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c8b2644 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | import torch
import torch.nn as nn
import clip
import copy
from torch.autograd import Function
from huggingface_hub import PyTorchModelHubMixin
from .utils import convert_weights_float
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, alpha):
ctx.alpha = alpha
return x.view_as(x)
@staticmethod
def backward(ctx, grad_output):
output = grad_output.neg() * ctx.alpha
return output, None
## taken from https://github.com/moein-shariatnia/OpenAI-CLIP/blob/master/modules.py
class ProjectionHead(nn.Module):
def __init__(
self,
embedding_dim,
projection_dim,
dropout=0
):
super().__init__()
self.projection = nn.Linear(embedding_dim, projection_dim)
self.gelu = nn.GELU()
self.fc = nn.Linear(projection_dim, projection_dim)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(projection_dim)
def forward(self, x):
projected = self.projection(x)
x = self.gelu(projected)
x = self.fc(x)
x = self.dropout(x)
x = x + projected
x = self.layer_norm(x)
return x
def init_weights(m): # TODO: do we need init for layernorm?
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.normal_(m.bias, std=1e-6)
class CSD_CLIP(nn.Module, PyTorchModelHubMixin, repo_url="https://github.com/learn2phoenix/CSD", pipeline_tag="image-feature-extraction", license="mit"):
"""backbone + projection head"""
def __init__(self, name='vit_large',content_proj_head='default'):
super(CSD_CLIP, self).__init__()
self.content_proj_head = content_proj_head
if name == 'vit_large':
clipmodel, _ = clip.load("ViT-L/14")
self.backbone = clipmodel.visual
self.embedding_dim = 1024
elif name == 'vit_base':
clipmodel, _ = clip.load("ViT-B/16")
self.backbone = clipmodel.visual
self.embedding_dim = 768
self.feat_dim = 512
else:
raise Exception('This model is not implemented')
convert_weights_float(self.backbone)
self.last_layer_style = copy.deepcopy(self.backbone.proj)
if content_proj_head == 'custom':
self.last_layer_content = ProjectionHead(self.embedding_dim,self.feat_dim)
self.last_layer_content.apply(init_weights)
else:
self.last_layer_content = copy.deepcopy(self.backbone.proj)
self.backbone.proj = None
@property
def dtype(self):
return self.backbone.conv1.weight.dtype
def forward(self, input_data, alpha=None):
feature = self.backbone(input_data)
if alpha is not None:
reverse_feature = ReverseLayerF.apply(feature, alpha)
else:
reverse_feature = feature
style_output = feature @ self.last_layer_style
style_output = nn.functional.normalize(style_output, dim=1, p=2)
# if alpha is not None:
if self.content_proj_head == 'custom':
content_output = self.last_layer_content(reverse_feature)
else:
content_output = reverse_feature @ self.last_layer_content
content_output = nn.functional.normalize(content_output, dim=1, p=2)
return feature, content_output, style_output
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