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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import torch
def round_width(width, multiplier, min_width=1, divisor=1, verbose=False):
if not multiplier:
return width
width *= multiplier
min_width = min_width or divisor
if verbose:
print(f"min width {min_width}")
print(f"width {width} divisor {divisor}")
print(f"other {int(width + divisor / 2) // divisor * divisor}")
width_out = max(min_width, int(width + divisor / 2) // divisor * divisor)
if width_out < 0.9 * width:
width_out += divisor
return int(width_out)
def validate_checkpoint_wrapper_import(checkpoint_wrapper):
"""
Check if checkpoint_wrapper is imported.
"""
if checkpoint_wrapper is None:
raise ImportError("Please install fairscale.")
def get_gkern(kernlen, std):
"""Returns a 2D Gaussian kernel array."""
def _gaussian_fn(kernlen, std):
n = torch.arange(0, kernlen).float()
n -= n.mean()
n /= std
w = torch.exp(-0.5 * n**2)
return w
gkern1d = _gaussian_fn(kernlen, std)
gkern2d = torch.outer(gkern1d, gkern1d)
return gkern2d / gkern2d.sum()
# --------------------------------------------------------
# 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_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
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_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.float)
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
# --------------------------------------------------------
# Interpolate position embeddings for high-resolution
# References:
# DeiT: https://github.com/facebookresearch/deit
# --------------------------------------------------------
def interpolate_pos_embed(model, checkpoint_model):
if "pos_embed" in checkpoint_model:
pos_embed_checkpoint = checkpoint_model["pos_embed"]
embedding_size = pos_embed_checkpoint.shape[-1]
num_patches = model.patch_embed.num_patches
num_extra_tokens = model.pos_embed.shape[-2] - num_patches
# height (== width) for the checkpoint position embedding
orig_size = int(
(pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5
)
# height (== width) for the new position embedding
new_size = int(num_patches**0.5)
# class_token and dist_token are kept unchanged
if orig_size != new_size:
print(
"Position interpolate from %dx%d to %dx%d"
% (orig_size, orig_size, new_size, new_size)
)
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
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,
)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model["pos_embed"] = new_pos_embed
def calc_mvit_feature_geometry(cfg):
feat_size = [
[
cfg.DATA.NUM_FRAMES // cfg.MVIT.PATCH_STRIDE[0]
if len(cfg.MVIT.PATCH_STRIDE) > 2
else 1,
cfg.DATA.TRAIN_CROP_SIZE // cfg.MVIT.PATCH_STRIDE[-2],
cfg.DATA.TRAIN_CROP_SIZE // cfg.MVIT.PATCH_STRIDE[-1],
]
for i in range(cfg.MVIT.DEPTH)
]
feat_stride = [
[
cfg.MVIT.PATCH_STRIDE[0] if len(cfg.MVIT.PATCH_STRIDE) > 2 else 1,
cfg.MVIT.PATCH_STRIDE[-2],
cfg.MVIT.PATCH_STRIDE[-1],
]
for i in range(cfg.MVIT.DEPTH)
]
for _, x in enumerate(cfg.MVIT.POOL_Q_STRIDE):
for i in range(cfg.MVIT.DEPTH):
if i >= x[0]:
for j in range(len(feat_size[i])):
feat_size[i][j] = feat_size[i][j] // x[j + 1]
feat_stride[i][j] = feat_stride[i][j] * x[j + 1]
return feat_size, feat_stride |