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from collections import OrderedDict
import torch
import torch.nn as nn
import abc
from transformers import PerceiverModel, PerceiverConfig
from transformers.models.perceiver.modeling_perceiver import build_position_encoding
from vidar.arch.networks.perceiver.externals.modeling_perceiver import PerceiverDepthDecoder, PerceiverRGBDecoder, build_position_encoding
from vidar.arch.blocks.depth.SigmoidToInvDepth import SigmoidToInvDepth
from vidar.arch.networks.decoders.DepthDecoder import DepthDecoder
from vidar.arch.networks.encoders.ResNetEncoder import ResNetEncoder
from vidar.utils.config import Config
from vidar.utils.networks import freeze_layers_and_norms
from vidar.utils.tensor import interpolate
from vidar.utils.types import is_int
class DownSampleRGB(nn.Module):
def __init__(self, out_dim):
super().__init__()
self.conv = torch.nn.Conv2d(3, out_dim, kernel_size=7, stride=2, padding=3)
self.norm = torch.nn.BatchNorm2d(out_dim)
self.actv = torch.nn.ReLU()
self.pool = torch.nn.MaxPool2d(2, stride=2)
def forward(self, x):
x = self.conv(x)
x = self.norm(x)
x = self.actv(x)
x = self.pool(x)
return x
class DefineNet(nn.Module):
def __init__(self, cfg):
super().__init__()
self.tasks = cfg.tasks
self.to_world = cfg.to_world
self.depth_range = cfg.depth_range
self.rgb_feat_dim = cfg.rgb_feat_dim
self.rgb_feat_type = cfg.rgb_feat_type
self.encoder_with_rgb = cfg.encoder_with_rgb
self.decoder_with_rgb = cfg.decoder_with_rgb
self.output_mode = cfg.output_mode
self.sample_encoding_rays = cfg.sample_encoding_rays
self.with_monodepth = cfg.with_monodepth
self.upsample_convex = cfg.upsample_convex
self.downsample_encoder = cfg.downsample_encoder
self.downsample_decoder = cfg.downsample_decoder
self.image_shape = [s // self.downsample_encoder for s in cfg.image_shape]
self.fourier_encoding_orig, _ = build_position_encoding(
position_encoding_type='fourier',
fourier_position_encoding_kwargs={
'num_bands': cfg.num_bands_orig,
'max_resolution': [cfg.max_resolution_orig] * 3,
'concat_pos': True,
'sine_only': False,
}
)
self.fourier_encoding_dirs, _ = build_position_encoding(
position_encoding_type='fourier',
fourier_position_encoding_kwargs={
'num_bands': cfg.num_bands_dirs,
'max_resolution': [cfg.num_bands_dirs] * 3,
'concat_pos': True,
'sine_only': False,
}
)
tot_encoder = self.fourier_encoding_orig.output_size() + \
self.fourier_encoding_dirs.output_size()
if self.encoder_with_rgb:
tot_encoder += self.rgb_feat_dim
tot_decoder = self.fourier_encoding_orig.output_size() + \
self.fourier_encoding_dirs.output_size()
if self.decoder_with_rgb:
tot_decoder += self.rgb_feat_dim
tot_decoder_depth = tot_decoder
tot_decoder_rgb = tot_decoder
self.config = PerceiverConfig(
train_size=self.image_shape,
d_latents=cfg.d_latents,
d_model=tot_encoder,
num_latents=cfg.num_latents,
hidden_act='gelu',
hidden_dropout_prob=cfg.hidden_dropout_prob,
initializer_range=0.02,
layer_norm_eps=1e-12,
num_blocks=1,
num_cross_attention_heads=cfg.num_cross_attention_heads,
num_self_attends_per_block=cfg.num_self_attends_per_block,
num_self_attention_heads=cfg.num_self_attention_heads,
qk_channels=None,
v_channels=None,
)
if 'depth' in self.tasks:
self.decoder = PerceiverDepthDecoder(
self.config,
num_channels=tot_decoder_depth,
use_query_residual=False,
output_num_channels=1,
position_encoding_type="none",
min_depth=self.depth_range[0],
max_depth=self.depth_range[1],
num_heads=cfg.decoder_num_heads,
upsample_mode=cfg.upsample_convex,
upsample_value=cfg.downsample_decoder,
output_mode=cfg.output_mode
)
if 'rgb' in self.tasks:
self.decoder_rgb = PerceiverRGBDecoder(
self.config,
num_channels=tot_decoder_rgb,
use_query_residual=False,
output_num_channels=3,
position_encoding_type="none",
num_heads=cfg.decoder_num_heads,
upsample_mode=cfg.upsample_convex,
upsample_value=cfg.downsample_decoder,
)
self.model = PerceiverModel(
self.config,
)
if self.rgb_feat_type == 'convnet':
self.feature = DownSampleRGB(out_dim=self.rgb_feat_dim)
elif self.rgb_feat_type in ['resnet', 'resnet_all', 'resnet_all_rgb']:
self.feature = ResNetEncoder(Config(version=18, pretrained=True, num_rgb_in=1))
if self.with_monodepth:
self.mono_encoder = ResNetEncoder(Config(version=18, pretrained=True, num_rgb_in=1))
self.mono_decoder = DepthDecoder(Config(
num_scales=4, use_skips=True, num_ch_enc=self.feature.num_ch_enc,
num_ch_out=1, activation='sigmoid',
))
self.sigmoid_to_depth = SigmoidToInvDepth(
min_depth=self.depth_range[0], max_depth=self.depth_range[1], return_depth=True)
def get_rgb_feat(self, rgb):
if self.rgb_feat_type == 'convnet':
return {
'feat': self.feature(rgb)
}
elif self.rgb_feat_type == 'resnet':
return {
'feat': self.feature(rgb)[1]
}
elif self.rgb_feat_type.startswith('resnet_all'):
all_feats = self.feature(rgb)
feats = all_feats[1:]
for i in range(1, len(feats)):
feats[i] = interpolate(
feats[i], size=feats[0], scale_factor=None, mode='bilinear', align_corners=True)
if self.rgb_feat_type.endswith('rgb'):
feats = feats + [interpolate(
rgb, size=feats[0], scale_factor=None, mode='bilinear', align_corners=True)]
feat = torch.cat(feats, 1)
return {
'all_feats': all_feats,
'feat': feat
}
def run_monodepth(self, rgb, freeze):
freeze_layers_and_norms(self.mono_encoder, flag_freeze=freeze)
freeze_layers_and_norms(self.mono_decoder, flag_freeze=freeze)
mono_features = self.mono_encoder(rgb)
mono_output = self.mono_decoder(mono_features)
sigmoids = [mono_output[('output', i)] for i in range(1)]
return self.sigmoid_to_depth(sigmoids)[0]
def embeddings(self, data, sources, downsample):
if 'rgb' in sources:
assert 'rgb' in data[0].keys()
b = [datum['rgb'].shape[0] for datum in data]
rgb = torch.cat([datum['rgb'] for datum in data], 0)
output_feats = self.get_rgb_feat(rgb)
feats = torch.split(output_feats['feat'], b)
for i in range(len(data)):
data[i]['feat'] = feats[i]
if self.with_monodepth:
depth = self.run_monodepth(rgb, freeze=False)
depth = torch.split(depth, b)
for i in range(len(data)):
data[i]['depth_mono'] = depth[i]
encodings = []
for datum in data:
encoding = OrderedDict()
if 'cam' in sources:
assert 'cam' in data[0].keys()
cam = datum['cam'].scaled(1. / downsample)
orig = cam.get_origin(flatten=True)
if self.to_world:
dirs = cam.get_viewdirs(normalize=True, flatten=True, to_world=True)
else:
dirs = cam.no_translation().get_viewdirs(normalize=True, flatten=True, to_world=True)
orig_encodings = self.fourier_encoding_orig(
index_dims=None, pos=orig, batch_size=orig.shape[0], device=orig.device)
dirs_encodings = self.fourier_encoding_dirs(
index_dims=None, pos=dirs, batch_size=dirs.shape[0], device=dirs.device)
encoding['cam'] = torch.cat([orig_encodings, dirs_encodings], -1)
if 'rgb' in sources:
rgb = datum['feat']
rgb_flat = rgb.view(*rgb.shape[:-2], -1).permute(0, 2, 1)
encoding['rgb'] = rgb_flat
encoding['all'] = torch.cat([val for val in encoding.values()], -1)
encodings.append(encoding)
return encodings
@staticmethod
def sample_decoder(data, embeddings, field, sample_queries, filter_invalid):
query_idx = []
if filter_invalid:
tot_min = []
for i in range(len(embeddings)):
for b in range(data[i]['rgb'].shape[0]):
tot_min.append((data[i]['rgb'][b].mean(0) >= 0).sum())
tot_min = min(tot_min)
tot = embeddings[0][field][0].shape[0]
tot = int(sample_queries * tot)
tot = min([tot, tot_min])
for i in range(len(embeddings)):
idx = []
for b in range(data[i]['rgb'].shape[0]):
if filter_invalid:
valid = data[i]['rgb'][b].mean(0, keepdim=True) >= 0
valid = valid.view(1, -1).permute(1, 0)
num = embeddings[i][field][0].shape[0]
all_idx = torch.arange(num, device=valid.device).unsqueeze(1)
valid_idx = all_idx[valid]
num = valid_idx.shape[0]
idx_i = torch.randperm(num)[tot:]
valid[valid_idx[idx_i]] = 0
idx_i = all_idx[valid]
else:
num = embeddings[i][field][0].shape[0]
tot = int(sample_queries * num)
idx_i = torch.randperm(num)[:tot]
idx.append(idx_i)
idx = torch.stack(idx, 0)
embeddings[i][field] = torch.stack(
[embeddings[i][field][b][idx[b]] for b in range(idx.shape[0])], 0)
query_idx.append(idx)
return query_idx, embeddings
def forward(self, encode_data, decode_data=None,
sample_queries=0, filter_invalid=False):
encode_field = 'all' if self.encoder_with_rgb else 'cam'
decode_field = 'all' if self.decoder_with_rgb else 'cam'
encode_sources = ['rgb', 'cam']
decode_sources = ['cam']
shape = encode_data[0]['cam'].hw
output = {}
encode_dict = self.encode(
data=encode_data, field=encode_field, sources=encode_sources
)
if 'depth_mono' in encode_data[0].keys():
output['depth_mono'] = [datum['depth_mono'] for datum in encode_data]
decode_embeddings = encode_dict['embeddings'] if decode_data is None else None
decode_dict = self.decode(
latent=encode_dict['latent'], shape=shape,
data=decode_data, embeddings=decode_embeddings,
field=decode_field, sources=decode_sources,
sample_queries=sample_queries, filter_invalid=filter_invalid
)
output.update(decode_dict['output'])
return {
'output': output,
'encode_embeddings': encode_dict['embeddings'],
'decode_embeddings': decode_dict['embeddings'],
'latent': encode_dict['latent'],
}
def encode(self, field, sources, data=None, embeddings=None):
assert data is not None or embeddings is not None
assert data is None or embeddings is None
if embeddings is None:
embeddings = self.embeddings(data, sources=sources, downsample=self.downsample_encoder)
all_embeddings = torch.cat([emb[field] for emb in embeddings], 1)
if self.training and self.sample_encoding_rays > 0:
tot = self.sample_encoding_rays if is_int(self.sample_encoding_rays) \
else int(self.sample_encoding_rays * all_embeddings.shape[1])
all_embeddings = torch.stack([all_embeddings[i, torch.randperm(all_embeddings.shape[1])[:tot], :]
for i in range(all_embeddings.shape[0])], 0)
return {
'embeddings': embeddings,
'latent': self.model(inputs=all_embeddings).last_hidden_state,
}
def decode(self, latent, field, sources=None, data=None, embeddings=None, shape=None,
sample_queries=0, filter_invalid=False):
assert data is not None or embeddings is not None
assert data is None or embeddings is None
if embeddings is None:
shape = data[0]['cam'].hw
shape = [s // self.downsample_decoder for s in shape]
embeddings = self.embeddings(data, sources=sources, downsample=self.downsample_decoder)
output = {}
if self.training and (sample_queries > 0): # or filter_invalid):
output['query_idx'], embeddings = self.sample_decoder(
data, embeddings, field, sample_queries, filter_invalid)
shape = None
if 'rgb' in self.tasks:
output['rgb'] = [
self.decoder_rgb(query=emb[field], z=latent, shape=shape).logits
for emb in embeddings]
if 'depth' in self.tasks:
output['depth'] = [
self.decoder(query=emb[field], z=latent, shape=shape).logits
for emb in embeddings]
return {
'embeddings': embeddings,
'output': output,
}
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