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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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from typing import List
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import torch
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from torch import nn
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from .decoders import MaskDecoder
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from .encoders import ImageEncoderViT, PromptEncoder
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class Sam(nn.Module):
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mask_threshold: float = 0.0
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image_format: str = 'RGB'
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def __init__(
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self,
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image_encoder: ImageEncoderViT,
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prompt_encoder: PromptEncoder,
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mask_decoder: MaskDecoder,
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pixel_mean: List[float] = (123.675, 116.28, 103.53),
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pixel_std: List[float] = (58.395, 57.12, 57.375)
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) -> None:
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"""
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SAM predicts object masks from an image and input prompts.
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Note:
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All forward() operations moved to SAMPredictor.
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Args:
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image_encoder (ImageEncoderViT): The backbone used to encode the image into image embeddings that allow for
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efficient mask prediction.
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prompt_encoder (PromptEncoder): Encodes various types of input prompts.
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mask_decoder (MaskDecoder): Predicts masks from the image embeddings and encoded prompts.
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pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
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pixel_std (list(float)): Std values for normalizing pixels in the input image.
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"""
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super().__init__()
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self.image_encoder = image_encoder
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self.prompt_encoder = prompt_encoder
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self.mask_decoder = mask_decoder
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self.register_buffer('pixel_mean', torch.Tensor(pixel_mean).view(-1, 1, 1), False)
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self.register_buffer('pixel_std', torch.Tensor(pixel_std).view(-1, 1, 1), False)
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