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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 psalm.eval.segmentation_evaluation.panoptic_evaluation import my_coco_panoptic_evaluator, my_SemSegEvaluator
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_panoptic_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/panoptic_segmentation'
segmentation: bool = True
eval_batch_size: int = 1
dataloader_num_workers: int = 4
seg_task: Optional[str] = field(default="panoptic")
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
conversation_lib.default_conversation = conversation_lib.conv_templates[data_args.version]
eval_dataset = COCO_panoptic_dataset(json_path=data_args.json_path, tokenizer=tokenizer,
data_args=data_args, is_train=False)
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)
MetadataCatalog.get('instruction_dataset').set(panoptic_json=eval_dataset.panoptic_json_path,
panoptic_root=eval_dataset.panoptic_gt_path)
evaluator = my_coco_panoptic_evaluator('instruction_dataset',
output_dir=data_args.output_dir, dataset_id_to_cont_id=eval_dataset.coco_id_to_cont_id, is_thing_list=eval_dataset.coco_is_thing)
sem_evaluator = my_SemSegEvaluator('instruction_dataset',
output_dir=data_args.output_dir, dataset_id_to_cont_id=eval_dataset.coco_id_to_cont_id, class_name=eval_dataset.coco_class_name[:-1],ignore_label=255)
evaluator.reset()
sem_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'],
is_thing_list=eval_dataset.coco_is_thing
)
if torch.cuda.is_available():
torch.cuda.synchronize()
evaluator.process(inputs['seg_info'], outputs)
sem_evaluator.process(inputs['seg_info'], outputs)
results = evaluator.evaluate()
sem_results = sem_evaluator.evaluate()
print(results)
print(sem_results)
if results is None:
results = {}
return results
def intersectionAndUnionGPU(output, target, K, ignore_index=255):
# 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1.
assert output.dim() in [1, 2, 3]
assert output.shape == target.shape
output = output.view(-1)
target = target.view(-1)
output[target == ignore_index] = ignore_index
intersection = output[output == target]
area_intersection = torch.histc(intersection, bins=K, min=0, max=K - 1)
area_output = torch.histc(output, bins=K, min=0, max=K - 1)
area_target = torch.histc(target, bins=K, min=0, max=K - 1)
area_union = area_output + area_target - area_intersection
return area_intersection, area_union, area_target
if __name__ == "__main__":
evaluation()
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