8648572809
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
63 lines
2.6 KiB
Python
63 lines
2.6 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 SAMModel(nn.Module):
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"""
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SAMModel (Segment Anything Model) is designed for object segmentation tasks. It uses image encoders to generate
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image embeddings, and prompt encoders to encode various types of input prompts. These embeddings are then used by
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the mask decoder to predict object masks.
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Attributes:
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mask_threshold (float): Threshold value for mask prediction.
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image_encoder (ImageEncoderViT): The backbone used to encode the image into embeddings.
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prompt_encoder (PromptEncoder): Encodes various types of input prompts.
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mask_decoder (MaskDecoder): Predicts object masks from the image and prompt embeddings.
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pixel_mean (List[float]): Mean pixel values for image normalization.
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pixel_std (List[float]): Standard deviation values for image normalization.
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"""
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mask_threshold: float = 0.0
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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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Initialize the SAMModel class to predict 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.
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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], optional): Mean values for normalizing pixels in the input image. Defaults to
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(123.675, 116.28, 103.53).
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pixel_std (List[float], optional): Std values for normalizing pixels in the input image. Defaults to
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(58.395, 57.12, 57.375).
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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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