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import numpy as np
import torch
import logging
logger = logging.getLogger(__name__)
# --------------------------------------------------------
# 3D sine-cosine position embedding
# References:
# MVD: https://github.com/ruiwang2021/mvd/blob/main/modeling_finetune.py
# --------------------------------------------------------
def get_3d_sincos_pos_embed(embed_dim, grid_size, t_size, cls_token=False):
"""
grid_size: int of the grid height and width
t_size: int of the temporal size
return:
pos_embed: [t_size*grid_size*grid_size, embed_dim] or [1+t_size*grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
assert embed_dim % 4 == 0
embed_dim_spatial = embed_dim // 4 * 3
embed_dim_temporal = embed_dim // 4
# spatial
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed_spatial = get_2d_sincos_pos_embed_from_grid(
embed_dim_spatial, grid
)
# temporal
grid_t = np.arange(t_size, dtype=np.float32)
pos_embed_temporal = get_1d_sincos_pos_embed_from_grid(
embed_dim_temporal, grid_t
)
# concate: [T, H, W] order
pos_embed_temporal = pos_embed_temporal[:, np.newaxis, :]
pos_embed_temporal = np.repeat(
pos_embed_temporal, grid_size**2, axis=1
) # [T, H*W, D // 4]
pos_embed_spatial = pos_embed_spatial[np.newaxis, :, :]
pos_embed_spatial = np.repeat(
pos_embed_spatial, t_size, axis=0
) # [T, H*W, D // 4 * 3]
pos_embed = np.concatenate([pos_embed_temporal, pos_embed_spatial], axis=-1)
pos_embed = pos_embed.reshape([-1, embed_dim]) # [T*H*W, D]
if cls_token:
pos_embed = np.concatenate(
[np.zeros([1, embed_dim]), pos_embed], axis=0
)
return pos_embed
# --------------------------------------------------------
# 2D sine-cosine position embedding
# References:
# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py
# MoCo v3: https://github.com/facebookresearch/moco-v3
# --------------------------------------------------------
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token:
pos_embed = np.concatenate(
[np.zeros([1, embed_dim]), pos_embed], axis=0
)
return pos_embed
def get_1d_sincos_pos_embed(embed_dim, t_size, cls_token=False):
"""
t_size: int of the temporal size
return:
pos_embed: [t_size, embed_dim] or [1+t_size, embed_dim] (w/ or w/o cls_token)
"""
grid_t = np.arange(t_size, dtype=np.float32)
pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, grid_t)
if cls_token:
pos_embed = np.concatenate(
[np.zeros([1, embed_dim]), pos_embed], axis=0
)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(
embed_dim // 2, grid[0]
) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(
embed_dim // 2, grid[1]
) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float32)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
def interpolate_pos_embed(checkpoint_model, model, orig_t_size=4, pos_name='vision_encoder.pos_embed'):
if pos_name in checkpoint_model:
pos_embed_checkpoint = checkpoint_model[pos_name]
embedding_size = pos_embed_checkpoint.shape[-1] # channel dim
num_patches = model.patch_embed.num_patches #
num_extra_tokens = model.pos_embed.shape[-2] - num_patches # 0/1
# we use 4 frames for pretraining
new_t_size = model.T
# height (== width) for the checkpoint position embedding
orig_size = int(((pos_embed_checkpoint.shape[-2] - num_extra_tokens)//(orig_t_size)) ** 0.5)
# height (== width) for the new position embedding
new_size = int((num_patches // (new_t_size))** 0.5)
# class_token and dist_token are kept unchanged
if orig_t_size != new_t_size:
logger.info(f"Temporal interpolate from {orig_t_size} to {new_t_size} ({pos_name})")
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
# B, L, C -> B， T, HW, C -> BHW, C, T (B = 1)
pos_tokens = pos_tokens.view(1, orig_t_size, -1, embedding_size)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, embedding_size, orig_t_size)
pos_tokens = torch.nn.functional.interpolate(pos_tokens, size=new_t_size, mode='linear')
pos_tokens = pos_tokens.view(1, -1, embedding_size, new_t_size)
pos_tokens = pos_tokens.permute(0, 3, 1, 2).reshape(1, -1, embedding_size)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model[pos_name] = new_pos_embed
pos_embed_checkpoint = new_pos_embed
# class_token and dist_token are kept unchanged
if orig_size != new_size:
logger.info(f"Position interpolate from {orig_size}x{orig_size} to {new_size}x{new_size} ({pos_name})")
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
# B, L, C -> BT, H, W, C -> BT, C, H, W
pos_tokens = pos_tokens.reshape(-1, new_t_size, orig_size, orig_size, embedding_size)
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
# BT, C, H, W -> BT, H, W, C -> B, T, H, W, C
pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, new_t_size, new_size, new_size, embedding_size)
pos_tokens = pos_tokens.flatten(1, 3) # B, L, C
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model[pos_name] = new_pos_embed
def interpolate_pos_embed_internvideo2(checkpoint_model, model, orig_t_size = 8):
# interpolate position embedding
for pos_name in ['pos_embed', 'clip_pos_embed']:
if pos_name in checkpoint_model:
pos_embed_checkpoint = checkpoint_model[pos_name]
embedding_size = pos_embed_checkpoint.shape[-1] # channel dim
num_patches = model.patch_embed.num_patches #
num_extra_tokens = model.pos_embed.shape[-2] - num_patches # 0/1
# we use 8 frames for pretraining
# new_t_size = args.num_frames * args.num_segments // model.patch_embed.tubelet_size
new_t_size = model.num_frames // model.tubelet_size
# height (== width) for the checkpoint position embedding
orig_size = int(((pos_embed_checkpoint.shape[-2] - num_extra_tokens)//(orig_t_size)) ** 0.5)
# height (== width) for the new position embedding
new_size = int((num_patches // (new_t_size))** 0.5)
# class_token and dist_token are kept unchanged
if orig_t_size != new_t_size:
logger.info(f"Temporal interpolate from {orig_t_size} to {new_t_size} ({pos_name})")
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
# B, L, C -> B， T, HW, C -> BHW, C, T (B = 1)
pos_tokens = pos_tokens.view(1, orig_t_size, -1, embedding_size)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, embedding_size, orig_t_size)
pos_tokens = torch.nn.functional.interpolate(pos_tokens, size=new_t_size, mode='linear')
pos_tokens = pos_tokens.view(1, -1, embedding_size, new_t_size)
pos_tokens = pos_tokens.permute(0, 3, 1, 2).reshape(1, -1, embedding_size)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model[pos_name] = new_pos_embed
pos_embed_checkpoint = new_pos_embed
# class_token and dist_token are kept unchanged
if orig_size != new_size:
logger.info(f"Position interpolate from {orig_size}x{orig_size} to {new_size}x{new_size} ({pos_name})")
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
# B, L, C -> BT, H, W, C -> BT, C, H, W
pos_tokens = pos_tokens.reshape(-1, new_t_size, orig_size, orig_size, embedding_size)
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
# BT, C, H, W -> BT, H, W, C -> B, T, H, W, C
pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, new_t_size, new_size, new_size, embedding_size)
pos_tokens = pos_tokens.flatten(1, 3) # B, L, C
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model[pos_name] = new_pos_embed
if 'pos_embed_spatial' in checkpoint_model or 'pos_embed_temporal' in checkpoint_model:
raise NotImplementedError
def interpolate_pos_embed_internvideo2_new(checkpoint_model, model, orig_t_size = 8):
pos_names = []
for k in checkpoint_model.keys():
if ('pos_embed' in k or 'clip_pos_embed' in k) and 'img_pos_embed' not in k:
pos_names.append(k)
logger.info(f"pos names list for interpolating: {pos_names}")
assert len(pos_names) > 0, checkpoint_model.keys()
if 'pos_embed_spatial' in checkpoint_model.keys() or 'pos_embed_temporal' in checkpoint_model.keys():
raise NotImplementedError
# interpolate position embedding
for pos_name in pos_names:
pos_embed_checkpoint = checkpoint_model[pos_name]
embedding_size = pos_embed_checkpoint.shape[-1] # channel dim
num_patches = model.patch_embed.num_patches #
num_extra_tokens = model.pos_embed.shape[-2] - num_patches # 0/1
# we use 8 frames for pretraining
# new_t_size = args.num_frames * args.num_segments // model.patch_embed.tubelet_size
new_t_size = model.num_frames // model.tubelet_size
# height (== width) for the checkpoint position embedding
orig_size = int(((pos_embed_checkpoint.shape[-2] - num_extra_tokens)//(orig_t_size)) ** 0.5)
# height (== width) for the new position embedding
new_size = int((num_patches // (new_t_size))** 0.5)
# class_token and dist_token are kept unchanged
if orig_t_size != new_t_size:
logger.info(f"Temporal interpolate from {orig_t_size} to {new_t_size} ({pos_name})")
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
# B, L, C -> B， T, HW, C -> BHW, C, T (B = 1)
pos_tokens = pos_tokens.view(1, orig_t_size, -1, embedding_size)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, embedding_size, orig_t_size)
pos_tokens = torch.nn.functional.interpolate(pos_tokens, size=new_t_size, mode='linear')
pos_tokens = pos_tokens.view(1, -1, embedding_size, new_t_size)
pos_tokens = pos_tokens.permute(0, 3, 1, 2).reshape(1, -1, embedding_size)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model[pos_name] = new_pos_embed
pos_embed_checkpoint = new_pos_embed
# class_token and dist_token are kept unchanged
if orig_size != new_size:
logger.info(f"Position interpolate from {orig_size}x{orig_size} to {new_size}x{new_size} ({pos_name})")
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
# B, L, C -> BT, H, W, C -> BT, C, H, W
pos_tokens = pos_tokens.reshape(-1, new_t_size, orig_size, orig_size, embedding_size)
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
# BT, C, H, W -> BT, H, W, C -> B, T, H, W, C
pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, new_t_size, new_size, new_size, embedding_size)
pos_tokens = pos_tokens.flatten(1, 3) # B, L, C
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model[pos_name] = new_pos_embed |