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Running on Zero
Running on Zero
| import math | |
| from typing import Any, Optional, Tuple | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| class PositionEmbeddingSine(nn.Module): | |
| def __init__( | |
| self, | |
| num_pos_feats, | |
| temperature: int = 10000, | |
| normalize: bool = True, | |
| scale: Optional[float] = None, | |
| warmup_cache: bool = True, | |
| image_size: int = 1024, | |
| strides: Tuple[int] = (4, 8, 16, 32), | |
| ): | |
| super().__init__() | |
| assert num_pos_feats % 2 == 0, "Expecting even model width" | |
| self.num_pos_feats = num_pos_feats // 2 | |
| self.temperature = temperature | |
| self.normalize = normalize | |
| if scale is not None and normalize is False: | |
| raise ValueError("normalize should be True if scale is passed") | |
| if scale is None: | |
| scale = 2 * math.pi | |
| self.scale = scale | |
| self.cache = {} | |
| if warmup_cache: | |
| device = torch.device("cpu") | |
| for stride in strides: | |
| cache_key = (image_size // stride, image_size // stride) | |
| self._pe(1, device, *cache_key) | |
| def _encode_xy(self, x, y): | |
| assert len(x) == len(y) and x.ndim == y.ndim == 1 | |
| x_embed = x * self.scale | |
| y_embed = y * self.scale | |
| dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) | |
| dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) | |
| pos_x = x_embed[:, None] / dim_t | |
| pos_y = y_embed[:, None] / dim_t | |
| pos_x = torch.stack( | |
| (pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2 | |
| ).flatten(1) | |
| pos_y = torch.stack( | |
| (pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2 | |
| ).flatten(1) | |
| return pos_x, pos_y | |
| def encode_boxes(self, x, y, w, h): | |
| pos_x, pos_y = self._encode_xy(x, y) | |
| pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1) | |
| return pos | |
| encode = encode_boxes | |
| def encode_points(self, x, y, labels): | |
| (bx, nx), (by, ny), (bl, nl) = x.shape, y.shape, labels.shape | |
| assert bx == by and nx == ny and bx == bl and nx == nl | |
| pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten()) | |
| pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1) | |
| pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2) | |
| return pos | |
| def _pe(self, B, device, *cache_key): | |
| H, W = cache_key | |
| if cache_key in self.cache: | |
| return self.cache[cache_key].to(device)[None].repeat(B, 1, 1, 1) | |
| y_embed = ( | |
| torch.arange(1, H + 1, dtype=torch.float32, device=device) | |
| .view(1, -1, 1) | |
| .repeat(B, 1, W) | |
| ) | |
| x_embed = ( | |
| torch.arange(1, W + 1, dtype=torch.float32, device=device) | |
| .view(1, 1, -1) | |
| .repeat(B, H, 1) | |
| ) | |
| if self.normalize: | |
| eps = 1e-6 | |
| y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale | |
| x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale | |
| dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=device) | |
| dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) | |
| pos_x = x_embed[:, :, :, None] / dim_t | |
| pos_y = y_embed[:, :, :, None] / dim_t | |
| pos_x = torch.stack( | |
| (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 | |
| ).flatten(3) | |
| pos_y = torch.stack( | |
| (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 | |
| ).flatten(3) | |
| pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) | |
| self.cache[cache_key] = pos[0] | |
| return pos | |
| def forward(self, x: torch.Tensor): | |
| B = x.shape[0] | |
| cache_key = (x.shape[-2], x.shape[-1]) | |
| return self._pe(B, x.device, *cache_key) | |
| class PositionEmbeddingRandom(nn.Module): | |
| def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None: | |
| super().__init__() | |
| if scale is None or scale <= 0.0: | |
| scale = 1.0 | |
| self.register_buffer( | |
| "positional_encoding_gaussian_matrix", | |
| scale * torch.randn((2, num_pos_feats)), | |
| ) | |
| def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor: | |
| coords = 2 * coords - 1 | |
| coords = coords @ self.positional_encoding_gaussian_matrix | |
| coords = 2 * np.pi * coords | |
| return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1) | |
| def forward(self, size: Tuple[int, int]) -> torch.Tensor: | |
| h, w = size | |
| device: Any = self.positional_encoding_gaussian_matrix.device | |
| grid = torch.ones((h, w), device=device, dtype=torch.float32) | |
| y_embed = grid.cumsum(dim=0) - 0.5 | |
| x_embed = grid.cumsum(dim=1) - 0.5 | |
| y_embed = y_embed / h | |
| x_embed = x_embed / w | |
| pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1)) | |
| return pe.permute(2, 0, 1) | |
| def forward_with_coords( | |
| self, coords_input: torch.Tensor, image_size: Tuple[int, int] | |
| ) -> torch.Tensor: | |
| coords = coords_input.clone() | |
| coords[:, :, 0] = coords[:, :, 0] / image_size[1] | |
| coords[:, :, 1] = coords[:, :, 1] / image_size[0] | |
| return self._pe_encoding(coords.to(torch.float)) | |
| def init_t_xy(end_x: int, end_y: int): | |
| t = torch.arange(end_x * end_y, dtype=torch.float32) | |
| t_x = (t % end_x).float() | |
| t_y = torch.div(t, end_x, rounding_mode="floor").float() | |
| return t_x, t_y | |
| def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0): | |
| freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) | |
| freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) | |
| t_x, t_y = init_t_xy(end_x, end_y) | |
| freqs_x = torch.outer(t_x, freqs_x) | |
| freqs_y = torch.outer(t_y, freqs_y) | |
| freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x) | |
| freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y) | |
| return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1) | |
| def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): | |
| ndim = x.ndim | |
| assert 0 <= 1 < ndim | |
| assert freqs_cis.shape == (x.shape[-2], x.shape[-1]) | |
| shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)] | |
| return freqs_cis.view(*shape) | |
| def apply_rotary_enc( | |
| xq: torch.Tensor, | |
| xk: torch.Tensor, | |
| freqs_cis: torch.Tensor, | |
| repeat_freqs_k: bool = False, | |
| ): | |
| xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) | |
| xk_ = ( | |
| torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) | |
| if xk.shape[-2] != 0 | |
| else None | |
| ) | |
| freqs_cis = reshape_for_broadcast(freqs_cis, xq_) | |
| xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) | |
| if xk_ is None: | |
| return xq_out.type_as(xq).to(xq.device), xk | |
| if repeat_freqs_k: | |
| r = xk_.shape[-2] // xq_.shape[-2] | |
| if freqs_cis.is_cuda: | |
| freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1) | |
| else: | |
| freqs_cis = freqs_cis.unsqueeze(2).expand(-1, -1, r, -1, -1).flatten(2, 3) | |
| xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) | |
| return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device) | |