Merge branch 'nested_transformer' of https://github.com/alexander-soare/pytorch-image-models into alexander-soare-nested_transformer
Ross Wightman
3 years ago
commit
6ae0ac6420
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"""
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Convert weights from https://github.com/google-research/nested-transformer
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NOTE: You'll need https://github.com/google/CommonLoopUtils, not included in requirements.txt
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"""
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import sys
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import numpy as np
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import torch
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from clu import checkpoint
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arch_depths = {
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'nest_base': [2, 2, 20],
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'nest_small': [2, 2, 20],
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'nest_tiny': [2, 2, 8],
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}
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def convert_nest(checkpoint_path, arch):
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"""
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Expects path to checkpoint which is a dir containing 4 files like in each of these folders
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- https://console.cloud.google.com/storage/browser/gresearch/nest-checkpoints
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`arch` is needed to
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Returns a state dict that can be used with `torch.nn.Module.load_state_dict`
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Hint: Follow timm.models.nest.Nest.__init__ and
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https://github.com/google-research/nested-transformer/blob/main/models/nest_net.py
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"""
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assert arch in ['nest_base', 'nest_small', 'nest_tiny'], "Your `arch` is not supported"
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flax_dict = checkpoint.load_state_dict(checkpoint_path)['optimizer']['target']
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state_dict = {}
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# Patch embedding
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state_dict['patch_embed.proj.weight'] = torch.tensor(
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flax_dict['PatchEmbedding_0']['Conv_0']['kernel']).permute(3, 2, 0, 1)
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state_dict['patch_embed.proj.bias'] = torch.tensor(flax_dict['PatchEmbedding_0']['Conv_0']['bias'])
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# Positional embeddings
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posemb_keys = [k for k in flax_dict.keys() if k.startswith('PositionEmbedding')]
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for i, k in enumerate(posemb_keys):
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state_dict[f'levels.{i}.pos_embed'] = torch.tensor(flax_dict[k]['pos_embedding'])
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# Transformer encoders
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depths = arch_depths[arch]
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for level in range(len(depths)):
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for layer in range(depths[level]):
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global_layer_ix = sum(depths[:level]) + layer
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# Norms
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for i in range(2):
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state_dict[f'levels.{level}.transformer_encoder.{layer}.norm{i+1}.weight'] = torch.tensor(
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flax_dict[f'EncoderNDBlock_{global_layer_ix}'][f'LayerNorm_{i}']['scale'])
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state_dict[f'levels.{level}.transformer_encoder.{layer}.norm{i+1}.bias'] = torch.tensor(
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flax_dict[f'EncoderNDBlock_{global_layer_ix}'][f'LayerNorm_{i}']['bias'])
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# Attention qkv
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w_q = flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['DenseGeneral_0']['kernel']
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w_kv = flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['DenseGeneral_1']['kernel']
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# Pay attention to dims here (maybe get pen and paper)
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w_kv = np.concatenate(np.split(w_kv, 2, -1), 1)
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w_qkv = np.concatenate([w_q, w_kv], 1)
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state_dict[f'levels.{level}.transformer_encoder.{layer}.attn.qkv.weight'] = torch.tensor(w_qkv).flatten(1).permute(1,0)
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b_q = flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['DenseGeneral_0']['bias']
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b_kv = flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['DenseGeneral_1']['bias']
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# Pay attention to dims here (maybe get pen and paper)
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b_kv = np.concatenate(np.split(b_kv, 2, -1), 0)
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b_qkv = np.concatenate([b_q, b_kv], 0)
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state_dict[f'levels.{level}.transformer_encoder.{layer}.attn.qkv.bias'] = torch.tensor(b_qkv).reshape(-1)
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# Attention proj
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w_proj = flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['proj_kernel']
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w_proj = torch.tensor(w_proj).permute(2, 1, 0).flatten(1)
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state_dict[f'levels.{level}.transformer_encoder.{layer}.attn.proj.weight'] = w_proj
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state_dict[f'levels.{level}.transformer_encoder.{layer}.attn.proj.bias'] = torch.tensor(
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flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MultiHeadAttention_0']['bias'])
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# MLP
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for i in range(2):
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state_dict[f'levels.{level}.transformer_encoder.{layer}.mlp.fc{i+1}.weight'] = torch.tensor(
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flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MlpBlock_0'][f'Dense_{i}']['kernel']).permute(1, 0)
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state_dict[f'levels.{level}.transformer_encoder.{layer}.mlp.fc{i+1}.bias'] = torch.tensor(
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flax_dict[f'EncoderNDBlock_{global_layer_ix}']['MlpBlock_0'][f'Dense_{i}']['bias'])
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# Block aggregations
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for level in range(len(depths)-1):
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# Convs
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state_dict[f'block_aggs.{level}.conv.weight'] = torch.tensor(
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flax_dict[f'ConvPool_{level}']['Conv_0']['kernel']).permute(3, 2, 0, 1)
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state_dict[f'block_aggs.{level}.conv.bias'] = torch.tensor(
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flax_dict[f'ConvPool_{level}']['Conv_0']['bias'])
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# Norms
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state_dict[f'block_aggs.{level}.norm.weight'] = torch.tensor(
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flax_dict[f'ConvPool_{level}']['LayerNorm_0']['scale'])
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state_dict[f'block_aggs.{level}.norm.bias'] = torch.tensor(
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flax_dict[f'ConvPool_{level}']['LayerNorm_0']['bias'])
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# Final norm
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state_dict[f'norm.weight'] = torch.tensor(flax_dict['LayerNorm_0']['scale'])
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state_dict[f'norm.bias'] = torch.tensor(flax_dict['LayerNorm_0']['bias'])
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# Classifier
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state_dict['head.weight'] = torch.tensor(flax_dict['Dense_0']['kernel']).permute(1, 0)
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state_dict['head.bias'] = torch.tensor(flax_dict['Dense_0']['bias'])
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return state_dict
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if __name__ == '__main__':
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variant = sys.argv[1] # base, small, or tiny
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state_dict = convert_nest(f'../nested-transformer/checkpoints/nest-{variant[0]}_imagenet', f'nest_{variant}')
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torch.save(state_dict, f'/home/alexander/.cache/torch/hub/checkpoints/jx_nest_{variant}.pth')
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""" Nested Transformer (NesT) in PyTorch
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A PyTorch implement of Aggregating Nested Transformers as described in:
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'Aggregating Nested Transformers'
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- https://arxiv.org/abs/2105.12723
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The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
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have been converted with convert/convert_nest_flax.py
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Acknowledgments:
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* The paper authors for sharing their research, code, and model weights
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* Ross Wightman's existing code off which I based this
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Copyright 2021 Alexander Soare
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"""
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import collections.abc
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from functools import partial
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import math
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import logging
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import numpy as np
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import torch
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from torch import nn
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import torch.nn.functional as F
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .layers import PatchEmbed, Mlp, DropPath, create_classifier, trunc_normal_
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from .layers.helpers import to_ntuple
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from .layers.create_conv2d import create_conv2d
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from .layers.pool2d_same import create_pool2d
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from .vision_transformer import Block
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from .registry import register_model
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from .helpers import build_model_with_cfg, named_apply
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from .vision_transformer import resize_pos_embed
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_logger = logging.getLogger(__name__)
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': [14, 14],
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'crop_pct': .875, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'patch_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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# (weights from official Google JAX impl)
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'nest_base': _cfg(),
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'nest_small': _cfg(),
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'nest_tiny': _cfg(),
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'jx_nest_base': _cfg(url='https://www.todo-this-is-a-placeholder.com/jx_nest_base.pth'), # TODO
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'jx_nest_small': _cfg(url='https://www.todo-this-is-a-placeholder.com/jx_nest_small.pth'), # TODO
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'jx_nest_tiny': _cfg(url='https://www.todo-this-is-a-placeholder.com/jx_nest_tiny.pth'), # TODO
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}
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class Attention(nn.Module):
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"""
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This is much like `.vision_transformer.Attention` but uses *localised* self attention by accepting an input with
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an extra "image block" dim
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"""
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def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
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super().__init__()
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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, 3*dim, 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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def forward(self, x):
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"""
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x is shape: B (batch_size), T (image blocks), N (seq length per image block), C (embed dim)
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"""
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B, T, N, C = x.shape
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# result of next line is (qkv, B, num (H)eads, T, N, (C')hannels per head)
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qkv = self.qkv(x).reshape(B, T, N, 3, self.num_heads, C // self.num_heads).permute(3, 0, 4, 1, 2, 5)
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q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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attn = (q @ k.transpose(-2, -1)) * self.scale # (B, H, T, N, N)
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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# (B, H, T, N, C'), permute -> (B, T, N, C', H)
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x = (attn @ v).permute(0, 2, 3, 4, 1).reshape(B, T, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x # (B, T, N, C)
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class TransformerLayer(Block):
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"""
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This is much like `.vision_transformer.Block` but:
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- Called TransformerLayer here to allow for "block" as defined in the paper ("non-overlapping image blocks")
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- Uses modified Attention layer that handles the "block" dimension
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"""
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0.,
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act_layer=nn.GELU, norm_layer=nn.LayerNorm):
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super().__init__(dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0.,
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act_layer=nn.GELU, norm_layer=nn.LayerNorm)
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self.norm1 = norm_layer(dim)
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self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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def forward(self, x):
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y = self.norm1(x)
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x = x + self.drop_path(self.attn(y))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class BlockAggregation(nn.Module):
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def __init__(self, in_channels, out_channels, norm_layer, pad_type=''):
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super().__init__()
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self.conv = create_conv2d(in_channels, out_channels, kernel_size=3, padding=pad_type, bias=True)
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self.norm = norm_layer(out_channels)
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self.pool = create_pool2d('max', kernel_size=3, stride=2, padding=pad_type)
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def forward(self, x):
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"""
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x is expected to have shape (B, C, H, W)
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"""
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assert x.shape[-2] % 2 == 0, 'BlockAggregation requires even input spatial dims'
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assert x.shape[-1] % 2 == 0, 'BlockAggregation requires even input spatial dims'
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x = self.conv(x)
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# Layer norm done over channel dim only
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x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
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x = self.pool(x)
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return x # (B, C, H//2, W//2)
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def blockify(x, block_size: int):
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"""image to blocks
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Args:
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x (Tensor): with shape (B, H, W, C)
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block_size (int): edge length of a single square block in units of H, W
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"""
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B, H, W, C = x.shape
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assert H % block_size == 0, '`block_size` must divide input height evenly'
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assert W % block_size == 0, '`block_size` must divide input width evenly'
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grid_height = H // block_size
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grid_width = W // block_size
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x = x.reshape(B, grid_height, block_size, grid_width, block_size, C)
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x = x.permute(0, 1, 3, 2, 4, 5)
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x = x.reshape(B, grid_height * grid_width, -1, C)
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return x # (B, T, N, C)
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def deblockify(x, block_size: int):
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"""blocks to image
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Args:
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x (Tensor): with shape (B, T, N, C) where T is number of blocks and N is sequence size per block
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block_size (int): edge length of a single square block in units of desired H, W
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"""
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B, T, _, C= x.shape
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grid_size = int(math.sqrt(T))
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x = x.reshape(B, grid_size, grid_size, block_size, block_size, C)
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x = x.permute(0, 1, 3, 2, 4, 5)
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height = width = grid_size * block_size
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x = x.reshape(B, height, width, C)
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return x # (B, H, W, C)
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class NestLevel(nn.Module):
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""" Single hierarchical level of a Nested Transformer
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"""
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def __init__(self, num_blocks, block_size, seq_length, num_heads, depth, embed_dim, mlp_ratio=4., qkv_bias=True,
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drop_rate=0., attn_drop_rate=0., drop_path_rates=[], norm_layer=None, act_layer=None):
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super().__init__()
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self.block_size = block_size
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self.pos_embed = nn.Parameter(torch.zeros(1, num_blocks, seq_length, embed_dim))
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# Transformer encoder
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if len(drop_path_rates):
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assert len(drop_path_rates) == depth, 'Must provide as many drop path rates as there are transformer layers'
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self.transformer_encoder = nn.Sequential(*[
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TransformerLayer(
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=drop_path_rates[i],
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norm_layer=norm_layer, act_layer=act_layer)
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for i in range(depth)])
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def forward(self, x):
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"""
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expects x as (B, C, H, W)
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"""
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# Switch to channels last for transformer
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x = x.permute(0, 2, 3, 1) # (B, H', W', C)
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x = blockify(x, self.block_size) # (B, T, N, C')
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x = x + self.pos_embed
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x = self.transformer_encoder(x) # (B, T, N, C')
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x = deblockify(x, self.block_size) # (B, H', W', C')
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# Channel-first for block aggregation, and generally to replicate convnet feature map at each stage
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x = x.permute(0, 3, 1, 2) # (B, C, H', W')
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return x
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class Nest(nn.Module):
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""" Nested Transformer (NesT)
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A PyTorch impl of : `Aggregating Nested Transformers`
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- https://arxiv.org/abs/2105.12723
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"""
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def __init__(self, img_size=224, in_chans=3, patch_size=4, num_levels=3, embed_dims=(128, 256, 512),
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num_heads=(4, 8, 16), depths=(2, 2, 20), num_classes=1000, mlp_ratio=4., qkv_bias=True, pad_type='',
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drop_rate=0., attn_drop_rate=0., drop_path_rate=0.5, norm_layer=None, act_layer=None, weight_init='',
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global_pool='avg'):
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"""
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Args:
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img_size (int, tuple): input image size
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in_chans (int): number of input channels
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patch_size (int): patch size
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num_levels (int): number of block hierarchies (T_d in the paper)
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embed_dims (int, tuple): embedding dimensions of each level
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num_heads (int, tuple): number of attention heads for each level
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depths (int, tuple): number of transformer layers for each level
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num_classes (int): number of classes for classification head
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim for MLP of transformer layers
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qkv_bias (bool): enable bias for qkv if True
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drop_rate (float): dropout rate for MLP of transformer layers, MSA final projection layer, and classifier
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attn_drop_rate (float): attention dropout rate
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drop_path_rate (float): stochastic depth rate
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norm_layer: (nn.Module): normalization layer for transformer layers
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act_layer: (nn.Module): activation layer in MLP of transformer layers
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weight_init: (str): weight init scheme
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global_pool: (str): type of pooling operation to apply to final feature map
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Notes:
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- Default values follow NesT-B from the original Jax code.
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- `embed_dims`, `num_heads`, `depths` should be ints or tuples with length `num_levels`.
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- For those following the paper, Table A1 may have errors!
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- https://github.com/google-research/nested-transformer/issues/2
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"""
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super().__init__()
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for param_name in ['embed_dims', 'num_heads', 'depths']:
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param_value = locals()[param_name]
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if isinstance(param_value, collections.abc.Sequence):
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assert len(param_value) == num_levels, f'Require `len({param_name}) == num_levels`'
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embed_dims = to_ntuple(num_levels)(embed_dims)
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num_heads = to_ntuple(num_levels)(num_heads)
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depths = to_ntuple(num_levels)(depths)
|
||||
self.num_classes = num_classes
|
||||
self.num_features = embed_dims[-1]
|
||||
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
||||
act_layer = act_layer or nn.GELU
|
||||
self.drop_rate = drop_rate
|
||||
self.num_levels = num_levels
|
||||
if isinstance(img_size, collections.abc.Sequence):
|
||||
assert img_size[0] == img_size[1], 'Model only handles square inputs'
|
||||
img_size = img_size[0]
|
||||
assert img_size % patch_size == 0, '`patch_size` must divide `img_size` evenly'
|
||||
self.patch_size = patch_size
|
||||
|
||||
# Number of blocks at each level
|
||||
self.num_blocks = 4**(np.arange(num_levels)[::-1])
|
||||
assert (img_size // patch_size) % np.sqrt(self.num_blocks[0]) == 0, \
|
||||
'First level blocks don\'t fit evenly. Check `img_size`, `patch_size`, and `num_levels`'
|
||||
|
||||
# Block edge size in units of patches
|
||||
# Hint: (img_size // patch_size) gives number of patches along edge of image. sqrt(self.num_blocks[0]) is the
|
||||
# number of blocks along edge of image
|
||||
self.block_size = int((img_size // patch_size) // np.sqrt(self.num_blocks[0]))
|
||||
|
||||
# Patch embedding
|
||||
self.patch_embed = PatchEmbed(
|
||||
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dims[0])
|
||||
self.num_patches = self.patch_embed.num_patches
|
||||
self.seq_length = self.num_patches // self.num_blocks[0]
|
||||
|
||||
# Build up each hierarchical level
|
||||
self.levels = nn.ModuleList([])
|
||||
self.block_aggs = nn.ModuleList([])
|
||||
drop_path_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
|
||||
for lix in range(self.num_levels):
|
||||
dpr = drop_path_rates[sum(depths[:lix]):sum(depths[:lix+1])]
|
||||
self.levels.append(NestLevel(
|
||||
self.num_blocks[lix], self.block_size, self.seq_length, num_heads[lix], depths[lix],
|
||||
embed_dims[lix], mlp_ratio, qkv_bias, drop_rate, attn_drop_rate, dpr, norm_layer,
|
||||
act_layer))
|
||||
if lix < self.num_levels - 1:
|
||||
self.block_aggs.append(BlockAggregation(
|
||||
embed_dims[lix], embed_dims[lix+1], norm_layer, pad_type=pad_type))
|
||||
else:
|
||||
# Required for zipped iteration over levels and ls_block_agg together
|
||||
self.block_aggs.append(nn.Identity())
|
||||
|
||||
# Final normalization layer
|
||||
self.norm = norm_layer(embed_dims[-1])
|
||||
|
||||
# Classifier
|
||||
self.global_pool, self.head = create_classifier(
|
||||
self.num_features, self.num_classes, pool_type=global_pool)
|
||||
|
||||
self.init_weights(weight_init)
|
||||
|
||||
def init_weights(self, mode=''):
|
||||
assert mode in ('jax', 'jax_nlhb', 'nlhb', '')
|
||||
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
|
||||
for level in self.levels:
|
||||
trunc_normal_(level.pos_embed, std=.02, a=-2, b=2)
|
||||
if mode.startswith('jax'):
|
||||
named_apply(partial(_init_nest_weights, head_bias=head_bias, jax_impl=True), self)
|
||||
else:
|
||||
named_apply(_init_nest_weights, self)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'pos_embed'}
|
||||
|
||||
def get_classifier(self):
|
||||
return self.head
|
||||
|
||||
def reset_classifier(self, num_classes, global_pool='avg'):
|
||||
self.num_classes = num_classes
|
||||
self.global_pool, self.head = create_classifier(
|
||||
self.num_features, self.num_classes, pool_type=global_pool)
|
||||
|
||||
def forward_features(self, x):
|
||||
""" x shape (B, C, H, W)
|
||||
"""
|
||||
B, _, H, W = x.shape
|
||||
x = self.patch_embed(x)
|
||||
x = x.reshape(B, H//self.patch_size, W//self.patch_size, -1) # (B, H', W', C')
|
||||
x = x.permute(0, 3, 1, 2)
|
||||
# NOTE: TorchScript won't let us subscript module lists with integer variables, so we iterate instead
|
||||
for level, block_agg in zip(self.levels, self.block_aggs):
|
||||
x = level(x)
|
||||
x = block_agg(x)
|
||||
# Layer norm done over channel dim only
|
||||
x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
""" x shape (B, C, H, W)
|
||||
"""
|
||||
x = self.forward_features(x)
|
||||
x = self.global_pool(x)
|
||||
if self.drop_rate > 0.:
|
||||
x = F.dropout(x, p=self.drop_rate, training=self.training)
|
||||
return self.head(x)
|
||||
|
||||
|
||||
def _init_nest_weights(module: nn.Module, name: str = '', head_bias: float = 0., jax_impl: bool = False):
|
||||
""" NesT weight initialization
|
||||
Can replicate Jax implementation. Otherwise follows vision_transformer.py
|
||||
"""
|
||||
if isinstance(module, nn.Linear):
|
||||
if name.startswith('head'):
|
||||
if jax_impl:
|
||||
trunc_normal_(module.weight, std=.02, a=-2, b=2)
|
||||
else:
|
||||
nn.init.zeros_(module.weight)
|
||||
nn.init.constant_(module.bias, head_bias)
|
||||
else:
|
||||
trunc_normal_(module.weight, std=.02, a=-2, b=2)
|
||||
if module.bias is not None:
|
||||
nn.init.zeros_(module.bias)
|
||||
elif jax_impl and isinstance(module, nn.Conv2d):
|
||||
trunc_normal_(module.weight, std=.02, a=-2, b=2)
|
||||
if module.bias is not None:
|
||||
nn.init.zeros_(module.bias)
|
||||
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
|
||||
nn.init.zeros_(module.bias)
|
||||
nn.init.ones_(module.weight)
|
||||
|
||||
|
||||
def resize_pos_embed(posemb, posemb_new):
|
||||
"""
|
||||
Rescale the grid of position embeddings when loading from state_dict
|
||||
Expected shape of position embeddings is (1, T, N, C), and considers only square images
|
||||
"""
|
||||
_logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
|
||||
seq_length_old = posemb.shape[2]
|
||||
num_blocks_new, seq_length_new = posemb_new.shape[1:3]
|
||||
size_new = int(math.sqrt(num_blocks_new*seq_length_new))
|
||||
# First change to (1, C, H, W)
|
||||
posemb = deblockify(posemb, int(math.sqrt(seq_length_old))).permute(0, 3, 1, 2)
|
||||
posemb = F.interpolate(posemb, size=[size_new, size_new], mode='bilinear')
|
||||
# Now change to new (1, T, N, C)
|
||||
posemb = blockify(posemb.permute(0, 2, 3, 1), int(math.sqrt(seq_length_new)))
|
||||
return posemb
|
||||
|
||||
|
||||
def checkpoint_filter_fn(state_dict, model):
|
||||
""" resize positional embeddings of pretrained weights """
|
||||
pos_embed_keys = [k for k in state_dict.keys() if k.startswith('pos_embed_')]
|
||||
for k in pos_embed_keys:
|
||||
if state_dict[k].shape != getattr(model, k).shape:
|
||||
state_dict[k] = resize_pos_embed(state_dict[k], getattr(model, k))
|
||||
return state_dict
|
||||
|
||||
|
||||
def _create_nest(variant, pretrained=False, default_cfg=None, **kwargs):
|
||||
if kwargs.get('features_only', None):
|
||||
raise RuntimeError('features_only not implemented for Vision Transformer models.')
|
||||
|
||||
default_cfg = default_cfg or default_cfgs[variant]
|
||||
model = build_model_with_cfg(
|
||||
Nest, variant, pretrained,
|
||||
default_cfg=default_cfg,
|
||||
pretrained_filter_fn=checkpoint_filter_fn,
|
||||
**kwargs)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def nest_base(pretrained=False, **kwargs):
|
||||
""" Nest-B @ 224x224
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
embed_dims=(128, 256, 512), num_heads=(4, 8, 16), depths=(2, 2, 20), drop_path_rate=0.5, **kwargs)
|
||||
model = _create_nest('nest_base', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def nest_small(pretrained=False, **kwargs):
|
||||
""" Nest-S @ 224x224
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 20), drop_path_rate=0.3, **kwargs)
|
||||
model = _create_nest('nest_small', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def nest_tiny(pretrained=False, **kwargs):
|
||||
""" Nest-T @ 224x224
|
||||
"""
|
||||
model_kwargs = dict(
|
||||
embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 8), drop_path_rate=0.2, **kwargs)
|
||||
model = _create_nest('nest_tiny', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def jx_nest_base(pretrained=False, **kwargs):
|
||||
""" Nest-B @ 224x224, Pretrained weights converted from official Jax impl.
|
||||
"""
|
||||
kwargs['pad_type'] = 'same'
|
||||
kwargs['weight_init'] = 'jax'
|
||||
model_kwargs = dict(
|
||||
embed_dims=(128, 256, 512), num_heads=(4, 8, 16), depths=(2, 2, 20), drop_path_rate=0.5, **kwargs)
|
||||
model = _create_nest('jx_nest_base', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def jx_nest_small(pretrained=False, **kwargs):
|
||||
""" Nest-S @ 224x224, Pretrained weights converted from official Jax impl.
|
||||
"""
|
||||
kwargs['pad_type'] = 'same'
|
||||
kwargs['weight_init'] = 'jax'
|
||||
model_kwargs = dict(
|
||||
embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 20), drop_path_rate=0.3, **kwargs)
|
||||
model = _create_nest('jx_nest_small', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def jx_nest_tiny(pretrained=False, **kwargs):
|
||||
""" Nest-T @ 224x224, Pretrained weights converted from official Jax impl.
|
||||
"""
|
||||
kwargs['pad_type'] = 'same'
|
||||
kwargs['weight_init'] = 'jax'
|
||||
model_kwargs = dict(
|
||||
embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 8), drop_path_rate=0.2, **kwargs)
|
||||
model = _create_nest('jx_nest_tiny', pretrained=pretrained, **model_kwargs)
|
||||
return model
|
||||
Loading…
Reference in new issue