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7667a87 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 | """Code is adapted from https://github.com/MIT-AI-Accelerator/neurips-2020-sevir. Their license is MIT License."""
from typing import Optional, Sequence
import re
from einops import rearrange
import numpy as np
import torch
from torch.nn import functional as F
from torchmetrics import Metric
from datamodule import SEVIRDataLoader
def _threshold(target, pred, T):
"""
Returns binary tensors t,p the same shape as target & pred. t = 1 wherever
target > t. p =1 wherever pred > t. p and t are set to 0 wherever EITHER
t or p are nan.
This is useful for counts that don't involve correct rejections.
Parameters
----------
target
torch.Tensor
pred
torch.Tensor
T
numeric_type: threshold
Returns
-------
t
p
"""
t = (target >= T).float()
p = (pred >= T).float()
is_nan = torch.logical_or(torch.isnan(target),
torch.isnan(pred))
t[is_nan] = 0
p[is_nan] = 0
return t, p
def _calc_hits_misses_fas(t, p):
hits = torch.sum(t * p)
misses = torch.sum(t * (1 - p))
fas = torch.sum((1 - t) * p)
return hits, misses, fas
def _pod(target, pred, T, eps=1e-6):
"""
Single channel version of probability_of_detection
"""
t, p = _threshold(target, pred, T)
hits, misses, fas = _calc_hits_misses_fas(t, p)
# return (hits + eps) / (hits + misses + eps)
return hits / (hits + misses + eps)
def _sucr(target, pred, T, eps=1e-6):
"""
Single channel version of success_rate
"""
t, p = _threshold(target, pred, T)
hits, misses, fas = _calc_hits_misses_fas(t, p)
# return (hits + eps) / (hits + fas + eps)
return hits / (hits + fas + eps)
def _csi(target, pred, T, eps=1e-6):
"""
Single channel version of csi
"""
t, p = _threshold(target, pred, T)
hits, misses, fas = _calc_hits_misses_fas(t, p)
# return (hits + eps) / (hits + misses + fas + eps)
return hits / (hits + misses + fas + eps)
def _bias(target, pred, T, eps=1e-6):
"""
Single channel version of csi
"""
t, p = _threshold(target, pred, T)
hits, misses, fas = _calc_hits_misses_fas(t, p)
# return (hits + fas + eps) / (hits + misses + eps)
return (hits + fas) / (hits + misses + eps)
class SEVIRSkillScore(Metric):
r"""
The calculation of skill scores in SEVIR challenge is slightly different:
`mCSI = sum(mCSI_t) / T`
See https://github.com/MIT-AI-Accelerator/sevir_challenges/blob/dev/radar_nowcasting/RadarNowcastBenchmarks.ipynb for more details.
"""
full_state_update: bool = True
def __init__(self,
layout: str = "NHWT",
mode: str = "0",
seq_len: Optional[int] = None,
preprocess_type: str = "sevir",
threshold_list: Sequence[int] = (16, 74, 133, 160, 181, 219),
metrics_list: Sequence[str] = ("csi", "bias", "sucr", "pod"),
eps: float = 1e-4,
):
r"""
Parameters
----------
seq_len
layout
mode: str
Should be in ("0", "1", "2")
"0":
cumulates hits/misses/fas of all test pixels
score_avg takes average over all thresholds
return
score_thresh shape = (1, )
score_avg shape = (1, )
"1":
cumulates hits/misses/fas of each step
score_avg takes average over all thresholds while keeps the seq_len dim
return
score_thresh shape = (seq_len, )
score_avg shape = (seq_len, )
"2":
cumulates hits/misses/fas of each step
score_avg takes average over all thresholds, then takes average over the seq_len dim
return
score_thresh shape = (1, )
score_avg shape = (1, )
preprocess_type: str
valid values are "sevir", f"sevir_pool{x}", where `x` is the pool scale factor
threshold_list
"""
super().__init__()
self.layout = layout
self.parse_layout()
assert preprocess_type == "sevir" or preprocess_type.startswith("sevir_pool")
self.preprocess_type = preprocess_type
self.threshold_list = threshold_list
self.metrics_list = metrics_list
self.eps = eps
self.mode = mode
self.seq_len = seq_len
if mode in ("0", ):
self.keep_seq_len_dim = False
state_shape = (len(self.threshold_list), )
elif mode in ("1", "2"):
self.keep_seq_len_dim = True
assert isinstance(self.seq_len, int), "seq_len must be provided when we need to keep seq_len dim."
state_shape = (len(self.threshold_list), self.seq_len)
else:
raise NotImplementedError(f"mode {mode} not supported!")
self.add_state("hits",
default=torch.zeros(state_shape),
dist_reduce_fx="sum")
self.add_state("misses",
default=torch.zeros(state_shape),
dist_reduce_fx="sum")
self.add_state("fas",
default=torch.zeros(state_shape),
dist_reduce_fx="sum")
@property
def hits_misses_fas_reduce_dims(self):
if not hasattr(self, "_hits_misses_fas_reduce_dims"):
seq_dim = self.layout.find('T')
self._hits_misses_fas_reduce_dims = list(range(len(self.layout)))
if self.keep_seq_len_dim:
self._hits_misses_fas_reduce_dims.pop(seq_dim)
return self._hits_misses_fas_reduce_dims
def parse_layout(self):
self.batch_axis = self.layout.find("N")
self.einops_layout = " ".join(self.layout)
self.einops_spatial_layout = "(N T) C H W"
@staticmethod
def pod(hits, misses, fas, eps):
return hits / (hits + misses + eps)
@staticmethod
def sucr(hits, misses, fas, eps):
return hits / (hits + fas + eps)
@staticmethod
def csi(hits, misses, fas, eps):
return hits / (hits + misses + fas + eps)
@staticmethod
def bias(hits, misses, fas, eps):
bias = (hits + fas) / (hits + misses + eps)
logbias = torch.pow(bias / torch.log(torch.tensor(2.0)), 2.0)
return logbias
def calc_seq_hits_misses_fas(self, pred, target, threshold):
"""
Parameters
----------
pred, target: torch.Tensor
threshold: int
Returns
-------
hits, misses, fas: torch.Tensor
each has shape (seq_len, )
"""
with torch.no_grad():
t, p = _threshold(target, pred, threshold)
hits = torch.sum(t * p, dim=self.hits_misses_fas_reduce_dims).int()
misses = torch.sum(t * (1 - p), dim=self.hits_misses_fas_reduce_dims).int()
fas = torch.sum((1 - t) * p, dim=self.hits_misses_fas_reduce_dims).int()
return hits, misses, fas
def preprocess(self, pred, target):
if self.preprocess_type == "sevir":
pred = SEVIRDataLoader.process_data_dict_back(
data_dict={'vil': pred.detach().float()})['vil']
target = SEVIRDataLoader.process_data_dict_back(
data_dict={'vil': target.detach().float()})['vil']
elif self.preprocess_type.startswith("sevir_pool"):
pred = SEVIRDataLoader.process_data_dict_back(
data_dict={'vil': pred.detach().float()})['vil']
target = SEVIRDataLoader.process_data_dict_back(
data_dict={'vil': target.detach().float()})['vil']
self.pool_scale = int(re.search(r'\d+', self.preprocess_type).group())
batch_size = target.shape[0]
pred = rearrange(pred, f"{self.einops_layout} -> {self.einops_spatial_layout}")
target = rearrange(target, f"{self.einops_layout} -> {self.einops_spatial_layout}")
pred = F.max_pool2d(pred, kernel_size=self.pool_scale, stride=self.pool_scale)
target = F.max_pool2d(target, kernel_size=self.pool_scale, stride=self.pool_scale)
pred = rearrange(pred, f"{self.einops_spatial_layout} -> {self.einops_layout}", N=batch_size)
target = rearrange(target, f"{self.einops_spatial_layout} -> {self.einops_layout}", N=batch_size)
else:
raise NotImplementedError
return pred, target
def update(self, pred: torch.Tensor, target: torch.Tensor):
pred, target = self.preprocess(pred, target)
for i, threshold in enumerate(self.threshold_list):
hits, misses, fas = self.calc_seq_hits_misses_fas(pred, target, threshold)
self.hits[i] += hits
self.misses[i] += misses
self.fas[i] += fas
def compute(self):
metrics_dict = {
'pod': self.pod,
'csi': self.csi,
'sucr': self.sucr,
'bias': self.bias
}
ret = {}
for threshold in self.threshold_list:
ret[threshold] = {}
ret["avg"] = {}
for metrics in self.metrics_list:
if self.keep_seq_len_dim:
score_avg = np.zeros((self.seq_len, ))
else:
score_avg = 0
# shape = (len(threshold_list), seq_len) if self.keep_seq_len_dim,
# else shape = (len(threshold_list),)
scores = metrics_dict[metrics](self.hits, self.misses, self.fas, self.eps)
scores = scores.detach().cpu().numpy()
for i, threshold in enumerate(self.threshold_list):
if self.keep_seq_len_dim:
score = scores[i] # shape = (seq_len, )
else:
score = scores[i].item() # shape = (1, )
if self.mode in ("0", "1"):
ret[threshold][metrics] = score
elif self.mode in ("2", ):
ret[threshold][metrics] = np.mean(score).item()
else:
raise NotImplementedError
score_avg += score
score_avg /= len(self.threshold_list)
if self.mode in ("0", "1"):
ret["avg"][metrics] = score_avg
elif self.mode in ("2",):
ret["avg"][metrics] = np.mean(score_avg).item()
else:
raise NotImplementedError
return ret
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