File size: 2,514 Bytes
7985e7e | 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 | import numpy as np
class DatasetNormalizer:
def __init__(self, dataset, normalizer, path_lengths=None):
dataset = flatten(dataset, path_lengths)
self.observation_dim = dataset["observations"].shape[1]
self.action_dim = dataset["actions"].shape[1]
if type(normalizer) == str:
normalizer = eval(normalizer)
self.normalizers = {}
for key, val in dataset.items():
try:
self.normalizers[key] = normalizer(val)
except Exception as e:
print(f"[ utils/normalization ] Skipping {key} | {normalizer} | {e}")
def __repr__(self):
string = ""
for key, normalizer in self.normalizers.items():
string += f"{key}: {normalizer}]\n"
return string
def __call__(self, *args, **kwargs):
return self.normalize(*args, **kwargs)
def normalize(self, x, key):
return self.normalizers[key].normalize(x)
def unnormalize(self, x, key):
return self.normalizers[key].unnormalize(x)
def get_field_normalizers(self):
return self.normalizers
def flatten(dataset, path_lengths):
flattened = {}
for key, xs in dataset.items():
assert len(xs) == len(path_lengths)
flattened[key] = np.concatenate(
[x[:length] for x, length in zip(xs, path_lengths)], axis=0
)
return flattened
class Normalizer:
def __init__(self, X):
self.X = X.astype(np.float32)
self.mins = X.min(axis=0)
self.maxs = X.max(axis=0)
def __repr__(self):
return (
f"""[ Normalizer ] dim: {self.mins.size}\n -: """
f"""{np.round(self.mins, 2)}\n +: {np.round(self.maxs, 2)}\n"""
)
def __call__(self, x):
return self.normalize(x)
def normalize(self, *args, **kwargs):
raise NotImplementedError()
def unnormalize(self, *args, **kwargs):
raise NotImplementedError()
class DebugNormalizer(Normalizer):
def normalize(self, x, *args, **kwargs):
return x
def unnormalize(self, x, *args, **kwargs):
return x
class LimitsNormalizer(Normalizer):
def normalize(self, x):
x = (x - self.mins) / (self.maxs - self.mins)
x = 2 * x - 1
return x
def unnormalize(self, x, eps=1e-4):
if x.max() > 1 + eps or x.min() < -1 - eps:
x = np.clip(x, -1, 1)
x = (x + 1) / 2.0
return x * (self.maxs - self.mins) + self.mins
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