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Eric Mintun
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segment_anything/modeling/sam.py
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174
segment_anything/modeling/sam.py
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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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import torch
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from torch import nn
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from torch.nn import functional as F
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from typing import Any, Dict, List, Tuple
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from .image_encoder import ImageEncoderViT
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from .mask_decoder import MaskDecoder
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from .prompt_encoder import 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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Arguments:
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image_encoder (ImageEncoderViT): The backbone used to encode the
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image into image embeddings that allow for 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
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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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@property
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def device(self) -> Any:
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return self.pixel_mean.device
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@torch.no_grad()
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def forward(
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self,
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batched_input: List[Dict[str, Any]],
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multimask_output: bool,
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) -> List[Dict[str, torch.Tensor]]:
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"""
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Predicts masks end-to-end from provided images and prompts.
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If prompts are not known in advance, using SamPredictor is
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recommended over calling the model directly.
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Arguments:
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batched_input (list(dict)): A list over input images, each a
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dictionary with the following keys. A prompt key can be
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excluded if it is not present.
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'image': The image as a torch tensor in 3xHxW format,
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already transformed for input to the model.
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'original_size': (tuple(int, int)) The original size of
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the image before transformation, as (H, W).
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'point_coords': (torch.Tensor) Batched point prompts for
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this image, with shape BxNx2. Already transformed to the
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input frame of the model.
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'point_labels': (torch.Tensor) Batched labels for point prompts,
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with shape BxN.
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'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
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Already transformed to the input frame of the model.
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'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
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in the form Bx1xHxW.
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multimask_output (bool): Whether the model should predict multiple
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disambiguating masks, or return a single mask.
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Returns:
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(list(dict)): A list over input images, where each element is
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as dictionary with the following keys.
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'masks': (torch.Tensor) Batched binary mask predictions,
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with shape BxCxHxW, where B is the number of input promts,
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C is determiend by multimask_output, and (H, W) is the
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original size of the image.
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'iou_predictions': (torch.Tensor) The model's predictions
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of mask quality, in shape BxC.
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'low_res_logits': (torch.Tensor) Low resolution logits with
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shape BxCxHxW, where H=W=256. Can be passed as mask input
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to subsequent iterations of prediction.
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"""
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input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
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image_embeddings = self.image_encoder(input_images)
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outputs = []
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for image_record, curr_embedding in zip(batched_input, image_embeddings):
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if "point_coords" in image_record:
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points = (image_record["point_coords"], image_record["point_labels"])
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else:
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points = None
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sparse_embeddings, dense_embeddings = self.prompt_encoder(
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points=points,
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boxes=image_record.get("boxes", None),
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masks=image_record.get("mask_inputs", None),
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)
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low_res_masks, iou_predictions = self.mask_decoder(
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image_embeddings=curr_embedding.unsqueeze(0),
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image_pe=self.prompt_encoder.get_dense_pe(),
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sparse_prompt_embeddings=sparse_embeddings,
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dense_prompt_embeddings=dense_embeddings,
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multimask_output=multimask_output,
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)
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masks = self.postprocess_masks(
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low_res_masks,
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input_size=image_record["image"].shape[-2:],
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original_size=image_record["original_size"],
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)
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masks = masks > self.mask_threshold
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outputs.append(
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{
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"masks": masks,
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"iou_predictions": iou_predictions,
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"low_res_logits": low_res_masks,
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}
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)
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return outputs
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def postprocess_masks(
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self,
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masks: torch.Tensor,
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input_size: Tuple[int, ...],
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original_size: Tuple[int, ...],
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) -> torch.Tensor:
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"""
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Remove padding and upscale masks to the original image size.
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Arguments:
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masks (torch.Tensor): Batched masks from the mask_decoder,
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in BxCxHxW format.
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input_size (tuple(int, int)): The size of the image input to the
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model, in (H, W) format. Used to remove padding.
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original_size (tuple(int, int)): The original size of the image
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before resizing for input to the model, in (H, W) format.
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Returns:
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(torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
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is given by original_size.
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"""
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masks = F.interpolate(
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masks,
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(self.image_encoder.img_size, self.image_encoder.img_size),
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mode="bilinear",
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align_corners=False,
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)
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masks = masks[..., : input_size[0], : input_size[1]]
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masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
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return masks
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def preprocess(self, x: torch.Tensor) -> torch.Tensor:
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"""Normalize pixel values and pad to a square input."""
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# Normalize colors
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x = (x - self.pixel_mean) / self.pixel_std
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# Pad
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h, w = x.shape[-2:]
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padh = self.image_encoder.img_size - h
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padw = self.image_encoder.img_size - w
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x = F.pad(x, (0, padw, 0, padh))
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return x
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