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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import random
import re
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from omegaconf import OmegaConf
from torch import distributions as pyd
from torch.distributions.utils import _standard_normal
class eval_mode:
def __init__(self, *models):
self.models = models
def __enter__(self):
self.prev_states = []
for model in self.models:
self.prev_states.append(model.training)
model.train(False)
def __exit__(self, *args):
for model, state in zip(self.models, self.prev_states):
model.train(state)
return False
def set_seed_everywhere(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
def soft_update_params(net, target_net, tau):
for param, target_param in zip(net.parameters(), target_net.parameters()):
target_param.data.copy_(tau * param.data +
(1 - tau) * target_param.data)
def to_torch(xs, device):
return tuple(torch.as_tensor(x, device=device) for x in xs)
def weight_init(m):
if isinstance(m, nn.Linear):
nn.init.orthogonal_(m.weight.data)
if hasattr(m.bias, 'data'):
m.bias.data.fill_(0.0)
elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
gain = nn.init.calculate_gain('relu')
nn.init.orthogonal_(m.weight.data, gain)
if hasattr(m.bias, 'data'):
m.bias.data.fill_(0.0)
class Until:
def __init__(self, until, action_repeat=1):
self._until = until
self._action_repeat = action_repeat
def __call__(self, step):
if self._until is None:
return True
until = self._until // self._action_repeat
return step < until
class Every:
def __init__(self, every, action_repeat=1):
self._every = every
self._action_repeat = action_repeat
def __call__(self, step):
if self._every is None:
return False
every = self._every // self._action_repeat
if step % every == 0:
return True
return False
class Timer:
def __init__(self):
self._start_time = time.time()
self._last_time = time.time()
def reset(self):
elapsed_time = time.time() - self._last_time
self._last_time = time.time()
total_time = time.time() - self._start_time
return elapsed_time, total_time
def total_time(self):
return time.time() - self._start_time
class TruncatedNormal(pyd.Normal):
def __init__(self, loc, scale, low=-1.0, high=1.0, eps=1e-6):
super().__init__(loc, scale, validate_args=False)
self.low = low
self.high = high
self.eps = eps
def _clamp(self, x):
clamped_x = torch.clamp(x, self.low + self.eps, self.high - self.eps)
x = x - x.detach() + clamped_x.detach()
return x
def sample(self, clip=None, sample_shape=torch.Size()):
shape = self._extended_shape(sample_shape)
eps = _standard_normal(shape,
dtype=self.loc.dtype,
device=self.loc.device)
eps *= self.scale
if clip is not None:
eps = torch.clamp(eps, -clip, clip)
x = self.loc + eps
return self._clamp(x)
def schedule(schdl, step):
try:
return float(schdl)
except ValueError:
match = re.match(r'linear\((.+),(.+),(.+)\)', schdl)
if match:
init, final, duration = [float(g) for g in match.groups()]
mix = np.clip(step / duration, 0.0, 1.0)
return (1.0 - mix) * init + mix * final
match = re.match(r'step_linear\((.+),(.+),(.+),(.+),(.+)\)', schdl)
if match:
init, final1, duration1, final2, duration2 = [
float(g) for g in match.groups()
]
if step <= duration1:
mix = np.clip(step / duration1, 0.0, 1.0)
return (1.0 - mix) * init + mix * final1
else:
mix = np.clip((step - duration1) / duration2, 0.0, 1.0)
return (1.0 - mix) * final1 + mix * final2
raise NotImplementedError(schdl)
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