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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 gzip
import html
import io
import math
from functools import lru_cache
from typing import Callable, List, Optional, Tuple
import ftfy
import numpy as np
import regex as re
import torch
import torch.nn as nn
from iopath.common.file_io import g_pathmgr
from timm.models.layers import trunc_normal_
from models.helpers import VerboseNNModule, cast_if_src_dtype
def get_sinusoid_encoding_table(n_position, d_hid):
"""Sinusoid position encoding table"""
# TODO: make it with torch instead of numpy
def get_position_angle_vec(position):
return [
position / np.power(10000, 2 * (hid_j // 2) / d_hid)
for hid_j in range(d_hid)
]
sinusoid_table = np.array(
[get_position_angle_vec(pos_i) for pos_i in range(n_position)]
)
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
return torch.FloatTensor(sinusoid_table).unsqueeze(0)
def interpolate_pos_encoding_2d(target_spatial_size, pos_embed):
N = pos_embed.shape[1]
if N == target_spatial_size:
return pos_embed
dim = pos_embed.shape[-1]
# nn.functional.interpolate doesn't work with bfloat16 so we cast to float32
pos_embed, updated = cast_if_src_dtype(pos_embed, torch.bfloat16, torch.float32)
pos_embed = nn.functional.interpolate(
pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(
0, 3, 1, 2
),
scale_factor=math.sqrt(target_spatial_size / N),
mode="bicubic",
)
if updated:
pos_embed, _ = cast_if_src_dtype(pos_embed, torch.float32, torch.bfloat16)
pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return pos_embed
def interpolate_pos_encoding(
npatch_per_img,
pos_embed,
patches_layout,
input_shape=None,
first_patch_idx=1,
):
assert first_patch_idx == 0 or first_patch_idx == 1, "there is 1 CLS token or none"
N = pos_embed.shape[1] - first_patch_idx # since it's 1 if cls_token exists
if npatch_per_img == N:
return pos_embed
assert (
patches_layout[-1] == patches_layout[-2]
), "Interpolation of pos embed not supported for non-square layouts"
class_emb = pos_embed[:, :first_patch_idx]
pos_embed = pos_embed[:, first_patch_idx:]
if input_shape is None or patches_layout[0] == 1:
# simple 2D pos embedding, no temporal component
pos_embed = interpolate_pos_encoding_2d(npatch_per_img, pos_embed)
elif patches_layout[0] > 1:
# pos embed has a temporal component
assert len(input_shape) == 4, "temporal interpolation not supported"
# we only support 2D interpolation in this case
num_frames = patches_layout[0]
num_spatial_tokens = patches_layout[1] * patches_layout[2]
pos_embed = pos_embed.view(1, num_frames, num_spatial_tokens, -1)
# interpolate embedding for zeroth frame
pos_embed = interpolate_pos_encoding_2d(
npatch_per_img, pos_embed[0, 0, ...].unsqueeze(0)
)
else:
raise ValueError("This type of interpolation isn't implemented")
return torch.cat((class_emb, pos_embed), dim=1)
def _get_pos_embedding(
npatch_per_img,
pos_embed,
patches_layout,
input_shape,
first_patch_idx=1,
):
pos_embed = interpolate_pos_encoding(
npatch_per_img,
pos_embed,
patches_layout,
input_shape=input_shape,
first_patch_idx=first_patch_idx,
)
return pos_embed
class PatchEmbedGeneric(nn.Module):
"""
PatchEmbed from Hydra
"""
def __init__(self, proj_stem, norm_layer: Optional[nn.Module] = None):
super().__init__()
if len(proj_stem) > 1:
self.proj = nn.Sequential(*proj_stem)
else:
# Special case to be able to load pre-trained models that were
# trained with a standard stem
self.proj = proj_stem[0]
self.norm_layer = norm_layer
def get_patch_layout(self, img_size):
with torch.no_grad():
dummy_img = torch.zeros(
[
1,
]
+ img_size
)
dummy_out = self.proj(dummy_img)
embed_dim = dummy_out.shape[1]
patches_layout = tuple(dummy_out.shape[2:])
num_patches = np.prod(patches_layout)
return patches_layout, num_patches, embed_dim
def forward(self, x):
x = self.proj(x)
# B C (T) H W -> B (T)HW C
x = x.flatten(2).transpose(1, 2)
if self.norm_layer is not None:
x = self.norm_layer(x)
return x
class SpatioTemporalPosEmbeddingHelper(VerboseNNModule):
def __init__(
self,
patches_layout: List,
num_patches: int,
num_cls_tokens: int,
embed_dim: int,
learnable: bool,
) -> None:
super().__init__()
self.num_cls_tokens = num_cls_tokens
self.patches_layout = patches_layout
self.num_patches = num_patches
self.num_tokens = num_cls_tokens + num_patches
self.learnable = learnable
if self.learnable:
self.pos_embed = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim))
trunc_normal_(self.pos_embed, std=0.02)
else:
self.register_buffer(
"pos_embed", get_sinusoid_encoding_table(self.num_tokens, embed_dim)
)
def get_pos_embedding(self, vision_input, all_vision_tokens):
input_shape = vision_input.shape
pos_embed = _get_pos_embedding(
all_vision_tokens.size(1) - self.num_cls_tokens,
pos_embed=self.pos_embed,
patches_layout=self.patches_layout,
input_shape=input_shape,
first_patch_idx=self.num_cls_tokens,
)
return pos_embed
class RGBDTPreprocessor(VerboseNNModule):
def __init__(
self,
rgbt_stem: PatchEmbedGeneric,
depth_stem: Optional[PatchEmbedGeneric],
img_size: Tuple = (3, 224, 224),
num_cls_tokens: int = 1,
pos_embed_fn: Optional[Callable] = None,
use_type_embed: bool = False,
init_param_style: str = "openclip",
) -> None:
super().__init__()
stem = rgbt_stem if rgbt_stem is not None else depth_stem
(
self.patches_layout,
self.num_patches,
self.embed_dim,
) = stem.get_patch_layout(img_size)
self.rgbt_stem = rgbt_stem
self.depth_stem = depth_stem
self.use_pos_embed = pos_embed_fn is not None
self.use_type_embed = use_type_embed
self.num_cls_tokens = num_cls_tokens
if self.use_pos_embed:
self.pos_embedding_helper = pos_embed_fn(
patches_layout=self.patches_layout,
num_cls_tokens=num_cls_tokens,
num_patches=self.num_patches,
embed_dim=self.embed_dim,
)
if self.num_cls_tokens > 0:
self.cls_token = nn.Parameter(
torch.zeros(1, self.num_cls_tokens, self.embed_dim)
)
if self.use_type_embed:
self.type_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
self.init_parameters(init_param_style)
@torch.no_grad()
def init_parameters(self, init_param_style):
if init_param_style == "openclip":
# OpenCLIP style initialization
scale = self.embed_dim**-0.5
if self.use_pos_embed:
nn.init.normal_(self.pos_embedding_helper.pos_embed)
self.pos_embedding_helper.pos_embed *= scale
if self.num_cls_tokens > 0:
nn.init.normal_(self.cls_token)
self.cls_token *= scale
elif init_param_style == "vit":
self.cls_token.data.fill_(0)
else:
raise ValueError(f"Unknown init {init_param_style}")
if self.use_type_embed:
nn.init.normal_(self.type_embed)
def tokenize_input_and_cls_pos(self, input, stem, mask):
# tokens is of shape B x L x D
tokens = stem(input)
assert tokens.ndim == 3
assert tokens.shape[2] == self.embed_dim
B = tokens.shape[0]
if self.num_cls_tokens > 0:
class_tokens = self.cls_token.expand(
B, -1, -1
) # stole class_tokens impl from Phil Wang, thanks
tokens = torch.cat((class_tokens, tokens), dim=1)
if self.use_pos_embed:
pos_embed = self.pos_embedding_helper.get_pos_embedding(input, tokens)
tokens = tokens + pos_embed
if self.use_type_embed:
tokens = tokens + self.type_embed.expand(B, -1, -1)
return tokens
def forward(self, vision=None, depth=None, patch_mask=None):
if patch_mask is not None:
raise NotImplementedError()
if vision is not None:
vision_tokens = self.tokenize_input_and_cls_pos(
vision, self.rgbt_stem, patch_mask
)
if depth is not None:
depth_tokens = self.tokenize_input_and_cls_pos(
depth, self.depth_stem, patch_mask
)
# aggregate tokens
if vision is not None and depth is not None:
final_tokens = vision_tokens + depth_tokens
else:
final_tokens = vision_tokens if vision is not None else depth_tokens
return_dict = {
"trunk": {
"tokens": final_tokens,
},
"head": {},
}
return return_dict
class AudioPreprocessor(RGBDTPreprocessor):
def __init__(self, audio_stem: PatchEmbedGeneric, **kwargs) -> None:
super().__init__(rgbt_stem=audio_stem, depth_stem=None, **kwargs)
def forward(self, audio=None):
return super().forward(vision=audio)
class ThermalPreprocessor(RGBDTPreprocessor):
def __init__(self, thermal_stem: PatchEmbedGeneric, **kwargs) -> None:
super().__init__(rgbt_stem=thermal_stem, depth_stem=None, **kwargs)
def forward(self, thermal=None):
return super().forward(vision=thermal)
def build_causal_attention_mask(context_length):
# lazily create causal attention mask, with full attention between the vision tokens
# pytorch uses additive attention mask; fill with -inf
mask = torch.empty(context_length, context_length, requires_grad=False)
mask.fill_(float("-inf"))
mask.triu_(1) # zero out the lower diagonal
return mask
class TextPreprocessor(VerboseNNModule):
def __init__(
self,
vocab_size: int,
context_length: int,
embed_dim: int,
causal_masking: bool,
supply_seq_len_to_head: bool = True,
num_cls_tokens: int = 0,
init_param_style: str = "openclip",
) -> None:
super().__init__()
self.vocab_size = vocab_size
self.context_length = context_length
self.token_embedding = nn.Embedding(vocab_size, embed_dim)
self.pos_embed = nn.Parameter(
torch.empty(1, self.context_length + num_cls_tokens, embed_dim)
)
self.causal_masking = causal_masking
if self.causal_masking:
mask = build_causal_attention_mask(self.context_length)
# register the mask as a buffer so it can be moved to the right device
self.register_buffer("mask", mask)
self.supply_seq_len_to_head = supply_seq_len_to_head
self.num_cls_tokens = num_cls_tokens
self.embed_dim = embed_dim
if num_cls_tokens > 0:
assert self.causal_masking is False, "Masking + CLS token isn't implemented"
self.cls_token = nn.Parameter(
torch.zeros(1, self.num_cls_tokens, embed_dim)
)
self.init_parameters(init_param_style)
@torch.no_grad()
def init_parameters(self, init_param_style="openclip"):
# OpenCLIP style initialization
nn.init.normal_(self.token_embedding.weight, std=0.02)
nn.init.normal_(self.pos_embed, std=0.01)
if init_param_style == "openclip":
# OpenCLIP style initialization
scale = self.embed_dim**-0.5
if self.num_cls_tokens > 0:
nn.init.normal_(self.cls_token)
self.cls_token *= scale
elif init_param_style == "vit":
self.cls_token.data.fill_(0)
else:
raise ValueError(f"Unknown init {init_param_style}")
def forward(self, text):
# text tokens are of shape B x L x D
text_tokens = self.token_embedding(text)
# concat CLS tokens if any
if self.num_cls_tokens > 0:
B = text_tokens.shape[0]
class_tokens = self.cls_token.expand(
B, -1, -1
) # stole class_tokens impl from Phil Wang, thanks
text_tokens = torch.cat((class_tokens, text_tokens), dim=1)
text_tokens = text_tokens + self.pos_embed
return_dict = {
"trunk": {
"tokens": text_tokens,
},
"head": {},
}
# Compute sequence length after adding CLS tokens
if self.supply_seq_len_to_head:
text_lengths = text.argmax(dim=-1)
return_dict["head"] = {
"seq_len": text_lengths,
}
if self.causal_masking:
return_dict["trunk"].update({"attn_mask": self.mask})
return return_dict
class Im2Video(nn.Module):
"""Convert an image into a trivial video."""
def __init__(self, time_dim=2):
super().__init__()
self.time_dim = time_dim
def forward(self, x):
if x.ndim == 4:
# B, C, H, W -> B, C, T, H, W
return x.unsqueeze(self.time_dim)
elif x.ndim == 5:
return x
else:
raise ValueError(f"Dimension incorrect {x.shape}")
class PadIm2Video(Im2Video):
def __init__(self, ntimes, pad_type, time_dim=2):
super().__init__(time_dim=time_dim)
assert ntimes > 0
assert pad_type in ["zero", "repeat"]
self.ntimes = ntimes
self.pad_type = pad_type
def forward(self, x):
x = super().forward(x)
if x.shape[self.time_dim] == 1:
if self.pad_type == "repeat":
new_shape = [1] * len(x.shape)
new_shape[self.time_dim] = self.ntimes
x = x.repeat(new_shape)
elif self.pad_type == "zero":
padarg = [0, 0] * len(x.shape)
padarg[2 * self.time_dim + 1] = self.ntimes - x.shape[self.time_dim]
x = nn.functional.pad(x, padarg)
return x
# Modified from github.com/openai/CLIP
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = (
list(range(ord("!"), ord("~") + 1))
+ list(range(ord("¡"), ord("¬") + 1))
+ list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def get_pairs(word):
"""Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
def basic_clean(text):
text = ftfy.fix_text(text)
text = html.unescape(html.unescape(text))
return text.strip()
def whitespace_clean(text):
text = re.sub(r"\s+", " ", text)
text = text.strip()
return text
class SimpleTokenizer(object):
def __init__(self, bpe_path: str, context_length=77):
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
with g_pathmgr.open(bpe_path, "rb") as fh:
bpe_bytes = io.BytesIO(fh.read())
merges: List[str] = gzip.open(bpe_bytes).read().decode("utf-8").split("\n")
merges = merges[1 : 49152 - 256 - 2 + 1]
merges: List[Tuple[str, ...]] = [tuple(merge.split()) for merge in merges]
vocab = list(bytes_to_unicode().values())
vocab = vocab + [v + "</w>" for v in vocab]
for merge in merges:
vocab.append("".join(merge))
vocab.extend(["<|startoftext|>", "<|endoftext|>"])
self.encoder = dict(zip(vocab, range(len(vocab))))
self.decoder = {v: k for k, v in self.encoder.items()}
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {
"<|startoftext|>": "<|startoftext|>",
"<|endoftext|>": "<|endoftext|>",
}
self.pat = re.compile(
r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
re.IGNORECASE,
)
self.context_length = context_length
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token[:-1]) + (token[-1] + "</w>",)
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except:
new_word.extend(word[i:])
break
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = " ".join(word)
self.cache[token] = word
return word
def encode(self, text):
bpe_tokens = []
text = whitespace_clean(basic_clean(text)).lower()
for token in re.findall(self.pat, text):
token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
bpe_tokens.extend(
self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
)
return bpe_tokens
def decode(self, tokens):
text = "".join([self.decoder[token] for token in tokens])
text = (
bytearray([self.byte_decoder[c] for c in text])
.decode("utf-8", errors="replace")
.replace("</w>", " ")
)
return text
def __call__(self, texts, context_length=None):
if not context_length:
context_length = self.context_length
if isinstance(texts, str):
texts = [texts]
sot_token = self.encoder["<|startoftext|>"]
eot_token = self.encoder["<|endoftext|>"]
all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts]
result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
for i, tokens in enumerate(all_tokens):
tokens = tokens[:context_length]
result[i, : len(tokens)] = torch.tensor(tokens)
if len(result) == 1:
return result[0]
return result
class IMUPreprocessor(VerboseNNModule):
def __init__(
self,
kernel_size: int,
imu_stem: PatchEmbedGeneric,
embed_dim: int,
img_size: Tuple = (6, 2000),
num_cls_tokens: int = 1,
pos_embed_fn: Optional[Callable] = None,
init_param_style: str = "openclip",
) -> None:
super().__init__()
self.imu_stem = imu_stem
self.embed_dim = embed_dim
self.use_pos_embed = pos_embed_fn is not None
self.num_cls_tokens = num_cls_tokens
self.kernel_size = kernel_size
self.pos_embed = nn.Parameter(
torch.empty(1, (img_size[1] // kernel_size) + num_cls_tokens, embed_dim)
)
if self.num_cls_tokens > 0:
self.cls_token = nn.Parameter(
torch.zeros(1, self.num_cls_tokens, self.embed_dim)
)
self.init_parameters(init_param_style)
@torch.no_grad()
def init_parameters(self, init_param_style):
nn.init.normal_(self.pos_embed, std=0.01)
if init_param_style == "openclip":
# OpenCLIP style initialization
scale = self.embed_dim**-0.5
if self.num_cls_tokens > 0:
nn.init.normal_(self.cls_token)
self.cls_token *= scale
elif init_param_style == "vit":
self.cls_token.data.fill_(0)
else:
raise ValueError(f"Unknown init {init_param_style}")
def tokenize_input_and_cls_pos(self, input, stem):
# tokens is of shape B x L x D
tokens = stem.norm_layer(stem.proj(input))
assert tokens.ndim == 3
assert tokens.shape[2] == self.embed_dim
B = tokens.shape[0]
if self.num_cls_tokens > 0:
class_tokens = self.cls_token.expand(
B, -1, -1
) # stole class_tokens impl from Phil Wang, thanks
tokens = torch.cat((class_tokens, tokens), dim=1)
if self.use_pos_embed:
tokens = tokens + self.pos_embed
return tokens
def forward(self, imu):
# Patchify
imu = imu.unfold(
-1,
self.kernel_size,
self.kernel_size,
).permute(0, 2, 1, 3)
imu = imu.reshape(imu.size(0), imu.size(1), -1)
imu_tokens = self.tokenize_input_and_cls_pos(
imu,
self.imu_stem,
)
return_dict = {
"trunk": {
"tokens": imu_tokens,
},
"head": {},
}
return return_dict
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