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from typing import List, Tuple
from .baseline_models import BASELINE_MODELS
def norm_type_from_model_name(model_name: str) -> Tuple[str, int]:
standardizing_models = [
"dofa_large",
"dofa_base",
"mmearth_atto",
"presto",
"anysat",
"prithvi",
]
for m in standardizing_models:
assert m in BASELINE_MODELS, f"{m} not in BASELINE_MODELS"
if model_name in standardizing_models:
norm_type = "standardize"
std_dividor = 2
elif model_name in BASELINE_MODELS:
norm_type = "norm_yes_clip_int"
std_dividor = 1
else:
norm_type = "norm_no_clip"
std_dividor = 1
return norm_type, std_dividor
def get_all_norm_strats(model_name, s1_or_s2: str = "s2") -> List:
std_multiplier_range = list(range(14, 27, 2))
norm_type, std_dividor = norm_type_from_model_name(model_name)
if s1_or_s2 == "s2":
datasets = ["dataset", "SATMAE", "S2A", "S2C", "OURS", "presto_s2"]
else:
if s1_or_s2 != "s1":
raise ValueError(f"Expected s1_or_s2 to be 's1' or 's2', got {s1_or_s2}")
datasets = ["dataset", "S1", "OURS_S1", "presto_s1"]
if model_name == "prithvi":
# the Prithvi norm bands only cover a subset of bands,
# so they are not applicable for other models
datasets.append("prithvi2")
# std_multiplier = 1.4, 1.6, ... 2.6
norm_stats = [
{"stats": s, "type": norm_type, "std_multiplier": m / (10 * std_dividor)}
for s in datasets
for m in std_multiplier_range
]
if s1_or_s2 == "s2":
norm_stats.append({"type": "satlas"})
return norm_stats
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