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import argparse
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
from distillanydepth.modeling.backbones.vit.ViT_DINO import vit_large, vit_giant2, vit_base
from distillanydepth.modeling.backbones.vit.ViT_DINO_reg import vit_large_reg, vit_giant2_reg
from timm.models.vision_transformer import vit_large_patch16_224, vit_large_patch14_224
def compute_depth_expectation(prob, depth_values):
depth_values = depth_values.view(*depth_values.shape, 1, 1)
depth = torch.sum(prob * depth_values, 1)
return depth
def _make_scratch(in_shape, out_shape, groups=1, expand=False):
scratch = nn.Module()
out_shape1 = out_shape
out_shape2 = out_shape
out_shape3 = out_shape
if len(in_shape) >= 4:
out_shape4 = out_shape
if expand:
out_shape1 = out_shape
out_shape2 = out_shape*2
out_shape3 = out_shape*4
if len(in_shape) >= 4:
out_shape4 = out_shape*8
scratch.layer1_rn = nn.Conv2d(
in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
scratch.layer2_rn = nn.Conv2d(
in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
scratch.layer3_rn = nn.Conv2d(
in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
if len(in_shape) >= 4:
scratch.layer4_rn = nn.Conv2d(
in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
return scratch
def _make_fusion_block(features, use_bn, size = None):
return FeatureFusionBlock(
features,
nn.ReLU(False),
deconv=False,
bn=use_bn,
expand=False,
align_corners=True,
size=size,
)
class ResidualConvUnit(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features, activation, bn):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.bn = bn
self.groups=1
self.conv1 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
)
self.conv2 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
)
if self.bn==True:
self.bn1 = nn.BatchNorm2d(features)
self.bn2 = nn.BatchNorm2d(features)
self.activation = activation
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: output
"""
out = self.activation(x)
out = self.conv1(out)
if self.bn==True:
out = self.bn1(out)
out = self.activation(out)
out = self.conv2(out)
if self.bn==True:
out = self.bn2(out)
if self.groups > 1:
out = self.conv_merge(out)
return self.skip_add.add(out, x)
class FeatureFusionBlock(nn.Module):
"""Feature fusion block.
"""
def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, size=None):
"""Init.
Args:
features (int): number of features
"""
super(FeatureFusionBlock, self).__init__()
self.deconv = deconv
self.align_corners = align_corners
self.groups=1
self.expand = expand
out_features = features
if self.expand==True:
out_features = features//2
self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
self.skip_add = nn.quantized.FloatFunctional()
self.size=size
def forward(self, *xs, size=None):
"""Forward pass.
Returns:
tensor: output
"""
output = xs[0]
if len(xs) == 2:
res = self.resConfUnit1(xs[1])
output = self.skip_add.add(output, res)
output = self.resConfUnit2(output)
if (size is None) and (self.size is None):
modifier = {"scale_factor": 2}
elif size is None:
modifier = {"size": self.size}
else:
modifier = {"size": size}
output = nn.functional.interpolate(
output, **modifier, mode="bilinear", align_corners=self.align_corners
)
output = self.out_conv(output)
return output
class DPTHead(nn.Module):
def __init__(
self,
mode,
in_channels,
features=256,
use_bn=False,
out_channels=[256, 512, 1024, 1024],
head_out_channels=1,
use_clstoken=False,
num_depth_regressor_anchor=512,
):
super(DPTHead, self).__init__()
self.use_clstoken = use_clstoken
self.projects = nn.ModuleList([
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channel,
kernel_size=1,
stride=1,
padding=0,
) for out_channel in out_channels
])
self.resize_layers = nn.ModuleList([
nn.ConvTranspose2d(
in_channels=out_channels[0],
out_channels=out_channels[0],
kernel_size=4,
stride=4,
padding=0),
nn.ConvTranspose2d(
in_channels=out_channels[1],
out_channels=out_channels[1],
kernel_size=2,
stride=2,
padding=0),
nn.Identity(),
nn.Conv2d(
in_channels=out_channels[3],
out_channels=out_channels[3],
kernel_size=3,
stride=2,
padding=1)
])
if use_clstoken:
self.readout_projects = nn.ModuleList()
for _ in range(len(self.projects)):
self.readout_projects.append(
nn.Sequential(
nn.Linear(2 * in_channels, in_channels),
nn.GELU()))
self.scratch = _make_scratch(
out_channels,
features,
groups=1,
expand=False,
)
self.scratch.stem_transpose = None
self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
head_features_1 = features
# if nclass > 1:
# self.scratch.output_conv = nn.Sequential(
# nn.Conv2d(head_features_1, head_features_1, kernel_size=3, stride=1, padding=1),
# nn.ReLU(True),
# nn.Conv2d(head_features_1, nclass, kernel_size=1, stride=1, padding=0),
# )
# else:
self.scratch.output_conv1 = nn.Conv2d(head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1)
# if 'metric' in mode:
# num_depth_regressor_anchor = 512
# # head_features_2 = num_depth_regressor_anchor
# self.scratch.output_conv2 = nn.Sequential(
# nn.Conv2d(head_features_1 // 2, num_depth_regressor_anchor, kernel_size=3, stride=1, padding=1),
# nn.ReLU(True),
# nn.Conv2d(num_depth_regressor_anchor, num_depth_regressor_anchor, kernel_size=1, stride=1, padding=0),
# # nn.Sigmoid()
# )
# elif 'disparity' in mode or 'rel_depth' in mode:
# head_features_2 = 32
# self.scratch.output_conv2 = nn.Sequential(
# nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),
# nn.ReLU(True),
# nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0),
# nn.ReLU(True),
# nn.Identity(),
# )
# else:
# raise NotImplementedError
head_features_2 = 32
self.scratch.output_conv2 = nn.Sequential(
nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),
nn.ReLU(True),
nn.Conv2d(head_features_2, head_out_channels, kernel_size=1, stride=1, padding=0),
# nn.ReLU(True),
# nn.Identity(),
)
def forward(self, out_features, patch_h, patch_w):
out = []
for i, x in enumerate(out_features):
if self.use_clstoken:
x, cls_token = x[0], x[1]
readout = cls_token.unsqueeze(1).expand_as(x)
x = self.readout_projects[i](torch.cat((x, readout), -1))
else:
x = x[0]
# import pdb;pdb.set_trace()
x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w))
x = self.projects[i](x)
x = self.resize_layers[i](x)
out.append(x)
layer_1, layer_2, layer_3, layer_4 = out
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:])
path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:])
path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:])
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv1(path_1)
out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
out = self.scratch.output_conv2(out)
# print(out.min())
# import pdb;pdb.set_trace()
return out
class DepthAnything(nn.Module, PyTorchModelHubMixin):
# @register_to_config
def __init__(
self,
encoder='vitl',
features=256,
out_channels=[256, 512, 1024, 1024],
head_out_channels=1,
wo_relu_1_2_channel=False,
use_bn=False,
use_clstoken=False,
# localhub=None
use_registers=False,
max_depth=1.0,
mode='disparity',
num_depth_regressor_anchor=512,
depth_normalize=(0.1, 150),
pretrain_type='dinov2', # sam, imagenet
del_mask_token=True,
):
super(DepthAnything, self).__init__()
self.pretrain_type = pretrain_type
self.max_depth = max_depth
self.mode = mode
assert encoder in ['vits', 'vitb', 'vitl', "vitg"]
self.intermediate_layer_idx = {
'vits': [2, 5, 8, 11],
'vitb': [2, 5, 8, 11],
'vitl': [4, 11, 17, 23],
'vitg': [9, 19, 29, 39]
}
self.backbone_name = encoder
# in case the Internet connection is not stable, please load the DINOv2 locally
# if localhub:
# assert type(localhub) == str
# # self.backbone = torch.hub.load(localhub, 'dinov2_{:}14'.format(encoder), source='local', pretrained=False)
# self.backbone = torch.hub.load(localhub, 'dinov2_{:}14'.format(encoder), source='local', pretrained=True)
# else:
# self.backbone = torch.hub.load('facebookresearch/dinov2', 'dinov2_{:}14'.format(encoder))
if use_registers:
if encoder == 'vitl':
checkpoint='data/weights/dinov2/dinov2_vitl14_reg4_pretrain.pth'
self.backbone = vit_large_reg(checkpoint=checkpoint)
elif encoder == 'vitg':
checkpoint='data/weights/dinov2/dinov2_vitg14_reg4_pretrain.pth'
self.backbone = vit_giant2_reg(checkpoint=checkpoint)
else:
raise NotImplementedError
else:
if encoder == 'vitl':
if pretrain_type == 'dinov2':
self.backbone = vit_large(checkpoint=None, del_mask_token=del_mask_token)
# import pdb;pdb.set_trace()
elif encoder == 'vitb':
self.backbone = vit_base(checkpoint=None, del_mask_token=del_mask_token)
elif encoder == 'vitg':
from geobench.depthanything_v2.dinov2 import DINOv2
checkpoint='data/weights/dinov2/dinov2_vitg14_pretrain.pth'
self.backbone = DINOv2(model_name=encoder)
miss, unexpected = self.backbone.load_state_dict(torch.load(checkpoint, map_location='cpu'), strict=False)
print('missing keys:', miss)
print('unexpected keys:', unexpected)
# import pdb;pdb.set_trace()
# self.backbone = vit_giant2(checkpoint=checkpoint)
else:
raise NotImplementedError
# dim = self.backbone.blocks[0].attn.qkv.in_features
dim = self.backbone.embed_dim
self.min_depth = depth_normalize[0]
self.max_depth = depth_normalize[1]
self.num_depth_regressor_anchor = num_depth_regressor_anchor
self.depth_head = DPTHead(mode, dim, features, use_bn,
out_channels=out_channels,
head_out_channels=head_out_channels,
use_clstoken=use_clstoken,
num_depth_regressor_anchor=num_depth_regressor_anchor,
)
# import pdb;pdb.set_trace()
self.wo_relu_1_2_channel = wo_relu_1_2_channel
def get_bins(self, bins_num):
depth_bins_vec = torch.linspace(math.log(self.min_depth), math.log(self.max_depth), bins_num, device='cuda')
depth_bins_vec = torch.exp(depth_bins_vec)
return depth_bins_vec
def register_depth_expectation_anchor(self, bins_num, B):
depth_bins_vec = self.get_bins(bins_num)
depth_bins_vec = depth_bins_vec.unsqueeze(0).repeat(B, 1)
self.register_buffer('depth_expectation_anchor', depth_bins_vec, persistent=False)
def forward(self, x):
bs, _, h, w = x.shape
# features = self.backbone.get_intermediate_layers(x, 4, return_class_token=True)
if self.pretrain_type=='dinov2':
features = self.backbone.get_intermediate_layers(x, self.intermediate_layer_idx[self.backbone_name], return_class_token=True)
patch_h, patch_w = h // 14, w // 14
elif self.pretrain_type=='imagenet':
features = self.backbone.get_intermediate_layers(x, self.intermediate_layer_idx[self.backbone_name], return_prefix_tokens=True)
patch_h, patch_w = h // 16, w // 16
else:
raise NotImplementedError
# import pdb;pdb.set_trace()
# if 'metric' in self.mode:
# prob_feature = self.depth_head(features, patch_h, patch_w)
# prob_feature = F.interpolate(prob_feature, size=(h, w), mode="bilinear", align_corners=True)
# prob = prob_feature.softmax(dim=1)
# if "depth_expectation_anchor" not in self._buffers:
# self.register_depth_expectation_anchor(self.num_depth_regressor_anchor, bs)
# depth = compute_depth_expectation(
# prob,
# self.depth_expectation_anchor[:bs, ...]
# ).unsqueeze(1)
# elif 'disparity' in self.mode or 'rel_depth' in self.mode:
# depth = self.depth_head(features, patch_h, patch_w)
# depth = F.interpolate(depth, size=(h, w), mode="bilinear", align_corners=True)
# # import pdb;pdb.set_trace()
# depth = F.relu(depth)
# else:
# raise NotImplementedError
depth = self.depth_head(features, patch_h, patch_w)
depth = F.interpolate(depth, size=(h, w), mode="bilinear", align_corners=True)
if not self.wo_relu_1_2_channel:
depth = F.relu(depth)
else:
depth[:, 2:] = F.relu(depth[:, 2:])
# import pdb;pdb.set_trace()
return depth, features[3][0]
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
"--encoder",
default="vits",
type=str,
choices=["vits", "vitb", "vitl", "vitg"],
)
args = parser.parse_args()
# model = DepthAnything.from_pretrained("LiheYoung/depth_anything_{:}14".format(args.encoder))
# model = DepthAnything.from_pretrained("LiheYoung/depth_anything_{:}14".format(args.encoder))
# print(model)
device = 'cuda'
image = torch.randn(1,3, 420, 420).to(device)
local_hub = "~/.cache/torch/hub/facebookresearch_dinov2_main/"
model = DepthAnything(localhub=local_hub,).to(device)
output = model(image)
import pdb;pdb.set_trace()
# .from_pretrained("LiheYoung/depth_anything_{:}14".format(args.encoder))
print(model) |