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PyCharm Code and Docs Inspect fixes v1 (#18461)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2025-01-03 01:16:18 +05:00 • committed by GitHub
parent 126867e355
commit 7f1a50e893
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26 changed files with 90 additions and 91 deletions
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22
ultralytics/models/sam/modules/blocks.py
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@ -502,11 +502,11 @@ def do_pool(x: torch.Tensor, pool: nn.Module, norm: nn.Module = None) -> torch.T
class MultiScaleAttention(nn.Module):
"""
Implements multi-scale self-attention with optional query pooling for efficient feature extraction.
Implements multiscale self-attention with optional query pooling for efficient feature extraction.
This class provides a flexible implementation of multi-scale attention, allowing for optional
This class provides a flexible implementation of multiscale attention, allowing for optional
downsampling of query features through pooling. It's designed to enhance the model's ability to
capture multi-scale information in visual tasks.
capture multiscale information in visual tasks.
Attributes:
dim (int): Input dimension of the feature map.
@ -518,7 +518,7 @@ class MultiScaleAttention(nn.Module):
proj (nn.Linear): Output projection.
Methods:
forward: Applies multi-scale attention to the input tensor.
forward: Applies multiscale attention to the input tensor.
Examples:
>>> import torch
@ -537,7 +537,7 @@ class MultiScaleAttention(nn.Module):
num_heads: int,
q_pool: nn.Module = None,
):
"""Initializes multi-scale attention with optional query pooling for efficient feature extraction."""
"""Initializes multiscale attention with optional query pooling for efficient feature extraction."""
super().__init__()
self.dim = dim
@ -552,7 +552,7 @@ class MultiScaleAttention(nn.Module):
self.proj = nn.Linear(dim_out, dim_out)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Applies multi-scale attention with optional query pooling to extract multi-scale features."""
"""Applies multiscale attention with optional query pooling to extract multiscale features."""
B, H, W, _ = x.shape
# qkv with shape (B, H * W, 3, nHead, C)
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1)
@ -582,9 +582,9 @@ class MultiScaleAttention(nn.Module):
class MultiScaleBlock(nn.Module):
"""
A multi-scale attention block with window partitioning and query pooling for efficient vision transformers.
A multiscale attention block with window partitioning and query pooling for efficient vision transformers.
This class implements a multi-scale attention mechanism with optional window partitioning and downsampling,
This class implements a multiscale attention mechanism with optional window partitioning and downsampling,
designed for use in vision transformer architectures.
Attributes:
@ -601,7 +601,7 @@ class MultiScaleBlock(nn.Module):
proj (nn.Linear | None): Projection layer for dimension mismatch.
Methods:
forward: Processes input tensor through the multi-scale block.
forward: Processes input tensor through the multiscale block.
Examples:
>>> block = MultiScaleBlock(dim=256, dim_out=512, num_heads=8, window_size=7)
@ -623,7 +623,7 @@ class MultiScaleBlock(nn.Module):
act_layer: nn.Module = nn.GELU,
window_size: int = 0,
):
"""Initializes a multi-scale attention block with window partitioning and optional query pooling."""
"""Initializes a multiscale attention block with window partitioning and optional query pooling."""
super().__init__()
if isinstance(norm_layer, str):
@ -660,7 +660,7 @@ class MultiScaleBlock(nn.Module):
self.proj = nn.Linear(dim, dim_out)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Processes input through multi-scale attention and MLP, with optional windowing and downsampling."""
"""Processes input through multiscale attention and MLP, with optional windowing and downsampling."""
shortcut = x # B, H, W, C
x = self.norm1(x)