354 lines
12 KiB
Python
354 lines
12 KiB
Python
from typing import Union, Dict
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import unittest
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import zarr
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import numpy as np
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import torch
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import torch.nn as nn
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from diffusion_policy.common.pytorch_util import dict_apply
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from diffusion_policy.model.common.dict_of_tensor_mixin import DictOfTensorMixin
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class LinearNormalizer(DictOfTensorMixin):
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avaliable_modes = ['limits', 'gaussian']
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@torch.no_grad()
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def fit(self,
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data: Union[Dict, torch.Tensor, np.ndarray, zarr.Array],
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last_n_dims=1,
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dtype=torch.float32,
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mode='limits',
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output_max=1.,
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output_min=-1.,
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range_eps=1e-4,
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fit_offset=True):
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if isinstance(data, dict):
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for key, value in data.items():
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self.params_dict[key] = _fit(value,
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last_n_dims=last_n_dims,
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dtype=dtype,
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mode=mode,
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output_max=output_max,
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output_min=output_min,
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range_eps=range_eps,
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fit_offset=fit_offset)
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else:
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self.params_dict['_default'] = _fit(data,
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last_n_dims=last_n_dims,
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dtype=dtype,
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mode=mode,
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output_max=output_max,
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output_min=output_min,
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range_eps=range_eps,
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fit_offset=fit_offset)
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def __call__(self, x: Union[Dict, torch.Tensor, np.ndarray]) -> torch.Tensor:
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return self.normalize(x)
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def __getitem__(self, key: str):
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return SingleFieldLinearNormalizer(self.params_dict[key])
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def __setitem__(self, key: str , value: 'SingleFieldLinearNormalizer'):
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self.params_dict[key] = value.params_dict
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def _normalize_impl(self, x, forward=True):
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if isinstance(x, dict):
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result = dict()
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for key, value in x.items():
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params = self.params_dict[key]
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result[key] = _normalize(value, params, forward=forward)
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return result
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else:
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if '_default' not in self.params_dict:
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raise RuntimeError("Not initialized")
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params = self.params_dict['_default']
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return _normalize(x, params, forward=forward)
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def normalize(self, x: Union[Dict, torch.Tensor, np.ndarray]) -> torch.Tensor:
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return self._normalize_impl(x, forward=True)
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def unnormalize(self, x: Union[Dict, torch.Tensor, np.ndarray]) -> torch.Tensor:
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return self._normalize_impl(x, forward=False)
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def get_input_stats(self) -> Dict:
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if len(self.params_dict) == 0:
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raise RuntimeError("Not initialized")
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if len(self.params_dict) == 1 and '_default' in self.params_dict:
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return self.params_dict['_default']['input_stats']
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result = dict()
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for key, value in self.params_dict.items():
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if key != '_default':
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result[key] = value['input_stats']
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return result
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def get_output_stats(self, key='_default'):
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input_stats = self.get_input_stats()
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if 'min' in input_stats:
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# no dict
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return dict_apply(input_stats, self.normalize)
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result = dict()
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for key, group in input_stats.items():
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this_dict = dict()
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for name, value in group.items():
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this_dict[name] = self.normalize({key:value})[key]
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result[key] = this_dict
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return result
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class SingleFieldLinearNormalizer(DictOfTensorMixin):
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avaliable_modes = ['limits', 'gaussian']
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@torch.no_grad()
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def fit(self,
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data: Union[torch.Tensor, np.ndarray, zarr.Array],
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last_n_dims=1,
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dtype=torch.float32,
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mode='limits',
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output_max=1.,
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output_min=-1.,
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range_eps=1e-4,
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fit_offset=True):
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self.params_dict = _fit(data,
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last_n_dims=last_n_dims,
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dtype=dtype,
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mode=mode,
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output_max=output_max,
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output_min=output_min,
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range_eps=range_eps,
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fit_offset=fit_offset)
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@classmethod
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def create_fit(cls, data: Union[torch.Tensor, np.ndarray, zarr.Array], **kwargs):
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obj = cls()
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obj.fit(data, **kwargs)
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return obj
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@classmethod
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def create_manual(cls,
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scale: Union[torch.Tensor, np.ndarray],
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offset: Union[torch.Tensor, np.ndarray],
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input_stats_dict: Dict[str, Union[torch.Tensor, np.ndarray]]):
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def to_tensor(x):
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if not isinstance(x, torch.Tensor):
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x = torch.from_numpy(x)
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x = x.flatten()
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return x
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# check
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for x in [offset] + list(input_stats_dict.values()):
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assert x.shape == scale.shape
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assert x.dtype == scale.dtype
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params_dict = nn.ParameterDict({
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'scale': to_tensor(scale),
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'offset': to_tensor(offset),
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'input_stats': nn.ParameterDict(
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dict_apply(input_stats_dict, to_tensor))
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})
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return cls(params_dict)
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@classmethod
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def create_identity(cls, dtype=torch.float32):
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scale = torch.tensor([1], dtype=dtype)
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offset = torch.tensor([0], dtype=dtype)
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input_stats_dict = {
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'min': torch.tensor([-1], dtype=dtype),
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'max': torch.tensor([1], dtype=dtype),
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'mean': torch.tensor([0], dtype=dtype),
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'std': torch.tensor([1], dtype=dtype)
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}
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return cls.create_manual(scale, offset, input_stats_dict)
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def normalize(self, x: Union[torch.Tensor, np.ndarray]) -> torch.Tensor:
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return _normalize(x, self.params_dict, forward=True)
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def unnormalize(self, x: Union[torch.Tensor, np.ndarray]) -> torch.Tensor:
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return _normalize(x, self.params_dict, forward=False)
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def get_input_stats(self):
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return self.params_dict['input_stats']
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def get_output_stats(self):
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return dict_apply(self.params_dict['input_stats'], self.normalize)
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def __call__(self, x: Union[torch.Tensor, np.ndarray]) -> torch.Tensor:
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return self.normalize(x)
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def _fit(data: Union[torch.Tensor, np.ndarray, zarr.Array],
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last_n_dims=1,
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dtype=torch.float32,
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mode='limits',
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output_max=1.,
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output_min=-1.,
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range_eps=1e-4,
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fit_offset=True):
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assert mode in ['limits', 'gaussian']
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assert last_n_dims >= 0
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assert output_max > output_min
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# convert data to torch and type
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if isinstance(data, zarr.Array):
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data = data[:]
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if isinstance(data, np.ndarray):
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data = torch.from_numpy(data)
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if dtype is not None:
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data = data.type(dtype)
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# convert shape
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dim = 1
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if last_n_dims > 0:
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dim = np.prod(data.shape[-last_n_dims:])
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data = data.reshape(-1,dim)
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# compute input stats min max mean std
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input_min, _ = data.min(axis=0)
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input_max, _ = data.max(axis=0)
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input_mean = data.mean(axis=0)
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input_std = data.std(axis=0)
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# compute scale and offset
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if mode == 'limits':
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if fit_offset:
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# unit scale
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input_range = input_max - input_min
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ignore_dim = input_range < range_eps
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input_range[ignore_dim] = output_max - output_min
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scale = (output_max - output_min) / input_range
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offset = output_min - scale * input_min
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offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]
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# ignore dims scaled to mean of output max and min
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else:
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# use this when data is pre-zero-centered.
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assert output_max > 0
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assert output_min < 0
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# unit abs
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output_abs = min(abs(output_min), abs(output_max))
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input_abs = torch.maximum(torch.abs(input_min), torch.abs(input_max))
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ignore_dim = input_abs < range_eps
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input_abs[ignore_dim] = output_abs
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# don't scale constant channels
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scale = output_abs / input_abs
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offset = torch.zeros_like(input_mean)
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elif mode == 'gaussian':
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ignore_dim = input_std < range_eps
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scale = input_std.clone()
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scale[ignore_dim] = 1
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scale = 1 / scale
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if fit_offset:
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offset = - input_mean * scale
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else:
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offset = torch.zeros_like(input_mean)
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# save
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this_params = nn.ParameterDict({
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'scale': scale,
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'offset': offset,
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'input_stats': nn.ParameterDict({
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'min': input_min,
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'max': input_max,
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'mean': input_mean,
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'std': input_std
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})
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})
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for p in this_params.parameters():
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p.requires_grad_(False)
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return this_params
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def _normalize(x, params, forward=True):
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assert 'scale' in params
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if isinstance(x, np.ndarray):
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x = torch.from_numpy(x)
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scale = params['scale']
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offset = params['offset']
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x = x.to(device=scale.device, dtype=scale.dtype)
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src_shape = x.shape
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x = x.reshape(-1, scale.shape[0])
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if forward:
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x = x * scale + offset
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else:
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x = (x - offset) / scale
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x = x.reshape(src_shape)
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return x
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def test():
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data = torch.zeros((100,10,9,2)).uniform_()
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data[...,0,0] = 0
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normalizer = SingleFieldLinearNormalizer()
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normalizer.fit(data, mode='limits', last_n_dims=2)
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datan = normalizer.normalize(data)
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assert datan.shape == data.shape
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assert np.allclose(datan.max(), 1.)
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assert np.allclose(datan.min(), -1.)
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dataun = normalizer.unnormalize(datan)
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assert torch.allclose(data, dataun, atol=1e-7)
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input_stats = normalizer.get_input_stats()
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output_stats = normalizer.get_output_stats()
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normalizer = SingleFieldLinearNormalizer()
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normalizer.fit(data, mode='limits', last_n_dims=1, fit_offset=False)
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datan = normalizer.normalize(data)
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assert datan.shape == data.shape
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assert np.allclose(datan.max(), 1., atol=1e-3)
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assert np.allclose(datan.min(), 0., atol=1e-3)
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dataun = normalizer.unnormalize(datan)
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assert torch.allclose(data, dataun, atol=1e-7)
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data = torch.zeros((100,10,9,2)).uniform_()
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normalizer = SingleFieldLinearNormalizer()
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normalizer.fit(data, mode='gaussian', last_n_dims=0)
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datan = normalizer.normalize(data)
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assert datan.shape == data.shape
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assert np.allclose(datan.mean(), 0., atol=1e-3)
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assert np.allclose(datan.std(), 1., atol=1e-3)
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dataun = normalizer.unnormalize(datan)
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assert torch.allclose(data, dataun, atol=1e-7)
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# dict
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data = torch.zeros((100,10,9,2)).uniform_()
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data[...,0,0] = 0
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normalizer = LinearNormalizer()
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normalizer.fit(data, mode='limits', last_n_dims=2)
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datan = normalizer.normalize(data)
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assert datan.shape == data.shape
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assert np.allclose(datan.max(), 1.)
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assert np.allclose(datan.min(), -1.)
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dataun = normalizer.unnormalize(datan)
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assert torch.allclose(data, dataun, atol=1e-7)
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input_stats = normalizer.get_input_stats()
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output_stats = normalizer.get_output_stats()
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data = {
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'obs': torch.zeros((1000,128,9,2)).uniform_() * 512,
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'action': torch.zeros((1000,128,2)).uniform_() * 512
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}
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normalizer = LinearNormalizer()
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normalizer.fit(data)
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datan = normalizer.normalize(data)
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dataun = normalizer.unnormalize(datan)
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for key in data:
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assert torch.allclose(data[key], dataun[key], atol=1e-4)
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input_stats = normalizer.get_input_stats()
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output_stats = normalizer.get_output_stats()
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state_dict = normalizer.state_dict()
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n = LinearNormalizer()
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n.load_state_dict(state_dict)
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datan = n.normalize(data)
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dataun = n.unnormalize(datan)
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for key in data:
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assert torch.allclose(data[key], dataun[key], atol=1e-4)
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