|
import torch |
|
import torch.nn.functional as F |
|
import torch.nn as nn |
|
from scipy import interpolate |
|
import numpy as np |
|
from einops import rearrange, repeat |
|
|
|
|
|
def _init_transformer_weights(module, initializer_range=0.02): |
|
"""Initialize the weights. Copied from transformers ViT/Bert model init""" |
|
if isinstance(module, (nn.Linear, nn.Conv2d)): |
|
|
|
|
|
module.weight.data.normal_(mean=0.0, std=initializer_range) |
|
if module.bias is not None: |
|
module.bias.data.zero_() |
|
elif isinstance(module, nn.Embedding): |
|
module.weight.data.normal_(mean=0.0, std=initializer_range) |
|
if module.padding_idx is not None: |
|
module.weight.data[module.padding_idx].zero_() |
|
elif isinstance(module, nn.LayerNorm): |
|
module.bias.data.zero_() |
|
module.weight.data.fill_(1.0) |
|
|
|
|
|
def interpolate_pos_embed(pos_embed_old, pos_embed_new, num_patches_new): |
|
""" |
|
Args: |
|
pos_embed_old: (1, L_old, d), pre-trained |
|
pos_embed_new: (1, L_new, d), newly initialized, to be replaced by interpolated weights |
|
num_patches_new: |
|
""" |
|
|
|
embedding_size = pos_embed_old.shape[-1] |
|
num_extra_tokens = pos_embed_new.shape[-2] - num_patches_new |
|
|
|
orig_size = int((pos_embed_old.shape[-2] - num_extra_tokens) ** 0.5) |
|
|
|
new_size = int(num_patches_new ** 0.5) |
|
|
|
if orig_size != new_size: |
|
|
|
|
|
extra_tokens = pos_embed_old[:, :num_extra_tokens] |
|
|
|
pos_tokens = pos_embed_old[:, num_extra_tokens:] |
|
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, size=(new_size, new_size), mode='bicubic', align_corners=False) |
|
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) |
|
interpolated_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) |
|
return interpolated_pos_embed |
|
else: |
|
return pos_embed_old |
|
|
|
|
|
def interpolate_pos_relative_bias_beit(state_dict_old, state_dict_new, patch_shape_new): |
|
""" |
|
Args: |
|
state_dict_old: loaded state dict |
|
state_dict_new: state dict for model with new image size |
|
patch_shape_new: new model patch_shape |
|
ref: https://github.com/microsoft/unilm/blob/master/beit/run_class_finetuning.py |
|
""" |
|
all_keys = list(state_dict_old.keys()) |
|
for key in all_keys: |
|
if "relative_position_index" in key: |
|
state_dict_old.pop(key) |
|
|
|
if "relative_position_bias_table" in key: |
|
rel_pos_bias = state_dict_old[key] |
|
src_num_pos, num_attn_heads = rel_pos_bias.size() |
|
dst_num_pos, _ = state_dict_new[key].size() |
|
dst_patch_shape = patch_shape_new |
|
if dst_patch_shape[0] != dst_patch_shape[1]: |
|
raise NotImplementedError() |
|
num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1) |
|
src_size = int((src_num_pos - num_extra_tokens) ** 0.5) |
|
dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5) |
|
if src_size != dst_size: |
|
extra_tokens = rel_pos_bias[-num_extra_tokens:, :] |
|
rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] |
|
|
|
def geometric_progression(a, r, n): |
|
return a * (1.0 - r ** n) / (1.0 - r) |
|
|
|
left, right = 1.01, 1.5 |
|
while right - left > 1e-6: |
|
q = (left + right) / 2.0 |
|
gp = geometric_progression(1, q, src_size // 2) |
|
if gp > dst_size // 2: |
|
right = q |
|
else: |
|
left = q |
|
|
|
|
|
|
|
|
|
dis = [] |
|
cur = 1 |
|
for i in range(src_size // 2): |
|
dis.append(cur) |
|
cur += q ** (i + 1) |
|
|
|
r_ids = [-_ for _ in reversed(dis)] |
|
|
|
x = r_ids + [0] + dis |
|
y = r_ids + [0] + dis |
|
|
|
t = dst_size // 2.0 |
|
dx = np.arange(-t, t + 0.1, 1.0) |
|
dy = np.arange(-t, t + 0.1, 1.0) |
|
|
|
all_rel_pos_bias = [] |
|
|
|
for i in range(num_attn_heads): |
|
z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy() |
|
f = interpolate.interp2d(x, y, z, kind='cubic') |
|
all_rel_pos_bias.append( |
|
torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device)) |
|
|
|
rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1) |
|
|
|
new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0) |
|
state_dict_old[key] = new_rel_pos_bias |
|
return state_dict_old |
|
|
|
|
|
def interpolate_pos_relative_bias_beit_3d(state_dict_old, state_dict_new, patch_shape_new, src_t_size=1): |
|
""" |
|
Args: |
|
state_dict_old: loaded state dict |
|
state_dict_new: state dict for model with new image size |
|
patch_shape_new: new model patch_shape |
|
ref: https://github.com/microsoft/unilm/blob/master/beit/run_class_finetuning.py |
|
""" |
|
all_keys = list(state_dict_old.keys()) |
|
for key in all_keys: |
|
if "relative_position_index" in key: |
|
state_dict_old.pop(key) |
|
|
|
if "relative_position_bias_table" in key: |
|
src_num_pos, num_attn_heads = state_dict_old[key].size() |
|
dst_num_pos, _ = state_dict_new[key].size() |
|
if src_num_pos == dst_num_pos: |
|
continue |
|
|
|
num_extra_tokens = dst_num_pos - np.prod([w * 2 - 1 for w in patch_shape_new]) |
|
|
|
src_s_size = int((src_num_pos - num_extra_tokens) / src_t_size) |
|
src_size = int(src_s_size ** 0.5) |
|
dst_size = patch_shape_new[-1] * 2 - 1 |
|
|
|
if src_size != dst_size: |
|
|
|
rel_pos_bias = state_dict_old[key] |
|
extra_tokens = rel_pos_bias[-num_extra_tokens:, :] |
|
rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] |
|
|
|
def geometric_progression(a, r, n): |
|
return a * (1.0 - r ** n) / (1.0 - r) |
|
|
|
left, right = 1.01, 1.5 |
|
while right - left > 1e-6: |
|
q = (left + right) / 2.0 |
|
gp = geometric_progression(1, q, src_size // 2) |
|
if gp > dst_size // 2: |
|
right = q |
|
else: |
|
left = q |
|
|
|
|
|
|
|
|
|
dis = [] |
|
cur = 1 |
|
for i in range(src_size // 2): |
|
dis.append(cur) |
|
cur += q ** (i + 1) |
|
|
|
r_ids = [-_ for _ in reversed(dis)] |
|
|
|
x = r_ids + [0] + dis |
|
y = r_ids + [0] + dis |
|
|
|
t = dst_size // 2.0 |
|
dx = np.arange(-t, t + 0.1, 1.0) |
|
dy = np.arange(-t, t + 0.1, 1.0) |
|
|
|
all_rel_pos_bias = [] |
|
|
|
for i in range(num_attn_heads): |
|
z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy() |
|
f = interpolate.interp2d(x, y, z, kind='cubic') |
|
all_rel_pos_bias.append( |
|
torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device)) |
|
|
|
rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1) |
|
|
|
new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0) |
|
state_dict_old[key] = new_rel_pos_bias |
|
|
|
dst_t_size = patch_shape_new[0] * 2 - 1 |
|
if src_t_size != dst_t_size: |
|
|
|
rel_pos_bias = state_dict_old[key] |
|
extra_tokens = rel_pos_bias[-num_extra_tokens:, :] |
|
rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] |
|
|
|
if src_t_size == 1: |
|
rel_pos_bias = repeat(rel_pos_bias, 's d -> (t s) d', t=dst_t_size) |
|
else: |
|
rel_pos_bias = rearrange(rel_pos_bias, '(t s) d -> s d t', t=src_t_size) |
|
rel_pos_bias = F.interpolate(rel_pos_bias, dst_t_size, mode='nearest') |
|
rel_pos_bias = rearrange(rel_pos_bias, 's d t -> (t s) d') |
|
new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0) |
|
state_dict_old[key] = new_rel_pos_bias |
|
|
|
return state_dict_old |
|
|
|
|
|
def tile(x, dim, n_tile): |
|
init_dim = x.size(dim) |
|
repeat_idx = [1] * x.dim() |
|
repeat_idx[dim] = n_tile |
|
x = x.repeat(*repeat_idx) |
|
order_index = torch.LongTensor(np.concatenate( |
|
[init_dim * np.arange(n_tile) + i for i in range(init_dim)])) |
|
return torch.index_select(x, dim, order_index.to(x.device)) |
|
|
|
|
|
def mask_logits(target, mask): |
|
return target * mask + (1 - mask) * (-1e10) |
|
|
|
|