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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from einops import rearrange
import torch
from torch.nn import functional as F
import numpy as np
from diffusers.models.embeddings import get_2d_sincos_pos_embed_from_grid
# ref diffusers.models.embeddings.get_2d_sincos_pos_embed
def get_2d_sincos_pos_embed(
embed_dim,
grid_size_w,
grid_size_h,
cls_token=False,
extra_tokens=0,
norm_length: bool = False,
max_length: float = 2048,
):
"""
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)
"""
if norm_length and grid_size_h <= max_length and grid_size_w <= max_length:
grid_h = np.linspace(0, max_length, grid_size_h)
grid_w = np.linspace(0, max_length, grid_size_w)
else:
grid_h = np.arange(grid_size_h, dtype=np.float32)
grid_w = np.arange(grid_size_w, dtype=np.float32)
grid = np.meshgrid(grid_h, grid_w) # here h goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size_h, grid_size_w])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate(
[np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0
)
return pos_embed
def resize_spatial_position_emb(
emb: torch.Tensor,
height: int,
width: int,
scale: float = None,
target_height: int = None,
target_width: int = None,
) -> torch.Tensor:
"""_summary_
Args:
emb (torch.Tensor): b ( h w) d
height (int): _description_
width (int): _description_
scale (float, optional): _description_. Defaults to None.
target_height (int, optional): _description_. Defaults to None.
target_width (int, optional): _description_. Defaults to None.
Returns:
torch.Tensor: b (target_height target_width) d
"""
if scale is not None:
target_height = int(height * scale)
target_width = int(width * scale)
emb = rearrange(emb, "(h w) (b d) ->b d h w", h=height, b=1)
emb = F.interpolate(
emb,
size=(target_height, target_width),
mode="bicubic",
align_corners=False,
)
emb = rearrange(emb, "b d h w-> (h w) (b d)")
return emb
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