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| # MIT License | |
| # Copyright (c) 2022 Intelligent Systems Lab Org | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| # File author: Shariq Farooq Bhat | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torchvision import transforms | |
| import PIL.Image | |
| from PIL import Image | |
| from typing import Union | |
| class DepthModel(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.device = 'cpu' | |
| def to(self, device) -> nn.Module: | |
| self.device = device | |
| return super().to(device) | |
| def forward(self, x, *args, **kwargs): | |
| raise NotImplementedError | |
| def _infer(self, x: torch.Tensor): | |
| """ | |
| Inference interface for the model | |
| Args: | |
| x (torch.Tensor): input tensor of shape (b, c, h, w) | |
| Returns: | |
| torch.Tensor: output tensor of shape (b, 1, h, w) | |
| """ | |
| return self(x)['metric_depth'] | |
| def _infer_with_pad_aug(self, x: torch.Tensor, pad_input: bool=True, fh: float=3, fw: float=3, upsampling_mode: str='bicubic', padding_mode="reflect", **kwargs) -> torch.Tensor: | |
| """ | |
| Inference interface for the model with padding augmentation | |
| Padding augmentation fixes the boundary artifacts in the output depth map. | |
| Boundary artifacts are sometimes caused by the fact that the model is trained on NYU raw dataset which has a black or white border around the image. | |
| This augmentation pads the input image and crops the prediction back to the original size / view. | |
| Note: This augmentation is not required for the models trained with 'avoid_boundary'=True. | |
| Args: | |
| x (torch.Tensor): input tensor of shape (b, c, h, w) | |
| pad_input (bool, optional): whether to pad the input or not. Defaults to True. | |
| fh (float, optional): height padding factor. The padding is calculated as sqrt(h/2) * fh. Defaults to 3. | |
| fw (float, optional): width padding factor. The padding is calculated as sqrt(w/2) * fw. Defaults to 3. | |
| upsampling_mode (str, optional): upsampling mode. Defaults to 'bicubic'. | |
| padding_mode (str, optional): padding mode. Defaults to "reflect". | |
| Returns: | |
| torch.Tensor: output tensor of shape (b, 1, h, w) | |
| """ | |
| # assert x is nchw and c = 3 | |
| assert x.dim() == 4, "x must be 4 dimensional, got {}".format(x.dim()) | |
| assert x.shape[1] == 3, "x must have 3 channels, got {}".format(x.shape[1]) | |
| if pad_input: | |
| assert fh > 0 or fw > 0, "atlease one of fh and fw must be greater than 0" | |
| pad_h = int(np.sqrt(x.shape[2]/2) * fh) | |
| pad_w = int(np.sqrt(x.shape[3]/2) * fw) | |
| padding = [pad_w, pad_w] | |
| if pad_h > 0: | |
| padding += [pad_h, pad_h] | |
| x = F.pad(x, padding, mode=padding_mode, **kwargs) | |
| out = self._infer(x) | |
| if out.shape[-2:] != x.shape[-2:]: | |
| out = F.interpolate(out, size=(x.shape[2], x.shape[3]), mode=upsampling_mode, align_corners=False) | |
| if pad_input: | |
| # crop to the original size, handling the case where pad_h and pad_w is 0 | |
| if pad_h > 0: | |
| out = out[:, :, pad_h:-pad_h,:] | |
| if pad_w > 0: | |
| out = out[:, :, :, pad_w:-pad_w] | |
| return out | |
| def infer_with_flip_aug(self, x, pad_input: bool=True, **kwargs) -> torch.Tensor: | |
| """ | |
| Inference interface for the model with horizontal flip augmentation | |
| Horizontal flip augmentation improves the accuracy of the model by averaging the output of the model with and without horizontal flip. | |
| Args: | |
| x (torch.Tensor): input tensor of shape (b, c, h, w) | |
| pad_input (bool, optional): whether to use padding augmentation. Defaults to True. | |
| Returns: | |
| torch.Tensor: output tensor of shape (b, 1, h, w) | |
| """ | |
| # infer with horizontal flip and average | |
| out = self._infer_with_pad_aug(x, pad_input=pad_input, **kwargs) | |
| out_flip = self._infer_with_pad_aug(torch.flip(x, dims=[3]), pad_input=pad_input, **kwargs) | |
| out = (out + torch.flip(out_flip, dims=[3])) / 2 | |
| return out | |
| def infer(self, x, pad_input: bool=True, with_flip_aug: bool=True, **kwargs) -> torch.Tensor: | |
| """ | |
| Inference interface for the model | |
| Args: | |
| x (torch.Tensor): input tensor of shape (b, c, h, w) | |
| pad_input (bool, optional): whether to use padding augmentation. Defaults to True. | |
| with_flip_aug (bool, optional): whether to use horizontal flip augmentation. Defaults to True. | |
| Returns: | |
| torch.Tensor: output tensor of shape (b, 1, h, w) | |
| """ | |
| if with_flip_aug: | |
| return self.infer_with_flip_aug(x, pad_input=pad_input, **kwargs) | |
| else: | |
| return self._infer_with_pad_aug(x, pad_input=pad_input, **kwargs) | |
| def infer_pil(self, pil_img, pad_input: bool=True, with_flip_aug: bool=True, output_type: str="numpy", **kwargs) -> Union[np.ndarray, PIL.Image.Image, torch.Tensor]: | |
| """ | |
| Inference interface for the model for PIL image | |
| Args: | |
| pil_img (PIL.Image.Image): input PIL image | |
| pad_input (bool, optional): whether to use padding augmentation. Defaults to True. | |
| with_flip_aug (bool, optional): whether to use horizontal flip augmentation. Defaults to True. | |
| output_type (str, optional): output type. Supported values are 'numpy', 'pil' and 'tensor'. Defaults to "numpy". | |
| """ | |
| x = transforms.ToTensor()(pil_img).unsqueeze(0).to(self.device) | |
| out_tensor = self.infer(x, pad_input=pad_input, with_flip_aug=with_flip_aug, **kwargs) | |
| if output_type == "numpy": | |
| return out_tensor.squeeze().cpu().numpy() | |
| elif output_type == "pil": | |
| # uint16 is required for depth pil image | |
| out_16bit_numpy = (out_tensor.squeeze().cpu().numpy()*256).astype(np.uint16) | |
| return Image.fromarray(out_16bit_numpy) | |
| elif output_type == "tensor": | |
| return out_tensor.squeeze().cpu() | |
| else: | |
| raise ValueError(f"output_type {output_type} not supported. Supported values are 'numpy', 'pil' and 'tensor'") | |