HongChuang/ultralytics-ascend
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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
parent c1882a4327
commit d27664216b
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63 changed files with 370 additions and 374 deletions
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11
ultralytics/models/sam/modules/decoders.py
View file

@ -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,

10
ultralytics/models/sam/modules/sam.py
View file

@ -417,7 +417,15 @@ class SAM2Model(torch.nn.Module):
>>> point_inputs = {"point_coords": torch.rand(1, 2, 2), "point_labels": torch.tensor([[1, 0]])}
>>> mask_inputs = torch.rand(1, 1, 512, 512)
>>> results = model._forward_sam_heads(backbone_features, point_inputs, mask_inputs)
>>> low_res_multimasks, high_res_multimasks, ious, low_res_masks, high_res_masks, obj_ptr, object_score_logits = results
>>> (
... low_res_multimasks,
... high_res_multimasks,
... ious,
... low_res_masks,
... high_res_masks,
... obj_ptr,
... object_score_logits,
... ) = results
"""
B = backbone_features.size(0)
device = backbone_features.device

2
ultralytics/models/sam/modules/tiny_encoder.py
View file

@ -716,7 +716,7 @@ class BasicLayer(nn.Module):
Examples:
>>> layer = BasicLayer(dim=96, input_resolution=(56, 56), depth=2, num_heads=3, window_size=7)
>>> x = torch.randn(1, 56*56, 96)
>>> x = torch.randn(1, 56 * 56, 96)
>>> output = layer(x)
>>> print(output.shape)
"""

2
ultralytics/models/sam/modules/utils.py
View file

@ -22,7 +22,7 @@ def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num
Examples:
>>> frame_idx = 5
>>> cond_frame_outputs = {1: 'a', 3: 'b', 7: 'c', 9: 'd'}
>>> cond_frame_outputs = {1: "a", 3: "b", 7: "c", 9: "d"}
>>> max_cond_frame_num = 2
>>> selected, unselected = select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num)
>>> print(selected)