78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
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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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import torch.nn as nn
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from torchvision.transforms import Normalize, Resize, ToTensor
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class SAM2Transforms(nn.Module):
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def __init__(
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self, resolution, mask_threshold, max_hole_area=0.0, max_sprinkle_area=0.0
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):
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"""
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Transforms for SAM2.
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"""
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super().__init__()
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self.resolution = resolution
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self.mask_threshold = mask_threshold
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self.max_hole_area = max_hole_area
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self.max_sprinkle_area = max_sprinkle_area
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self.mean = [0.485, 0.456, 0.406]
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self.std = [0.229, 0.224, 0.225]
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self.to_tensor = ToTensor()
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self.transforms = torch.jit.script(
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nn.Sequential(
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Resize((self.resolution, self.resolution)),
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Normalize(self.mean, self.std),
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)
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)
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def __call__(self, x):
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x = self.to_tensor(x)
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return self.transforms(x)
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def forward_batch(self, img_list):
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img_batch = [self.transforms(self.to_tensor(img)) for img in img_list]
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img_batch = torch.stack(img_batch, dim=0)
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return img_batch
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def transform_coords(
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self, coords: torch.Tensor, normalize=False, orig_hw=None
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) -> torch.Tensor:
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"""
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Expects a torch tensor with length 2 in the last dimension. The coordinates can be in absolute image or normalized coordinates,
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If the coords are in absolute image coordinates, normalize should be set to True and original image size is required.
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Returns
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Un-normalized coordinates in the range of [0, 1] which is expected by the SAM2 model.
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"""
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if normalize:
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assert orig_hw is not None
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h, w = orig_hw
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coords = coords.clone()
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coords[..., 0] = coords[..., 0] / w
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coords[..., 1] = coords[..., 1] / h
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coords = coords * self.resolution # unnormalize coords
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return coords
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def transform_boxes(
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self, boxes: torch.Tensor, normalize=False, orig_hw=None
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) -> torch.Tensor:
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"""
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Expects a tensor of shape Bx4. The coordinates can be in absolute image or normalized coordinates,
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if the coords are in absolute image coordinates, normalize should be set to True and original image size is required.
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"""
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boxes = self.transform_coords(boxes.reshape(-1, 2, 2), normalize, orig_hw)
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return boxes
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def postprocess_masks(self, masks: torch.Tensor, orig_hw) -> torch.Tensor:
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"""
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Perform PostProcessing on output masks.
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"""
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return masks
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