mirror of
https://github.com/brycedrennan/imaginAIry
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219 lines
6.6 KiB
Python
219 lines
6.6 KiB
Python
"""Code for depth estimation with MiDaS models"""
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# based on https://github.com/isl-org/MiDaS
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from functools import lru_cache
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import cv2
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import torch
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from einops import rearrange
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from torch import nn
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from torchvision.transforms import Compose
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from imaginairy.modules.midas.midas.dpt_depth import DPTDepthModel
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from imaginairy.modules.midas.midas.midas_net import MidasNet
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from imaginairy.modules.midas.midas.midas_net_custom import MidasNet_small
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from imaginairy.modules.midas.midas.transforms import (
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NormalizeImage,
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PrepareForNet,
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Resize,
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)
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from imaginairy.utils import get_device
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ISL_PATHS = {
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"dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
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"dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
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"midas_v21": "",
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"midas_v21_small": "",
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}
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def disabled_train(self, mode=True):
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"""
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Overwrite model.train with this function to make sure train/eval mode
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does not change anymore.
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"""
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return self
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def load_midas_transform(model_type="dpt_hybrid"):
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# https://github.com/isl-org/MiDaS/blob/master/run.py
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# load transform only
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if model_type == "dpt_large": # DPT-Large
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net_w, net_h = 384, 384
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resize_mode = "minimal"
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normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
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elif model_type == "dpt_hybrid": # DPT-Hybrid
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net_w, net_h = 384, 384
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resize_mode = "minimal"
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normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
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elif model_type == "midas_v21":
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net_w, net_h = 384, 384
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resize_mode = "upper_bound"
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normalization = NormalizeImage(
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
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)
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elif model_type == "midas_v21_small":
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net_w, net_h = 256, 256
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resize_mode = "upper_bound"
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normalization = NormalizeImage(
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
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)
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else:
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msg = f"model_type '{model_type}' not implemented, use: --model_type large"
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raise NotImplementedError(msg)
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transform = Compose(
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[
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Resize(
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net_w,
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net_h,
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resize_target=None,
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keep_aspect_ratio=True,
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ensure_multiple_of=32,
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resize_method=resize_mode,
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image_interpolation_method=cv2.INTER_CUBIC,
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),
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normalization,
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PrepareForNet(),
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]
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)
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return transform
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@lru_cache(maxsize=1)
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def load_model(model_type):
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# https://github.com/isl-org/MiDaS/blob/master/run.py
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# load network
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model_path = ISL_PATHS[model_type]
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if model_type == "dpt_large": # DPT-Large
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model = DPTDepthModel(
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path=model_path,
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backbone="vitl16_384",
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non_negative=True,
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)
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net_w, net_h = 384, 384
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resize_mode = "minimal"
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normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
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elif model_type == "dpt_hybrid": # DPT-Hybrid
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model = DPTDepthModel(
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path=model_path,
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backbone="vitb_rn50_384",
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non_negative=True,
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)
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net_w, net_h = 384, 384
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resize_mode = "minimal"
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normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
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elif model_type == "midas_v21":
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model = MidasNet(model_path, non_negative=True)
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net_w, net_h = 384, 384
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resize_mode = "upper_bound"
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normalization = NormalizeImage(
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
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)
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elif model_type == "midas_v21_small":
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model = MidasNet_small(
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model_path,
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features=64,
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backbone="efficientnet_lite3",
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exportable=True,
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non_negative=True,
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blocks={"expand": True},
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)
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net_w, net_h = 256, 256
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resize_mode = "upper_bound"
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normalization = NormalizeImage(
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
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)
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else:
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msg = f"model_type '{model_type}' not implemented, use: --model_type large"
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raise NotImplementedError(msg)
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transform = Compose(
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[
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Resize(
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net_w,
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net_h,
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resize_target=None,
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keep_aspect_ratio=True,
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ensure_multiple_of=32,
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resize_method=resize_mode,
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image_interpolation_method=cv2.INTER_CUBIC,
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),
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normalization,
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PrepareForNet(),
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]
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)
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return model.eval(), transform
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@lru_cache
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def midas_device():
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# mps returns incorrect results ~50% of the time
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return torch.device("cuda" if torch.cuda.is_available() else "cpu")
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@lru_cache
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def load_midas(model_type="dpt_hybrid"):
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model = MiDaSInference(model_type)
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model.to(midas_device())
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return model
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def torch_image_to_depth_map(image_t: torch.Tensor, model_type="dpt_hybrid"):
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model = load_midas(model_type)
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transform = load_midas_transform(model_type)
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image_t = rearrange(image_t, "b c h w -> b h w c")[0]
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image_np = ((image_t + 1.0) * 0.5).detach().cpu().numpy()
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image_np = transform({"image": image_np})["image"]
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image_t = torch.from_numpy(image_np[None, ...])
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image_t = image_t.to(device=midas_device())
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depth_t = model(image_t)
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depth_min = torch.amin(depth_t, dim=[1, 2, 3], keepdim=True)
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depth_max = torch.amax(depth_t, dim=[1, 2, 3], keepdim=True)
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depth_t = (depth_t - depth_min) / (depth_max - depth_min)
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return depth_t.to(get_device())
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class MiDaSInference(nn.Module):
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MODEL_TYPES_TORCH_HUB = ["DPT_Large", "DPT_Hybrid", "MiDaS_small"]
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MODEL_TYPES_ISL = [
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"dpt_large",
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"dpt_hybrid",
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"midas_v21",
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"midas_v21_small",
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]
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def __init__(self, model_type):
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super().__init__()
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assert model_type in self.MODEL_TYPES_ISL
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model, _ = load_model(model_type)
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self.model = model
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self.model.train = disabled_train
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self.model.eval()
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def forward(self, x):
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# x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
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# NOTE: we expect that the correct transform has been called during dataloading.
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with torch.no_grad():
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prediction = self.model(x)
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prediction = torch.nn.functional.interpolate(
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prediction.unsqueeze(1),
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size=x.shape[2:],
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mode="bicubic",
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align_corners=False,
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)
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assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
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return prediction
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