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import math |
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import logging |
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import torch |
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import torch.nn.functional as F |
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_ |
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from torch import nn |
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import torch.utils.checkpoint as checkpoint |
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from functools import partial |
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from einops import rearrange |
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logger = logging.getLogger(__name__) |
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import numpy as np |
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import torch |
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import logging |
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logger = logging.getLogger(__name__) |
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def get_3d_sincos_pos_embed(embed_dim, grid_size, t_size, cls_token=False): |
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""" |
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grid_size: int of the grid height and width |
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t_size: int of the temporal size |
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return: |
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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) |
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""" |
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assert embed_dim % 4 == 0 |
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embed_dim_spatial = embed_dim // 4 * 3 |
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embed_dim_temporal = embed_dim // 4 |
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grid_h = np.arange(grid_size, dtype=np.float32) |
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grid_w = np.arange(grid_size, dtype=np.float32) |
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grid = np.meshgrid(grid_w, grid_h) |
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grid = np.stack(grid, axis=0) |
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grid = grid.reshape([2, 1, grid_size, grid_size]) |
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pos_embed_spatial = get_2d_sincos_pos_embed_from_grid( |
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embed_dim_spatial, grid |
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) |
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grid_t = np.arange(t_size, dtype=np.float32) |
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pos_embed_temporal = get_1d_sincos_pos_embed_from_grid( |
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embed_dim_temporal, grid_t |
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) |
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pos_embed_temporal = pos_embed_temporal[:, np.newaxis, :] |
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pos_embed_temporal = np.repeat( |
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pos_embed_temporal, grid_size**2, axis=1 |
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) |
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pos_embed_spatial = pos_embed_spatial[np.newaxis, :, :] |
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pos_embed_spatial = np.repeat( |
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pos_embed_spatial, t_size, axis=0 |
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) |
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pos_embed = np.concatenate([pos_embed_temporal, pos_embed_spatial], axis=-1) |
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pos_embed = pos_embed.reshape([-1, embed_dim]) |
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if cls_token: |
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pos_embed = np.concatenate( |
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[np.zeros([1, embed_dim]), pos_embed], axis=0 |
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) |
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return pos_embed |
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def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): |
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""" |
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grid_size: int of the grid height and width |
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return: |
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pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) |
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""" |
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grid_h = np.arange(grid_size, dtype=np.float32) |
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grid_w = np.arange(grid_size, dtype=np.float32) |
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grid = np.meshgrid(grid_w, grid_h) |
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grid = np.stack(grid, axis=0) |
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grid = grid.reshape([2, 1, grid_size, grid_size]) |
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pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) |
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if cls_token: |
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pos_embed = np.concatenate( |
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[np.zeros([1, embed_dim]), pos_embed], axis=0 |
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) |
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return pos_embed |
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def get_1d_sincos_pos_embed(embed_dim, t_size, cls_token=False): |
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""" |
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t_size: int of the temporal size |
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return: |
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pos_embed: [t_size, embed_dim] or [1+t_size, embed_dim] (w/ or w/o cls_token) |
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""" |
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grid_t = np.arange(t_size, dtype=np.float32) |
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pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, grid_t) |
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if cls_token: |
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pos_embed = np.concatenate( |
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[np.zeros([1, embed_dim]), pos_embed], axis=0 |
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) |
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return pos_embed |
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def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): |
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assert embed_dim % 2 == 0 |
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emb_h = get_1d_sincos_pos_embed_from_grid( |
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embed_dim // 2, grid[0] |
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) |
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emb_w = get_1d_sincos_pos_embed_from_grid( |
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embed_dim // 2, grid[1] |
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) |
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emb = np.concatenate([emb_h, emb_w], axis=1) |
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return emb |
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def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): |
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""" |
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embed_dim: output dimension for each position |
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pos: a list of positions to be encoded: size (M,) |
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out: (M, D) |
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""" |
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assert embed_dim % 2 == 0 |
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omega = np.arange(embed_dim // 2, dtype=np.float32) |
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omega /= embed_dim / 2.0 |
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omega = 1.0 / 10000**omega |
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pos = pos.reshape(-1) |
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out = np.einsum("m,d->md", pos, omega) |
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emb_sin = np.sin(out) |
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emb_cos = np.cos(out) |
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emb = np.concatenate([emb_sin, emb_cos], axis=1) |
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return emb |
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def interpolate_pos_embed_internvideo2(checkpoint_model, model, orig_t_size = 8): |
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for pos_name in ['pos_embed', 'clip_pos_embed']: |
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if pos_name in checkpoint_model: |
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pos_embed_checkpoint = checkpoint_model[pos_name] |
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embedding_size = pos_embed_checkpoint.shape[-1] |
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num_patches = model.patch_embed.num_patches |
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num_extra_tokens = model.pos_embed.shape[-2] - num_patches |
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new_t_size = model.num_frames // model.tubelet_size |
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orig_size = int(((pos_embed_checkpoint.shape[-2] - num_extra_tokens)//(orig_t_size)) ** 0.5) |
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new_size = int((num_patches // (new_t_size))** 0.5) |
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if orig_t_size != new_t_size: |
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logger.info(f"Temporal interpolate from {orig_t_size} to {new_t_size} ({pos_name})") |
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extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] |
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pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] |
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pos_tokens = pos_tokens.view(1, orig_t_size, -1, embedding_size) |
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pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, embedding_size, orig_t_size) |
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pos_tokens = torch.nn.functional.interpolate(pos_tokens, size=new_t_size, mode='linear') |
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pos_tokens = pos_tokens.view(1, -1, embedding_size, new_t_size) |
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pos_tokens = pos_tokens.permute(0, 3, 1, 2).reshape(1, -1, embedding_size) |
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new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) |
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checkpoint_model[pos_name] = new_pos_embed |
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pos_embed_checkpoint = new_pos_embed |
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if orig_size != new_size: |
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logger.info(f"Position interpolate from {orig_size}x{orig_size} to {new_size}x{new_size} ({pos_name})") |
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extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] |
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pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] |
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pos_tokens = pos_tokens.reshape(-1, new_t_size, orig_size, orig_size, embedding_size) |
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pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) |
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pos_tokens = torch.nn.functional.interpolate( |
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pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) |
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pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, new_t_size, new_size, new_size, embedding_size) |
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pos_tokens = pos_tokens.flatten(1, 3) |
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new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) |
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checkpoint_model[pos_name] = new_pos_embed |
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if 'pos_embed_spatial' in checkpoint_model or 'pos_embed_temporal' in checkpoint_model: |
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raise NotImplementedError |
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def interpolate_pos_embed_internvideo2_new(checkpoint_model, model, orig_t_size = 8): |
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pos_names = [] |
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for k in checkpoint_model.keys(): |
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if ('pos_embed' in k or 'clip_pos_embed' in k) and 'img_pos_embed' not in k: |
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pos_names.append(k) |
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logger.info(f"pos names list for interpolating: {pos_names}") |
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assert len(pos_names) > 0, checkpoint_model.keys() |
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if 'pos_embed_spatial' in checkpoint_model.keys() or 'pos_embed_temporal' in checkpoint_model.keys(): |
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raise NotImplementedError |
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for pos_name in pos_names: |
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pos_embed_checkpoint = checkpoint_model[pos_name] |
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embedding_size = pos_embed_checkpoint.shape[-1] |
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num_patches = model.patch_embed.num_patches |
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num_extra_tokens = model.pos_embed.shape[-2] - num_patches |
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new_t_size = model.num_frames // model.tubelet_size |
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orig_size = int(((pos_embed_checkpoint.shape[-2] - num_extra_tokens)//(orig_t_size)) ** 0.5) |
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new_size = int((num_patches // (new_t_size))** 0.5) |
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if orig_t_size != new_t_size: |
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logger.info(f"Temporal interpolate from {orig_t_size} to {new_t_size} ({pos_name})") |
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extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] |
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pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] |
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pos_tokens = pos_tokens.view(1, orig_t_size, -1, embedding_size) |
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pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, embedding_size, orig_t_size) |
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pos_tokens = torch.nn.functional.interpolate(pos_tokens, size=new_t_size, mode='linear') |
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pos_tokens = pos_tokens.view(1, -1, embedding_size, new_t_size) |
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pos_tokens = pos_tokens.permute(0, 3, 1, 2).reshape(1, -1, embedding_size) |
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new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) |
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checkpoint_model[pos_name] = new_pos_embed |
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pos_embed_checkpoint = new_pos_embed |
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if orig_size != new_size: |
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logger.info(f"Position interpolate from {orig_size}x{orig_size} to {new_size}x{new_size} ({pos_name})") |
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extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] |
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pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] |
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pos_tokens = pos_tokens.reshape(-1, new_t_size, orig_size, orig_size, embedding_size) |
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pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) |
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pos_tokens = torch.nn.functional.interpolate( |
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pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) |
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pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, new_t_size, new_size, new_size, embedding_size) |
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pos_tokens = pos_tokens.flatten(1, 3) |
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new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) |
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checkpoint_model[pos_name] = new_pos_embed |
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def interpolate_pos_embed(checkpoint_model, model, orig_t_size=4, pos_name='vision_encoder.pos_embed'): |
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if pos_name in checkpoint_model: |
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pos_embed_checkpoint = checkpoint_model[pos_name] |
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embedding_size = pos_embed_checkpoint.shape[-1] |
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num_patches = model.patch_embed.num_patches |
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num_extra_tokens = model.pos_embed.shape[-2] - num_patches |
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new_t_size = model.T |
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orig_size = int(((pos_embed_checkpoint.shape[-2] - num_extra_tokens)//(orig_t_size)) ** 0.5) |
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new_size = int((num_patches // (new_t_size))** 0.5) |
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if orig_t_size != new_t_size: |
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print(f"Temporal interpolate from {orig_t_size} to {new_t_size} ({pos_name})") |
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extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] |
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pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] |
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pos_tokens = pos_tokens.view(1, orig_t_size, -1, embedding_size) |
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pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, embedding_size, orig_t_size) |
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pos_tokens = torch.nn.functional.interpolate(pos_tokens, size=new_t_size, mode='linear') |
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pos_tokens = pos_tokens.view(1, -1, embedding_size, new_t_size) |
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pos_tokens = pos_tokens.permute(0, 3, 1, 2).reshape(1, -1, embedding_size) |
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new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) |
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checkpoint_model[pos_name] = new_pos_embed |
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pos_embed_checkpoint = new_pos_embed |
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if orig_size != new_size: |
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print(f"Position interpolate from {orig_size}x{orig_size} to {new_size}x{new_size} ({pos_name})") |
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extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] |
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pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] |
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pos_tokens = pos_tokens.reshape(-1, new_t_size, orig_size, orig_size, embedding_size) |
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pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) |
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pos_tokens = torch.nn.functional.interpolate( |
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pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) |
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pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, new_t_size, new_size, new_size, embedding_size) |
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pos_tokens = pos_tokens.flatten(1, 3) |
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new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) |
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checkpoint_model[pos_name] = new_pos_embed |
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else: |
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raise NotImplementedError |
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class CrossAttention(nn.Module): |
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def __init__( |
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self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., |
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proj_drop=0., attn_head_dim=None, out_dim=None): |
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super().__init__() |
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if out_dim is None: |
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out_dim = dim |
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self.num_heads = num_heads |
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head_dim = dim // num_heads |
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if attn_head_dim is not None: |
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head_dim = attn_head_dim |
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all_head_dim = head_dim * self.num_heads |
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self.scale = qk_scale or head_dim ** -0.5 |
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assert all_head_dim == dim |
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self.q = nn.Linear(dim, all_head_dim, bias=False) |
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self.k = nn.Linear(dim, all_head_dim, bias=False) |
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self.v = nn.Linear(dim, all_head_dim, bias=False) |
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if qkv_bias: |
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self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) |
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self.k_bias = nn.Parameter(torch.zeros(all_head_dim)) |
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self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) |
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else: |
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self.q_bias = None |
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self.k_bias = None |
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self.v_bias = None |
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self.attn_drop = nn.Dropout(attn_drop) |
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self.proj = nn.Linear(all_head_dim, out_dim) |
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self.proj_drop = nn.Dropout(proj_drop) |
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def forward(self, x, k=None, v=None): |
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B, N, C = x.shape |
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N_k = k.shape[1] |
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N_v = v.shape[1] |
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q_bias, k_bias, v_bias = None, None, None |
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if self.q_bias is not None: |
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q_bias = self.q_bias |
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k_bias = self.k_bias |
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v_bias = self.v_bias |
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q = F.linear(input=x, weight=self.q.weight, bias=q_bias) |
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q = q.reshape(B, N, 1, self.num_heads, -1).permute(2, 0, 3, 1, 4).squeeze(0) |
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k = F.linear(input=k, weight=self.k.weight, bias=k_bias) |
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k = k.reshape(B, N_k, 1, self.num_heads, -1).permute(2, 0, 3, 1, 4).squeeze(0) |
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v = F.linear(input=v, weight=self.v.weight, bias=v_bias) |
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v = v.reshape(B, N_v, 1, self.num_heads, -1).permute(2, 0, 3, 1, 4).squeeze(0) |
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q = q * self.scale |
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attn = (q @ k.transpose(-2, -1)) |
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attn = attn.softmax(dim=-1) |
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attn = self.attn_drop(attn) |
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x = (attn @ v).transpose(1, 2).reshape(B, N, -1) |
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x = self.proj(x) |
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x = self.proj_drop(x) |
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return x |
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class AttentiveBlock(nn.Module): |
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def __init__(self, dim, num_heads, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., |
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drop_path=0., norm_layer=nn.LayerNorm, attn_head_dim=None, out_dim=None): |
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super().__init__() |
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self.norm1_q = norm_layer(dim) |
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self.norm1_k = norm_layer(dim) |
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self.norm1_v = norm_layer(dim) |
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self.cross_attn = CrossAttention( |
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dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, |
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proj_drop=drop, attn_head_dim=attn_head_dim, out_dim=out_dim) |
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() |
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def forward(self, x_q, x_kv, pos_q, pos_k, bool_masked_pos, rel_pos_bias=None): |
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x_q = self.norm1_q(x_q + pos_q) |
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x_k = self.norm1_k(x_kv + pos_k) |
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x_v = self.norm1_v(x_kv) |
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x = self.cross_attn(x_q, k=x_k, v=x_v) |
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return x |
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class AttentionPoolingBlock(AttentiveBlock): |
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def forward(self, x): |
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x_q = x.mean(1, keepdim=True) |
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x_kv, pos_q, pos_k = x, 0, 0 |
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x = super().forward(x_q, x_kv, pos_q, pos_k, bool_masked_pos=None, rel_pos_bias=None) |
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x = x.squeeze(1) |
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return x |
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class RMSNorm(nn.Module): |
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def __init__(self, hidden_size, eps=1e-6): |
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super().__init__() |
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self.weight = nn.Parameter(torch.ones(hidden_size)) |
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self.variance_epsilon = eps |
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def forward(self, hidden_states): |
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input_dtype = hidden_states.dtype |
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hidden_states = hidden_states.to(torch.float32) |
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variance = hidden_states.pow(2).mean(-1, keepdim=True) |
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) |
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return self.weight * hidden_states.to(input_dtype) |
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class Attention(nn.Module): |
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def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., use_flash_attn=False, |
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causal=False, norm_layer=nn.LayerNorm, qk_normalization=False, use_fused_rmsnorm=False): |
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super().__init__() |
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assert dim % num_heads == 0, 'dim should be divisible by num_heads' |
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self.num_heads = num_heads |
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head_dim = dim // num_heads |
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self.scale = head_dim ** -0.5 |
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) |
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self.attn_drop = nn.Dropout(attn_drop) |
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self.proj = nn.Linear(dim, dim) |
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self.proj_drop = nn.Dropout(proj_drop) |
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self.use_flash_attn = use_flash_attn |
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if use_flash_attn: |
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self.causal = causal |
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self.inner_attn = FlashAttention(attention_dropout=attn_drop) |
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self.qk_normalization = qk_normalization |
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self.q_norm = norm_layer(dim) if qk_normalization else nn.Identity() |
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self.k_norm = norm_layer(dim) if qk_normalization else nn.Identity() |
|
self.use_fused_rmsnorm = use_fused_rmsnorm |
|
|
|
def _naive_attn(self, x): |
|
B, N, C = x.shape |
|
|
|
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) |
|
q, k, v = qkv.unbind(0) |
|
|
|
if self.qk_normalization: |
|
B_, H_, N_, D_ = q.shape |
|
q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2) |
|
k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2) |
|
|
|
attn = ((q * self.scale) @ k.transpose(-2, -1)) |
|
|
|
attn = attn.softmax(dim=-1) |
|
attn = self.attn_drop(attn) |
|
|
|
x = (attn @ v).transpose(1, 2).reshape(B, N, C) |
|
|
|
|
|
x = self.proj(x) |
|
x = self.proj_drop(x) |
|
return x |
|
|
|
def _flash_attn(self, x, key_padding_mask=None, need_weights=False): |
|
|
|
qkv = self.qkv(x) |
|
qkv = rearrange(qkv, "b s (three h d) -> b s three h d", three=3, h=self.num_heads) |
|
|
|
if self.qk_normalization: |
|
q, k, v = qkv.unbind(2) |
|
if self.use_fused_rmsnorm: |
|
q = self.q_norm(q.flatten(-2, -1))[0].view(q.shape) |
|
k = self.k_norm(k.flatten(-2, -1))[0].view(k.shape) |
|
else: |
|
q = self.q_norm(q.flatten(-2, -1)).view(q.shape) |
|
k = self.k_norm(k.flatten(-2, -1)).view(k.shape) |
|
qkv = torch.stack([q, k, v], dim=2) |
|
|
|
context, _ = self.inner_attn( |
|
qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=self.causal |
|
) |
|
outs = self.proj(rearrange(context, "b s h d -> b s (h d)")) |
|
outs = self.proj_drop(outs) |
|
return outs |
|
|
|
def forward(self, x): |
|
x = self._naive_attn(x) if not self.use_flash_attn else self._flash_attn(x) |
|
return x |
|
|
|
|
|
class Mlp(nn.Module): |
|
""" MLP as used in Vision Transformer, MLP-Mixer and related networks |
|
""" |
|
|
|
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, |
|
bias=True, drop=0.): |
|
super().__init__() |
|
out_features = out_features or in_features |
|
hidden_features = hidden_features or in_features |
|
bias = to_2tuple(bias) |
|
drop_probs = to_2tuple(drop) |
|
|
|
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0]) |
|
self.act = act_layer() |
|
self.drop1 = nn.Dropout(drop_probs[0]) |
|
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1]) |
|
self.drop2 = nn.Dropout(drop_probs[1]) |
|
|
|
def forward(self, x): |
|
x = self.fc1(x) |
|
x = self.act(x) |
|
x = self.drop1(x) |
|
x = self.fc2(x) |
|
x = self.drop2(x) |
|
return x |
|
|
|
|
|
class Block(nn.Module): |
|
|
|
def __init__( |
|
self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., init_values=None, |
|
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_flash_attn=False, use_fused_mlp=False, |
|
fused_mlp_heuristic=1, with_cp=False, qk_normalization=False, layerscale_no_force_fp32=False, |
|
use_fused_rmsnorm=False): |
|
super().__init__() |
|
|
|
self.norm1 = norm_layer(dim) |
|
self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, |
|
use_flash_attn=use_flash_attn, causal=False, norm_layer=norm_layer, |
|
qk_normalization=qk_normalization, |
|
use_fused_rmsnorm=use_fused_rmsnorm) |
|
self.ls1 = nn.Parameter(init_values * torch.ones(dim)) |
|
|
|
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity() |
|
|
|
self.norm2 = norm_layer(dim) |
|
mlp_hidden_dim = int(dim * mlp_ratio) |
|
if use_fused_mlp: |
|
self.mlp = FusedMLP(in_features=dim, hidden_features=mlp_hidden_dim, heuristic=fused_mlp_heuristic) |
|
else: |
|
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) |
|
self.ls2 = nn.Parameter(init_values * torch.ones(dim)) |
|
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity() |
|
|
|
self.with_cp = with_cp |
|
self.use_fused_rmsnorm = use_fused_rmsnorm |
|
|
|
def forward(self, x, residual=None): |
|
|
|
def _inner_forward(x, residual=None): |
|
if self.use_fused_rmsnorm: |
|
x, residual = self.norm1(x, residual) |
|
x = self.drop_path1(self.ls1 * self.attn(x) ) |
|
x, residual = self.norm2(x, residual) |
|
x = self.drop_path2(self.ls2 * self.mlp(x) ) |
|
return x, residual |
|
else: |
|
assert residual is None |
|
x = x + self.drop_path1(self.ls1 * self.attn(self.norm1(x))) |
|
x = x + self.drop_path2(self.ls2 * self.mlp(self.norm2(x))) |
|
return x |
|
|
|
if self.with_cp: |
|
|
|
return checkpoint.checkpoint(_inner_forward, x, residual) |
|
else: |
|
return _inner_forward(x, residual=residual) |
|
|
|
|
|
class PatchEmbed(nn.Module): |
|
""" 3D Image to Patch Embedding |
|
""" |
|
|
|
def __init__( |
|
self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, |
|
num_frames=8, tubelet_size=1, norm_layer=None |
|
): |
|
super().__init__() |
|
img_size = to_2tuple(img_size) |
|
patch_size = to_2tuple(patch_size) |
|
self.tubelet_size = tubelet_size |
|
self.img_size = img_size |
|
self.patch_size = patch_size |
|
self.grid_size = ( |
|
num_frames // tubelet_size, |
|
img_size[0] // patch_size[0], |
|
img_size[1] // patch_size[1] |
|
) |
|
self.num_patches = self.grid_size[0] * self.grid_size[1] * self.grid_size[2] |
|
self.num_img_patches = self.grid_size[1] * self.grid_size[2] |
|
|
|
self.proj = nn.Conv3d( |
|
in_channels=in_chans, out_channels=embed_dim, |
|
kernel_size=(tubelet_size, patch_size[0], patch_size[1]), |
|
stride=(tubelet_size, patch_size[0], patch_size[1]) |
|
) |
|
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() |
|
|
|
def forward(self, x): |
|
x = self.proj(x) |
|
x = x.flatten(3).permute(0, 2, 3, 1) |
|
x = self.norm(x) |
|
return x |
|
|
|
class PretrainVisionTransformer_clean(nn.Module): |
|
def __init__( |
|
self, |
|
in_chans: int = 3, |
|
patch_size: int = 14, |
|
img_size: int = 224, |
|
qkv_bias: bool = False, |
|
drop_path_rate: float = 0.25, |
|
embed_dim: int = 1408, |
|
num_heads: int = 16, |
|
mlp_ratio: float = 48/11, |
|
init_values: float = 1e-5, |
|
qk_normalization: bool = True, |
|
depth: int = 40, |
|
use_flash_attn: bool = True, |
|
use_fused_rmsnorm: bool = True, |
|
use_fused_mlp: bool = True, |
|
fused_mlp_heuristic: int = 1, |
|
attn_pool_num_heads: int = 16, |
|
clip_embed_dim: int = 768, |
|
layerscale_no_force_fp32: bool = False, |
|
num_frames: int = 8, |
|
tubelet_size: int = 1, |
|
sep_pos_embed: bool = False, |
|
sep_image_video_pos_embed: bool = False, |
|
use_checkpoint: bool = False, |
|
checkpoint_num: int = 0, |
|
|
|
x_vis_return_idx=-1, |
|
x_vis_only=False |
|
): |
|
super().__init__() |
|
|
|
self.num_frames = num_frames |
|
self.tubelet_size = tubelet_size |
|
assert use_flash_attn == use_fused_rmsnorm == use_fused_mlp, 'use_flash_attn, use_fused_rmsnorm and use_fused_mlp should be consistent' |
|
|
|
self.use_flash_attn = use_flash_attn |
|
self.embed_dim = embed_dim |
|
|
|
logger.info(f"Origin depth: {depth}") |
|
depth = depth + x_vis_return_idx + 1 |
|
logger.info(f"New depth: {depth}") |
|
self.depth = depth |
|
|
|
self.x_vis_only = x_vis_only |
|
|
|
if use_fused_rmsnorm: |
|
norm_layer_for_blocks = partial(DropoutAddRMSNorm, eps=1e-6, prenorm=True) |
|
else: |
|
norm_layer_for_blocks = partial(RMSNorm, eps=1e-6) |
|
self.norm_layer_for_blocks = norm_layer_for_blocks |
|
self.patch_embed = PatchEmbed( |
|
img_size, patch_size, in_chans, embed_dim, |
|
num_frames=num_frames, tubelet_size=tubelet_size, |
|
) |
|
num_patches = self.patch_embed.num_patches |
|
num_img_patches = self.patch_embed.num_img_patches |
|
|
|
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) |
|
|
|
|
|
self.sep_pos_embed = sep_pos_embed |
|
self.sep_image_video_pos_embed = sep_image_video_pos_embed |
|
if sep_pos_embed: |
|
raise NotImplementedError |
|
else: |
|
if sep_image_video_pos_embed: |
|
logger.info("Use joint position embedding, for image and video we use different pos_embed.") |
|
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) |
|
self.img_pos_embed = nn.Parameter(torch.zeros(1, num_img_patches + 1, embed_dim)) |
|
else: |
|
logger.info("Use joint position embedding, for image and video we use same pos_embed.") |
|
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) |
|
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] |
|
|
|
with_cp_list = [False] * depth |
|
if use_checkpoint: |
|
for idx in range(depth): |
|
if idx < checkpoint_num: |
|
with_cp_list[idx] = True |
|
logger.info(f"Droppath rate: {dpr}") |
|
logger.info(f"Checkpoint list: {with_cp_list}") |
|
|
|
self.blocks = nn.ModuleList([ |
|
Block(embed_dim, num_heads, mlp_ratio, qkv_bias=qkv_bias, |
|
norm_layer=norm_layer_for_blocks, |
|
drop_path=dpr[i], init_values=init_values, attn_drop=0., |
|
use_flash_attn=use_flash_attn, use_fused_mlp=use_fused_mlp, |
|
fused_mlp_heuristic=fused_mlp_heuristic, |
|
with_cp=with_cp_list[i], |
|
qk_normalization=qk_normalization, |
|
layerscale_no_force_fp32=layerscale_no_force_fp32, |
|
use_fused_rmsnorm=use_fused_rmsnorm) |
|
for i in range(depth)]) |
|
|
|
if not self.x_vis_only: |
|
self.clip_projector = AttentionPoolingBlock( |
|
dim=embed_dim, num_heads=attn_pool_num_heads, qkv_bias=True, qk_scale=None, |
|
drop=0., attn_drop=0., norm_layer=partial(nn.LayerNorm, eps=1e-5), out_dim=clip_embed_dim) |
|
|
|
|
|
|
|
self.init_pos_embed() |
|
|
|
|
|
|
|
|
|
def init_pos_embed(self): |
|
logger.info("Init pos_embed from sincos pos_embed") |
|
if self.sep_pos_embed: |
|
raise NotImplementedError |
|
else: |
|
pos_embed = get_3d_sincos_pos_embed( |
|
self.pos_embed.shape[-1], |
|
self.patch_embed.grid_size[1], |
|
self.patch_embed.grid_size[0], |
|
cls_token=True |
|
) |
|
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) |
|
|
|
if self.sep_image_video_pos_embed: |
|
img_pos_embed = get_3d_sincos_pos_embed( |
|
self.pos_embed.shape[-1], |
|
self.patch_embed.grid_size[1], |
|
1, |
|
cls_token=True |
|
) |
|
self.img_pos_embed.data.copy_(torch.from_numpy(img_pos_embed).float().unsqueeze(0)) |
|
|
|
|
|
def _init_weights(self, m): |
|
if isinstance(m, nn.Linear): |
|
trunc_normal_(m.weight, std=.02) |
|
if isinstance(m, nn.Linear) and m.bias is not None: |
|
nn.init.constant_(m.bias, 0) |
|
elif isinstance(m, nn.LayerNorm): |
|
nn.init.constant_(m.bias, 0) |
|
nn.init.constant_(m.weight, 1.0) |
|
|
|
def fix_init_weight(self): |
|
def rescale(param, layer_id): |
|
param.div_(math.sqrt(2.0 * layer_id)) |
|
|
|
for layer_id, layer in enumerate(self.blocks): |
|
rescale(layer.attn.proj.weight.data, layer_id + 1) |
|
rescale(layer.mlp.fc2.weight.data, layer_id + 1) |
|
|
|
@property |
|
def dtype(self): |
|
return self.patch_embed.proj.weight.dtype |
|
|
|
def get_num_layers(self): |
|
return len(self.blocks) |
|
|
|
@torch.jit.ignore |
|
def no_weight_decay(self): |
|
return { |
|
'pos_embed', |
|
'pos_embed_spatial', |
|
'pos_embed_temporal', |
|
'pos_embed_cls', |
|
'img_pos_embed', |
|
'cls_token' |
|
} |
|
|
|
def expand_pos_embed(self, pos_embed, new_t_size, L, use_vitar_fuzzing=False): |
|
''' |
|
@param: |
|
pos_embed: original pos_embed, (1, T*L + 1, embed_dim) |
|
T: frames |
|
L: w * h |
|
method: interpolation method |
|
''' |
|
pos_embed_checkpoint = pos_embed |
|
embedding_size = pos_embed_checkpoint.shape[-1] |
|
num_extra_tokens = 1 |
|
|
|
|
|
orig_size = int(((pos_embed_checkpoint.shape[-2] - num_extra_tokens)//(self.num_frames / self.patch_embed.tubelet_size)) ** 0.5) |
|
|
|
new_size = int(L ** 0.5) |
|
|
|
|
|
if self.num_frames != new_t_size: |
|
logger.info(f"Temporal interpolate from {self.num_frames} to {new_t_size} ") |
|
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] |
|
|
|
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] |
|
|
|
pos_tokens = pos_tokens.view(1, self.num_frames, -1, embedding_size) |
|
pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, embedding_size, self.num_frames) |
|
pos_tokens = torch.nn.functional.interpolate(pos_tokens.cpu(), size=new_t_size, mode='linear').cuda() |
|
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) |
|
pos_embed_checkpoint = new_pos_embed |
|
|
|
|
|
if orig_size != new_size: |
|
logger.info(f"Position interpolate from {orig_size}x{orig_size} to {new_size}x{new_size}") |
|
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] |
|
|
|
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] |
|
|
|
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.cpu(), size=(new_size, new_size), mode='bicubic', align_corners=False).cuda() |
|
|
|
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) |
|
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) |
|
|
|
if use_vitar_fuzzing: |
|
... |
|
|
|
return new_pos_embed |
|
|
|
|
|
def forward(self, x, mask=None, use_image=False): |
|
x = self.patch_embed(x.type(self.dtype)) |
|
|
|
B, T, L, C = x.shape |
|
x = x.view([B, T * L, C]) |
|
|
|
|
|
cls_tokens = self.cls_token.expand(B, -1, -1) |
|
x = torch.cat((cls_tokens, x), dim=1) |
|
|
|
|
|
if self.sep_pos_embed: |
|
raise NotImplementedError |
|
else: |
|
if use_image: |
|
if self.sep_image_video_pos_embed: |
|
pos_embed = self.img_pos_embed |
|
else: |
|
|
|
|
|
cls_pos_embed = self.pos_embed[:, 0:1, :] |
|
|
|
|
|
img_pos_embed = self.pos_embed[:, 1:, :].view(1, self.num_frames, self.patch_embed.num_patches // self.num_frames, self.embed_dim).mean(dim=1) |
|
|
|
|
|
pos_embed = torch.cat([cls_pos_embed, img_pos_embed], dim=1) |
|
|
|
else: |
|
pos_embed = self.pos_embed |
|
|
|
if pos_embed[0].shape != x[0].shape: |
|
|
|
pos_embed = self.expand_pos_embed(pos_embed, T, L) |
|
assert pos_embed[0].shape == x[0].shape, f'pos embed shape: {pos_embed.shape} not match x[0].shape {x[0].shape}' |
|
|
|
x = x + pos_embed |
|
|
|
|
|
if mask is not None: |
|
x = x[~mask].reshape(B, -1, C) |
|
else: |
|
x = x.reshape(B, -1, C) |
|
|
|
residual = None |
|
|
|
for idx, blk in enumerate(self.blocks): |
|
if isinstance(x, tuple) and len(x) == 2: |
|
x, residual = x |
|
x = blk(x, residual=residual) |
|
|
|
if isinstance(x, tuple) and len(x) == 2: |
|
x, residual = x |
|
if residual is not None: |
|
x = x + residual |
|
|
|
x_vis = x |
|
if self.x_vis_only: |
|
return x_vis |
|
else: |
|
x_pool_vis = self.clip_projector(x_vis) |
|
return x_vis, x_pool_vis, None, None |
|
|
|
|
|
def pretrain_internvideo2_giant_patch14_224_clean(config): |
|
model = PretrainVisionTransformer_clean( |
|
in_chans=3, img_size=224, patch_size=14, |
|
embed_dim=1408, depth=40, num_heads=16, mlp_ratio=48/11, |
|
attn_pool_num_heads=16, qkv_bias=False, |
|
drop_path_rate=0.25, |
|
init_values=0.00001, |
|
qk_normalization=True, |
|
use_flash_attn=config.vision_encoder.get('use_flash_attn', False), |
|
use_fused_rmsnorm=config.vision_encoder.get('use_fused_rmsnorm', False), |
|
use_fused_mlp=config.vision_encoder.get('use_fused_mlp', False), |
|
fused_mlp_heuristic=1, |
|
layerscale_no_force_fp32=True, |
|
num_frames=config.vision_encoder.num_frames, |
|
tubelet_size=config.vision_encoder.tubelet_size, |
|
sep_pos_embed=False, |
|
sep_image_video_pos_embed=config.vision_encoder.sep_image_video_pos_embed, |
|
use_checkpoint=config.vision_encoder.use_checkpoint, |
|
checkpoint_num=config.vision_encoder.checkpoint_num, |
|
x_vis_return_idx=config.vision_encoder.x_vis_return_idx, |
|
x_vis_only=config.vision_encoder.x_vis_only, |
|
) |
|
|
|
if config.vision_encoder.pretrained is not None: |
|
logger.info(f"Loading pretrained weights from {config.vision_encoder.pretrained}") |
|
state_dict = torch.load(config.vision_encoder.pretrained, map_location='cpu') |
|
interpolate_pos_embed_internvideo2(state_dict, model, orig_t_size=4) |
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message = model.load_state_dict(state_dict, strict=False) |
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logger.info(message) |
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else: |
|
logger.info("No pretrained weights!!!") |
|
return model |
|
|
|
|
|
|
|
def pretrain_internvideo2_6b_patch14_224_clean(config): |
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model = PretrainVisionTransformer_clean( |
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in_chans=3, img_size=224, patch_size=14, |
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embed_dim=3200, depth=48, num_heads=25, mlp_ratio=4, |
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clip_embed_dim=config.vision_encoder.clip_embed_dim, |
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attn_pool_num_heads=16, qkv_bias=False, |
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drop_path_rate=0.3, |
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init_values=0.00001, |
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qk_normalization=True, |
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use_flash_attn=config.vision_encoder.get('use_flash_attn', True), |
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use_fused_rmsnorm=config.vision_encoder.get('use_fused_rmsnorm', True), |
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use_fused_mlp=config.vision_encoder.get('use_fused_mlp', True), |
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fused_mlp_heuristic=1, |
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layerscale_no_force_fp32=True, |
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num_frames=config.vision_encoder.num_frames, |
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tubelet_size=config.vision_encoder.tubelet_size, |
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sep_pos_embed=False, |
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sep_image_video_pos_embed=config.vision_encoder.sep_image_video_pos_embed, |
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use_checkpoint=config.vision_encoder.use_checkpoint, |
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checkpoint_num=config.vision_encoder.checkpoint_num, |
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x_vis_return_idx=config.vision_encoder.x_vis_return_idx, |
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x_vis_only=config.vision_encoder.x_vis_only |
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) |
|
|
|
if config.vision_encoder.pretrained is not None: |
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logger.info(f"Loading pretrained weights from {config.vision_encoder.pretrained}") |
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state_dict = torch.load(config.vision_encoder.pretrained, map_location='cpu') |
|
interpolate_pos_embed_internvideo2(state_dict, model, orig_t_size=8) |
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msg = model.load_state_dict(state_dict, strict=False) |
|
logger.info(msg) |
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else: |
|
logger.info("No pretrained weights!!!") |
|
return model |