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[HIL-SERL PORT] Fix linter issues #588

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merged 1 commit into from
Dec 23, 2024
Merged

[HIL-SERL PORT] Fix linter issues #588

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20 changes: 8 additions & 12 deletions lerobot/common/policies/sac/modeling_sac.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,21 +19,18 @@

from collections import deque
from copy import deepcopy
from functools import partial
from typing import Callable, Optional, Sequence, Tuple

import einops

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F # noqa: N812
from huggingface_hub import PyTorchModelHubMixin
from torch import Tensor

from huggingface_hub import PyTorchModelHubMixin
from lerobot.common.policies.normalize import Normalize, Unnormalize
from lerobot.common.policies.sac.configuration_sac import SACConfig
import numpy as np
from typing import Callable, Optional, Tuple, Sequence



class SACPolicy(
Expand Down Expand Up @@ -290,10 +287,7 @@ def forward(
observations = observations.to(self.device)
actions = actions.to(self.device)

if self.encoder is not None:
obs_enc = self.encoder(observations)
else:
obs_enc = observations
obs_enc = observations if self.encoder is None else self.encoder(observations)

inputs = torch.cat([obs_enc, actions], dim=-1)
x = self.network(inputs)
Expand Down Expand Up @@ -563,6 +557,8 @@ def forward(
# 3. Optionally, the values can be further transformed to fit within arbitrary bounds [low, high] using an affine transformation
# This type of distribution is commonly used in reinforcement learning, particularly for continuous action spaces
class TanhMultivariateNormalDiag(torch.distributions.TransformedDistribution):
DEFAULT_SAMPLE_SHAPE = torch.Size()

def __init__(
self,
loc: torch.Tensor,
Expand Down Expand Up @@ -611,7 +607,7 @@ def mode(self) -> torch.Tensor:

return mode

def rsample(self, sample_shape=torch.Size()) -> torch.Tensor:
def rsample(self, sample_shape=DEFAULT_SAMPLE_SHAPE) -> torch.Tensor:
"""
Reparameterized sample from the distribution
"""
Expand Down Expand Up @@ -643,7 +639,7 @@ def log_prob(self, value: torch.Tensor) -> torch.Tensor:

return log_prob

def sample_and_log_prob(self, sample_shape=torch.Size()) -> Tuple[torch.Tensor, torch.Tensor]:
def sample_and_log_prob(self, sample_shape=DEFAULT_SAMPLE_SHAPE) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Sample from the distribution and compute log probability
"""
Expand Down