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from enum import Enum
import numpy as np
import torch
import torch.distributed as dist
IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"
SHORT_QUESTION_LIST = [
DEFAULT_IMAGE_TOKEN + "\n" + "Can you segment the {class_name} in this image?",
DEFAULT_IMAGE_TOKEN + "\n" + "Please segment the {class_name} in this image.",
DEFAULT_IMAGE_TOKEN
+ "\n"
+ "What is {class_name} in this image? Please respond with segmentation mask.",
DEFAULT_IMAGE_TOKEN
+ "\n"
+ "What is {class_name} in this image? Please output segmentation mask.",
]
LONG_QUESTION_LIST = [
DEFAULT_IMAGE_TOKEN + "\n" + "{sent} Please respond with segmentation mask.",
DEFAULT_IMAGE_TOKEN + "\n" + "{sent} Please output segmentation mask.",
]
EXPLANATORY_QUESTION_LIST = [
"Please output segmentation mask and explain why.",
"Please output segmentation mask and explain the reason.",
"Please output segmentation mask and give some explanation.",
]
ANSWER_LIST = [
"It is [SEG].",
"Sure, [SEG].",
"Sure, it is [SEG].",
"Sure, the segmentation result is [SEG].",
"[SEG].",
]
class Summary(Enum):
NONE = 0
AVERAGE = 1
SUM = 2
COUNT = 3
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=":f", summary_type=Summary.AVERAGE):
self.name = name
self.fmt = fmt
self.summary_type = summary_type
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def all_reduce(self):
device = "cuda" if torch.cuda.is_available() else "cpu"
if isinstance(self.sum, np.ndarray):
total = torch.tensor(
self.sum.tolist()
+ [
self.count,
],
dtype=torch.float32,
device=device,
)
else:
total = torch.tensor(
[self.sum, self.count], dtype=torch.float32, device=device
)
dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False)
if total.shape[0] > 2:
self.sum, self.count = total[:-1].cpu().numpy(), total[-1].cpu().item()
else:
self.sum, self.count = total.tolist()
self.avg = self.sum / (self.count + 1e-5)
def __str__(self):
fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
return fmtstr.format(**self.__dict__)
def summary(self):
fmtstr = ""
if self.summary_type is Summary.NONE:
fmtstr = ""
elif self.summary_type is Summary.AVERAGE:
fmtstr = "{name} {avg:.3f}"
elif self.summary_type is Summary.SUM:
fmtstr = "{name} {sum:.3f}"
elif self.summary_type is Summary.COUNT:
fmtstr = "{name} {count:.3f}"
else:
raise ValueError("invalid summary type %r" % self.summary_type)
return fmtstr.format(**self.__dict__)
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
class ProgressMeter(object):
def __init__(self, num_batches, meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def display(self, batch):
entries = [self.prefix + self.batch_fmtstr.format(batch)]
entries += [str(meter) for meter in self.meters]
print("\t".join(entries))
def display_summary(self):
entries = [" *"]
entries += [meter.summary() for meter in self.meters]
print(" ".join(entries))
def _get_batch_fmtstr(self, num_batches):
num_digits = len(str(num_batches // 1))
fmt = "{:" + str(num_digits) + "d}"
return "[" + fmt + "/" + fmt.format(num_batches) + "]"
def dict_to_cuda(input_dict):
for k, v in input_dict.items():
if isinstance(input_dict[k], torch.Tensor):
input_dict[k] = v.cuda(non_blocking=True)
elif (
isinstance(input_dict[k], list)
and len(input_dict[k]) > 0
and isinstance(input_dict[k][0], torch.Tensor)
):
input_dict[k] = [ele.cuda(non_blocking=True) for ele in v]
return input_dict
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