294 lines
12 KiB
Python
294 lines
12 KiB
Python
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from typing import Tuple
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import torch
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import torch.nn.functional as F
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def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num):
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"""
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Selects the closest conditioning frames to a given frame index.
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Args:
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frame_idx (int): Current frame index.
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cond_frame_outputs (Dict[int, Any]): Dictionary of conditioning frame outputs keyed by frame indices.
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max_cond_frame_num (int): Maximum number of conditioning frames to select.
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Returns:
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(Tuple[Dict[int, Any], Dict[int, Any]]): A tuple containing two dictionaries:
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- selected_outputs: Selected items from cond_frame_outputs.
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- unselected_outputs: Items not selected from cond_frame_outputs.
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Examples:
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>>> frame_idx = 5
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>>> cond_frame_outputs = {1: "a", 3: "b", 7: "c", 9: "d"}
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>>> max_cond_frame_num = 2
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>>> selected, unselected = select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num)
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>>> print(selected)
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{3: 'b', 7: 'c'}
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>>> print(unselected)
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{1: 'a', 9: 'd'}
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"""
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if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num:
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selected_outputs = cond_frame_outputs
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unselected_outputs = {}
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else:
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assert max_cond_frame_num >= 2, "we should allow using 2+ conditioning frames"
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selected_outputs = {}
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# the closest conditioning frame before `frame_idx` (if any)
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idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None)
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if idx_before is not None:
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selected_outputs[idx_before] = cond_frame_outputs[idx_before]
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# the closest conditioning frame after `frame_idx` (if any)
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idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None)
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if idx_after is not None:
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selected_outputs[idx_after] = cond_frame_outputs[idx_after]
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# add other temporally closest conditioning frames until reaching a total
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# of `max_cond_frame_num` conditioning frames.
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num_remain = max_cond_frame_num - len(selected_outputs)
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inds_remain = sorted(
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(t for t in cond_frame_outputs if t not in selected_outputs),
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key=lambda x: abs(x - frame_idx),
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)[:num_remain]
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selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain)
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unselected_outputs = {t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs}
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return selected_outputs, unselected_outputs
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def get_1d_sine_pe(pos_inds, dim, temperature=10000):
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"""Generates 1D sinusoidal positional embeddings for given positions and dimensions."""
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pe_dim = dim // 2
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dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device)
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dim_t = temperature ** (2 * (dim_t // 2) / pe_dim)
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pos_embed = pos_inds.unsqueeze(-1) / dim_t
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pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1)
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return pos_embed
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def init_t_xy(end_x: int, end_y: int):
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"""Initializes 1D and 2D coordinate tensors for a grid of specified dimensions."""
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t = torch.arange(end_x * end_y, dtype=torch.float32)
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t_x = (t % end_x).float()
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t_y = torch.div(t, end_x, rounding_mode="floor").float()
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return t_x, t_y
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def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0):
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"""Computes axial complex exponential positional encodings for 2D spatial positions in a grid."""
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freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))
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freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))
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t_x, t_y = init_t_xy(end_x, end_y)
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freqs_x = torch.outer(t_x, freqs_x)
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freqs_y = torch.outer(t_y, freqs_y)
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freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x)
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freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y)
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return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1)
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def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
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"""Reshapes frequency tensor for broadcasting with input tensor, ensuring dimensional compatibility."""
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ndim = x.ndim
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assert 0 <= 1 < ndim
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assert freqs_cis.shape == (x.shape[-2], x.shape[-1])
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shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)]
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return freqs_cis.view(*shape)
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def apply_rotary_enc(
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xq: torch.Tensor,
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xk: torch.Tensor,
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freqs_cis: torch.Tensor,
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repeat_freqs_k: bool = False,
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):
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"""Applies rotary positional encoding to query and key tensors using complex-valued frequency components."""
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xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
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xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) if xk.shape[-2] != 0 else None
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freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
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xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
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if xk_ is None:
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# no keys to rotate, due to dropout
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return xq_out.type_as(xq).to(xq.device), xk
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# repeat freqs along seq_len dim to match k seq_len
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if repeat_freqs_k:
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r = xk_.shape[-2] // xq_.shape[-2]
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freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1)
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xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
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return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device)
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def window_partition(x, window_size):
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"""
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Partitions input tensor into non-overlapping windows with padding if needed.
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Args:
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x (torch.Tensor): Input tensor with shape (B, H, W, C).
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window_size (int): Size of each window.
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Returns:
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(Tuple[torch.Tensor, Tuple[int, int]]): A tuple containing:
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- windows (torch.Tensor): Partitioned windows with shape (B * num_windows, window_size, window_size, C).
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- (Hp, Wp) (Tuple[int, int]): Padded height and width before partition.
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Examples:
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>>> x = torch.randn(1, 16, 16, 3)
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>>> windows, (Hp, Wp) = window_partition(x, window_size=4)
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>>> print(windows.shape, Hp, Wp)
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torch.Size([16, 4, 4, 3]) 16 16
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"""
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B, H, W, C = x.shape
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pad_h = (window_size - H % window_size) % window_size
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pad_w = (window_size - W % window_size) % window_size
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if pad_h > 0 or pad_w > 0:
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x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
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Hp, Wp = H + pad_h, W + pad_w
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x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
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return windows, (Hp, Wp)
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def window_unpartition(windows, window_size, pad_hw, hw):
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"""
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Unpartitions windowed sequences into original sequences and removes padding.
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This function reverses the windowing process, reconstructing the original input from windowed segments
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and removing any padding that was added during the windowing process.
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Args:
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windows (torch.Tensor): Input tensor of windowed sequences with shape (B * num_windows, window_size,
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window_size, C), where B is the batch size, num_windows is the number of windows, window_size is
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the size of each window, and C is the number of channels.
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window_size (int): Size of each window.
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pad_hw (Tuple[int, int]): Padded height and width (Hp, Wp) of the input before windowing.
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hw (Tuple[int, int]): Original height and width (H, W) of the input before padding and windowing.
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Returns:
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(torch.Tensor): Unpartitioned sequences with shape (B, H, W, C), where B is the batch size, H and W
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are the original height and width, and C is the number of channels.
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Examples:
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>>> windows = torch.rand(32, 8, 8, 64) # 32 windows of size 8x8 with 64 channels
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>>> pad_hw = (16, 16) # Padded height and width
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>>> hw = (15, 14) # Original height and width
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>>> x = window_unpartition(windows, window_size=8, pad_hw=pad_hw, hw=hw)
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>>> print(x.shape)
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torch.Size([1, 15, 14, 64])
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"""
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Hp, Wp = pad_hw
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H, W = hw
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B = windows.shape[0] // (Hp * Wp // window_size // window_size)
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x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
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if Hp > H or Wp > W:
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x = x[:, :H, :W, :].contiguous()
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return x
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def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
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"""
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Extracts relative positional embeddings based on query and key sizes.
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Args:
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q_size (int): Size of the query.
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k_size (int): Size of the key.
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rel_pos (torch.Tensor): Relative position embeddings with shape (L, C), where L is the maximum relative
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distance and C is the embedding dimension.
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Returns:
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(torch.Tensor): Extracted positional embeddings according to relative positions, with shape (q_size,
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k_size, C).
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Examples:
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>>> q_size, k_size = 8, 16
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>>> rel_pos = torch.randn(31, 64) # 31 = 2 * max(8, 16) - 1
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>>> extracted_pos = get_rel_pos(q_size, k_size, rel_pos)
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>>> print(extracted_pos.shape)
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torch.Size([8, 16, 64])
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"""
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max_rel_dist = int(2 * max(q_size, k_size) - 1)
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# Interpolate rel pos if needed.
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if rel_pos.shape[0] != max_rel_dist:
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# Interpolate rel pos.
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rel_pos_resized = F.interpolate(
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rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
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size=max_rel_dist,
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mode="linear",
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)
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rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
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else:
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rel_pos_resized = rel_pos
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# Scale the coords with short length if shapes for q and k are different.
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q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
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k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
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relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
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return rel_pos_resized[relative_coords.long()]
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def add_decomposed_rel_pos(
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attn: torch.Tensor,
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q: torch.Tensor,
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rel_pos_h: torch.Tensor,
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rel_pos_w: torch.Tensor,
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q_size: Tuple[int, int],
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k_size: Tuple[int, int],
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) -> torch.Tensor:
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"""
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Adds decomposed Relative Positional Embeddings to the attention map.
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This function calculates and applies decomposed Relative Positional Embeddings as described in the MVITv2
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paper. It enhances the attention mechanism by incorporating spatial relationships between query and key
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positions.
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Args:
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attn (torch.Tensor): Attention map with shape (B, q_h * q_w, k_h * k_w).
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q (torch.Tensor): Query tensor in the attention layer with shape (B, q_h * q_w, C).
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rel_pos_h (torch.Tensor): Relative position embeddings for height axis with shape (Lh, C).
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rel_pos_w (torch.Tensor): Relative position embeddings for width axis with shape (Lw, C).
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q_size (Tuple[int, int]): Spatial sequence size of query q as (q_h, q_w).
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k_size (Tuple[int, int]): Spatial sequence size of key k as (k_h, k_w).
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Returns:
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(torch.Tensor): Updated attention map with added relative positional embeddings, shape
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(B, q_h * q_w, k_h * k_w).
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Examples:
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>>> B, C, q_h, q_w, k_h, k_w = 1, 64, 8, 8, 8, 8
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>>> attn = torch.rand(B, q_h * q_w, k_h * k_w)
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>>> q = torch.rand(B, q_h * q_w, C)
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>>> rel_pos_h = torch.rand(2 * max(q_h, k_h) - 1, C)
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>>> rel_pos_w = torch.rand(2 * max(q_w, k_w) - 1, C)
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>>> q_size, k_size = (q_h, q_w), (k_h, k_w)
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>>> updated_attn = add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size)
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>>> print(updated_attn.shape)
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torch.Size([1, 64, 64])
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References:
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https://github.com/facebookresearch/mvit/blob/main/mvit/models/attention.py
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"""
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q_h, q_w = q_size
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k_h, k_w = k_size
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Rh = get_rel_pos(q_h, k_h, rel_pos_h)
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Rw = get_rel_pos(q_w, k_w, rel_pos_w)
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B, _, dim = q.shape
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r_q = q.reshape(B, q_h, q_w, dim)
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rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
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rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
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attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(
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B, q_h * q_w, k_h * k_w
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)
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return attn
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