes // prob.shape[2]
labels = topk_indexes % prob.shape[2]
if not_to_xyxy:
boxes = out_bbox
else:
boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)
boxes = torch.gather(
boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
# and from relative [0, 1] to absolute [0, height] coordinates
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
boxes = boxes * scale_fct[:, None, :]
results = [{'scores': s, 'labels': l, 'boxes': b}
for s, l, b in zip(scores, labels, boxes)]
return results
def main(args):
# config
cfg = SLConfig.fromfile(args.config_file)
# build model
model = load_model(args.config_file, args.checkpoint_path)
model = model.to(args.device)
model = model.eval()
# build dataloader
transform = T.Compose(
[
T.RandomResize([800], max_size=1333),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
dataset = CocoDetection(
args.image_dir, args.anno_path, transforms=transform)
data_loader = DataLoader(
dataset, batch_size=1, shuffle=False, num_workers=args.num_workers, collate_fn=collate_fn)
# build post processor
tokenlizer = get_tokenlizer.get_tokenlizer(cfg.text_encoder_type)
postprocessor = PostProcessCocoGrounding(
coco_api=dataset.coco, tokenlizer=tokenlizer)
# build evaluator
evaluator = CocoGroundingEvaluator(
dataset.coco, iou_types=("bbox",), useCats=True)
# build captions
category_dict = dataset.coco.dataset['categories']
cat_list = [item['name'] for item in category_dict]
caption = " . ".join(cat_list) + ' .'
print("Input text prompt:", caption)
# run inference
start = time.time()
for i, (images, targets) in enumerate(data_loader):
# get images and captions
images = images.tensors.to(args.device)
bs = images.shape[0]
input_captions = [caption] * bs
# feed to the model
outputs = model(images, captions=input_captions)
orig_target_sizes = torch.stack(
[t["orig_size"] for t in targets], dim=0).to(images.device)
results = postprocessor(outputs, orig_target_sizes)
cocogrounding_res = {
target["image_id"]: output for target, output in zip(targets, results)}
evaluator.update(cocogrounding_res)
if (i+1) % 30 == 0:
used_time = time.time() - start
eta = len(data_loader) / (i+1e-5) * used_time - used_time
print(
f"processed {i}/{len(data_loader)} images. time: {used_time:.2f}s, ETA: {eta:.2f}s")
evaluator.synchronize_between_processes()
evaluator.accumulate()
evaluator.summarize()
print("Final results:", evaluator.coco_eval["bbox"].stats.tolist())
if __name__ == "__main__":
parser = argparse.ArgumentParser(
"Grounding DINO eval on COCO", add_help=True)
# load model
parser.add_argument("--config_file", "-c", type=str,
required=True, help="path to config file")
parser.add_argument(
"--checkpoint_path", "-p", type=str, required=True, help="path to checkpoint file"
)
parser.add_argument("--device", type=str, default="cuda",
help="running device (default: cuda)")
# post processing
parser.add_argument("--num_select", type=int, default=300,
help="number of topk to select")
# coco info
parser.add_argument("--anno_path", type=str,
required=True, help="coco root")
parser.add_argument("--image_dir", type=str,
required=True, help="coco image dir")
parser.add_argument("--num_workers", type=int, default=4,
help="number of workers for dataloader")
args = parser.parse_args()
main(args)
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