diff --git a/README.md b/README.md index 4cd599f1..ca283605 100644 --- a/README.md +++ b/README.md @@ -26,9 +26,9 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor ### May 14, 2021 * Add EfficientNet-V2 official model defs w/ ported weights from official [Tensorflow/Keras](https://github.com/google/automl/tree/master/efficientnetv2) impl. * 1k trained variants: `tf_efficientnetv2_s/m/l` - * 21k trained variants: `tf_efficientnetv2_s/m/l_21k` - * 21k pretrained -> 1k fine-tuned: `tf_efficientnetv2_s/m/l_21ft1k` - * v2 models w/ v1 scaling: `tf_efficientnet_v2_b0` through `b3` + * 21k trained variants: `tf_efficientnetv2_s/m/l_in21k` + * 21k pretrained -> 1k fine-tuned: `tf_efficientnetv2_s/m/l_in21ft1k` + * v2 models w/ v1 scaling: `tf_efficientnetv2_b0` through `b3` * Rename my prev V2 guess `efficientnet_v2s` -> `efficientnetv2_rw_s` * Some blank `efficientnetv2_*` models in-place for future native PyTorch training diff --git a/inference.py b/inference.py index 89efb1fb..5fcf1e60 100755 --- a/inference.py +++ b/inference.py @@ -114,13 +114,13 @@ def main(): _logger.info('Predict: [{0}/{1}] Time {batch_time.val:.3f} ({batch_time.avg:.3f})'.format( batch_idx, len(loader), batch_time=batch_time)) - topk_ids = np.concatenate(topk_ids, axis=0).squeeze() + topk_ids = np.concatenate(topk_ids, axis=0) with open(os.path.join(args.output_dir, './topk_ids.csv'), 'w') as out_file: filenames = loader.dataset.filenames(basename=True) for filename, label in zip(filenames, topk_ids): - out_file.write('{0},{1},{2},{3},{4},{5}\n'.format( - filename, label[0], label[1], label[2], label[3], label[4])) + out_file.write('{0},{1}\n'.format( + filename, ','.join([ str(v) for v in label]))) if __name__ == '__main__': diff --git a/tests/test_models.py b/tests/test_models.py index 1e1de498..5ff9fb33 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -15,7 +15,9 @@ if hasattr(torch._C, '_jit_set_profiling_executor'): torch._C._jit_set_profiling_mode(False) # transformer models don't support many of the spatial / feature based model functionalities -NON_STD_FILTERS = ['vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', 'mixer_*', 'levit*', 'visformer*'] +NON_STD_FILTERS = [ + 'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*', + 'convit_*', 'levit*', 'visformer*'] NUM_NON_STD = len(NON_STD_FILTERS) # exclude models that cause specific test failures diff --git a/timm/models/__init__.py b/timm/models/__init__.py index 821012e2..1a21de09 100644 --- a/timm/models/__init__.py +++ b/timm/models/__init__.py @@ -2,6 +2,7 @@ from .byoanet import * from .byobnet import * from .cait import * from .coat import * +from .convit import * from .cspnet import * from .densenet import * from .dla import * @@ -42,6 +43,7 @@ from .vision_transformer_hybrid import * from .vovnet import * from .xception import * from .xception_aligned import * +from .twins import * from .factory import create_model, split_model_name, safe_model_name from .helpers import load_checkpoint, resume_checkpoint, model_parameters diff --git a/timm/models/convit.py b/timm/models/convit.py new file mode 100644 index 00000000..f6ae3ec1 --- /dev/null +++ b/timm/models/convit.py @@ -0,0 +1,350 @@ +""" ConViT Model + +@article{d2021convit, + title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases}, + author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent}, + journal={arXiv preprint arXiv:2103.10697}, + year={2021} +} + +Paper link: https://arxiv.org/abs/2103.10697 +Original code: https://github.com/facebookresearch/convit, original copyright below +""" +# Copyright (c) 2015-present, Facebook, Inc. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# +'''These modules are adapted from those of timm, see +https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py +''' + +import torch +import torch.nn as nn +from functools import partial +import torch.nn.functional as F + +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD +from .helpers import build_model_with_cfg +from .layers import DropPath, to_2tuple, trunc_normal_, PatchEmbed, Mlp +from .registry import register_model +from .vision_transformer_hybrid import HybridEmbed + +import torch +import torch.nn as nn + + +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'first_conv': 'patch_embed.proj', 'classifier': 'head', + **kwargs + } + + +default_cfgs = { + # ConViT + 'convit_tiny': _cfg( + url="https://dl.fbaipublicfiles.com/convit/convit_tiny.pth"), + 'convit_small': _cfg( + url="https://dl.fbaipublicfiles.com/convit/convit_small.pth"), + 'convit_base': _cfg( + url="https://dl.fbaipublicfiles.com/convit/convit_base.pth") +} + + +class GPSA(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., + locality_strength=1.): + super().__init__() + self.num_heads = num_heads + self.dim = dim + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + self.locality_strength = locality_strength + + self.qk = nn.Linear(dim, dim * 2, bias=qkv_bias) + self.v = nn.Linear(dim, dim, bias=qkv_bias) + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.pos_proj = nn.Linear(3, num_heads) + self.proj_drop = nn.Dropout(proj_drop) + self.locality_strength = locality_strength + self.gating_param = nn.Parameter(torch.ones(self.num_heads)) + self.rel_indices: torch.Tensor = torch.zeros(1, 1, 1, 3) # silly torchscript hack, won't work with None + + def forward(self, x): + B, N, C = x.shape + if self.rel_indices is None or self.rel_indices.shape[1] != N: + self.rel_indices = self.get_rel_indices(N) + attn = self.get_attention(x) + v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def get_attention(self, x): + B, N, C = x.shape + qk = self.qk(x).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k = qk[0], qk[1] + pos_score = self.rel_indices.expand(B, -1, -1, -1) + pos_score = self.pos_proj(pos_score).permute(0, 3, 1, 2) + patch_score = (q @ k.transpose(-2, -1)) * self.scale + patch_score = patch_score.softmax(dim=-1) + pos_score = pos_score.softmax(dim=-1) + + gating = self.gating_param.view(1, -1, 1, 1) + attn = (1. - torch.sigmoid(gating)) * patch_score + torch.sigmoid(gating) * pos_score + attn /= attn.sum(dim=-1).unsqueeze(-1) + attn = self.attn_drop(attn) + return attn + + def get_attention_map(self, x, return_map=False): + attn_map = self.get_attention(x).mean(0) # average over batch + distances = self.rel_indices.squeeze()[:, :, -1] ** .5 + dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / distances.size(0) + if return_map: + return dist, attn_map + else: + return dist + + def local_init(self): + self.v.weight.data.copy_(torch.eye(self.dim)) + locality_distance = 1 # max(1,1/locality_strength**.5) + + kernel_size = int(self.num_heads ** .5) + center = (kernel_size - 1) / 2 if kernel_size % 2 == 0 else kernel_size // 2 + for h1 in range(kernel_size): + for h2 in range(kernel_size): + position = h1 + kernel_size * h2 + self.pos_proj.weight.data[position, 2] = -1 + self.pos_proj.weight.data[position, 1] = 2 * (h1 - center) * locality_distance + self.pos_proj.weight.data[position, 0] = 2 * (h2 - center) * locality_distance + self.pos_proj.weight.data *= self.locality_strength + + def get_rel_indices(self, num_patches: int) -> torch.Tensor: + img_size = int(num_patches ** .5) + rel_indices = torch.zeros(1, num_patches, num_patches, 3) + ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1) + indx = ind.repeat(img_size, img_size) + indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1) + indd = indx ** 2 + indy ** 2 + rel_indices[:, :, :, 2] = indd.unsqueeze(0) + rel_indices[:, :, :, 1] = indy.unsqueeze(0) + rel_indices[:, :, :, 0] = indx.unsqueeze(0) + device = self.qk.weight.device + return rel_indices.to(device) + + +class MHSA(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def get_attention_map(self, x, return_map=False): + 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[0], qkv[1], qkv[2] + attn_map = (q @ k.transpose(-2, -1)) * self.scale + attn_map = attn_map.softmax(dim=-1).mean(0) + + img_size = int(N ** .5) + ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1) + indx = ind.repeat(img_size, img_size) + indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1) + indd = indx ** 2 + indy ** 2 + distances = indd ** .5 + distances = distances.to('cuda') + + dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / N + if return_map: + return dist, attn_map + else: + return dist + + def forward(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[0], qkv[1], qkv[2] + + attn = (q @ k.transpose(-2, -1)) * self.scale + 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 + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_gpsa=True, **kwargs): + super().__init__() + self.norm1 = norm_layer(dim) + self.use_gpsa = use_gpsa + if self.use_gpsa: + self.attn = GPSA( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, + proj_drop=drop, **kwargs) + else: + self.attn = MHSA( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, + proj_drop=drop, **kwargs) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x): + x = x + self.drop_path(self.attn(self.norm1(x))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class ConViT(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm, global_pool=None, + local_up_to_layer=3, locality_strength=1., use_pos_embed=True): + super().__init__() + embed_dim *= num_heads + self.num_classes = num_classes + self.local_up_to_layer = local_up_to_layer + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + self.locality_strength = locality_strength + self.use_pos_embed = use_pos_embed + + if hybrid_backbone is not None: + self.patch_embed = HybridEmbed( + hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim) + else: + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + self.num_patches = num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + if self.use_pos_embed: + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + trunc_normal_(self.pos_embed, std=.02) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + use_gpsa=True, + locality_strength=locality_strength) + if i < local_up_to_layer else + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + use_gpsa=False) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + # Classifier head + self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')] + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + for n, m in self.named_modules(): + if hasattr(m, 'local_init'): + m.local_init() + + 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) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x): + B = x.shape[0] + x = self.patch_embed(x) + + cls_tokens = self.cls_token.expand(B, -1, -1) + + if self.use_pos_embed: + x = x + self.pos_embed + x = self.pos_drop(x) + + for u, blk in enumerate(self.blocks): + if u == self.local_up_to_layer: + x = torch.cat((cls_tokens, x), dim=1) + x = blk(x) + + x = self.norm(x) + return x[:, 0] + + def forward(self, x): + x = self.forward_features(x) + x = self.head(x) + return x + + +def _create_convit(variant, pretrained=False, **kwargs): + return build_model_with_cfg( + ConViT, variant, pretrained, + default_cfg=default_cfgs[variant], + **kwargs) + + +@register_model +def convit_tiny(pretrained=False, **kwargs): + model_args = dict( + local_up_to_layer=10, locality_strength=1.0, embed_dim=48, + num_heads=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + model = _create_convit(variant='convit_tiny', pretrained=pretrained, **model_args) + return model + + +@register_model +def convit_small(pretrained=False, **kwargs): + model_args = dict( + local_up_to_layer=10, locality_strength=1.0, embed_dim=48, + num_heads=9, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + model = _create_convit(variant='convit_small', pretrained=pretrained, **model_args) + return model + + +@register_model +def convit_base(pretrained=False, **kwargs): + model_args = dict( + local_up_to_layer=10, locality_strength=1.0, embed_dim=48, + num_heads=16, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + model = _create_convit(variant='convit_base', pretrained=pretrained, **model_args) + return model diff --git a/timm/models/efficientnet.py b/timm/models/efficientnet.py index 1716e92d..8aa61ec5 100644 --- a/timm/models/efficientnet.py +++ b/timm/models/efficientnet.py @@ -162,6 +162,9 @@ default_cfgs = { 'efficientnetv2_rw_s': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2_288-a6477665.pth', input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0), + 'efficientnetv2_rw_m': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_rw_m_agc-3d90cb1e.pth', + input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0), 'efficientnetv2_s': _cfg( url='', @@ -173,7 +176,6 @@ default_cfgs = { url='', input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), - 'tf_efficientnet_b0': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth', input_size=(3, 224, 224)), @@ -332,28 +334,28 @@ default_cfgs = { mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), - 'tf_efficientnetv2_s_21ft1k': _cfg( + 'tf_efficientnetv2_s_in21ft1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21ft1k-d7dafa41.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0), - 'tf_efficientnetv2_m_21ft1k': _cfg( + 'tf_efficientnetv2_m_in21ft1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21ft1k-bf41664a.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), - 'tf_efficientnetv2_l_21ft1k': _cfg( + 'tf_efficientnetv2_l_in21ft1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21ft1k-60127a9d.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), - 'tf_efficientnetv2_s_21k': _cfg( + 'tf_efficientnetv2_s_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21k-6337ad01.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0), - 'tf_efficientnetv2_m_21k': _cfg( + 'tf_efficientnetv2_m_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21k-361418a2.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), - 'tf_efficientnetv2_l_21k': _cfg( + 'tf_efficientnetv2_l_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21k-91a19ec9.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), @@ -1461,7 +1463,7 @@ def efficientnet_b3_pruned(pretrained=False, **kwargs): @register_model def efficientnetv2_rw_s(pretrained=False, **kwargs): - """ EfficientNet-V2 Small. + """ EfficientNet-V2 Small RW variant. NOTE: This is my initial (pre official code release) w/ some differences. See efficientnetv2_s and tf_efficientnetv2_s for versions that match the official w/ PyTorch vs TF padding """ @@ -1469,6 +1471,16 @@ def efficientnetv2_rw_s(pretrained=False, **kwargs): return model +@register_model +def efficientnetv2_rw_m(pretrained=False, **kwargs): + """ EfficientNet-V2 Medium RW variant. + """ + model = _gen_efficientnetv2_s( + 'efficientnetv2_rw_m', channel_multiplier=1.2, depth_multiplier=(1.2,) * 4 + (1.6,) * 2, rw=True, + pretrained=pretrained, **kwargs) + return model + + @register_model def efficientnetv2_s(pretrained=False, **kwargs): """ EfficientNet-V2 Small. """ @@ -1929,62 +1941,62 @@ def tf_efficientnetv2_l(pretrained=False, **kwargs): @register_model -def tf_efficientnetv2_s_21ft1k(pretrained=False, **kwargs): +def tf_efficientnetv2_s_in21ft1k(pretrained=False, **kwargs): """ EfficientNet-V2 Small. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_s('tf_efficientnetv2_s_21ft1k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_s('tf_efficientnetv2_s_in21ft1k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_m_21ft1k(pretrained=False, **kwargs): +def tf_efficientnetv2_m_in21ft1k(pretrained=False, **kwargs): """ EfficientNet-V2 Medium. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_m('tf_efficientnetv2_m_21ft1k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_m('tf_efficientnetv2_m_in21ft1k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_l_21ft1k(pretrained=False, **kwargs): +def tf_efficientnetv2_l_in21ft1k(pretrained=False, **kwargs): """ EfficientNet-V2 Large. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_l('tf_efficientnetv2_l_21ft1k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_l('tf_efficientnetv2_l_in21ft1k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_s_21k(pretrained=False, **kwargs): +def tf_efficientnetv2_s_in21k(pretrained=False, **kwargs): """ EfficientNet-V2 Small w/ ImageNet-21k pretrained weights. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_s('tf_efficientnetv2_s_21k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_s('tf_efficientnetv2_s_in21k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_m_21k(pretrained=False, **kwargs): +def tf_efficientnetv2_m_in21k(pretrained=False, **kwargs): """ EfficientNet-V2 Medium w/ ImageNet-21k pretrained weights. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_m('tf_efficientnetv2_m_21k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_m('tf_efficientnetv2_m_in21k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_l_21k(pretrained=False, **kwargs): +def tf_efficientnetv2_l_in21k(pretrained=False, **kwargs): """ EfficientNet-V2 Large w/ ImageNet-21k pretrained weights. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_l('tf_efficientnetv2_l_21k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_l('tf_efficientnetv2_l_in21k', pretrained=pretrained, **kwargs) return model diff --git a/timm/models/efficientnet_builder.py b/timm/models/efficientnet_builder.py index 9d5853c7..30739454 100644 --- a/timm/models/efficientnet_builder.py +++ b/timm/models/efficientnet_builder.py @@ -237,7 +237,11 @@ def _scale_stage_depth(stack_args, repeats, depth_multiplier=1.0, depth_trunc='c def decode_arch_def(arch_def, depth_multiplier=1.0, depth_trunc='ceil', experts_multiplier=1, fix_first_last=False): arch_args = [] - for stack_idx, block_strings in enumerate(arch_def): + if isinstance(depth_multiplier, tuple): + assert len(depth_multiplier) == len(arch_def) + else: + depth_multiplier = (depth_multiplier,) * len(arch_def) + for stack_idx, (block_strings, multiplier) in enumerate(zip(arch_def, depth_multiplier)): assert isinstance(block_strings, list) stack_args = [] repeats = [] @@ -251,7 +255,7 @@ def decode_arch_def(arch_def, depth_multiplier=1.0, depth_trunc='ceil', experts_ if fix_first_last and (stack_idx == 0 or stack_idx == len(arch_def) - 1): arch_args.append(_scale_stage_depth(stack_args, repeats, 1.0, depth_trunc)) else: - arch_args.append(_scale_stage_depth(stack_args, repeats, depth_multiplier, depth_trunc)) + arch_args.append(_scale_stage_depth(stack_args, repeats, multiplier, depth_trunc)) return arch_args diff --git a/timm/models/layers/__init__.py b/timm/models/layers/__init__.py index 522c27e1..cd192281 100644 --- a/timm/models/layers/__init__.py +++ b/timm/models/layers/__init__.py @@ -21,7 +21,7 @@ from .inplace_abn import InplaceAbn from .involution import Involution from .linear import Linear from .mixed_conv2d import MixedConv2d -from .mlp import Mlp, GluMlp +from .mlp import Mlp, GluMlp, GatedMlp from .norm import GroupNorm from .norm_act import BatchNormAct2d, GroupNormAct from .padding import get_padding, get_same_padding, pad_same diff --git a/timm/models/layers/mlp.py b/timm/models/layers/mlp.py index b65c8d07..b3f8de11 100644 --- a/timm/models/layers/mlp.py +++ b/timm/models/layers/mlp.py @@ -34,9 +34,10 @@ class GluMlp(nn.Module): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features * 2) + assert hidden_features % 2 == 0 + self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) + self.fc2 = nn.Linear(hidden_features // 2, out_features) self.drop = nn.Dropout(drop) def forward(self, x): @@ -47,3 +48,32 @@ class GluMlp(nn.Module): x = self.fc2(x) x = self.drop(x) return x + + +class GatedMlp(nn.Module): + """ MLP as used in gMLP + """ + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, + gate_layer=None, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + if gate_layer is not None: + assert hidden_features % 2 == 0 + self.gate = gate_layer(hidden_features) + hidden_features = hidden_features // 2 # FIXME base reduction on gate property? + else: + self.gate = nn.Identity() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.gate(x) + x = self.fc2(x) + x = self.drop(x) + return x diff --git a/timm/models/mlp_mixer.py b/timm/models/mlp_mixer.py index c2c96e6c..92ca115b 100644 --- a/timm/models/mlp_mixer.py +++ b/timm/models/mlp_mixer.py @@ -1,4 +1,6 @@ -""" MLP-Mixer in PyTorch +""" MLP-Mixer, ResMLP, and gMLP in PyTorch + +This impl originally based on MLP-Mixer paper. Official JAX impl: https://github.com/google-research/vision_transformer/blob/linen/vit_jax/models_mixer.py @@ -12,6 +14,25 @@ Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2 year={2021} } +Also supporting preliminary (not verified) implementations of ResMlp, gMLP, and possibly more... + +Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404 +@misc{touvron2021resmlp, + title={ResMLP: Feedforward networks for image classification with data-efficient training}, + author={Hugo Touvron and Piotr Bojanowski and Mathilde Caron and Matthieu Cord and Alaaeldin El-Nouby and + Edouard Grave and Armand Joulin and Gabriel Synnaeve and Jakob Verbeek and Hervé Jégou}, + year={2021}, + eprint={2105.03404}, +} + +Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050 +@misc{liu2021pay, + title={Pay Attention to MLPs}, + author={Hanxiao Liu and Zihang Dai and David R. So and Quoc V. Le}, + year={2021}, + eprint={2105.08050}, +} + A thank you to paper authors for releasing code and weights. Hacked together by / Copyright 2021 Ross Wightman @@ -25,7 +46,7 @@ import torch.nn as nn from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .helpers import build_model_with_cfg, overlay_external_default_cfg -from .layers import PatchEmbed, Mlp, GluMlp, DropPath, lecun_normal_ +from .layers import PatchEmbed, Mlp, GluMlp, GatedMlp, DropPath, lecun_normal_, to_2tuple from .registry import register_model @@ -43,7 +64,6 @@ def _cfg(url='', **kwargs): default_cfgs = dict( mixer_s32_224=_cfg(), mixer_s16_224=_cfg(), - mixer_s16_glu_224=_cfg(), mixer_b32_224=_cfg(), mixer_b16_224=_cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224-76587d61.pth', @@ -60,15 +80,38 @@ default_cfgs = dict( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_l16_224_in21k-846aa33c.pth', num_classes=21843 ), + # Mixer ImageNet-21K-P pretraining + mixer_b16_224_miil_in21k=_cfg( + url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil_in21k.pth', + mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221, + ), + mixer_b16_224_miil=_cfg( + url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil.pth', + mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', + ), + + gmixer_12_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD), + gmixer_24_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD), + + resmlp_12_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD), + resmlp_24_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD), + resmlp_36_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD), + + gmlp_ti16_224=_cfg(), + gmlp_s16_224=_cfg(), + gmlp_b16_224=_cfg(), ) class MixerBlock(nn.Module): - + """ Residual Block w/ token mixing and channel MLPs + Based on: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601 + """ def __init__( - self, dim, seq_len, tokens_dim, channels_dim, - mlp_layer=Mlp, norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, drop=0., drop_path=0.): + self, dim, seq_len, mlp_ratio=(0.5, 4.0), mlp_layer=Mlp, + norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, drop=0., drop_path=0.): super().__init__() + tokens_dim, channels_dim = [int(x * dim) for x in to_2tuple(mlp_ratio)] self.norm1 = norm_layer(dim) self.mlp_tokens = mlp_layer(seq_len, tokens_dim, act_layer=act_layer, drop=drop) self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() @@ -81,6 +124,78 @@ class MixerBlock(nn.Module): return x +class Affine(nn.Module): + def __init__(self, dim): + super().__init__() + self.alpha = nn.Parameter(torch.ones((1, 1, dim))) + self.beta = nn.Parameter(torch.zeros((1, 1, dim))) + + def forward(self, x): + return torch.addcmul(self.beta, self.alpha, x) + + +class ResBlock(nn.Module): + """ Residual MLP block w/ LayerScale and Affine 'norm' + + Based on: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404 + """ + def __init__( + self, dim, seq_len, mlp_ratio=4, mlp_layer=Mlp, norm_layer=Affine, + act_layer=nn.GELU, init_values=1e-4, drop=0., drop_path=0.): + super().__init__() + channel_dim = int(dim * mlp_ratio) + self.norm1 = norm_layer(dim) + self.linear_tokens = nn.Linear(seq_len, seq_len) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + self.mlp_channels = mlp_layer(dim, channel_dim, act_layer=act_layer, drop=drop) + self.ls1 = nn.Parameter(init_values * torch.ones(dim)) + self.ls2 = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x): + x = x + self.drop_path(self.ls1 * self.linear_tokens(self.norm1(x).transpose(1, 2)).transpose(1, 2)) + x = x + self.drop_path(self.ls2 * self.mlp_channels(self.norm2(x))) + return x + + +class SpatialGatingUnit(nn.Module): + """ Spatial Gating Unit + + Based on: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050 + """ + def __init__(self, dim, seq_len, norm_layer=nn.LayerNorm): + super().__init__() + gate_dim = dim // 2 + self.norm = norm_layer(gate_dim) + self.proj = nn.Linear(seq_len, seq_len) + + def forward(self, x): + u, v = x.chunk(2, dim=-1) + v = self.norm(v) + v = self.proj(v.transpose(-1, -2)) + return u * v.transpose(-1, -2) + + +class SpatialGatingBlock(nn.Module): + """ Residual Block w/ Spatial Gating + + Based on: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050 + """ + def __init__( + self, dim, seq_len, mlp_ratio=4, mlp_layer=GatedMlp, + norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, drop=0., drop_path=0.): + super().__init__() + channel_dim = int(dim * mlp_ratio) + self.norm = norm_layer(dim) + sgu = partial(SpatialGatingUnit, seq_len=seq_len) + self.mlp_channels = mlp_layer(dim, channel_dim, act_layer=act_layer, gate_layer=sgu, drop=drop) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + + def forward(self, x): + x = x + self.drop_path(self.mlp_channels(self.norm(x))) + return x + + class MlpMixer(nn.Module): def __init__( @@ -91,24 +206,27 @@ class MlpMixer(nn.Module): patch_size=16, num_blocks=8, hidden_dim=512, - tokens_dim=256, - channels_dim=2048, + mlp_ratio=(0.5, 4.0), + block_layer=MixerBlock, mlp_layer=Mlp, norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, - drop=0., - drop_path=0., + drop_rate=0., + drop_path_rate=0., nlhb=False, + stem_norm=False, ): super().__init__() self.num_classes = num_classes - self.stem = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=hidden_dim) - # FIXME drop_path (stochastic depth scaling rule?) + self.stem = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=hidden_dim, + norm_layer=norm_layer if stem_norm else None) + # FIXME drop_path (stochastic depth scaling rule or all the same?) self.blocks = nn.Sequential(*[ - MixerBlock( - hidden_dim, self.stem.num_patches, tokens_dim, channels_dim, - mlp_layer=mlp_layer, norm_layer=norm_layer, act_layer=act_layer, drop=drop, drop_path=drop_path) + block_layer( + hidden_dim, self.stem.num_patches, mlp_ratio, mlp_layer=mlp_layer, norm_layer=norm_layer, + act_layer=act_layer, drop=drop_rate, drop_path=drop_path_rate) for _ in range(num_blocks)]) self.norm = norm_layer(hidden_dim) self.head = nn.Linear(hidden_dim, self.num_classes) # zero init @@ -136,6 +254,9 @@ def _init_weights(m, n: str, head_bias: float = 0.): if n.startswith('head'): nn.init.zeros_(m.weight) nn.init.constant_(m.bias, head_bias) + elif n.endswith('gate.proj'): + nn.init.normal_(m.weight, std=1e-4) + nn.init.ones_(m.bias) else: nn.init.xavier_uniform_(m.weight) if m.bias is not None: @@ -177,8 +298,9 @@ def _create_mixer(variant, pretrained=False, default_cfg=None, **kwargs): @register_model def mixer_s32_224(pretrained=False, **kwargs): """ Mixer-S/32 224x224 + Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601 """ - model_args = dict(patch_size=32, num_blocks=8, hidden_dim=512, tokens_dim=256, channels_dim=2048, **kwargs) + model_args = dict(patch_size=32, num_blocks=8, hidden_dim=512, **kwargs) model = _create_mixer('mixer_s32_224', pretrained=pretrained, **model_args) return model @@ -186,28 +308,19 @@ def mixer_s32_224(pretrained=False, **kwargs): @register_model def mixer_s16_224(pretrained=False, **kwargs): """ Mixer-S/16 224x224 + Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601 """ - model_args = dict(patch_size=16, num_blocks=8, hidden_dim=512, tokens_dim=256, channels_dim=2048, **kwargs) + model_args = dict(patch_size=16, num_blocks=8, hidden_dim=512, **kwargs) model = _create_mixer('mixer_s16_224', pretrained=pretrained, **model_args) return model -@register_model -def mixer_s16_glu_224(pretrained=False, **kwargs): - """ Mixer-S/16 224x224 - """ - model_args = dict( - patch_size=16, num_blocks=8, hidden_dim=512, tokens_dim=256, channels_dim=1536, - mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs) - model = _create_mixer('mixer_s16_glu_224', pretrained=pretrained, **model_args) - return model - - @register_model def mixer_b32_224(pretrained=False, **kwargs): """ Mixer-B/32 224x224 + Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601 """ - model_args = dict(patch_size=32, num_blocks=12, hidden_dim=768, tokens_dim=384, channels_dim=3072, **kwargs) + model_args = dict(patch_size=32, num_blocks=12, hidden_dim=768, **kwargs) model = _create_mixer('mixer_b32_224', pretrained=pretrained, **model_args) return model @@ -215,8 +328,9 @@ def mixer_b32_224(pretrained=False, **kwargs): @register_model def mixer_b16_224(pretrained=False, **kwargs): """ Mixer-B/16 224x224. ImageNet-1k pretrained weights. + Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601 """ - model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, tokens_dim=384, channels_dim=3072, **kwargs) + model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, **kwargs) model = _create_mixer('mixer_b16_224', pretrained=pretrained, **model_args) return model @@ -224,8 +338,9 @@ def mixer_b16_224(pretrained=False, **kwargs): @register_model def mixer_b16_224_in21k(pretrained=False, **kwargs): """ Mixer-B/16 224x224. ImageNet-21k pretrained weights. + Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601 """ - model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, tokens_dim=384, channels_dim=3072, **kwargs) + model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, **kwargs) model = _create_mixer('mixer_b16_224_in21k', pretrained=pretrained, **model_args) return model @@ -233,8 +348,9 @@ def mixer_b16_224_in21k(pretrained=False, **kwargs): @register_model def mixer_l32_224(pretrained=False, **kwargs): """ Mixer-L/32 224x224. + Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601 """ - model_args = dict(patch_size=32, num_blocks=24, hidden_dim=1024, tokens_dim=512, channels_dim=4096, **kwargs) + model_args = dict(patch_size=32, num_blocks=24, hidden_dim=1024, **kwargs) model = _create_mixer('mixer_l32_224', pretrained=pretrained, **model_args) return model @@ -242,8 +358,9 @@ def mixer_l32_224(pretrained=False, **kwargs): @register_model def mixer_l16_224(pretrained=False, **kwargs): """ Mixer-L/16 224x224. ImageNet-1k pretrained weights. + Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601 """ - model_args = dict(patch_size=16, num_blocks=24, hidden_dim=1024, tokens_dim=512, channels_dim=4096, **kwargs) + model_args = dict(patch_size=16, num_blocks=24, hidden_dim=1024, **kwargs) model = _create_mixer('mixer_l16_224', pretrained=pretrained, **model_args) return model @@ -251,7 +368,118 @@ def mixer_l16_224(pretrained=False, **kwargs): @register_model def mixer_l16_224_in21k(pretrained=False, **kwargs): """ Mixer-L/16 224x224. ImageNet-21k pretrained weights. + Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601 """ - model_args = dict(patch_size=16, num_blocks=24, hidden_dim=1024, tokens_dim=512, channels_dim=4096, **kwargs) + model_args = dict(patch_size=16, num_blocks=24, hidden_dim=1024, **kwargs) model = _create_mixer('mixer_l16_224_in21k', pretrained=pretrained, **model_args) return model + +@register_model +def mixer_b16_224_miil(pretrained=False, **kwargs): + """ Mixer-B/16 224x224. ImageNet-21k pretrained weights. + Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K + """ + model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, **kwargs) + model = _create_mixer('mixer_b16_224_miil', pretrained=pretrained, **model_args) + return model + +@register_model +def mixer_b16_224_miil_in21k(pretrained=False, **kwargs): + """ Mixer-B/16 224x224. ImageNet-1k pretrained weights. + Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K + """ + model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, **kwargs) + model = _create_mixer('mixer_b16_224_miil_in21k', pretrained=pretrained, **model_args) + return model + +@register_model +def gmixer_12_224(pretrained=False, **kwargs): + """ Glu-Mixer-12 224x224 (short & fat) + Experiment by Ross Wightman, adding (Si)GLU to MLP-Mixer + """ + model_args = dict( + patch_size=20, num_blocks=12, hidden_dim=512, mlp_ratio=(1.0, 6.0), + mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs) + model = _create_mixer('gmixer_12_224', pretrained=pretrained, **model_args) + return model + + +@register_model +def gmixer_24_224(pretrained=False, **kwargs): + """ Glu-Mixer-24 224x224 (tall & slim) + Experiment by Ross Wightman, adding (Si)GLU to MLP-Mixer + """ + model_args = dict( + patch_size=20, num_blocks=24, hidden_dim=384, mlp_ratio=(1.0, 6.0), + mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs) + model = _create_mixer('gmixer_24_224', pretrained=pretrained, **model_args) + return model + + +@register_model +def resmlp_12_224(pretrained=False, **kwargs): + """ ResMLP-12 + Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404 + """ + model_args = dict( + patch_size=16, num_blocks=12, hidden_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs) + model = _create_mixer('resmlp_12_224', pretrained=pretrained, **model_args) + return model + + +@register_model +def resmlp_24_224(pretrained=False, **kwargs): + """ ResMLP-24 + Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404 + """ + model_args = dict( + patch_size=16, num_blocks=24, hidden_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs) + model = _create_mixer('resmlp_24_224', pretrained=pretrained, **model_args) + return model + + +@register_model +def resmlp_36_224(pretrained=False, **kwargs): + """ ResMLP-36 + Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404 + """ + model_args = dict( + patch_size=16, num_blocks=36, hidden_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs) + model = _create_mixer('resmlp_36_224', pretrained=pretrained, **model_args) + return model + + +@register_model +def gmlp_ti16_224(pretrained=False, **kwargs): + """ gMLP-Tiny + Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050 + """ + model_args = dict( + patch_size=16, num_blocks=30, hidden_dim=128, mlp_ratio=6, block_layer=SpatialGatingBlock, + mlp_layer=GatedMlp, **kwargs) + model = _create_mixer('gmlp_ti16_224', pretrained=pretrained, **model_args) + return model + + +@register_model +def gmlp_s16_224(pretrained=False, **kwargs): + """ gMLP-Small + Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050 + """ + model_args = dict( + patch_size=16, num_blocks=30, hidden_dim=256, mlp_ratio=6, block_layer=SpatialGatingBlock, + mlp_layer=GatedMlp, **kwargs) + model = _create_mixer('gmlp_s16_224', pretrained=pretrained, **model_args) + return model + + +@register_model +def gmlp_b16_224(pretrained=False, **kwargs): + """ gMLP-Base + Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050 + """ + model_args = dict( + patch_size=16, num_blocks=30, hidden_dim=512, mlp_ratio=6, block_layer=SpatialGatingBlock, + mlp_layer=GatedMlp, **kwargs) + model = _create_mixer('gmlp_b16_224', pretrained=pretrained, **model_args) + return model diff --git a/timm/models/tnt.py b/timm/models/tnt.py index cc732677..8e038718 100644 --- a/timm/models/tnt.py +++ b/timm/models/tnt.py @@ -14,7 +14,9 @@ from functools import partial from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from timm.models.helpers import load_pretrained from timm.models.layers import Mlp, DropPath, trunc_normal_ +from timm.models.layers.helpers import to_2tuple from timm.models.registry import register_model +from timm.models.vision_transformer import resize_pos_embed def _cfg(url='', **kwargs): @@ -118,11 +120,15 @@ class PixelEmbed(nn.Module): """ def __init__(self, img_size=224, patch_size=16, in_chans=3, in_dim=48, stride=4): super().__init__() - num_patches = (img_size // patch_size) ** 2 + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + # grid_size property necessary for resizing positional embedding + self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + num_patches = (self.grid_size[0]) * (self.grid_size[1]) self.img_size = img_size self.num_patches = num_patches self.in_dim = in_dim - new_patch_size = math.ceil(patch_size / stride) + new_patch_size = [math.ceil(ps / stride) for ps in patch_size] self.new_patch_size = new_patch_size self.proj = nn.Conv2d(in_chans, self.in_dim, kernel_size=7, padding=3, stride=stride) @@ -130,11 +136,11 @@ class PixelEmbed(nn.Module): def forward(self, x, pixel_pos): B, C, H, W = x.shape - assert H == self.img_size and W == self.img_size, \ - f"Input image size ({H}*{W}) doesn't match model ({self.img_size}*{self.img_size})." + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." x = self.proj(x) x = self.unfold(x) - x = x.transpose(1, 2).reshape(B * self.num_patches, self.in_dim, self.new_patch_size, self.new_patch_size) + x = x.transpose(1, 2).reshape(B * self.num_patches, self.in_dim, self.new_patch_size[0], self.new_patch_size[1]) x = x + pixel_pos x = x.reshape(B * self.num_patches, self.in_dim, -1).transpose(1, 2) return x @@ -155,7 +161,7 @@ class TNT(nn.Module): num_patches = self.pixel_embed.num_patches self.num_patches = num_patches new_patch_size = self.pixel_embed.new_patch_size - num_pixel = new_patch_size ** 2 + num_pixel = new_patch_size[0] * new_patch_size[1] self.norm1_proj = norm_layer(num_pixel * in_dim) self.proj = nn.Linear(num_pixel * in_dim, embed_dim) @@ -163,7 +169,7 @@ class TNT(nn.Module): self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.patch_pos = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) - self.pixel_pos = nn.Parameter(torch.zeros(1, in_dim, new_patch_size, new_patch_size)) + self.pixel_pos = nn.Parameter(torch.zeros(1, in_dim, new_patch_size[0], new_patch_size[1])) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule @@ -224,6 +230,14 @@ class TNT(nn.Module): return x +def checkpoint_filter_fn(state_dict, model): + """ convert patch embedding weight from manual patchify + linear proj to conv""" + if state_dict['patch_pos'].shape != model.patch_pos.shape: + state_dict['patch_pos'] = resize_pos_embed(state_dict['patch_pos'], + model.patch_pos, getattr(model, 'num_tokens', 1), model.pixel_embed.grid_size) + return state_dict + + @register_model def tnt_s_patch16_224(pretrained=False, **kwargs): model = TNT(patch_size=16, embed_dim=384, in_dim=24, depth=12, num_heads=6, in_num_head=4, @@ -231,7 +245,8 @@ def tnt_s_patch16_224(pretrained=False, **kwargs): model.default_cfg = default_cfgs['tnt_s_patch16_224'] if pretrained: load_pretrained( - model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3)) + model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), + filter_fn=checkpoint_filter_fn) return model diff --git a/timm/models/twins.py b/timm/models/twins.py new file mode 100644 index 00000000..a534d174 --- /dev/null +++ b/timm/models/twins.py @@ -0,0 +1,431 @@ +""" Twins +A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers` + - https://arxiv.org/pdf/2104.13840.pdf + +Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below + +""" +# -------------------------------------------------------- +# Twins +# Copyright (c) 2021 Meituan +# Licensed under The Apache 2.0 License [see LICENSE for details] +# Written by Xinjie Li, Xiangxiang Chu +# -------------------------------------------------------- +import math +from copy import deepcopy +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from functools import partial + +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD +from .layers import Mlp, DropPath, to_2tuple, trunc_normal_ +from .registry import register_model +from .vision_transformer import Attention +from .helpers import build_model_with_cfg, overlay_external_default_cfg + + +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, + 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'first_conv': 'patch_embed.proj', 'classifier': 'head', + **kwargs + } + + +default_cfgs = { + 'twins_pcpvt_small': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_small-e70e7e7a.pth', + ), + 'twins_pcpvt_base': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_base-e5ecb09b.pth', + ), + 'twins_pcpvt_large': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_large-d273f802.pth', + ), + 'twins_svt_small': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_svt_small-42e5f78c.pth', + ), + 'twins_svt_base': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_svt_base-c2265010.pth', + ), + 'twins_svt_large': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_svt_large-90f6aaa9.pth', + ), +} + +Size_ = Tuple[int, int] + + +class LocallyGroupedAttn(nn.Module): + """ LSA: self attention within a group + """ + def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., ws=1): + assert ws != 1 + super(LocallyGroupedAttn, self).__init__() + assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." + + self.dim = dim + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=True) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.ws = ws + + def forward(self, x, size: Size_): + # There are two implementations for this function, zero padding or mask. We don't observe obvious difference for + # both. You can choose any one, we recommend forward_padding because it's neat. However, + # the masking implementation is more reasonable and accurate. + B, N, C = x.shape + H, W = size + x = x.view(B, H, W, C) + pad_l = pad_t = 0 + pad_r = (self.ws - W % self.ws) % self.ws + pad_b = (self.ws - H % self.ws) % self.ws + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + _h, _w = Hp // self.ws, Wp // self.ws + x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3) + qkv = self.qkv(x).reshape( + B, _h * _w, self.ws * self.ws, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5) + q, k, v = qkv[0], qkv[1], qkv[2] + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.ws, self.ws, C) + x = attn.transpose(2, 3).reshape(B, _h * self.ws, _w * self.ws, C) + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + x = x.reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + # def forward_mask(self, x, size: Size_): + # B, N, C = x.shape + # H, W = size + # x = x.view(B, H, W, C) + # pad_l = pad_t = 0 + # pad_r = (self.ws - W % self.ws) % self.ws + # pad_b = (self.ws - H % self.ws) % self.ws + # x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + # _, Hp, Wp, _ = x.shape + # _h, _w = Hp // self.ws, Wp // self.ws + # mask = torch.zeros((1, Hp, Wp), device=x.device) + # mask[:, -pad_b:, :].fill_(1) + # mask[:, :, -pad_r:].fill_(1) + # + # x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3) # B, _h, _w, ws, ws, C + # mask = mask.reshape(1, _h, self.ws, _w, self.ws).transpose(2, 3).reshape(1, _h * _w, self.ws * self.ws) + # attn_mask = mask.unsqueeze(2) - mask.unsqueeze(3) # 1, _h*_w, ws*ws, ws*ws + # attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-1000.0)).masked_fill(attn_mask == 0, float(0.0)) + # qkv = self.qkv(x).reshape( + # B, _h * _w, self.ws * self.ws, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5) + # # n_h, B, _w*_h, nhead, ws*ws, dim + # q, k, v = qkv[0], qkv[1], qkv[2] # B, _h*_w, n_head, ws*ws, dim_head + # attn = (q @ k.transpose(-2, -1)) * self.scale # B, _h*_w, n_head, ws*ws, ws*ws + # attn = attn + attn_mask.unsqueeze(2) + # attn = attn.softmax(dim=-1) + # attn = self.attn_drop(attn) # attn @v -> B, _h*_w, n_head, ws*ws, dim_head + # attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.ws, self.ws, C) + # x = attn.transpose(2, 3).reshape(B, _h * self.ws, _w * self.ws, C) + # if pad_r > 0 or pad_b > 0: + # x = x[:, :H, :W, :].contiguous() + # x = x.reshape(B, N, C) + # x = self.proj(x) + # x = self.proj_drop(x) + # return x + + +class GlobalSubSampleAttn(nn.Module): + """ GSA: using a key to summarize the information for a group to be efficient. + """ + def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., sr_ratio=1): + super().__init__() + assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." + + self.dim = dim + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim ** -0.5 + + self.q = nn.Linear(dim, dim, bias=True) + self.kv = nn.Linear(dim, dim * 2, bias=True) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.sr_ratio = sr_ratio + if sr_ratio > 1: + self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) + self.norm = nn.LayerNorm(dim) + else: + self.sr = None + self.norm = None + + def forward(self, x, size: Size_): + B, N, C = x.shape + q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) + + if self.sr is not None: + x = x.permute(0, 2, 1).reshape(B, C, *size) + x = self.sr(x).reshape(B, C, -1).permute(0, 2, 1) + x = self.norm(x) + kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + k, v = kv[0], kv[1] + + attn = (q @ k.transpose(-2, -1)) * self.scale + 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 + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, ws=None): + super().__init__() + self.norm1 = norm_layer(dim) + if ws is None: + self.attn = Attention(dim, num_heads, False, None, attn_drop, drop) + elif ws == 1: + self.attn = GlobalSubSampleAttn(dim, num_heads, attn_drop, drop, sr_ratio) + else: + self.attn = LocallyGroupedAttn(dim, num_heads, attn_drop, drop, ws) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x, size: Size_): + x = x + self.drop_path(self.attn(self.norm1(x), size)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PosConv(nn.Module): + # PEG from https://arxiv.org/abs/2102.10882 + def __init__(self, in_chans, embed_dim=768, stride=1): + super(PosConv, self).__init__() + self.proj = nn.Sequential(nn.Conv2d(in_chans, embed_dim, 3, stride, 1, bias=True, groups=embed_dim), ) + self.stride = stride + + def forward(self, x, size: Size_): + B, N, C = x.shape + cnn_feat_token = x.transpose(1, 2).view(B, C, *size) + x = self.proj(cnn_feat_token) + if self.stride == 1: + x += cnn_feat_token + x = x.flatten(2).transpose(1, 2) + return x + + def no_weight_decay(self): + return ['proj.%d.weight' % i for i in range(4)] + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + + self.img_size = img_size + self.patch_size = patch_size + assert img_size[0] % patch_size[0] == 0 and img_size[1] % patch_size[1] == 0, \ + f"img_size {img_size} should be divided by patch_size {patch_size}." + self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1] + self.num_patches = self.H * self.W + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + self.norm = nn.LayerNorm(embed_dim) + + def forward(self, x) -> Tuple[torch.Tensor, Size_]: + B, C, H, W = x.shape + + x = self.proj(x).flatten(2).transpose(1, 2) + x = self.norm(x) + out_size = (H // self.patch_size[0], W // self.patch_size[1]) + + return x, out_size + + +class Twins(nn.Module): + """ Twins Vision Transfomer (Revisiting Spatial Attention) + + Adapted from PVT (PyramidVisionTransformer) class at https://github.com/whai362/PVT.git + """ + def __init__( + self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dims=(64, 128, 256, 512), + num_heads=(1, 2, 4, 8), mlp_ratios=(4, 4, 4, 4), drop_rate=0., attn_drop_rate=0., drop_path_rate=0., + norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=(3, 4, 6, 3), sr_ratios=(8, 4, 2, 1), wss=None, + block_cls=Block): + super().__init__() + self.num_classes = num_classes + self.depths = depths + + img_size = to_2tuple(img_size) + prev_chs = in_chans + self.patch_embeds = nn.ModuleList() + self.pos_drops = nn.ModuleList() + for i in range(len(depths)): + self.patch_embeds.append(PatchEmbed(img_size, patch_size, prev_chs, embed_dims[i])) + self.pos_drops.append(nn.Dropout(p=drop_rate)) + prev_chs = embed_dims[i] + img_size = tuple(t // patch_size for t in img_size) + patch_size = 2 + + self.blocks = nn.ModuleList() + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + cur = 0 + for k in range(len(depths)): + _block = nn.ModuleList([block_cls( + dim=embed_dims[k], num_heads=num_heads[k], mlp_ratio=mlp_ratios[k], drop=drop_rate, + attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[k], + ws=1 if wss is None or i % 2 == 1 else wss[k]) for i in range(depths[k])]) + self.blocks.append(_block) + cur += depths[k] + + self.pos_block = nn.ModuleList([PosConv(embed_dim, embed_dim) for embed_dim in embed_dims]) + + self.norm = norm_layer(embed_dims[-1]) + + # classification head + self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity() + + # init weights + self.apply(self._init_weights) + + @torch.jit.ignore + def no_weight_decay(self): + return set(['pos_block.' + n for n, p in self.pos_block.named_parameters()]) + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + 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) + elif isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + fan_out //= m.groups + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1.0) + m.bias.data.zero_() + + def forward_features(self, x): + B = x.shape[0] + for i, (embed, drop, blocks, pos_blk) in enumerate( + zip(self.patch_embeds, self.pos_drops, self.blocks, self.pos_block)): + x, size = embed(x) + x = drop(x) + for j, blk in enumerate(blocks): + x = blk(x, size) + if j == 0: + x = pos_blk(x, size) # PEG here + if i < len(self.depths) - 1: + x = x.reshape(B, *size, -1).permute(0, 3, 1, 2).contiguous() + x = self.norm(x) + return x.mean(dim=1) # GAP here + + def forward(self, x): + x = self.forward_features(x) + x = self.head(x) + return x + + +def _create_twins(variant, pretrained=False, default_cfg=None, **kwargs): + if default_cfg is None: + default_cfg = deepcopy(default_cfgs[variant]) + overlay_external_default_cfg(default_cfg, kwargs) + default_num_classes = default_cfg['num_classes'] + default_img_size = default_cfg['input_size'][-2:] + + num_classes = kwargs.pop('num_classes', default_num_classes) + img_size = kwargs.pop('img_size', default_img_size) + if kwargs.get('features_only', None): + raise RuntimeError('features_only not implemented for Vision Transformer models.') + + model = build_model_with_cfg( + Twins, variant, pretrained, + default_cfg=default_cfg, + img_size=img_size, + num_classes=num_classes, + **kwargs) + + return model + + +@register_model +def twins_pcpvt_small(pretrained=False, **kwargs): + model_kwargs = dict( + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], + depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('twins_pcpvt_small', pretrained=pretrained, **model_kwargs) + + +@register_model +def twins_pcpvt_base(pretrained=False, **kwargs): + model_kwargs = dict( + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], + depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('twins_pcpvt_base', pretrained=pretrained, **model_kwargs) + + +@register_model +def twins_pcpvt_large(pretrained=False, **kwargs): + model_kwargs = dict( + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], + depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('twins_pcpvt_large', pretrained=pretrained, **model_kwargs) + + +@register_model +def twins_svt_small(pretrained=False, **kwargs): + model_kwargs = dict( + patch_size=4, embed_dims=[64, 128, 256, 512], num_heads=[2, 4, 8, 16], mlp_ratios=[4, 4, 4, 4], + depths=[2, 2, 10, 4], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('twins_svt_small', pretrained=pretrained, **model_kwargs) + + +@register_model +def twins_svt_base(pretrained=False, **kwargs): + model_kwargs = dict( + patch_size=4, embed_dims=[96, 192, 384, 768], num_heads=[3, 6, 12, 24], mlp_ratios=[4, 4, 4, 4], + depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('twins_svt_base', pretrained=pretrained, **model_kwargs) + + +@register_model +def twins_svt_large(pretrained=False, **kwargs): + model_kwargs = dict( + patch_size=4, embed_dims=[128, 256, 512, 1024], num_heads=[4, 8, 16, 32], mlp_ratios=[4, 4, 4, 4], + depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('twins_svt_large', pretrained=pretrained, **model_kwargs) diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py index cc7e0903..bef6dfb0 100644 --- a/timm/models/vision_transformer.py +++ b/timm/models/vision_transformer.py @@ -352,7 +352,7 @@ def _init_vit_weights(m, n: str = '', head_bias: float = 0., jax_impl: bool = Fa nn.init.ones_(m.weight) -def resize_pos_embed(posemb, posemb_new, num_tokens=1): +def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()): # Rescale the grid of position embeddings when loading from state_dict. Adapted from # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224 _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape) @@ -363,11 +363,13 @@ def resize_pos_embed(posemb, posemb_new, num_tokens=1): else: posemb_tok, posemb_grid = posemb[:, :0], posemb[0] gs_old = int(math.sqrt(len(posemb_grid))) - gs_new = int(math.sqrt(ntok_new)) - _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new) + if not len(gs_new): # backwards compatibility + gs_new = [int(math.sqrt(ntok_new))] * 2 + assert len(gs_new) >= 2 + _logger.info('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new) posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) - posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bilinear') - posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -1) + posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bilinear') + posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb @@ -385,7 +387,8 @@ def checkpoint_filter_fn(state_dict, model): v = v.reshape(O, -1, H, W) elif k == 'pos_embed' and v.shape != model.pos_embed.shape: # To resize pos embedding when using model at different size from pretrained weights - v = resize_pos_embed(v, model.pos_embed, getattr(model, 'num_tokens', 1)) + v = resize_pos_embed(v, model.pos_embed, getattr(model, 'num_tokens', 1), + model.patch_embed.grid_size) out_dict[k] = v return out_dict