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https://github.com/invoke-ai/InvokeAI
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wip: depth_anything_v2 initial implementation
This commit is contained in:
blessedcoolant
parent
b70ac88684
commit
4f0dfbd34d
@ -1,66 +1,46 @@
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from pathlib import Path
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from typing import Literal
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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from einops import repeat
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from PIL import Image
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from torchvision.transforms import Compose
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from invokeai.app.services.config.config_default import get_config
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from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
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from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
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from invokeai.backend.image_util.depth_anything.v2.dpt import DepthAnythingV2
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from invokeai.backend.util.logging import InvokeAILogger
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config = get_config()
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logger = InvokeAILogger.get_logger(config=config)
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DEPTH_ANYTHING_MODELS = {
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"large": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitl14.pth?download=true",
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"base": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitb14.pth?download=true",
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"small": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vits14.pth?download=true",
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"large": "https://huggingface.co/depth-anything/Depth-Anything-V2-Large/resolve/main/depth_anything_v2_vitl.pth?download=true",
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"base": "https://huggingface.co/depth-anything/Depth-Anything-V2-Base/resolve/main/depth_anything_v2_vitb.pth?download=true",
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"small": "https://huggingface.co/depth-anything/Depth-Anything-V2-Small/resolve/main/depth_anything_v2_vits.pth?download=true",
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}
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transform = Compose(
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[
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Resize(
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width=518,
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height=518,
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resize_target=False,
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keep_aspect_ratio=True,
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ensure_multiple_of=14,
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resize_method="lower_bound",
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image_interpolation_method=cv2.INTER_CUBIC,
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),
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NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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PrepareForNet(),
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]
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)
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class DepthAnythingDetector:
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def __init__(self, model: DPT_DINOv2, device: torch.device) -> None:
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def __init__(self, model: DepthAnythingV2, device: torch.device) -> None:
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self.model = model
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self.device = device
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@staticmethod
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def load_model(
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model_path: Path, device: torch.device, model_size: Literal["large", "base", "small"] = "small"
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) -> DPT_DINOv2:
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model_path: Path, device: torch.device, model_size: Literal["large", "base", "small", "giant"] = "small"
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) -> DepthAnythingV2:
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match model_size:
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case "small":
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model = DPT_DINOv2(encoder="vits", features=64, out_channels=[48, 96, 192, 384])
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model = DepthAnythingV2(encoder="vits", features=64, out_channels=[48, 96, 192, 384])
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case "base":
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model = DPT_DINOv2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768])
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model = DepthAnythingV2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768])
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case "large":
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model = DPT_DINOv2(encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024])
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model = DepthAnythingV2(encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024])
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case "giant":
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model = DepthAnythingV2(encoder="vitg", features=384, out_channels=[1536, 1536, 1536, 1536])
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model.load_state_dict(torch.load(model_path.as_posix(), map_location="cpu"))
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model.eval()
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model.to(device)
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return model
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@ -70,18 +50,13 @@ class DepthAnythingDetector:
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return image
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np_image = np.array(image, dtype=np.uint8)
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np_image = np_image[:, :, ::-1] / 255.0
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image_height, image_width = np_image.shape[:2]
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np_image = transform({"image": np_image})["image"]
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tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(self.device)
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with torch.no_grad():
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depth = self.model(tensor_image)
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depth = F.interpolate(depth[None], (image_height, image_width), mode="bilinear", align_corners=False)[0, 0]
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depth = self.model.infer_image(np_image)
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depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
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depth_map = repeat(depth, "h w -> h w 3").cpu().numpy().astype(np.uint8)
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depth_map = repeat(depth, "h w -> h w 3").astype(np.uint8)
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depth_map = Image.fromarray(depth_map)
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new_height = int(image_height * (resolution / image_width))
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411
invokeai/backend/image_util/depth_anything/v2/dinov2.py
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411
invokeai/backend/image_util/depth_anything/v2/dinov2.py
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@ -0,0 +1,411 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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#
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# This source code is licensed under the Apache License, Version 2.0
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# found in the LICENSE file in the root directory of this source tree.
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# References:
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# https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
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# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
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import logging
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import math
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from functools import partial
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from typing import Callable, Sequence, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint
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from torch.nn.init import trunc_normal_
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from .dinov2_layers import MemEffAttention, Mlp
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from .dinov2_layers import NestedTensorBlock as Block
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from .dinov2_layers import PatchEmbed, SwiGLUFFNFused
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logger = logging.getLogger("dinov2")
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def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module:
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if not depth_first and include_root:
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fn(module=module, name=name)
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for child_name, child_module in module.named_children():
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child_name = ".".join((name, child_name)) if name else child_name
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named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
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if depth_first and include_root:
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fn(module=module, name=name)
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return module
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class BlockChunk(nn.ModuleList):
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def forward(self, x):
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for b in self:
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x = b(x)
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return x
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class DinoVisionTransformer(nn.Module):
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def __init__(
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self,
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img_size=224,
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patch_size=16,
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in_chans=3,
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embed_dim=768,
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depth=12,
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num_heads=12,
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mlp_ratio=4.0,
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qkv_bias=True,
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ffn_bias=True,
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proj_bias=True,
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drop_path_rate=0.0,
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drop_path_uniform=False,
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init_values=None, # for layerscale: None or 0 => no layerscale
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embed_layer=PatchEmbed,
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act_layer=nn.GELU,
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block_fn=Block,
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ffn_layer="mlp",
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block_chunks=1,
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num_register_tokens=0,
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interpolate_antialias=False,
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interpolate_offset=0.1,
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):
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"""
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Args:
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img_size (int, tuple): input image size
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patch_size (int, tuple): patch size
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in_chans (int): number of input channels
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embed_dim (int): embedding dimension
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depth (int): depth of transformer
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num_heads (int): number of attention heads
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim
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qkv_bias (bool): enable bias for qkv if True
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proj_bias (bool): enable bias for proj in attn if True
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ffn_bias (bool): enable bias for ffn if True
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drop_path_rate (float): stochastic depth rate
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drop_path_uniform (bool): apply uniform drop rate across blocks
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weight_init (str): weight init scheme
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init_values (float): layer-scale init values
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embed_layer (nn.Module): patch embedding layer
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act_layer (nn.Module): MLP activation layer
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block_fn (nn.Module): transformer block class
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ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity"
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block_chunks: (int) split block sequence into block_chunks units for FSDP wrap
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num_register_tokens: (int) number of extra cls tokens (so-called "registers")
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interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings
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interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings
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"""
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super().__init__()
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norm_layer = partial(nn.LayerNorm, eps=1e-6)
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self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
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self.num_tokens = 1
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self.n_blocks = depth
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self.num_heads = num_heads
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self.patch_size = patch_size
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self.num_register_tokens = num_register_tokens
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self.interpolate_antialias = interpolate_antialias
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self.interpolate_offset = interpolate_offset
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self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
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num_patches = self.patch_embed.num_patches
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
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assert num_register_tokens >= 0
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self.register_tokens = (
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nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None
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)
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if drop_path_uniform is True:
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dpr = [drop_path_rate] * depth
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else:
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
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if ffn_layer == "mlp":
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logger.info("using MLP layer as FFN")
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ffn_layer = Mlp
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elif ffn_layer == "swiglufused" or ffn_layer == "swiglu":
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logger.info("using SwiGLU layer as FFN")
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ffn_layer = SwiGLUFFNFused
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elif ffn_layer == "identity":
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logger.info("using Identity layer as FFN")
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def f(*args, **kwargs):
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return nn.Identity()
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ffn_layer = f
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else:
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raise NotImplementedError
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blocks_list = [
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block_fn(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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proj_bias=proj_bias,
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ffn_bias=ffn_bias,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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act_layer=act_layer,
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ffn_layer=ffn_layer,
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init_values=init_values,
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)
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for i in range(depth)
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]
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if block_chunks > 0:
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self.chunked_blocks = True
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chunked_blocks = []
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chunksize = depth // block_chunks
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for i in range(0, depth, chunksize):
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# this is to keep the block index consistent if we chunk the block list
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chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize])
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self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks])
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else:
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self.chunked_blocks = False
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self.blocks = nn.ModuleList(blocks_list)
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self.norm = norm_layer(embed_dim)
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self.head = nn.Identity()
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self.mask_token = nn.Parameter(torch.zeros(1, embed_dim))
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self.init_weights()
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def init_weights(self):
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trunc_normal_(self.pos_embed, std=0.02)
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nn.init.normal_(self.cls_token, std=1e-6)
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if self.register_tokens is not None:
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nn.init.normal_(self.register_tokens, std=1e-6)
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named_apply(init_weights_vit_timm, self)
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def interpolate_pos_encoding(self, x, w, h):
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previous_dtype = x.dtype
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npatch = x.shape[1] - 1
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N = self.pos_embed.shape[1] - 1
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if npatch == N and w == h:
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return self.pos_embed
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pos_embed = self.pos_embed.float()
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class_pos_embed = pos_embed[:, 0]
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patch_pos_embed = pos_embed[:, 1:]
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dim = x.shape[-1]
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w0 = w // self.patch_size
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h0 = h // self.patch_size
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# we add a small number to avoid floating point error in the interpolation
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# see discussion at https://github.com/facebookresearch/dino/issues/8
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# DINOv2 with register modify the interpolate_offset from 0.1 to 0.0
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w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset
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# w0, h0 = w0 + 0.1, h0 + 0.1
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sqrt_N = math.sqrt(N)
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sx, sy = float(w0) / sqrt_N, float(h0) / sqrt_N
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patch_pos_embed = nn.functional.interpolate(
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patch_pos_embed.reshape(1, int(sqrt_N), int(sqrt_N), dim).permute(0, 3, 1, 2),
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scale_factor=(sx, sy),
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# (int(w0), int(h0)), # to solve the upsampling shape issue
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mode="bicubic",
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antialias=self.interpolate_antialias,
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)
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assert int(w0) == patch_pos_embed.shape[-2]
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assert int(h0) == patch_pos_embed.shape[-1]
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patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
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return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)
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def prepare_tokens_with_masks(self, x, masks=None):
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B, nc, w, h = x.shape
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x = self.patch_embed(x)
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if masks is not None:
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x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)
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x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
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x = x + self.interpolate_pos_encoding(x, w, h)
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if self.register_tokens is not None:
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x = torch.cat(
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(
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x[:, :1],
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self.register_tokens.expand(x.shape[0], -1, -1),
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x[:, 1:],
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),
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dim=1,
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)
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return x
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def forward_features_list(self, x_list, masks_list):
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x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)]
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for blk in self.blocks:
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x = blk(x)
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all_x = x
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output = []
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for x, masks in zip(all_x, masks_list):
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x_norm = self.norm(x)
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output.append(
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{
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"x_norm_clstoken": x_norm[:, 0],
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"x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
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"x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
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"x_prenorm": x,
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"masks": masks,
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}
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)
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return output
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def forward_features(self, x, masks=None):
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if isinstance(x, list):
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return self.forward_features_list(x, masks)
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x = self.prepare_tokens_with_masks(x, masks)
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for blk in self.blocks:
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x = blk(x)
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x_norm = self.norm(x)
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return {
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"x_norm_clstoken": x_norm[:, 0],
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"x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
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"x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
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"x_prenorm": x,
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"masks": masks,
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}
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def _get_intermediate_layers_not_chunked(self, x, n=1):
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x = self.prepare_tokens_with_masks(x)
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# If n is an int, take the n last blocks. If it's a list, take them
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output, total_block_len = [], len(self.blocks)
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blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
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for i, blk in enumerate(self.blocks):
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x = blk(x)
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if i in blocks_to_take:
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output.append(x)
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assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
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return output
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def _get_intermediate_layers_chunked(self, x, n=1):
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x = self.prepare_tokens_with_masks(x)
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output, i, total_block_len = [], 0, len(self.blocks[-1])
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# If n is an int, take the n last blocks. If it's a list, take them
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blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
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for block_chunk in self.blocks:
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for blk in block_chunk[i:]: # Passing the nn.Identity()
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x = blk(x)
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if i in blocks_to_take:
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output.append(x)
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i += 1
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assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
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return output
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def get_intermediate_layers(
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self,
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x: torch.Tensor,
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n: Union[int, Sequence] = 1, # Layers or n last layers to take
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reshape: bool = False,
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return_class_token: bool = False,
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norm=True,
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) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
|
||||
if self.chunked_blocks:
|
||||
outputs = self._get_intermediate_layers_chunked(x, n)
|
||||
else:
|
||||
outputs = self._get_intermediate_layers_not_chunked(x, n)
|
||||
if norm:
|
||||
outputs = [self.norm(out) for out in outputs]
|
||||
class_tokens = [out[:, 0] for out in outputs]
|
||||
outputs = [out[:, 1 + self.num_register_tokens :] for out in outputs]
|
||||
if reshape:
|
||||
B, _, w, h = x.shape
|
||||
outputs = [
|
||||
out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous()
|
||||
for out in outputs
|
||||
]
|
||||
if return_class_token:
|
||||
return tuple(zip(outputs, class_tokens))
|
||||
return tuple(outputs)
|
||||
|
||||
def forward(self, *args, is_training=False, **kwargs):
|
||||
ret = self.forward_features(*args, **kwargs)
|
||||
if is_training:
|
||||
return ret
|
||||
else:
|
||||
return self.head(ret["x_norm_clstoken"])
|
||||
|
||||
|
||||
def init_weights_vit_timm(module: nn.Module, name: str = ""):
|
||||
"""ViT weight initialization, original timm impl (for reproducibility)"""
|
||||
if isinstance(module, nn.Linear):
|
||||
trunc_normal_(module.weight, std=0.02)
|
||||
if module.bias is not None:
|
||||
nn.init.zeros_(module.bias)
|
||||
|
||||
|
||||
def vit_small(patch_size=16, num_register_tokens=0, **kwargs):
|
||||
model = DinoVisionTransformer(
|
||||
patch_size=patch_size,
|
||||
embed_dim=384,
|
||||
depth=12,
|
||||
num_heads=6,
|
||||
mlp_ratio=4,
|
||||
block_fn=partial(Block, attn_class=MemEffAttention),
|
||||
num_register_tokens=num_register_tokens,
|
||||
**kwargs,
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
|
||||
model = DinoVisionTransformer(
|
||||
patch_size=patch_size,
|
||||
embed_dim=768,
|
||||
depth=12,
|
||||
num_heads=12,
|
||||
mlp_ratio=4,
|
||||
block_fn=partial(Block, attn_class=MemEffAttention),
|
||||
num_register_tokens=num_register_tokens,
|
||||
**kwargs,
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
|
||||
model = DinoVisionTransformer(
|
||||
patch_size=patch_size,
|
||||
embed_dim=1024,
|
||||
depth=24,
|
||||
num_heads=16,
|
||||
mlp_ratio=4,
|
||||
block_fn=partial(Block, attn_class=MemEffAttention),
|
||||
num_register_tokens=num_register_tokens,
|
||||
**kwargs,
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
|
||||
"""
|
||||
Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
|
||||
"""
|
||||
model = DinoVisionTransformer(
|
||||
patch_size=patch_size,
|
||||
embed_dim=1536,
|
||||
depth=40,
|
||||
num_heads=24,
|
||||
mlp_ratio=4,
|
||||
block_fn=partial(Block, attn_class=MemEffAttention),
|
||||
num_register_tokens=num_register_tokens,
|
||||
**kwargs,
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def DINOv2(model_name):
|
||||
model_zoo = {"vits": vit_small, "vitb": vit_base, "vitl": vit_large, "vitg": vit_giant2}
|
||||
|
||||
return model_zoo[model_name](
|
||||
img_size=518,
|
||||
patch_size=14,
|
||||
init_values=1.0,
|
||||
ffn_layer="mlp" if model_name != "vitg" else "swiglufused",
|
||||
block_chunks=0,
|
||||
num_register_tokens=0,
|
||||
interpolate_antialias=False,
|
||||
interpolate_offset=0.1,
|
||||
)
|
||||
@ -0,0 +1,11 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
from .attention import MemEffAttention
|
||||
from .block import NestedTensorBlock
|
||||
from .mlp import Mlp
|
||||
from .patch_embed import PatchEmbed
|
||||
from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
|
||||
@ -0,0 +1,79 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# References:
|
||||
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
|
||||
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
|
||||
|
||||
import logging
|
||||
|
||||
from torch import Tensor, nn
|
||||
|
||||
logger = logging.getLogger("dinov2")
|
||||
|
||||
|
||||
try:
|
||||
from xformers.ops import fmha, memory_efficient_attention, unbind
|
||||
|
||||
XFORMERS_AVAILABLE = True
|
||||
except ImportError:
|
||||
logger.warning("xFormers not available")
|
||||
XFORMERS_AVAILABLE = False
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
num_heads: int = 8,
|
||||
qkv_bias: bool = False,
|
||||
proj_bias: bool = True,
|
||||
attn_drop: float = 0.0,
|
||||
proj_drop: float = 0.0,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = head_dim**-0.5
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim, bias=proj_bias)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
B, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
|
||||
q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
|
||||
attn = q @ k.transpose(-2, -1)
|
||||
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class MemEffAttention(Attention):
|
||||
def forward(self, x: Tensor, attn_bias=None) -> Tensor:
|
||||
if not XFORMERS_AVAILABLE:
|
||||
assert attn_bias is None, "xFormers is required for nested tensors usage"
|
||||
return super().forward(x)
|
||||
|
||||
B, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
|
||||
|
||||
q, k, v = unbind(qkv, 2)
|
||||
|
||||
x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
|
||||
x = x.reshape([B, N, C])
|
||||
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
@ -0,0 +1,250 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# References:
|
||||
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
|
||||
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
|
||||
|
||||
import logging
|
||||
from typing import Any, Callable, Dict, List, Tuple
|
||||
|
||||
import torch
|
||||
from torch import Tensor, nn
|
||||
|
||||
from .attention import Attention, MemEffAttention
|
||||
from .drop_path import DropPath
|
||||
from .layer_scale import LayerScale
|
||||
from .mlp import Mlp
|
||||
|
||||
logger = logging.getLogger("dinov2")
|
||||
|
||||
|
||||
try:
|
||||
from xformers.ops import fmha, index_select_cat, scaled_index_add
|
||||
|
||||
XFORMERS_AVAILABLE = True
|
||||
except ImportError:
|
||||
logger.warning("xFormers not available")
|
||||
XFORMERS_AVAILABLE = False
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
num_heads: int,
|
||||
mlp_ratio: float = 4.0,
|
||||
qkv_bias: bool = False,
|
||||
proj_bias: bool = True,
|
||||
ffn_bias: bool = True,
|
||||
drop: float = 0.0,
|
||||
attn_drop: float = 0.0,
|
||||
init_values=None,
|
||||
drop_path: float = 0.0,
|
||||
act_layer: Callable[..., nn.Module] = nn.GELU,
|
||||
norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
|
||||
attn_class: Callable[..., nn.Module] = Attention,
|
||||
ffn_layer: Callable[..., nn.Module] = Mlp,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
# print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = attn_class(
|
||||
dim,
|
||||
num_heads=num_heads,
|
||||
qkv_bias=qkv_bias,
|
||||
proj_bias=proj_bias,
|
||||
attn_drop=attn_drop,
|
||||
proj_drop=drop,
|
||||
)
|
||||
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
|
||||
self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
||||
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = ffn_layer(
|
||||
in_features=dim,
|
||||
hidden_features=mlp_hidden_dim,
|
||||
act_layer=act_layer,
|
||||
drop=drop,
|
||||
bias=ffn_bias,
|
||||
)
|
||||
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
|
||||
self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
||||
|
||||
self.sample_drop_ratio = drop_path
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
def attn_residual_func(x: Tensor) -> Tensor:
|
||||
return self.ls1(self.attn(self.norm1(x)))
|
||||
|
||||
def ffn_residual_func(x: Tensor) -> Tensor:
|
||||
return self.ls2(self.mlp(self.norm2(x)))
|
||||
|
||||
if self.training and self.sample_drop_ratio > 0.1:
|
||||
# the overhead is compensated only for a drop path rate larger than 0.1
|
||||
x = drop_add_residual_stochastic_depth(
|
||||
x,
|
||||
residual_func=attn_residual_func,
|
||||
sample_drop_ratio=self.sample_drop_ratio,
|
||||
)
|
||||
x = drop_add_residual_stochastic_depth(
|
||||
x,
|
||||
residual_func=ffn_residual_func,
|
||||
sample_drop_ratio=self.sample_drop_ratio,
|
||||
)
|
||||
elif self.training and self.sample_drop_ratio > 0.0:
|
||||
x = x + self.drop_path1(attn_residual_func(x))
|
||||
x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2
|
||||
else:
|
||||
x = x + attn_residual_func(x)
|
||||
x = x + ffn_residual_func(x)
|
||||
return x
|
||||
|
||||
|
||||
def drop_add_residual_stochastic_depth(
|
||||
x: Tensor,
|
||||
residual_func: Callable[[Tensor], Tensor],
|
||||
sample_drop_ratio: float = 0.0,
|
||||
) -> Tensor:
|
||||
# 1) extract subset using permutation
|
||||
b, n, d = x.shape
|
||||
sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
|
||||
brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
|
||||
x_subset = x[brange]
|
||||
|
||||
# 2) apply residual_func to get residual
|
||||
residual = residual_func(x_subset)
|
||||
|
||||
x_flat = x.flatten(1)
|
||||
residual = residual.flatten(1)
|
||||
|
||||
residual_scale_factor = b / sample_subset_size
|
||||
|
||||
# 3) add the residual
|
||||
x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
|
||||
return x_plus_residual.view_as(x)
|
||||
|
||||
|
||||
def get_branges_scales(x, sample_drop_ratio=0.0):
|
||||
b, n, d = x.shape
|
||||
sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
|
||||
brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
|
||||
residual_scale_factor = b / sample_subset_size
|
||||
return brange, residual_scale_factor
|
||||
|
||||
|
||||
def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):
|
||||
if scaling_vector is None:
|
||||
x_flat = x.flatten(1)
|
||||
residual = residual.flatten(1)
|
||||
x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
|
||||
else:
|
||||
x_plus_residual = scaled_index_add(
|
||||
x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor
|
||||
)
|
||||
return x_plus_residual
|
||||
|
||||
|
||||
attn_bias_cache: Dict[Tuple, Any] = {}
|
||||
|
||||
|
||||
def get_attn_bias_and_cat(x_list, branges=None):
|
||||
"""
|
||||
this will perform the index select, cat the tensors, and provide the attn_bias from cache
|
||||
"""
|
||||
batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
|
||||
all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))
|
||||
if all_shapes not in attn_bias_cache.keys():
|
||||
seqlens = []
|
||||
for b, x in zip(batch_sizes, x_list):
|
||||
for _ in range(b):
|
||||
seqlens.append(x.shape[1])
|
||||
attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
|
||||
attn_bias._batch_sizes = batch_sizes
|
||||
attn_bias_cache[all_shapes] = attn_bias
|
||||
|
||||
if branges is not None:
|
||||
cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1])
|
||||
else:
|
||||
tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)
|
||||
cat_tensors = torch.cat(tensors_bs1, dim=1)
|
||||
|
||||
return attn_bias_cache[all_shapes], cat_tensors
|
||||
|
||||
|
||||
def drop_add_residual_stochastic_depth_list(
|
||||
x_list: List[Tensor],
|
||||
residual_func: Callable[[Tensor, Any], Tensor],
|
||||
sample_drop_ratio: float = 0.0,
|
||||
scaling_vector=None,
|
||||
) -> Tensor:
|
||||
# 1) generate random set of indices for dropping samples in the batch
|
||||
branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]
|
||||
branges = [s[0] for s in branges_scales]
|
||||
residual_scale_factors = [s[1] for s in branges_scales]
|
||||
|
||||
# 2) get attention bias and index+concat the tensors
|
||||
attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
|
||||
|
||||
# 3) apply residual_func to get residual, and split the result
|
||||
residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore
|
||||
|
||||
outputs = []
|
||||
for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors):
|
||||
outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x))
|
||||
return outputs
|
||||
|
||||
|
||||
class NestedTensorBlock(Block):
|
||||
def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
|
||||
"""
|
||||
x_list contains a list of tensors to nest together and run
|
||||
"""
|
||||
assert isinstance(self.attn, MemEffAttention)
|
||||
|
||||
if self.training and self.sample_drop_ratio > 0.0:
|
||||
|
||||
def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
|
||||
return self.attn(self.norm1(x), attn_bias=attn_bias)
|
||||
|
||||
def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
|
||||
return self.mlp(self.norm2(x))
|
||||
|
||||
x_list = drop_add_residual_stochastic_depth_list(
|
||||
x_list,
|
||||
residual_func=attn_residual_func,
|
||||
sample_drop_ratio=self.sample_drop_ratio,
|
||||
scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,
|
||||
)
|
||||
x_list = drop_add_residual_stochastic_depth_list(
|
||||
x_list,
|
||||
residual_func=ffn_residual_func,
|
||||
sample_drop_ratio=self.sample_drop_ratio,
|
||||
scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,
|
||||
)
|
||||
return x_list
|
||||
else:
|
||||
|
||||
def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
|
||||
return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))
|
||||
|
||||
def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
|
||||
return self.ls2(self.mlp(self.norm2(x)))
|
||||
|
||||
attn_bias, x = get_attn_bias_and_cat(x_list)
|
||||
x = x + attn_residual_func(x, attn_bias=attn_bias)
|
||||
x = x + ffn_residual_func(x)
|
||||
return attn_bias.split(x)
|
||||
|
||||
def forward(self, x_or_x_list):
|
||||
if isinstance(x_or_x_list, Tensor):
|
||||
return super().forward(x_or_x_list)
|
||||
elif isinstance(x_or_x_list, list):
|
||||
assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage"
|
||||
return self.forward_nested(x_or_x_list)
|
||||
else:
|
||||
raise AssertionError
|
||||
@ -0,0 +1,35 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# References:
|
||||
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
|
||||
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
|
||||
|
||||
|
||||
from torch import nn
|
||||
|
||||
|
||||
def drop_path(x, drop_prob: float = 0.0, training: bool = False):
|
||||
if drop_prob == 0.0 or not training:
|
||||
return x
|
||||
keep_prob = 1 - drop_prob
|
||||
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
|
||||
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
|
||||
if keep_prob > 0.0:
|
||||
random_tensor.div_(keep_prob)
|
||||
output = x * random_tensor
|
||||
return output
|
||||
|
||||
|
||||
class DropPath(nn.Module):
|
||||
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
|
||||
|
||||
def __init__(self, drop_prob=None):
|
||||
super(DropPath, self).__init__()
|
||||
self.drop_prob = drop_prob
|
||||
|
||||
def forward(self, x):
|
||||
return drop_path(x, self.drop_prob, self.training)
|
||||
@ -0,0 +1,27 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110
|
||||
|
||||
from typing import Union
|
||||
|
||||
import torch
|
||||
from torch import Tensor, nn
|
||||
|
||||
|
||||
class LayerScale(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
init_values: Union[float, Tensor] = 1e-5,
|
||||
inplace: bool = False,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.inplace = inplace
|
||||
self.gamma = nn.Parameter(init_values * torch.ones(dim))
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
return x.mul_(self.gamma) if self.inplace else x * self.gamma
|
||||
@ -0,0 +1,41 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# References:
|
||||
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
|
||||
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py
|
||||
|
||||
|
||||
from typing import Callable, Optional
|
||||
|
||||
from torch import Tensor, nn
|
||||
|
||||
|
||||
class Mlp(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_features: int,
|
||||
hidden_features: Optional[int] = None,
|
||||
out_features: Optional[int] = None,
|
||||
act_layer: Callable[..., nn.Module] = nn.GELU,
|
||||
drop: float = 0.0,
|
||||
bias: bool = True,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
@ -0,0 +1,89 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
# References:
|
||||
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
|
||||
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
|
||||
|
||||
from typing import Callable, Optional, Tuple, Union
|
||||
|
||||
import torch.nn as nn
|
||||
from torch import Tensor
|
||||
|
||||
|
||||
def make_2tuple(x):
|
||||
if isinstance(x, tuple):
|
||||
assert len(x) == 2
|
||||
return x
|
||||
|
||||
assert isinstance(x, int)
|
||||
return (x, x)
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
"""
|
||||
2D image to patch embedding: (B,C,H,W) -> (B,N,D)
|
||||
|
||||
Args:
|
||||
img_size: Image size.
|
||||
patch_size: Patch token size.
|
||||
in_chans: Number of input image channels.
|
||||
embed_dim: Number of linear projection output channels.
|
||||
norm_layer: Normalization layer.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
img_size: Union[int, Tuple[int, int]] = 224,
|
||||
patch_size: Union[int, Tuple[int, int]] = 16,
|
||||
in_chans: int = 3,
|
||||
embed_dim: int = 768,
|
||||
norm_layer: Optional[Callable] = None,
|
||||
flatten_embedding: bool = True,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
image_HW = make_2tuple(img_size)
|
||||
patch_HW = make_2tuple(patch_size)
|
||||
patch_grid_size = (
|
||||
image_HW[0] // patch_HW[0],
|
||||
image_HW[1] // patch_HW[1],
|
||||
)
|
||||
|
||||
self.img_size = image_HW
|
||||
self.patch_size = patch_HW
|
||||
self.patches_resolution = patch_grid_size
|
||||
self.num_patches = patch_grid_size[0] * patch_grid_size[1]
|
||||
|
||||
self.in_chans = in_chans
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
self.flatten_embedding = flatten_embedding
|
||||
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW)
|
||||
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
_, _, H, W = x.shape
|
||||
patch_H, patch_W = self.patch_size
|
||||
|
||||
assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
|
||||
assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
|
||||
|
||||
x = self.proj(x) # B C H W
|
||||
H, W = x.size(2), x.size(3)
|
||||
x = x.flatten(2).transpose(1, 2) # B HW C
|
||||
x = self.norm(x)
|
||||
if not self.flatten_embedding:
|
||||
x = x.reshape(-1, H, W, self.embed_dim) # B H W C
|
||||
return x
|
||||
|
||||
def flops(self) -> float:
|
||||
Ho, Wo = self.patches_resolution
|
||||
flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
|
||||
if self.norm is not None:
|
||||
flops += Ho * Wo * self.embed_dim
|
||||
return flops
|
||||
@ -0,0 +1,63 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
from typing import Callable, Optional
|
||||
|
||||
import torch.nn.functional as F
|
||||
from torch import Tensor, nn
|
||||
|
||||
|
||||
class SwiGLUFFN(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_features: int,
|
||||
hidden_features: Optional[int] = None,
|
||||
out_features: Optional[int] = None,
|
||||
act_layer: Callable[..., nn.Module] = None,
|
||||
drop: float = 0.0,
|
||||
bias: bool = True,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
|
||||
self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
x12 = self.w12(x)
|
||||
x1, x2 = x12.chunk(2, dim=-1)
|
||||
hidden = F.silu(x1) * x2
|
||||
return self.w3(hidden)
|
||||
|
||||
|
||||
try:
|
||||
from xformers.ops import SwiGLU
|
||||
|
||||
XFORMERS_AVAILABLE = True
|
||||
except ImportError:
|
||||
SwiGLU = SwiGLUFFN
|
||||
XFORMERS_AVAILABLE = False
|
||||
|
||||
|
||||
class SwiGLUFFNFused(SwiGLU):
|
||||
def __init__(
|
||||
self,
|
||||
in_features: int,
|
||||
hidden_features: Optional[int] = None,
|
||||
out_features: Optional[int] = None,
|
||||
act_layer: Callable[..., nn.Module] = None,
|
||||
drop: float = 0.0,
|
||||
bias: bool = True,
|
||||
) -> None:
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
|
||||
super().__init__(
|
||||
in_features=in_features,
|
||||
hidden_features=hidden_features,
|
||||
out_features=out_features,
|
||||
bias=bias,
|
||||
)
|
||||
@ -1,12 +1,12 @@
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torchvision.transforms import Compose
|
||||
|
||||
from invokeai.backend.image_util.depth_anything.model.blocks import FeatureFusionBlock, _make_scratch
|
||||
|
||||
torchhub_path = Path(__file__).parent.parent / "torchhub"
|
||||
from .dinov2 import DINOv2
|
||||
from .utils.blocks import FeatureFusionBlock, _make_scratch
|
||||
from .utils.transform import NormalizeImage, PrepareForNet, Resize
|
||||
|
||||
|
||||
def _make_fusion_block(features, use_bn, size=None):
|
||||
@ -21,11 +21,26 @@ def _make_fusion_block(features, use_bn, size=None):
|
||||
)
|
||||
|
||||
|
||||
class ConvBlock(nn.Module):
|
||||
def __init__(self, in_feature, out_feature):
|
||||
super().__init__()
|
||||
|
||||
self.conv_block = nn.Sequential(
|
||||
nn.Conv2d(in_feature, out_feature, kernel_size=3, stride=1, padding=1),
|
||||
nn.BatchNorm2d(out_feature),
|
||||
nn.ReLU(True),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv_block(x)
|
||||
|
||||
|
||||
class DPTHead(nn.Module):
|
||||
def __init__(self, nclass, in_channels, features, out_channels, use_bn=False, use_clstoken=False):
|
||||
def __init__(
|
||||
self, in_channels, features=256, use_bn=False, out_channels=[256, 512, 1024, 1024], use_clstoken=False
|
||||
):
|
||||
super(DPTHead, self).__init__()
|
||||
|
||||
self.nclass = nclass
|
||||
self.use_clstoken = use_clstoken
|
||||
|
||||
self.projects = nn.ModuleList(
|
||||
@ -78,24 +93,14 @@ class DPTHead(nn.Module):
|
||||
head_features_1 = features
|
||||
head_features_2 = 32
|
||||
|
||||
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
|
||||
)
|
||||
|
||||
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(),
|
||||
)
|
||||
self.scratch.output_conv1 = nn.Conv2d(head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1)
|
||||
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(),
|
||||
)
|
||||
|
||||
def forward(self, out_features, patch_h, patch_w):
|
||||
out = []
|
||||
@ -133,51 +138,73 @@ class DPTHead(nn.Module):
|
||||
return out
|
||||
|
||||
|
||||
class DPT_DINOv2(nn.Module):
|
||||
class DepthAnythingV2(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
features,
|
||||
out_channels,
|
||||
encoder="vitl",
|
||||
use_bn=False,
|
||||
use_clstoken=False,
|
||||
self, encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024], use_bn=False, use_clstoken=False
|
||||
):
|
||||
super(DPT_DINOv2, self).__init__()
|
||||
super(DepthAnythingV2, self).__init__()
|
||||
|
||||
assert encoder in ["vits", "vitb", "vitl"]
|
||||
self.intermediate_layer_idx = {
|
||||
"vits": [2, 5, 8, 11],
|
||||
"vitb": [2, 5, 8, 11],
|
||||
"vitl": [4, 11, 17, 23],
|
||||
"vitg": [9, 19, 29, 39],
|
||||
}
|
||||
|
||||
# # in case the Internet connection is not stable, please load the DINOv2 locally
|
||||
# if use_local:
|
||||
# self.pretrained = torch.hub.load(
|
||||
# torchhub_path / "facebookresearch_dinov2_main",
|
||||
# "dinov2_{:}14".format(encoder),
|
||||
# source="local",
|
||||
# pretrained=False,
|
||||
# )
|
||||
# else:
|
||||
# self.pretrained = torch.hub.load(
|
||||
# "facebookresearch/dinov2",
|
||||
# "dinov2_{:}14".format(encoder),
|
||||
# )
|
||||
self.encoder = encoder
|
||||
self.pretrained = DINOv2(model_name=encoder)
|
||||
|
||||
self.pretrained = torch.hub.load(
|
||||
"facebookresearch/dinov2",
|
||||
"dinov2_{:}14".format(encoder),
|
||||
self.depth_head = DPTHead(
|
||||
self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken
|
||||
)
|
||||
|
||||
dim = self.pretrained.blocks[0].attn.qkv.in_features
|
||||
|
||||
self.depth_head = DPTHead(1, dim, features, out_channels=out_channels, use_bn=use_bn, use_clstoken=use_clstoken)
|
||||
|
||||
def forward(self, x):
|
||||
h, w = x.shape[-2:]
|
||||
patch_h, patch_w = x.shape[-2] // 14, x.shape[-1] // 14
|
||||
|
||||
features = self.pretrained.get_intermediate_layers(x, 4, return_class_token=True)
|
||||
|
||||
patch_h, patch_w = h // 14, w // 14
|
||||
features = self.pretrained.get_intermediate_layers(
|
||||
x, self.intermediate_layer_idx[self.encoder], return_class_token=True
|
||||
)
|
||||
|
||||
depth = self.depth_head(features, patch_h, patch_w)
|
||||
depth = F.interpolate(depth, size=(h, w), mode="bilinear", align_corners=True)
|
||||
depth = F.relu(depth)
|
||||
|
||||
return depth.squeeze(1)
|
||||
|
||||
@torch.no_grad()
|
||||
def infer_image(self, raw_image, input_size=518):
|
||||
image, (h, w) = self.image2tensor(raw_image, input_size)
|
||||
|
||||
depth = self.forward(image)
|
||||
|
||||
depth = F.interpolate(depth[:, None], (h, w), mode="bilinear", align_corners=True)[0, 0]
|
||||
|
||||
return depth.cpu().numpy()
|
||||
|
||||
def image2tensor(self, raw_image, input_size=518):
|
||||
transform = Compose(
|
||||
[
|
||||
Resize(
|
||||
width=input_size,
|
||||
height=input_size,
|
||||
resize_target=False,
|
||||
keep_aspect_ratio=True,
|
||||
ensure_multiple_of=14,
|
||||
resize_method="lower_bound",
|
||||
image_interpolation_method=cv2.INTER_CUBIC,
|
||||
),
|
||||
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
||||
PrepareForNet(),
|
||||
]
|
||||
)
|
||||
|
||||
h, w = raw_image.shape[:2]
|
||||
|
||||
image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0
|
||||
|
||||
image = transform({"image": image})["image"]
|
||||
image = torch.from_numpy(image).unsqueeze(0)
|
||||
|
||||
DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
|
||||
image = image.to(DEVICE)
|
||||
|
||||
return image, (h, w)
|
||||
@ -53,7 +53,7 @@ class ResidualConvUnit(nn.Module):
|
||||
|
||||
self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
|
||||
|
||||
if self.bn:
|
||||
if self.bn == True:
|
||||
self.bn1 = nn.BatchNorm2d(features)
|
||||
self.bn2 = nn.BatchNorm2d(features)
|
||||
|
||||
@ -73,12 +73,12 @@ class ResidualConvUnit(nn.Module):
|
||||
|
||||
out = self.activation(x)
|
||||
out = self.conv1(out)
|
||||
if self.bn:
|
||||
if self.bn == True:
|
||||
out = self.bn1(out)
|
||||
|
||||
out = self.activation(out)
|
||||
out = self.conv2(out)
|
||||
if self.bn:
|
||||
if self.bn == True:
|
||||
out = self.bn2(out)
|
||||
|
||||
if self.groups > 1:
|
||||
@ -105,7 +105,7 @@ class FeatureFusionBlock(nn.Module):
|
||||
|
||||
self.expand = expand
|
||||
out_features = features
|
||||
if self.expand:
|
||||
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)
|
||||
@ -1,47 +1,5 @@
|
||||
import math
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
|
||||
"""Rezise the sample to ensure the given size. Keeps aspect ratio.
|
||||
|
||||
Args:
|
||||
sample (dict): sample
|
||||
size (tuple): image size
|
||||
|
||||
Returns:
|
||||
tuple: new size
|
||||
"""
|
||||
shape = list(sample["disparity"].shape)
|
||||
|
||||
if shape[0] >= size[0] and shape[1] >= size[1]:
|
||||
return sample
|
||||
|
||||
scale = [0, 0]
|
||||
scale[0] = size[0] / shape[0]
|
||||
scale[1] = size[1] / shape[1]
|
||||
|
||||
scale = max(scale)
|
||||
|
||||
shape[0] = math.ceil(scale * shape[0])
|
||||
shape[1] = math.ceil(scale * shape[1])
|
||||
|
||||
# resize
|
||||
sample["image"] = cv2.resize(sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method)
|
||||
|
||||
sample["disparity"] = cv2.resize(sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST)
|
||||
sample["mask"] = cv2.resize(
|
||||
sample["mask"].astype(np.float32),
|
||||
tuple(shape[::-1]),
|
||||
interpolation=cv2.INTER_NEAREST,
|
||||
)
|
||||
sample["mask"] = sample["mask"].astype(bool)
|
||||
|
||||
return tuple(shape)
|
||||
|
||||
|
||||
class Resize(object):
|
||||
@ -76,8 +34,7 @@ class Resize(object):
|
||||
Defaults to 1.
|
||||
resize_method (str, optional):
|
||||
"lower_bound": Output will be at least as large as the given size.
|
||||
"upper_bound": Output will be at max as large as the given size. (Output size might be smaller
|
||||
than given size.)
|
||||
"upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
|
||||
"minimal": Scale as least as possible. (Output size might be smaller than given size.)
|
||||
Defaults to "lower_bound".
|
||||
"""
|
||||
@ -152,40 +109,17 @@ class Resize(object):
|
||||
width, height = self.get_size(sample["image"].shape[1], sample["image"].shape[0])
|
||||
|
||||
# resize sample
|
||||
sample["image"] = cv2.resize(
|
||||
sample["image"],
|
||||
(width, height),
|
||||
interpolation=self.__image_interpolation_method,
|
||||
)
|
||||
sample["image"] = cv2.resize(sample["image"], (width, height), interpolation=self.__image_interpolation_method)
|
||||
|
||||
if self.__resize_target:
|
||||
if "disparity" in sample:
|
||||
sample["disparity"] = cv2.resize(
|
||||
sample["disparity"],
|
||||
(width, height),
|
||||
interpolation=cv2.INTER_NEAREST,
|
||||
)
|
||||
|
||||
if "depth" in sample:
|
||||
sample["depth"] = cv2.resize(sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST)
|
||||
|
||||
if "semseg_mask" in sample:
|
||||
# sample["semseg_mask"] = cv2.resize(
|
||||
# sample["semseg_mask"], (width, height), interpolation=cv2.INTER_NEAREST
|
||||
# )
|
||||
sample["semseg_mask"] = F.interpolate(
|
||||
torch.from_numpy(sample["semseg_mask"]).float()[None, None, ...], (height, width), mode="nearest"
|
||||
).numpy()[0, 0]
|
||||
|
||||
if "mask" in sample:
|
||||
sample["mask"] = cv2.resize(
|
||||
sample["mask"].astype(np.float32),
|
||||
(width, height),
|
||||
interpolation=cv2.INTER_NEAREST,
|
||||
sample["mask"].astype(np.float32), (width, height), interpolation=cv2.INTER_NEAREST
|
||||
)
|
||||
# sample["mask"] = sample["mask"].astype(bool)
|
||||
|
||||
# print(sample['image'].shape, sample['depth'].shape)
|
||||
return sample
|
||||
|
||||
|
||||
@ -212,16 +146,12 @@ class PrepareForNet(object):
|
||||
image = np.transpose(sample["image"], (2, 0, 1))
|
||||
sample["image"] = np.ascontiguousarray(image).astype(np.float32)
|
||||
|
||||
if "mask" in sample:
|
||||
sample["mask"] = sample["mask"].astype(np.float32)
|
||||
sample["mask"] = np.ascontiguousarray(sample["mask"])
|
||||
|
||||
if "depth" in sample:
|
||||
depth = sample["depth"].astype(np.float32)
|
||||
sample["depth"] = np.ascontiguousarray(depth)
|
||||
|
||||
if "semseg_mask" in sample:
|
||||
sample["semseg_mask"] = sample["semseg_mask"].astype(np.float32)
|
||||
sample["semseg_mask"] = np.ascontiguousarray(sample["semseg_mask"])
|
||||
if "mask" in sample:
|
||||
sample["mask"] = sample["mask"].astype(np.float32)
|
||||
sample["mask"] = np.ascontiguousarray(sample["mask"])
|
||||
|
||||
return sample
|
||||
Loading…
Reference in New Issue
Block a user