major package upgrade&new weights
This commit is contained in:
rmaphoh
+92
-92
@@ -1,92 +1,92 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# Partly revised by YZ @UCL&Moorfields
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# --------------------------------------------------------
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import numpy as np
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import torch
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# --------------------------------------------------------
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# 2D sine-cosine position embedding
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# References:
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# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py
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# MoCo v3: https://github.com/facebookresearch/moco-v3
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# --------------------------------------------------------
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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) # here w goes first
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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([np.zeros([1, embed_dim]), pos_embed], axis=0)
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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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# use half of dimensions to encode grid_h
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emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
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emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
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emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
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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.float)
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omega /= embed_dim / 2.
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omega = 1. / 10000**omega # (D/2,)
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pos = pos.reshape(-1) # (M,)
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out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
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emb_sin = np.sin(out) # (M, D/2)
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emb_cos = np.cos(out) # (M, D/2)
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emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
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return emb
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# --------------------------------------------------------
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# Interpolate position embeddings for high-resolution
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# References:
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# DeiT: https://github.com/facebookresearch/deit
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# --------------------------------------------------------
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def interpolate_pos_embed(model, checkpoint_model):
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if 'pos_embed' in checkpoint_model:
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pos_embed_checkpoint = checkpoint_model['pos_embed']
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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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# height (== width) for the checkpoint position embedding
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orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
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# height (== width) for the new position embedding
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new_size = int(num_patches ** 0.5)
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# class_token and dist_token are kept unchanged
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if orig_size != new_size:
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print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
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extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
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# only the position tokens are interpolated
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pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
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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).flatten(1, 2)
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new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
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checkpoint_model['pos_embed'] = new_pos_embed
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# Partly revised by YZ @UCL&Moorfields
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# --------------------------------------------------------
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import numpy as np
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import torch
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# --------------------------------------------------------
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# 2D sine-cosine position embedding
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# References:
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# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py
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# MoCo v3: https://github.com/facebookresearch/moco-v3
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# --------------------------------------------------------
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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) # here w goes first
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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([np.zeros([1, embed_dim]), pos_embed], axis=0)
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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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# use half of dimensions to encode grid_h
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emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
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emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
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emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
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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.float)
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omega /= embed_dim / 2.
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omega = 1. / 10000**omega # (D/2,)
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pos = pos.reshape(-1) # (M,)
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out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
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emb_sin = np.sin(out) # (M, D/2)
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emb_cos = np.cos(out) # (M, D/2)
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emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
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return emb
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# --------------------------------------------------------
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# Interpolate position embeddings for high-resolution
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# References:
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# DeiT: https://github.com/facebookresearch/deit
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# --------------------------------------------------------
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def interpolate_pos_embed(model, checkpoint_model):
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if 'pos_embed' in checkpoint_model:
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pos_embed_checkpoint = checkpoint_model['pos_embed']
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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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# height (== width) for the checkpoint position embedding
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orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
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# height (== width) for the new position embedding
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new_size = int(num_patches ** 0.5)
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# class_token and dist_token are kept unchanged
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if orig_size != new_size:
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print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
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extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
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# only the position tokens are interpolated
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pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
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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).flatten(1, 2)
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new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
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checkpoint_model['pos_embed'] = new_pos_embed
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