AdBind / imagebind /models /imagebind_model.py
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#!/usr/bin/env python3
# Portions 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.
import os
from functools import partial
from types import SimpleNamespace
import torch
import torch.nn as nn
from imagebind.models.helpers import (EinOpsRearrange, LearnableLogitScaling, Normalize,
SelectElement, SelectEOSAndProject)
from imagebind.models.multimodal_preprocessors import (AudioPreprocessor,
IMUPreprocessor, PadIm2Video,
PatchEmbedGeneric,
RGBDTPreprocessor,
SpatioTemporalPosEmbeddingHelper,
TextPreprocessor,
ThermalPreprocessor)
from imagebind.models.transformer import MultiheadAttention, SimpleTransformer
ModalityType = SimpleNamespace(
VISION="vision",
TEXT="text",
AUDIO="audio",
THERMAL="thermal",
DEPTH="depth",
IMU="imu",
)
class ImageBindModel(nn.Module):
def __init__(
self,
video_frames=2,
kernel_size=(2, 14, 14),
audio_kernel_size=16,
audio_stride=10,
out_embed_dim=768,
vision_embed_dim=1024,
vision_num_blocks=24,
vision_num_heads=16,
audio_embed_dim=768,
audio_num_blocks=12,
audio_num_heads=12,
audio_num_mel_bins=128,
audio_target_len=204,
audio_drop_path=0.1,
text_embed_dim=768,
text_num_blocks=12,
text_num_heads=12,
depth_embed_dim=384,
depth_kernel_size=16,
depth_num_blocks=12,
depth_num_heads=8,
depth_drop_path=0.0,
thermal_embed_dim=768,
thermal_kernel_size=16,
thermal_num_blocks=12,
thermal_num_heads=12,
thermal_drop_path=0.0,
imu_embed_dim=512,
imu_kernel_size=8,
imu_num_blocks=6,
imu_num_heads=8,
imu_drop_path=0.7,
):
super().__init__()
self.modality_preprocessors = self._create_modality_preprocessors(
video_frames,
vision_embed_dim,
kernel_size,
text_embed_dim,
audio_embed_dim,
audio_kernel_size,
audio_stride,
audio_num_mel_bins,
audio_target_len,
depth_embed_dim,
depth_kernel_size,
thermal_embed_dim,
thermal_kernel_size,
imu_embed_dim,
)
self.modality_trunks = self._create_modality_trunks(
vision_embed_dim,
vision_num_blocks,
vision_num_heads,
text_embed_dim,
text_num_blocks,
text_num_heads,
audio_embed_dim,
audio_num_blocks,
audio_num_heads,
audio_drop_path,
depth_embed_dim,
depth_num_blocks,
depth_num_heads,
depth_drop_path,
thermal_embed_dim,
thermal_num_blocks,
thermal_num_heads,
thermal_drop_path,
imu_embed_dim,
imu_num_blocks,
imu_num_heads,
imu_drop_path,
)
self.modality_heads = self._create_modality_heads(
out_embed_dim,
vision_embed_dim,
text_embed_dim,
audio_embed_dim,
depth_embed_dim,
thermal_embed_dim,
imu_embed_dim,
)
self.modality_postprocessors = self._create_modality_postprocessors(
out_embed_dim
)
def _create_modality_preprocessors(
self,
video_frames=2,
vision_embed_dim=1024,
kernel_size=(2, 14, 14),
text_embed_dim=768,
audio_embed_dim=768,
audio_kernel_size=16,
audio_stride=10,
audio_num_mel_bins=128,
audio_target_len=204,
depth_embed_dim=768,
depth_kernel_size=16,
thermal_embed_dim=768,
thermal_kernel_size=16,
imu_embed_dim=512,
):
rgbt_stem = PatchEmbedGeneric(
proj_stem=[
PadIm2Video(pad_type="repeat", ntimes=2),
nn.Conv3d(
in_channels=3,
kernel_size=kernel_size,
out_channels=vision_embed_dim,
stride=kernel_size,
bias=False,
),
]
)
rgbt_preprocessor = RGBDTPreprocessor(
img_size=[3, video_frames, 224, 224],
num_cls_tokens=1,
pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
rgbt_stem=rgbt_stem,
depth_stem=None,
)
text_preprocessor = TextPreprocessor(
context_length=77,
vocab_size=49408,
embed_dim=text_embed_dim,
causal_masking=True,
)
audio_stem = PatchEmbedGeneric(
proj_stem=[
nn.Conv2d(
in_channels=1,
kernel_size=audio_kernel_size,
stride=audio_stride,
out_channels=audio_embed_dim,
bias=False,
),
],
norm_layer=nn.LayerNorm(normalized_shape=audio_embed_dim),
)
audio_preprocessor = AudioPreprocessor(
img_size=[1, audio_num_mel_bins, audio_target_len],
num_cls_tokens=1,
pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
audio_stem=audio_stem,
)
depth_stem = PatchEmbedGeneric(
[
nn.Conv2d(
kernel_size=depth_kernel_size,
in_channels=1,
out_channels=depth_embed_dim,
stride=depth_kernel_size,
bias=False,
),
],
norm_layer=nn.LayerNorm(normalized_shape=depth_embed_dim),
)
depth_preprocessor = RGBDTPreprocessor(
img_size=[1, 224, 224],
num_cls_tokens=1,
pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
rgbt_stem=None,
depth_stem=depth_stem,
)
thermal_stem = PatchEmbedGeneric(
[
nn.Conv2d(
kernel_size=thermal_kernel_size,
in_channels=1,
out_channels=thermal_embed_dim,
stride=thermal_kernel_size,
bias=False,
),
],
norm_layer=nn.LayerNorm(normalized_shape=thermal_embed_dim),
)
thermal_preprocessor = ThermalPreprocessor(
img_size=[1, 224, 224],
num_cls_tokens=1,
pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
thermal_stem=thermal_stem,
)
imu_stem = PatchEmbedGeneric(
[
nn.Linear(
in_features=48,
out_features=imu_embed_dim,
bias=False,
),
],
norm_layer=nn.LayerNorm(normalized_shape=imu_embed_dim),
)
imu_preprocessor = IMUPreprocessor(
img_size=[6, 2000],
num_cls_tokens=1,
kernel_size=8,
embed_dim=imu_embed_dim,
pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
imu_stem=imu_stem,
)
modality_preprocessors = {
ModalityType.VISION: rgbt_preprocessor,
ModalityType.TEXT: text_preprocessor,
ModalityType.AUDIO: audio_preprocessor,
ModalityType.DEPTH: depth_preprocessor,
ModalityType.THERMAL: thermal_preprocessor,
ModalityType.IMU: imu_preprocessor,
}
return nn.ModuleDict(modality_preprocessors)
def _create_modality_trunks(
self,
vision_embed_dim=1024,
vision_num_blocks=24,
vision_num_heads=16,
text_embed_dim=768,
text_num_blocks=12,
text_num_heads=12,
audio_embed_dim=768,
audio_num_blocks=12,
audio_num_heads=12,
audio_drop_path=0.0,
depth_embed_dim=768,
depth_num_blocks=12,
depth_num_heads=12,
depth_drop_path=0.0,
thermal_embed_dim=768,
thermal_num_blocks=12,
thermal_num_heads=12,
thermal_drop_path=0.0,
imu_embed_dim=512,
imu_num_blocks=6,
imu_num_heads=8,
imu_drop_path=0.7,
):
def instantiate_trunk(
embed_dim, num_blocks, num_heads, pre_transformer_ln, add_bias_kv, drop_path
):
return SimpleTransformer(
embed_dim=embed_dim,
num_blocks=num_blocks,
ffn_dropout_rate=0.0,
drop_path_rate=drop_path,
attn_target=partial(
MultiheadAttention,
embed_dim=embed_dim,
num_heads=num_heads,
bias=True,
add_bias_kv=add_bias_kv,
),
pre_transformer_layer=nn.Sequential(
nn.LayerNorm(embed_dim, eps=1e-6)
if pre_transformer_ln
else nn.Identity(),
EinOpsRearrange("b l d -> l b d"),
),
post_transformer_layer=EinOpsRearrange("l b d -> b l d"),
)
modality_trunks = {}
modality_trunks[ModalityType.VISION] = instantiate_trunk(
vision_embed_dim,
vision_num_blocks,
vision_num_heads,
pre_transformer_ln=True,
add_bias_kv=False,
drop_path=0.0,
)
modality_trunks[ModalityType.TEXT] = instantiate_trunk(
text_embed_dim,
text_num_blocks,
text_num_heads,
pre_transformer_ln=False,
add_bias_kv=False,
drop_path=0.0,
)
modality_trunks[ModalityType.AUDIO] = instantiate_trunk(
audio_embed_dim,
audio_num_blocks,
audio_num_heads,
pre_transformer_ln=False,
add_bias_kv=True,
drop_path=audio_drop_path,
)
modality_trunks[ModalityType.DEPTH] = instantiate_trunk(
depth_embed_dim,
depth_num_blocks,
depth_num_heads,
pre_transformer_ln=False,
add_bias_kv=True,
drop_path=depth_drop_path,
)
modality_trunks[ModalityType.THERMAL] = instantiate_trunk(
thermal_embed_dim,
thermal_num_blocks,
thermal_num_heads,
pre_transformer_ln=False,
add_bias_kv=True,
drop_path=thermal_drop_path,
)
modality_trunks[ModalityType.IMU] = instantiate_trunk(
imu_embed_dim,
imu_num_blocks,
imu_num_heads,
pre_transformer_ln=False,
add_bias_kv=True,
drop_path=imu_drop_path,
)
return nn.ModuleDict(modality_trunks)
def _create_modality_heads(
self,
out_embed_dim,
vision_embed_dim,
text_embed_dim,
audio_embed_dim,
depth_embed_dim,
thermal_embed_dim,
imu_embed_dim,
):
modality_heads = {}
modality_heads[ModalityType.VISION] = nn.Sequential(
nn.LayerNorm(normalized_shape=vision_embed_dim, eps=1e-6),
SelectElement(index=0),
nn.Linear(vision_embed_dim, out_embed_dim, bias=False),
)
modality_heads[ModalityType.TEXT] = SelectEOSAndProject(
proj=nn.Sequential(
nn.LayerNorm(normalized_shape=text_embed_dim, eps=1e-6),
nn.Linear(text_embed_dim, out_embed_dim, bias=False),
)
)
modality_heads[ModalityType.AUDIO] = nn.Sequential(
nn.LayerNorm(normalized_shape=audio_embed_dim, eps=1e-6),
SelectElement(index=0),
nn.Linear(audio_embed_dim, out_embed_dim, bias=False),
)
modality_heads[ModalityType.DEPTH] = nn.Sequential(
nn.LayerNorm(normalized_shape=depth_embed_dim, eps=1e-6),
SelectElement(index=0),
nn.Linear(depth_embed_dim, out_embed_dim, bias=False),
)
modality_heads[ModalityType.THERMAL] = nn.Sequential(
nn.LayerNorm(normalized_shape=thermal_embed_dim, eps=1e-6),
SelectElement(index=0),
nn.Linear(thermal_embed_dim, out_embed_dim, bias=False),
)
modality_heads[ModalityType.IMU] = nn.Sequential(
nn.LayerNorm(normalized_shape=imu_embed_dim, eps=1e-6),
SelectElement(index=0),
nn.Dropout(p=0.5),
nn.Linear(imu_embed_dim, out_embed_dim, bias=False),
)
return nn.ModuleDict(modality_heads)
def _create_modality_postprocessors(self, out_embed_dim):
modality_postprocessors = {}
modality_postprocessors[ModalityType.VISION] = Normalize(dim=-1)
modality_postprocessors[ModalityType.TEXT] = nn.Sequential(
Normalize(dim=-1), LearnableLogitScaling(learnable=True)
)
modality_postprocessors[ModalityType.AUDIO] = nn.Sequential(
Normalize(dim=-1),
LearnableLogitScaling(logit_scale_init=20.0, learnable=False),
)
modality_postprocessors[ModalityType.DEPTH] = nn.Sequential(
Normalize(dim=-1),
LearnableLogitScaling(logit_scale_init=5.0, learnable=False),
)
modality_postprocessors[ModalityType.THERMAL] = nn.Sequential(
Normalize(dim=-1),
LearnableLogitScaling(logit_scale_init=10.0, learnable=False),
)
modality_postprocessors[ModalityType.IMU] = nn.Sequential(
Normalize(dim=-1),
LearnableLogitScaling(logit_scale_init=5.0, learnable=False),
)
return nn.ModuleDict(modality_postprocessors)
def forward(self, inputs):
outputs = {}
for modality_key, modality_value in inputs.items():
reduce_list = (
modality_value.ndim >= 5
) # Audio and Video inputs consist of multiple clips
if reduce_list:
B, S = modality_value.shape[:2]
modality_value = modality_value.reshape(
B * S, *modality_value.shape[2:]
)
if modality_value is not None:
modality_value = self.modality_preprocessors[modality_key](
**{modality_key: modality_value}
)
trunk_inputs = modality_value["trunk"]
head_inputs = modality_value["head"]
modality_value = self.modality_trunks[modality_key](**trunk_inputs)
modality_value = self.modality_heads[modality_key](
modality_value, **head_inputs
)
modality_value = self.modality_postprocessors[modality_key](
modality_value
)
if reduce_list:
modality_value = modality_value.reshape(B, S, -1)
modality_value = modality_value.mean(dim=1)
outputs[modality_key] = modality_value
return outputs
def imagebind_huge(pretrained=False):
model = ImageBindModel(
vision_embed_dim=1280,
vision_num_blocks=32,
vision_num_heads=16,
text_embed_dim=1024,
text_num_blocks=24,
text_num_heads=16,
out_embed_dim=1024,
audio_drop_path=0.1,
imu_drop_path=0.7,
)
if pretrained:
if not os.path.exists(".checkpoints/imagebind_huge.pth"):
print(
"Downloading imagebind weights to .checkpoints/imagebind_huge.pth ..."
)
os.makedirs(".checkpoints", exist_ok=True)
torch.hub.download_url_to_file(
"https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth",
".checkpoints/imagebind_huge.pth",
progress=True,
)
model.load_state_dict(torch.load(".checkpoints/imagebind_huge.pth"))
return model