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import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from pycocotools import mask
import numpy as np
import cv2
from psalm.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, \
DEFAULT_IM_END_TOKEN, DEFAULT_SEG_TOKEN, SEG_TOKEN_INDEX, CLS_TOKEN_INDEX
from psalm.conversation import conv_templates, SeparatorStyle
from psalm.model.builder import load_pretrained_model
from psalm.utils import disable_torch_init
from psalm.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from psalm.eval.segmentation_evaluation.instance_evaluation import InstanceSegEvaluator, my_coco_evaluator
from transformers import StoppingCriteria, StoppingCriteriaList
from torch.utils.data import Dataset, DataLoader
from psalm import conversation as conversation_lib
from psalm.model.datasets_mapper.coco_instance_mapper import COCOInstanceNewBaselineDatasetMapperForEval
from PIL import Image
import math
import copy
from detectron2.structures import BoxMode
from detectron2.evaluation import inference_on_dataset, COCOEvaluator
from detectron2.data import MetadataCatalog, DatasetCatalog
from typing import Dict, Optional, Sequence, List
from dataclasses import dataclass, field
from psalm.train.train_datasets import DataCollatorForCOCODatasetV2, COCO_instance_dataset
import transformers
@dataclass
class DataArguments:
data_path: str = field(default=None,
metadata={"help": "Path to the training data."})
lazy_preprocess: bool = False
is_multimodal: bool = False
image_folder: Optional[str] = field(default='/path/to/val2017')
model_path: Optional[str] = field(default="/path/to/model")
mask_config: Optional[str] = field(default="./psalm/mask_config/maskformer2_swin_base_384_bs16_50ep.yaml")
image_aspect_ratio: str = 'square'
image_grid_pinpoints: Optional[str] = field(default=None)
json_path: str = '/path/to/coco'
model_map_name: str = 'psalm'
version: str = 'llava_phi'
output_dir: str = './output/instance_segmentation'
segmentation: bool = True
eval_batch_size: int = 1
dataloader_num_workers: int = 4
seg_task: Optional[str] = field(default="instance")
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], encounters=1):
super().__init__()
self.stops = stops
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
last_token = input_ids[0][-1]
for stop in self.stops:
if stop == last_token:
return True
return False
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
def evaluation():
parser = transformers.HfArgumentParser(DataArguments)
data_args = parser.parse_args_into_dataclasses()[0]
disable_torch_init()
model_path = os.path.expanduser(data_args.model_path)
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name,mask_config=data_args.mask_config,model_args=data_args)
data_args.image_processor = image_processor
data_args.is_multimodal = True
gt_json_path = data_args.json_path
with open(gt_json_path) as f:
gt_data = json.load(f)
conversation_lib.default_conversation = conversation_lib.conv_templates[data_args.version]
eval_dataset = COCO_instance_dataset(json_path=data_args.json_path, tokenizer=tokenizer,
data_args=data_args)
data_collator = DataCollatorForCOCODatasetV2(tokenizer=tokenizer)
dataloader_params = {
"batch_size": data_args.eval_batch_size,
"num_workers": data_args.dataloader_num_workers,
}
eval_dataloader = DataLoader(eval_dataset, batch_size=dataloader_params['batch_size'], collate_fn=data_collator,
num_workers=dataloader_params['num_workers'])
def load_instruction_dataset():
return eval_dataset
DatasetCatalog.register('instruction_dataset', load_instruction_dataset)
origin_coco_ids = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49,
50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,
82, 84, 85, 86, 87, 88, 89, 90
]
coco_class_ids = eval_dataset.coco_class_ids if hasattr(eval_dataset,'coco_class_ids') else origin_coco_ids
thing_dataset_id_to_contiguous_id = {coco_id: cont_id for cont_id, coco_id in enumerate(coco_class_ids)}
MetadataCatalog.get('instruction_dataset').set(thing_classes=eval_dataset.thing_classes if hasattr(eval_dataset,'thing_classes') else MetadataCatalog.get('coco_2017_train').thing_classes,
thing_dataset_id_to_contiguous_id=thing_dataset_id_to_contiguous_id)
evaluator = my_coco_evaluator('instruction_dataset', tasks=('segm',),
output_dir=data_args.output_dir, distributed=False)
evaluator.reset()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(dtype=torch.float32, device=device).eval()
with torch.no_grad():
for idx, inputs in tqdm(enumerate(eval_dataloader), total=len(eval_dataloader)):
inputs = {k: v.to(device) if torch.is_tensor(v) else v for k, v in inputs.items()}
outputs = model.eval_seg(
input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
images=inputs['images'].float(),
seg_info=inputs['seg_info'],
class_name_embedding_indices=inputs['class_name_embedding_indices'],
class_name_ids=inputs['class_name_ids'],
cls_indices=inputs['cls_indices'],
labels=inputs['labels']
)
if torch.cuda.is_available():
torch.cuda.synchronize()
evaluator.process(inputs['seg_info'], outputs)
results = evaluator.evaluate()
print(results)
if results is None:
results = {}
return results
if __name__ == "__main__":
evaluation()
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