mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
144 lines
4.5 KiB
Python
144 lines
4.5 KiB
Python
import numpy as np
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import tree
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from ray.rllib.models.repeated_values import RepeatedValues
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from ray.rllib.utils.framework import try_import_torch
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torch, _ = try_import_torch()
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def explained_variance(y, pred):
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y_var = torch.var(y, dim=[0])
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diff_var = torch.var(y - pred, dim=[0])
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min_ = torch.Tensor([-1.0])
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return torch.max(
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min_.to(device=torch.device("cuda"))
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if torch.cuda.is_available() else min_,
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1 - (diff_var / y_var))
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def global_norm(tensors):
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"""Returns the global L2 norm over a list of tensors.
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output = sqrt(SUM(t ** 2 for t in tensors)),
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where SUM reduces over all tensors and over all elements in tensors.
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Args:
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tensors (List[torch.Tensor]): The list of tensors to calculate the
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global norm over.
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"""
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# List of single tensors' L2 norms: SQRT(SUM(xi^2)) over all xi in tensor.
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single_l2s = [
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torch.pow(torch.sum(torch.pow(t, 2.0)), 0.5) for t in tensors
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]
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# Compute global norm from all single tensors' L2 norms.
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return torch.pow(sum(torch.pow(l2, 2.0) for l2 in single_l2s), 0.5)
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def huber_loss(x, delta=1.0):
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"""Reference: https://en.wikipedia.org/wiki/Huber_loss"""
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return torch.where(
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torch.abs(x) < delta,
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torch.pow(x, 2.0) * 0.5, delta * (torch.abs(x) - 0.5 * delta))
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def l2_loss(x):
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"""Computes half the L2 norm of a tensor without the sqrt.
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output = sum(x ** 2) / 2
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"""
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return torch.sum(torch.pow(x, 2.0)) / 2.0
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def reduce_mean_ignore_inf(x, axis):
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"""Same as torch.mean() but ignores -inf values."""
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mask = torch.ne(x, float("-inf"))
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x_zeroed = torch.where(mask, x, torch.zeros_like(x))
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return torch.sum(x_zeroed, axis) / torch.sum(mask.float(), axis)
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def minimize_and_clip(optimizer, clip_val=10):
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"""Clips gradients found in `optimizer.param_groups` to given value.
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Ensures the norm of the gradients for each variable is clipped to
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`clip_val`
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"""
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for param_group in optimizer.param_groups:
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for p in param_group["params"]:
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if p.grad is not None:
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torch.nn.utils.clip_grad_norm_(p.grad, clip_val)
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def sequence_mask(lengths, maxlen=None, dtype=None):
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"""Offers same behavior as tf.sequence_mask for torch.
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Thanks to Dimitris Papatheodorou
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(https://discuss.pytorch.org/t/pytorch-equivalent-for-tf-sequence-mask/
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39036).
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"""
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if maxlen is None:
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maxlen = int(lengths.max())
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mask = ~(torch.ones((len(lengths), maxlen)).to(
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lengths.device).cumsum(dim=1).t() > lengths).t()
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mask.type(dtype or torch.bool)
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return mask
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def convert_to_non_torch_type(stats):
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"""Converts values in `stats` to non-Tensor numpy or python types.
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Args:
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stats (any): Any (possibly nested) struct, the values in which will be
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converted and returned as a new struct with all torch tensors
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being converted to numpy types.
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Returns:
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Any: A new struct with the same structure as `stats`, but with all
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values converted to non-torch Tensor types.
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"""
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# The mapping function used to numpyize torch Tensors.
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def mapping(item):
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if isinstance(item, torch.Tensor):
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return item.cpu().item() if len(item.size()) == 0 else \
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item.cpu().detach().numpy()
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else:
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return item
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return tree.map_structure(mapping, stats)
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def convert_to_torch_tensor(stats, device=None):
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"""Converts any struct to torch.Tensors.
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stats (any): Any (possibly nested) struct, the values in which will be
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converted and returned as a new struct with all leaves converted
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to torch tensors.
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Returns:
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Any: A new struct with the same structure as `stats`, but with all
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values converted to torch Tensor types.
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"""
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def mapping(item):
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# Already torch tensor -> make sure it's on right device.
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if torch.is_tensor(item):
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return item if device is None else item.to(device)
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# Special handling of "Repeated" values.
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elif isinstance(item, RepeatedValues):
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return RepeatedValues(
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tree.map_structure(mapping, item.values),
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item.lengths, item.max_len)
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tensor = torch.from_numpy(np.asarray(item))
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# Floatify all float64 tensors.
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if tensor.dtype == torch.double:
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tensor = tensor.float()
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return tensor if device is None else tensor.to(device)
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return tree.map_structure(mapping, stats)
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def atanh(x):
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return 0.5 * torch.log((1 + x) / (1 - x))
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