Added usage helpers

.gitignore

@@ -0,0 +1,2 @@
+.models/
+__pycache__

model.py

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+# * SPDX-License-Identifier: Apache-2.0
+# * © 2023 ETH Zurich and other contributors, see AUTHORS.txt for details
+
+import os
+from typing import List
+
+import torch
+from PIL import Image
+from fastapi import HTTPException
+from mtc_api_utils.base_model import MLBaseModel
+from mtc_api_utils.init_api import download_if_not_exists
+from torchvision import transforms
+
+from aesthetics_model.api_types import AestheticsModelResponse
+from aesthetics_model.config import AestheticsModelConfig
+from aesthetics_model.model.vision_transformer import vit_large_patch16_224_in21k
+
+
+class AestheticModel(MLBaseModel):
+ model = None
+ transform = None
+ device = None
+ model_checkpoint_path = None
+
+ def init_model(self):
+ print("Initializing Aesthetic Model...")
+ # Download image archive
+ self.model_checkpoint_path = download_if_not_exists(
+ artifact_url=os.path.join(AestheticsModelConfig.model_base_url, AestheticsModelConfig.model_url),
+ download_dir=AestheticsModelConfig.model_checkpoint_path,
+ polybox_auth=AestheticsModelConfig.polybox_credentials,
+ is_tar=False,
+ )
+ print("Model downloaded, loading to device..")
+
+ self.model, self.transform, self.device = self.load_model()
+ print("Model initialized")
+
+ def is_ready(self):
+ return self.model is not None
+
+ def inference(self, images: List[Image.Image]) -> List[AestheticsModelResponse]:
+ if not self.is_ready():
+ raise HTTPException(status_code=503, detail="Model is not ready")
+
+ results = []
+ for image in images:
+ im = self.validate_and_resize_image(image, max_size=AestheticsModelConfig.max_image_size)
+ image_tensor = self.transform(im).unsqueeze(0).to(self.device)
+ with torch.no_grad():
+ image_embedding = self.model.forward_features(image_tensor)
+ score = self.model.head(image_embedding).squeeze().to("cpu")
+ result: AestheticsModelResponse = AestheticsModelResponse(
+ aesthetics_score=float(score),
+ aesthetics_embedding=image_embedding.to("cpu").tolist(),
+ # TODO: success is going to be always true
+ success=True
+ )
+ results.append(result)
+ return results
+
+ def load_model(self):
+ # Check if a gpu and cuda is available
+ use_cuda = torch.cuda.is_available()
+ device = torch.device("cuda:0" if use_cuda else "cpu")
+ print(f"Running on {device}")
+
+ model = vit_large_patch16_224_in21k()
+ model.reset_classifier(num_classes=1)
+ model.load_state_dict(torch.load(self.model_checkpoint_path, map_location=device))
+
+ model.eval()
+ model.to(device)
+
+ transform = transforms.Compose(
+ [transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]
+ )
+
+ return model, transform, device
+
+ def validate_and_resize_image(self, image: Image.Image, max_size: int):
+ """Check if an image is to big, if it is too big resize it. Transform to RGB if B&W image.
+
+ Args:
+ img : PIL Image
+ max_size : int
+ """
+ if not image.mode == "RGB":
+ image = image.convert(mode="RGB")
+
+ size_factor = (max_size + 0.0) / max(image.size)
+ if size_factor < 1:
+ new_h = round(image.size[0] * size_factor)
+ new_w = round(image.size[1] * size_factor)
+ image = image.resize((new_w, new_h), resample=Image.HAMMING)
+ return image
+
+
+if __name__ == '__main__':
+ model = AestheticModel()
+ model.__wait_until_ready__()
+
+ example_images = ["../tests/test_image.png"] # model.get_examples()
+
+ # Perform inference with underlying model
+ images = [model.model(Image.open(image)) for image in example_images]
+
+ model_result = model.inference(images=images)
+ print(model_result)

test.py

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+# from transformers import AutoModel
+from huggingface_hub import hf_hub_download
+from vision_transformer import vit_large_patch16_224_in21k
+import torch
+import numpy as np
+
+REPO_ID = "ethz-mtc/aesthetics_vit"
+FILENAME="pytorch_model.bin"
+
+path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, cache_dir=".models")
+print(path)
+REPO_ID = "ethz-mtc/shot_scale_classifier-resnet50"
+path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, cache_dir=".models")
+print(path)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+model = vit_large_patch16_224_in21k()
+model.reset_classifier(num_classes=1)
+model.load_state_dict(torch.load(path, map_location=device))
+
+print(
+ f"Model has {sum(np.prod(p.shape) for p in model.parameters()):,} parameters."
+)

vision_transformer.py

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+# * SPDX-License-Identifier: Apache-2.0
+# * © 2023 ETH Zurich and other contributors, see AUTHORS.txt for details
+
+# Based on timm library: git+https://github.com/rwightman/pytorch-image-models.git@95feb1da41c1fe95ce9634b83db343e08224a8c5
+""" Vision Transformer (ViT) in PyTorch
+
+A PyTorch implement of Vision Transformers as described in
+'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
+
+The official jax code is released and available at https://github.com/google-research/vision_transformer
+
+Acknowledgments:
+* The paper authors for releasing code and weights, thanks!
+* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
+for some einops/einsum fun
+* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
+* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
+
+DeiT model defs and weights from https://github.com/facebookresearch/deit,
+paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import math
+import logging
+from functools import partial
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from timm.models.layers import StdConv2dSame, DropPath, to_2tuple, trunc_normal_
+from timm.models.resnet import resnet26d, resnet50d
+from timm.models.resnetv2 import ResNetV2
+from timm.models.registry import register_model
+
+_logger = logging.getLogger(__name__)
+
+
+def _cfg(url='', **kwargs):
+ return {
+ 'url': url,
+ 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+ 'crop_pct': .9, 'interpolation': 'bicubic',
+ 'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225),
+ 'first_conv': 'patch_embed.proj', 'classifier': 'head',
+ **kwargs
+ }
+
+
+default_cfgs = {
+ # patch models (my experiments)
+ 'vit_small_patch16_224': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
+ ),
+
+ # patch models (weights ported from official Google JAX impl)
+ 'vit_base_patch16_224': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
+ mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+ ),
+ 'vit_base_patch32_224': _cfg(
+ url='', # no official model weights for this combo, only for in21k
+ mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+ 'vit_base_patch16_384': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
+ input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+ 'vit_base_patch32_384': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
+ input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+ 'vit_large_patch16_224': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
+ mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+ 'vit_large_patch32_224': _cfg(
+ url='', # no official model weights for this combo, only for in21k
+ mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+ 'vit_large_patch16_384': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
+ input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+ 'vit_large_patch32_384': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
+ input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+
+ # patch models, imagenet21k (weights ported from official Google JAX impl)
+ 'vit_base_patch16_224_in21k': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch16_224_in21k-e5005f0a.pth',
+ num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+ 'vit_base_patch32_224_in21k': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch32_224_in21k-8db57226.pth',
+ num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+ 'vit_large_patch16_224_in21k': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch16_224_in21k-606da67d.pth',
+ num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+ 'vit_large_patch32_224_in21k': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',
+ num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+ 'vit_huge_patch14_224_in21k': _cfg(
+ url='', # FIXME I have weights for this but > 2GB limit for github release binaries
+ num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+
+ # hybrid models (weights ported from official Google JAX impl)
+ 'vit_base_resnet50_224_in21k': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth',
+ num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.9, first_conv='patch_embed.backbone.stem.conv'),
+ 'vit_base_resnet50_384': _cfg(
+ url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
+ input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'),
+
+ # hybrid models (my experiments)
+ 'vit_small_resnet26d_224': _cfg(),
+ 'vit_small_resnet50d_s3_224': _cfg(),
+ 'vit_base_resnet26d_224': _cfg(),
+ 'vit_base_resnet50d_224': _cfg(),
+
+ # deit models (FB weights)
+ 'vit_deit_tiny_patch16_224': _cfg(
+ url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth'),
+ 'vit_deit_small_patch16_224': _cfg(
+ url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'),
+ 'vit_deit_base_patch16_224': _cfg(
+ url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',),
+ 'vit_deit_base_patch16_384': _cfg(
+ url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth',
+ input_size=(3, 384, 384), crop_pct=1.0),
+ 'vit_deit_tiny_distilled_patch16_224': _cfg(
+ url='https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth'),
+ 'vit_deit_small_distilled_patch16_224': _cfg(
+ url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth'),
+ 'vit_deit_base_distilled_patch16_224': _cfg(
+ url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth', ),
+ 'vit_deit_base_distilled_patch16_384': _cfg(
+ url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth',
+ input_size=(3, 384, 384), crop_pct=1.0),
+}
+
+
+class Mlp(nn.Module):
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, 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()
+ 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.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class Attention(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
+ # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+ 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 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] # make torchscript happy (cannot use tensor as tuple)
+
+ attn = (q @ k.transpose(-2, -1)) * self.scale
+ # TODO: this is where the masking of the pads should happen
+ #if mask is not None:
+ # attn = attn.masked_fill(mask == 0, -1e9) or float('-1e20')
+
+ 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):
+ super().__init__()
+ self.norm1 = norm_layer(dim)
+ self.attn = Attention(
+ dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+ # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+ 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 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)
+ num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.num_patches = num_patches
+
+ self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+ def forward(self, x):
+ B, C, H, W = x.shape
+ # FIXME look at relaxing size constraints
+ #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).flatten(2).transpose(1, 2)
+ #print("Patch embedding output shape {} for input image {}".format(x.shape, [B,C,H,W]))
+ #print("Number of patches: {}".format(self.num_patches))
+ return x
+
+
+class HybridEmbed(nn.Module):
+ """ CNN Feature Map Embedding
+ Extract feature map from CNN, flatten, project to embedding dim.
+ """
+ def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
+ super().__init__()
+ assert isinstance(backbone, nn.Module)
+ img_size = to_2tuple(img_size)
+ self.img_size = img_size
+ self.backbone = backbone
+ if feature_size is None:
+ with torch.no_grad():
+ # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
+ # map for all networks, the feature metadata has reliable channel and stride info, but using
+ # stride to calc feature dim requires info about padding of each stage that isn't captured.
+ training = backbone.training
+ if training:
+ backbone.eval()
+ o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
+ if isinstance(o, (list, tuple)):
+ o = o[-1] # last feature if backbone outputs list/tuple of features
+ feature_size = o.shape[-2:]
+ feature_dim = o.shape[1]
+ backbone.train(training)
+ else:
+ feature_size = to_2tuple(feature_size)
+ if hasattr(self.backbone, 'feature_info'):
+ feature_dim = self.backbone.feature_info.channels()[-1]
+ else:
+ feature_dim = self.backbone.num_features
+ self.num_patches = feature_size[0] * feature_size[1]
+ self.proj = nn.Conv2d(feature_dim, embed_dim, 1)
+
+ def forward(self, x):
+ x = self.backbone(x)
+ if isinstance(x, (list, tuple)):
+ x = x[-1] # last feature if backbone outputs list/tuple of features
+ x = self.proj(x).flatten(2).transpose(1, 2)
+ return x
+
+
+class VisionTransformer(nn.Module):
+ """ Vision Transformer
+
+ A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
+ https://arxiv.org/abs/2010.11929
+ """
+ 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=True, qk_scale=None, representation_size=None,
+ drop_rate=0., attn_drop_rate=0., drop_path_rate=0., hybrid_backbone=None, norm_layer=None, variable_input_len=True):
+ """
+ Args:
+ img_size (int, tuple): input image size
+ patch_size (int, tuple): patch size
+ in_chans (int): number of input channels
+ num_classes (int): number of classes for classification head
+ embed_dim (int): embedding dimension
+ depth (int): depth of transformer
+ num_heads (int): number of attention heads
+ mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+ qkv_bias (bool): enable bias for qkv if True
+ qk_scale (float): override default qk scale of head_dim ** -0.5 if set
+ representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
+ drop_rate (float): dropout rate
+ attn_drop_rate (float): attention dropout rate
+ drop_path_rate (float): stochastic depth rate
+ hybrid_backbone (nn.Module): CNN backbone to use in-place of PatchEmbed module
+ norm_layer: (nn.Module): normalization layer
+ """
+ super().__init__()
+ self.num_classes = num_classes
+ self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
+ norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
+
+ 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.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+ self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
+ 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
+ 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)
+ for i in range(depth)])
+ self.norm = norm_layer(embed_dim)
+
+ # Representation layer
+ if representation_size:
+ self.num_features = representation_size
+ self.pre_logits = nn.Sequential(OrderedDict([
+ ('fc', nn.Linear(embed_dim, representation_size)),
+ ('act', nn.Tanh())
+ ]))
+ else:
+ self.pre_logits = nn.Identity()
+
+ # Classifier head
+ self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+ trunc_normal_(self.pos_embed, std=.02)
+ trunc_normal_(self.cls_token, std=.02)
+ self.apply(self._init_weights)
+
+ self.variable_input_len = variable_input_len
+ self.patch_size = patch_size
+
+ 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) -> torch.Tensor:
+ #B = x.shape[0]
+ B, C, H, W = x.shape
+ x = self.patch_embed(x)
+
+ cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
+ x = torch.cat((cls_tokens, x), dim=1)
+ #print("ViT shapes of x and pos_embed: {} | {}".format(x.shape, self.pos_embed.shape))
+ #for an image of size 710,541: ViT shapes of x and pos_embed: torch.Size([1, 1453, 768]) | torch.Size([1, 197, 768])
+ pos_embed = None
+ if self.variable_input_len:
+ patches_height = int(H/self.patch_size)
+ patches_width = int(W/self.patch_size)
+
+ pos_embed = resize_pos_embed(self.pos_embed, patches_height*patches_width, patches_height, patches_width)
+
+ else:
+ pos_embed = self.pos_embed
+
+ x = x + pos_embed
+
+ x = self.pos_drop(x)
+
+ for blk in self.blocks:
+ x = blk(x)
+
+ #print("Shapes after block: {}".format(x.shape)) # Shapes after block: torch.Size([1, 1453, 768])
+ #temp = self.norm(x)
+ #print("Shapes after norm original: {}".format(temp.shape)) # Shapes after norm original: torch.Size([1, 1453, 768])
+
+ x = self.norm(x)[:, 0] # Important! The state of the class token at the output of the transformer encoder serves as the image representation
+
+ #print("Shapes after norm: {}".format(x.shape)) # Shapes after norm: torch.Size([1, 768])
+ x = self.pre_logits(x)
+ 
+ return x
+
+ def forward(self, x):
+ x = self.forward_features(x)
+ #print("Shapes before head: {}".format(x.shape)) # Shapes before head: torch.Size([1, 768])
+ x = self.head(x)
+ return x
+
+
+class DistilledVisionTransformer(VisionTransformer):
+ """ Vision Transformer with distillation token.
+
+ Paper: `Training data-efficient image transformers & distillation through attention` -
+ https://arxiv.org/abs/2012.12877
+
+ This impl of distilled ViT is taken from https://github.com/facebookresearch/deit
+ """
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.dist_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
+ num_patches = self.patch_embed.num_patches
+ self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, self.embed_dim))
+ self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if self.num_classes > 0 else nn.Identity()
+
+ trunc_normal_(self.dist_token, std=.02)
+ trunc_normal_(self.pos_embed, std=.02)
+ self.head_dist.apply(self._init_weights)
+
+ def forward_features(self, x):
+ B = x.shape[0]
+ x = self.patch_embed(x)
+
+ cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
+ dist_token = self.dist_token.expand(B, -1, -1)
+ x = torch.cat((cls_tokens, dist_token, x), dim=1)
+
+ x = x + self.pos_embed
+ x = self.pos_drop(x)
+
+ for blk in self.blocks:
+ x = blk(x)
+
+ x = self.norm(x)
+ return x[:, 0], x[:, 1]
+
+ def forward(self, x):
+ x, x_dist = self.forward_features(x)
+ x = self.head(x)
+ x_dist = self.head_dist(x_dist)
+ if self.training:
+ return x, x_dist
+ else:
+ # during inference, return the average of both classifier predictions
+ return (x + x_dist) / 2
+
+
+def resize_pos_embed(posemb, ntok_new, new_height, new_width):
+ #uzpaka: make it work for non-square images
+
+ # 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)
+ #ntok_new = posemb_new.shape[1]
+ if True:
+ posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]
+ ntok_new -= 1
+ else:
+ posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
+ gs_old = int(math.sqrt(len(posemb_grid)))
+
+ gs_new_h = new_height
+ gs_new_w = new_width
+ if new_height is None or new_width is None:
+ gs_new = int(math.sqrt(ntok_new))
+ gs_new_h = gs_new
+ gs_new_w = gs_new
+
+ #_logger.info('Position embedding grid-size from %s to %s', 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_h, gs_new_w), mode='bilinear')
+ posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new_h * gs_new_w, -1)
+ posemb_ret = torch.cat([posemb_tok, posemb_grid], dim=1)
+ return posemb_ret
+
+
+def checkpoint_filter_fn(state_dict, model):
+ """ convert patch embedding weight from manual patchify + linear proj to conv"""
+ out_dict = {}
+ if 'model' in state_dict:
+ # For deit models
+ state_dict = state_dict['model']
+ for k, v in state_dict.items():
+ if 'patch_embed.proj.weight' in k and len(v.shape) < 4:
+ # For old models that I trained prior to conv based patchification
+ O, I, H, W = model.patch_embed.proj.weight.shape
+ 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)
+ out_dict[k] = v
+ return out_dict
+
+
+def _create_vision_transformer(variant, pretrained=False, distilled=False, **kwargs):
+ default_cfg = default_cfgs[variant]
+ default_num_classes = default_cfg['num_classes']
+ default_img_size = default_cfg['input_size'][-1]
+
+ num_classes = kwargs.pop('num_classes', default_num_classes)
+ img_size = kwargs.pop('img_size', default_img_size)
+ repr_size = kwargs.pop('representation_size', None)
+ if repr_size is not None and num_classes != default_num_classes:
+ # Remove representation layer if fine-tuning. This may not always be the desired action,
+ # but I feel better than doing nothing by default for fine-tuning. Perhaps a better interface?
+ _logger.warning("Removing representation layer for fine-tuning.")
+ repr_size = None
+
+ model_cls = DistilledVisionTransformer if distilled else VisionTransformer
+ model = model_cls(img_size=img_size, num_classes=num_classes, representation_size=repr_size, **kwargs)
+ model.default_cfg = default_cfg
+ return model
+
+
+@register_model
+def vit_small_patch16_224(pretrained=False, **kwargs):
+ """ My custom 'small' ViT model. Depth=8, heads=8= mlp_ratio=3."""
+ model_kwargs = dict(
+ patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3.,
+ qkv_bias=False, norm_layer=nn.LayerNorm, **kwargs)
+ if pretrained:
+ # NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model
+ model_kwargs.setdefault('qk_scale', 768 ** -0.5)
+ model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_base_patch16_224(pretrained=False, **kwargs):
+ """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
+ model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_base_patch32_224(pretrained=False, **kwargs):
+ """ ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
+ """
+ model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
+ model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_base_patch16_384(pretrained=False, **kwargs):
+ """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
+ model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_base_patch32_384(pretrained=False, **kwargs):
+ """ ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
+ """
+ model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
+ model = _create_vision_transformer('vit_base_patch32_384', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_large_patch16_224(pretrained=False, **kwargs):
+ """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
+ model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_large_patch32_224(pretrained=False, **kwargs):
+ """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
+ """
+ model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
+ model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_large_patch16_384(pretrained=False, **kwargs):
+ """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
+ model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_large_patch32_384(pretrained=False, **kwargs):
+ """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
+ """
+ model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
+ model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
+ """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+ """
+ model_kwargs = dict(
+ patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
+ model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
+ """ ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+ """
+ model_kwargs = dict(
+ patch_size=32, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
+ model = _create_vision_transformer('vit_base_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
+ """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+ """
+ model_kwargs = dict(
+ patch_size=16, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
+ model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
+ """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+ """
+ model_kwargs = dict(
+ patch_size=32, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
+ model = _create_vision_transformer('vit_large_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
+ """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+ NOTE: converted weights not currently available, too large for github release hosting.
+ """
+ model_kwargs = dict(
+ patch_size=14, embed_dim=1280, depth=32, num_heads=16, representation_size=1280, **kwargs)
+ model = _create_vision_transformer('vit_huge_patch14_224_in21k', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
+ """ R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
+ """
+ # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head
+ backbone = ResNetV2(
+ layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
+ preact=False, stem_type='same', conv_layer=StdConv2dSame)
+ model_kwargs = dict(
+ embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone,
+ representation_size=768, **kwargs)
+ model = _create_vision_transformer('vit_base_resnet50_224_in21k', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_base_resnet50_384(pretrained=False, **kwargs):
+ """ R50+ViT-B/16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
+ ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
+ """
+ # create a ResNetV2 w/o pre-activation, that uses StdConv and GroupNorm and has 3 stages, no head
+ backbone = ResNetV2(
+ layers=(3, 4, 9), num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
+ preact=False, stem_type='same', conv_layer=StdConv2dSame)
+ model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+ model = _create_vision_transformer('vit_base_resnet50_384', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_small_resnet26d_224(pretrained=False, **kwargs):
+ """ Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights.
+ """
+ backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
+ model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
+ model = _create_vision_transformer('vit_small_resnet26d_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_small_resnet50d_s3_224(pretrained=False, **kwargs):
+ """ Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights.
+ """
+ backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[3])
+ model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
+ model = _create_vision_transformer('vit_small_resnet50d_s3_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_base_resnet26d_224(pretrained=False, **kwargs):
+ """ Custom ViT base hybrid w/ ResNet26D stride 32. No pretrained weights.
+ """
+ backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
+ model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+ model = _create_vision_transformer('vit_base_resnet26d_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_base_resnet50d_224(pretrained=False, **kwargs):
+ """ Custom ViT base hybrid w/ ResNet50D stride 32. No pretrained weights.
+ """
+ backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
+ model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, hybrid_backbone=backbone, **kwargs)
+ model = _create_vision_transformer('vit_base_resnet50d_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_deit_tiny_patch16_224(pretrained=False, **kwargs):
+ """ DeiT-tiny model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+ ImageNet-1k weights from https://github.com/facebookresearch/deit.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
+ model = _create_vision_transformer('vit_deit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_deit_small_patch16_224(pretrained=False, **kwargs):
+ """ DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+ ImageNet-1k weights from https://github.com/facebookresearch/deit.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
+ model = _create_vision_transformer('vit_deit_small_patch16_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_deit_base_patch16_224(pretrained=False, **kwargs):
+ """ DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+ ImageNet-1k weights from https://github.com/facebookresearch/deit.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
+ model = _create_vision_transformer('vit_deit_base_patch16_224', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_deit_base_patch16_384(pretrained=False, **kwargs):
+ """ DeiT base model @ 384x384 from paper (https://arxiv.org/abs/2012.12877).
+ ImageNet-1k weights from https://github.com/facebookresearch/deit.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
+ model = _create_vision_transformer('vit_deit_base_patch16_384', pretrained=pretrained, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_deit_tiny_distilled_patch16_224(pretrained=False, **kwargs):
+ """ DeiT-tiny distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+ ImageNet-1k weights from https://github.com/facebookresearch/deit.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
+ model = _create_vision_transformer(
+ 'vit_deit_tiny_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_deit_small_distilled_patch16_224(pretrained=False, **kwargs):
+ """ DeiT-small distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+ ImageNet-1k weights from https://github.com/facebookresearch/deit.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
+ model = _create_vision_transformer(
+ 'vit_deit_small_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_deit_base_distilled_patch16_224(pretrained=False, **kwargs):
+ """ DeiT-base distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
+ ImageNet-1k weights from https://github.com/facebookresearch/deit.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
+ model = _create_vision_transformer(
+ 'vit_deit_base_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
+ return model
+
+
+@register_model
+def vit_deit_base_distilled_patch16_384(pretrained=False, **kwargs):
+ """ DeiT-base distilled model @ 384x384 from paper (https://arxiv.org/abs/2012.12877).
+ ImageNet-1k weights from https://github.com/facebookresearch/deit.
+ """
+ model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
+ model = _create_vision_transformer(
+ 'vit_deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
+ return model