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import torch.nn as nn
import torchvision.models as models
import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
class ImageModel(nn.Module):
def __init__(self, num_genre) -> None:
super(ImageModel, self).__init__()
# Feature extraction layer.
# Input 200x200
self.features = models.mobilenet_v3_large(weights="IMAGENET1K_V2")
in_features = self.features.classifier[0].in_features
self.features.classifier = nn.Identity()
self.head_score = nn.Sequential(
nn.Dropout(p=0.5),
nn.Linear(in_features=in_features, out_features=512, bias=True),
nn.SiLU(),
nn.Dropout(p=0.2),
nn.Linear(in_features=512, out_features=1, bias=True)
)
self.head_award = nn.Sequential(
nn.Dropout(p=0.5),
nn.Linear(in_features=in_features, out_features=512, bias=True),
nn.SiLU(),
nn.Dropout(p=0.2),
nn.Linear(in_features=512, out_features=1, bias=True)
)
self.head_genre = nn.Sequential(
nn.Dropout(p=0.5),
nn.Linear(in_features=in_features, out_features=1024, bias=True),
nn.Hardswish(),
nn.Dropout(p=0.2),
nn.Linear(in_features=1024, out_features=num_genre, bias=True)
)
# Initialize model weights.
self._initialize_weights()
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.features(x)
return self.head_score(x), self.head_award(x), self.head_genre(x)
# The filter weight of each layer is a Gaussian distribution with zero mean and standard deviation initialized by random extraction 0.001 (deviation is 0).
def _initialize_weights(model):
"""
Initializes weights of all layers in a PyTorch model.
Args:
model (nn.Module): The model to initialize weights for.
"""
for m in model.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
elif isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
class TextModel(nn.Module):
def __init__(self, model_name, num_genre):
super(TextModel, self).__init__()
config = AutoConfig.from_pretrained(model_name)
in_features = config.hidden_size
self.transformer = AutoModel.from_pretrained(model_name)
self.head_score = nn.Sequential(
# nn.Dropout(p=0.5),
# nn.Linear(in_features=in_features, out_features=512, bias=True),
# nn.SiLU(),
nn.Dropout(p=0.2),
nn.Linear(in_features=in_features, out_features=1, bias=True)
)
self.head_award = nn.Sequential(
# nn.Dropout(p=0.5),
# nn.Linear(in_features=in_features, out_features=512, bias=True),
# nn.SiLU(),
nn.Dropout(p=0.2),
nn.Linear(in_features=in_features, out_features=1, bias=True)
)
self.head_genre = nn.Sequential(
# nn.Linear(in_features=in_features, out_features=1024, bias=True),
# nn.Hardswish(),
nn.Dropout(p=0.2),
nn.Linear(in_features=in_features, out_features=num_genre, bias=True)
)
def forward(self, x):
x = self.transformer(input_ids=x[0], attention_mask=x[1])['pooler_output']
return self.head_score(x), self.head_award(x), self.head_genre(x)
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