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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>
This commit is contained in:
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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2
ultralytics/models/sam/amg.py
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@ -76,7 +76,7 @@ def build_all_layer_point_grids(n_per_side: int, n_layers: int, scale_per_layer:
def generate_crop_boxes(
im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
) -> Tuple[List[List[int]], List[int]]:
"""Generates crop boxes of varying sizes for multi-scale image processing, with layered overlapping regions."""
"""Generates crop boxes of varying sizes for multiscale image processing, with layered overlapping regions."""
crop_boxes, layer_idxs = [], []
im_h, im_w = im_size
short_side = min(im_h, im_w)

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)

4
ultralytics/models/sam/modules/sam.py
View file

@ -425,7 +425,7 @@ class SAM2Model(torch.nn.Module):
low_res_masks: Tensor of shape (B, 1, H*4, W*4) with the best low-resolution mask.
high_res_masks: Tensor of shape (B, 1, H*16, W*16) with the best high-resolution mask.
obj_ptr: Tensor of shape (B, C) with object pointer vector for the output mask.
object_score_logits: Tensor of shape (B,) with object score logits.
object_score_logits: Tensor of shape (B) with object score logits.
Where M is 3 if multimask_output=True, and 1 if multimask_output=False.
@ -643,7 +643,7 @@ class SAM2Model(torch.nn.Module):
if not is_init_cond_frame:
# Retrieve the memories encoded with the maskmem backbone
to_cat_memory, to_cat_memory_pos_embed = [], []
# Add conditioning frames's output first (all cond frames have t_pos=0 for
# Add conditioning frame's output first (all cond frames have t_pos=0 for
# when getting temporal positional embedding below)
assert len(output_dict["cond_frame_outputs"]) > 0
# Select a maximum number of temporally closest cond frames for cross attention

59
ultralytics/models/sam/predict.py
View file

@ -1096,7 +1096,7 @@ class SAM2VideoPredictor(SAM2Predictor):
# to `propagate_in_video_preflight`).
consolidated_frame_inds = self.inference_state["consolidated_frame_inds"]
for is_cond in {False, True}:
# Separately consolidate conditioning and non-conditioning temp outptus
# Separately consolidate conditioning and non-conditioning temp outputs
storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
# Find all the frames that contain temporary outputs for any objects
# (these should be the frames that have just received clicks for mask inputs
@ -1161,36 +1161,35 @@ class SAM2VideoPredictor(SAM2Predictor):
assert predictor.dataset is not None
assert predictor.dataset.mode == "video"
inference_state = {}
inference_state["num_frames"] = predictor.dataset.frames
# inputs on each frame
inference_state["point_inputs_per_obj"] = {}
inference_state["mask_inputs_per_obj"] = {}
# values that don't change across frames (so we only need to hold one copy of them)
inference_state["constants"] = {}
# mapping between client-side object id and model-side object index
inference_state["obj_id_to_idx"] = OrderedDict()
inference_state["obj_idx_to_id"] = OrderedDict()
inference_state["obj_ids"] = []
# A storage to hold the model's tracking results and states on each frame
inference_state["output_dict"] = {
"cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
"non_cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
inference_state = {
"num_frames": predictor.dataset.frames,
"point_inputs_per_obj": {}, # inputs points on each frame
"mask_inputs_per_obj": {}, # inputs mask on each frame
"constants": {}, # values that don't change across frames (so we only need to hold one copy of them)
# mapping between client-side object id and model-side object index
"obj_id_to_idx": OrderedDict(),
"obj_idx_to_id": OrderedDict(),
"obj_ids": [],
# A storage to hold the model's tracking results and states on each frame
"output_dict": {
"cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
"non_cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
},
# Slice (view) of each object tracking results, sharing the same memory with "output_dict"
"output_dict_per_obj": {},
# A temporary storage to hold new outputs when user interact with a frame
# to add clicks or mask (it's merged into "output_dict" before propagation starts)
"temp_output_dict_per_obj": {},
# Frames that already holds consolidated outputs from click or mask inputs
# (we directly use their consolidated outputs during tracking)
"consolidated_frame_inds": {
"cond_frame_outputs": set(), # set containing frame indices
"non_cond_frame_outputs": set(), # set containing frame indices
},
# metadata for each tracking frame (e.g. which direction it's tracked)
"tracking_has_started": False,
"frames_already_tracked": [],
}
# Slice (view) of each object tracking results, sharing the same memory with "output_dict"
inference_state["output_dict_per_obj"] = {}
# A temporary storage to hold new outputs when user interact with a frame
# to add clicks or mask (it's merged into "output_dict" before propagation starts)
inference_state["temp_output_dict_per_obj"] = {}
# Frames that already holds consolidated outputs from click or mask inputs
# (we directly use their consolidated outputs during tracking)
inference_state["consolidated_frame_inds"] = {
"cond_frame_outputs": set(), # set containing frame indices
"non_cond_frame_outputs": set(), # set containing frame indices
}
# metadata for each tracking frame (e.g. which direction it's tracked)
inference_state["tracking_has_started"] = False
inference_state["frames_already_tracked"] = []
predictor.inference_state = inference_state
def get_im_features(self, im, batch=1):