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from equi_diffpo.model.common.normalizer import SingleFieldLinearNormalizer
from equi_diffpo.common.pytorch_util import dict_apply, dict_apply_reduce, dict_apply_split
import numpy as np
def get_range_normalizer_from_stat(stat, output_max=1, output_min=-1, range_eps=1e-7):
# -1, 1 normalization
input_max = stat['max']
input_min = stat['min']
input_range = input_max - input_min
ignore_dim = input_range < range_eps
input_range[ignore_dim] = output_max - output_min
scale = (output_max - output_min) / input_range
offset = output_min - scale * input_min
offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]
return SingleFieldLinearNormalizer.create_manual(
scale=scale,
offset=offset,
input_stats_dict=stat
)
def get_range_symmetric_normalizer_from_stat(stat, output_max=1, output_min=-1, range_eps=1e-7):
# -1, 1 normalization
input_max = stat['max']
input_min = stat['min']
abs_max = np.max([np.abs(stat['max'][:2]), np.abs(stat['min'][:2])])
input_max[:2] = abs_max
input_min[:2] = -abs_max
input_range = input_max - input_min
ignore_dim = input_range < range_eps
input_range[ignore_dim] = output_max - output_min
scale = (output_max - output_min) / input_range
offset = output_min - scale * input_min
offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]
return SingleFieldLinearNormalizer.create_manual(
scale=scale,
offset=offset,
input_stats_dict=stat
)
def get_voxel_identity_normalizer():
scale = np.array([1], dtype=np.float32)
offset = np.array([0], dtype=np.float32)
stat = {
'min': np.array([0], dtype=np.float32),
'max': np.array([1], dtype=np.float32),
'mean': np.array([0.5], dtype=np.float32),
'std': np.array([np.sqrt(1/12)], dtype=np.float32)
}
return SingleFieldLinearNormalizer.create_manual(
scale=scale,
offset=offset,
input_stats_dict=stat
)
def get_image_range_normalizer():
scale = np.array([2], dtype=np.float32)
offset = np.array([-1], dtype=np.float32)
stat = {
'min': np.array([0], dtype=np.float32),
'max': np.array([1], dtype=np.float32),
'mean': np.array([0.5], dtype=np.float32),
'std': np.array([np.sqrt(1/12)], dtype=np.float32)
}
return SingleFieldLinearNormalizer.create_manual(
scale=scale,
offset=offset,
input_stats_dict=stat
)
def get_identity_normalizer_from_stat(stat):
scale = np.ones_like(stat['min'])
offset = np.zeros_like(stat['min'])
return SingleFieldLinearNormalizer.create_manual(
scale=scale,
offset=offset,
input_stats_dict=stat
)
def robomimic_abs_action_normalizer_from_stat(stat, rotation_transformer):
result = dict_apply_split(
stat, lambda x: {
'pos': x[...,:3],
'rot': x[...,3:6],
'gripper': x[...,6:]
})
def get_pos_param_info(stat, output_max=1, output_min=-1, range_eps=1e-7):
# -1, 1 normalization
input_max = stat['max']
input_min = stat['min']
input_range = input_max - input_min
ignore_dim = input_range < range_eps
input_range[ignore_dim] = output_max - output_min
scale = (output_max - output_min) / input_range
offset = output_min - scale * input_min
offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]
return {'scale': scale, 'offset': offset}, stat
def get_rot_param_info(stat):
example = rotation_transformer.forward(stat['mean'])
scale = np.ones_like(example)
offset = np.zeros_like(example)
info = {
'max': np.ones_like(example),
'min': np.full_like(example, -1),
'mean': np.zeros_like(example),
'std': np.ones_like(example)
}
return {'scale': scale, 'offset': offset}, info
def get_gripper_param_info(stat):
example = stat['max']
scale = np.ones_like(example)
offset = np.zeros_like(example)
info = {
'max': np.ones_like(example),
'min': np.full_like(example, -1),
'mean': np.zeros_like(example),
'std': np.ones_like(example)
}
return {'scale': scale, 'offset': offset}, info
pos_param, pos_info = get_pos_param_info(result['pos'])
rot_param, rot_info = get_rot_param_info(result['rot'])
gripper_param, gripper_info = get_gripper_param_info(result['gripper'])
param = dict_apply_reduce(
[pos_param, rot_param, gripper_param],
lambda x: np.concatenate(x,axis=-1))
info = dict_apply_reduce(
[pos_info, rot_info, gripper_info],
lambda x: np.concatenate(x,axis=-1))
return SingleFieldLinearNormalizer.create_manual(
scale=param['scale'],
offset=param['offset'],
input_stats_dict=info
)
def robomimic_abs_action_only_normalizer_from_stat(stat):
result = dict_apply_split(
stat, lambda x: {
'pos': x[...,:3],
'other': x[...,3:]
})
def get_pos_param_info(stat, output_max=1, output_min=-1, range_eps=1e-7):
# -1, 1 normalization
input_max = stat['max']
input_min = stat['min']
input_range = input_max - input_min
ignore_dim = input_range < range_eps
input_range[ignore_dim] = output_max - output_min
scale = (output_max - output_min) / input_range
offset = output_min - scale * input_min
offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]
return {'scale': scale, 'offset': offset}, stat
def get_other_param_info(stat):
example = stat['max']
scale = np.ones_like(example)
offset = np.zeros_like(example)
info = {
'max': np.ones_like(example),
'min': np.full_like(example, -1),
'mean': np.zeros_like(example),
'std': np.ones_like(example)
}
return {'scale': scale, 'offset': offset}, info
pos_param, pos_info = get_pos_param_info(result['pos'])
other_param, other_info = get_other_param_info(result['other'])
param = dict_apply_reduce(
[pos_param, other_param],
lambda x: np.concatenate(x,axis=-1))
info = dict_apply_reduce(
[pos_info, other_info],
lambda x: np.concatenate(x,axis=-1))
return SingleFieldLinearNormalizer.create_manual(
scale=param['scale'],
offset=param['offset'],
input_stats_dict=info
)
def robomimic_abs_action_only_symmetric_normalizer_from_stat(stat):
result = dict_apply_split(
stat, lambda x: {
'pos': x[...,:3],
'other': x[...,3:]
})
def get_pos_param_info(stat, output_max=1, output_min=-1, range_eps=1e-7):
# -1, 1 normalization
input_max = stat['max']
input_min = stat['min']
abs_max = np.max([np.abs(stat['max'][:2]), np.abs(stat['min'][:2])])
input_max[:2] = abs_max
input_min[:2] = -abs_max
input_range = input_max - input_min
ignore_dim = input_range < range_eps
input_range[ignore_dim] = output_max - output_min
scale = (output_max - output_min) / input_range
offset = output_min - scale * input_min
offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]
return {'scale': scale, 'offset': offset}, stat
def get_other_param_info(stat):
example = stat['max']
scale = np.ones_like(example)
offset = np.zeros_like(example)
info = {
'max': np.ones_like(example),
'min': np.full_like(example, -1),
'mean': np.zeros_like(example),
'std': np.ones_like(example)
}
return {'scale': scale, 'offset': offset}, info
pos_param, pos_info = get_pos_param_info(result['pos'])
other_param, other_info = get_other_param_info(result['other'])
param = dict_apply_reduce(
[pos_param, other_param],
lambda x: np.concatenate(x,axis=-1))
info = dict_apply_reduce(
[pos_info, other_info],
lambda x: np.concatenate(x,axis=-1))
return SingleFieldLinearNormalizer.create_manual(
scale=param['scale'],
offset=param['offset'],
input_stats_dict=info
)
def robomimic_abs_action_only_dual_arm_normalizer_from_stat(stat):
Da = stat['max'].shape[-1]
Dah = Da // 2
result = dict_apply_split(
stat, lambda x: {
'pos0': x[...,:3],
'other0': x[...,3:Dah],
'pos1': x[...,Dah:Dah+3],
'other1': x[...,Dah+3:]
})
def get_pos_param_info(stat, output_max=1, output_min=-1, range_eps=1e-7):
# -1, 1 normalization
input_max = stat['max']
input_min = stat['min']
input_range = input_max - input_min
ignore_dim = input_range < range_eps
input_range[ignore_dim] = output_max - output_min
scale = (output_max - output_min) / input_range
offset = output_min - scale * input_min
offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]
return {'scale': scale, 'offset': offset}, stat
def get_other_param_info(stat):
example = stat['max']
scale = np.ones_like(example)
offset = np.zeros_like(example)
info = {
'max': np.ones_like(example),
'min': np.full_like(example, -1),
'mean': np.zeros_like(example),
'std': np.ones_like(example)
}
return {'scale': scale, 'offset': offset}, info
pos0_param, pos0_info = get_pos_param_info(result['pos0'])
pos1_param, pos1_info = get_pos_param_info(result['pos1'])
other0_param, other0_info = get_other_param_info(result['other0'])
other1_param, other1_info = get_other_param_info(result['other1'])
param = dict_apply_reduce(
[pos0_param, other0_param, pos1_param, other1_param],
lambda x: np.concatenate(x,axis=-1))
info = dict_apply_reduce(
[pos0_info, other0_info, pos1_info, other1_info],
lambda x: np.concatenate(x,axis=-1))
return SingleFieldLinearNormalizer.create_manual(
scale=param['scale'],
offset=param['offset'],
input_stats_dict=info
)
def array_to_stats(arr: np.ndarray):
stat = {
'min': np.min(arr, axis=0),
'max': np.max(arr, axis=0),
'mean': np.mean(arr, axis=0),
'std': np.std(arr, axis=0)
}
return stat
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