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# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# The deconvolution code is based on Simple Baseline.
# (https://github.com/microsoft/human-pose-estimation.pytorch/blob/master/lib/models/pose_resnet.py)
# Modified by Zigang Geng (zigang@mail.ustc.edu.cn).
# ------------------------------------------------------------------------------
import torch
import torch.nn as nn
from timm.models.layers import trunc_normal_, DropPath
from mmcv.cnn import (build_conv_layer, build_norm_layer, build_upsample_layer,
constant_init, normal_init)
from omegaconf import OmegaConf
from ldm.util import instantiate_from_config
import torch.nn.functional as F
from evp.models import UNetWrapper, TextAdapterDepth
class VPDDepthEncoder(nn.Module):
def __init__(self, out_dim=1024, ldm_prior=[320, 640, 1280+1280], sd_path=None, text_dim=768,
dataset='nyu'
):
super().__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(ldm_prior[0], ldm_prior[0], 3, stride=2, padding=1),
nn.GroupNorm(16, ldm_prior[0]),
nn.ReLU(),
nn.Conv2d(ldm_prior[0], ldm_prior[0], 3, stride=2, padding=1),
)
self.layer2 = nn.Sequential(
nn.Conv2d(ldm_prior[1], ldm_prior[1], 3, stride=2, padding=1),
)
self.out_layer = nn.Sequential(
nn.Conv2d(sum(ldm_prior), out_dim, 1),
nn.GroupNorm(16, out_dim),
nn.ReLU(),
)
self.apply(self._init_weights)
### stable diffusion layers
config = OmegaConf.load('./v1-inference.yaml')
if sd_path is None:
config.model.params.ckpt_path = '../checkpoints/v1-5-pruned-emaonly.ckpt'
else:
config.model.params.ckpt_path = f'../{sd_path}'
sd_model = instantiate_from_config(config.model)
self.encoder_vq = sd_model.first_stage_model
self.unet = UNetWrapper(sd_model.model, use_attn=False)
del sd_model.cond_stage_model
del self.encoder_vq.decoder
del self.unet.unet.diffusion_model.out
for param in self.encoder_vq.parameters():
param.requires_grad = False
if dataset == 'nyu':
self.text_adapter = TextAdapterDepth(text_dim=text_dim)
class_embeddings = torch.load('nyu_class_embeddings.pth')
else:
raise NotImplementedError
self.register_buffer('class_embeddings', class_embeddings)
self.gamma = nn.Parameter(torch.ones(text_dim) * 1e-4)
def _init_weights(self, m):
if isinstance(m, (nn.Conv2d, nn.Linear)):
trunc_normal_(m.weight, std=.02)
nn.init.constant_(m.bias, 0)
def forward_features(self, feats):
x = self.ldm_to_net[0](feats[0])
for i in range(3):
if i > 0:
x = x + self.ldm_to_net[i](feats[i])
x = self.layers[i](x)
x = self.upsample_layers[i](x)
return self.out_conv(x)
def forward(self, x, class_ids=None,img_paths=None):
with torch.no_grad():
latents = self.encoder_vq.encode(x).mode().detach()
if class_ids is not None:
class_embeddings = self.class_embeddings[class_ids.tolist()]
else:
class_embeddings = self.class_embeddings
c_crossattn = self.text_adapter(latents, class_embeddings, self.gamma) # NOTE: here the c_crossattn should be expand_dim as latents
t = torch.ones((x.shape[0],), device=x.device).long()
# import pdb; pdb.set_trace()
outs = self.unet(latents, t, c_crossattn=[c_crossattn])
feats = [outs[0], outs[1], torch.cat([outs[2], F.interpolate(outs[3], scale_factor=2)], dim=1)]
x = torch.cat([self.layer1(feats[0]), self.layer2(feats[1]), feats[2]], dim=1)
return self.out_layer(x)
class VPDDepth(nn.Module):
def __init__(self, args=None):
super().__init__()
self.max_depth = args.max_depth
embed_dim = 192
channels_in = embed_dim*8
channels_out = embed_dim
if args.dataset == 'nyudepthv2':
self.encoder = VPDDepthEncoder(out_dim=channels_in, dataset='nyu')
else:
raise NotImplementedError
self.decoder = Decoder(channels_in, channels_out, args)
self.decoder.init_weights()
self.last_layer_depth = nn.Sequential(
nn.Conv2d(channels_out, channels_out, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False),
nn.Conv2d(channels_out, 1, kernel_size=3, stride=1, padding=1))
for m in self.last_layer_depth.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001, bias=0)
def forward(self, x, class_ids=None,img_paths=None):
# import pdb; pdb.set_trace()
b, c, h, w = x.shape
x = x*2.0 - 1.0 # normalize to [-1, 1]
if h == 480 and w == 480:
new_x = torch.zeros(b, c, 512, 512, device=x.device)
new_x[:, :, 0:480, 0:480] = x
x = new_x
elif h==352 and w==352:
new_x = torch.zeros(b, c, 384, 384, device=x.device)
new_x[:, :, 0:352, 0:352] = x
x = new_x
elif h == 512 and w == 512:
pass
else:
raise NotImplementedError
conv_feats = self.encoder(x, class_ids)
if h == 480 or h == 352:
conv_feats = conv_feats[:, :, :-1, :-1]
out = self.decoder([conv_feats])
out_depth = self.last_layer_depth(out)
out_depth = torch.sigmoid(out_depth) * self.max_depth
return {'pred_d': out_depth}
class Decoder(nn.Module):
def __init__(self, in_channels, out_channels, args):
super().__init__()
self.deconv = args.num_deconv
self.in_channels = in_channels
# import pdb; pdb.set_trace()
self.deconv_layers = self._make_deconv_layer(
args.num_deconv,
args.num_filters,
args.deconv_kernels,
)
conv_layers = []
conv_layers.append(
build_conv_layer(
dict(type='Conv2d'),
in_channels=args.num_filters[-1],
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1))
conv_layers.append(
build_norm_layer(dict(type='BN'), out_channels)[1])
conv_layers.append(nn.ReLU(inplace=True))
self.conv_layers = nn.Sequential(*conv_layers)
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
def forward(self, conv_feats):
# import pdb; pdb.set_trace()
out = self.deconv_layers(conv_feats[0])
out = self.conv_layers(out)
out = self.up(out)
out = self.up(out)
return out
def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
"""Make deconv layers."""
layers = []
in_planes = self.in_channels
for i in range(num_layers):
kernel, padding, output_padding = \
self._get_deconv_cfg(num_kernels[i])
planes = num_filters[i]
layers.append(
build_upsample_layer(
dict(type='deconv'),
in_channels=in_planes,
out_channels=planes,
kernel_size=kernel,
stride=2,
padding=padding,
output_padding=output_padding,
bias=False))
layers.append(nn.BatchNorm2d(planes))
layers.append(nn.ReLU(inplace=True))
in_planes = planes
return nn.Sequential(*layers)
def _get_deconv_cfg(self, deconv_kernel):
"""Get configurations for deconv layers."""
if deconv_kernel == 4:
padding = 1
output_padding = 0
elif deconv_kernel == 3:
padding = 1
output_padding = 1
elif deconv_kernel == 2:
padding = 0
output_padding = 0
else:
raise ValueError(f'Not supported num_kernels ({deconv_kernel}).')
return deconv_kernel, padding, output_padding
def init_weights(self):
"""Initialize model weights."""
for m in self.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001, bias=0)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
elif isinstance(m, nn.ConvTranspose2d):
normal_init(m, std=0.001)
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