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Ruff format docstring Python code (#15792)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-08-25 01:08:07 +08:00 • committed by GitHub
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63 changed files with 370 additions and 374 deletions
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11
ultralytics/models/sam/modules/decoders.py
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@ -32,8 +32,9 @@ class MaskDecoder(nn.Module):
Examples:
>>> decoder = MaskDecoder(transformer_dim=256, transformer=transformer_module)
>>> masks, iou_pred = decoder(image_embeddings, image_pe, sparse_prompt_embeddings,
... dense_prompt_embeddings, multimask_output=True)
>>> masks, iou_pred = decoder(
... image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, multimask_output=True
... )
>>> print(f"Predicted masks shape: {masks.shape}, IoU predictions shape: {iou_pred.shape}")
"""
@ -213,7 +214,8 @@ class SAM2MaskDecoder(nn.Module):
>>> dense_prompt_embeddings = torch.rand(1, 256, 64, 64)
>>> decoder = SAM2MaskDecoder(256, transformer)
>>> masks, iou_pred, sam_tokens_out, obj_score_logits = decoder.forward(
... image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False)
... image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False
... )
"""
def __init__(
@ -345,7 +347,8 @@ class SAM2MaskDecoder(nn.Module):
>>> dense_prompt_embeddings = torch.rand(1, 256, 64, 64)
>>> decoder = SAM2MaskDecoder(256, transformer)
>>> masks, iou_pred, sam_tokens_out, obj_score_logits = decoder.forward(
... image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False)
... image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, True, False
... )
"""
masks, iou_pred, mask_tokens_out, object_score_logits = self.predict_masks(
image_embeddings=image_embeddings,