File size: 5,493 Bytes
91c1e9e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | from enum import Enum
import numpy as np
import torch
import torch.distributed as dist
IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200
DEFAULT_EOS_TOKEN = "</s>"
DEFAULT_BOS_TOKEN = "<s>"
DEFAULT_UNK_TOKEN = "<unk>"
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"
DEFAULT_BBOX_TOKEN = "<bbox>"
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 to_cuda(e):
return (
e.cuda(non_blocking=True)
if e is not None and not isinstance(e, (str, int))
else e
)
def dict_to_cuda(input_dict):
for k, v in input_dict.items():
if isinstance(v, torch.Tensor):
input_dict[k] = to_cuda(v)
elif isinstance(input_dict[k], list):
input_dict[k] = list_to_cuda(input_dict[k])
elif isinstance(input_dict[k], tuple):
input_dict[k] = tuple_to_cuda(input_dict[k])
elif isinstance(v, dict):
input_dict[k] = dict_to_cuda(v)
return input_dict
def list_to_cuda(input_list):
for i in range(len(input_list)):
if isinstance(input_list[i], torch.Tensor):
input_list[i] = to_cuda(input_list[i])
elif isinstance(input_list[i], dict):
input_list[i] = dict_to_cuda(input_list[i])
elif isinstance(input_list[i], list):
input_list[i] = list_to_cuda(input_list[i])
elif isinstance(input_list[i], tuple):
input_list[i] = tuple_to_cuda(input_list[i])
return input_list
def tuple_to_cuda(input_tuple):
return tuple(
(
to_cuda(e)
if isinstance(e, torch.Tensor)
else (
dict_to_cuda(e)
if isinstance(e, dict)
else (
list_to_cuda(e)
if isinstance(e, list)
else tuple_to_cuda(e) if isinstance(e, tuple) else e
)
)
)
for e in input_tuple
)
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