File size: 4,467 Bytes
fa0eee7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
from collections import OrderedDict
from typing import Dict, Final, Optional, Tuple, Union
import torch
import torch.nn as nn
from transformers import CLIPVisionModelWithProjection, logging
from transformers.modeling_outputs import ImageClassifierOutputWithNoAttention
from transformers.models.clip.configuration_clip import CLIPVisionConfig
logging.set_verbosity_error()
URLS_LINEAR: Final[Dict[str, str]] = {
"sac+logos+ava1-l14-linearMSE": "https://github.com/christophschuhmann/improved-aesthetic-predictor/raw/main/sac%2Blogos%2Bava1-l14-linearMSE.pth",
"ava+logos-l14-linearMSE": "https://github.com/christophschuhmann/improved-aesthetic-predictor/raw/main/ava%2Blogos-l14-linearMSE.pth",
}
URLS_RELU: Final[Dict[str, str]] = {
"ava+logos-l14-reluMSE": "https://github.com/christophschuhmann/improved-aesthetic-predictor/raw/main/ava%2Blogos-l14-reluMSE.pth",
}
class AestheticsPredictorV2Linear(CLIPVisionModelWithProjection):
def __init__(self, config: CLIPVisionConfig) -> None:
super().__init__(config)
self.layers = nn.Sequential(
nn.Linear(config.projection_dim, 1024),
nn.Dropout(0.2),
nn.Linear(1024, 128),
nn.Dropout(0.2),
nn.Linear(128, 64),
nn.Dropout(0.1),
nn.Linear(64, 16),
nn.Linear(16, 1),
)
self.post_init()
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
outputs = super().forward(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
image_embeds = outputs[0] # image_embeds
image_embeds /= image_embeds.norm(dim=-1, keepdim=True)
prediction = self.layers(image_embeds)
loss = None
if labels is not None:
loss_fct = nn.MSELoss()
loss = loss_fct()
if not return_dict:
return (loss, prediction, image_embeds)
return ImageClassifierOutputWithNoAttention(
loss=loss,
logits=prediction,
hidden_states=image_embeds,
)
class AestheticsPredictorV2ReLU(AestheticsPredictorV2Linear):
def __init__(self, config: CLIPVisionConfig):
super().__init__(config)
self.layers = nn.Sequential(
nn.Linear(config.projection_dim, 1024),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(1024, 128),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(128, 64),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(64, 16),
nn.ReLU(),
nn.Linear(16, 1),
)
self.post_init()
def convert_v2_linear_from_openai_clip(
predictor_head_name: str,
openai_model_name: str = "openai/clip-vit-large-patch14",
) -> AestheticsPredictorV2Linear:
model = AestheticsPredictorV2Linear.from_pretrained(openai_model_name)
state_dict = torch.hub.load_state_dict_from_url(
URLS_LINEAR[predictor_head_name], map_location="cpu"
)
assert isinstance(state_dict, OrderedDict)
# remove `layers.` from the key of the state_dict
state_dict = OrderedDict(
((k.replace("layers.", ""), v) for k, v in state_dict.items())
)
model.layers.load_state_dict(state_dict)
model.eval()
return model
def convert_v2_relu_from_openai_clip(
predictor_head_name: str,
openai_model_name: str = "openai/clip-vit-large-patch14",
) -> AestheticsPredictorV2ReLU:
model = AestheticsPredictorV2ReLU.from_pretrained(openai_model_name)
state_dict = torch.hub.load_state_dict_from_url(
URLS_RELU[predictor_head_name], map_location="cpu"
)
assert isinstance(state_dict, OrderedDict)
# remove `layers.` from the key of the state_dict
state_dict = OrderedDict(
((k.replace("layers.", ""), v) for k, v in state_dict.items())
)
model.layers.load_state_dict(state_dict)
model.eval()
return model
|