Update modeling.py
Browse files- modeling.py +3 -3
modeling.py
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@@ -4,7 +4,7 @@ from transformers import CLIPModel
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from peft import LoraConfig, get_peft_model
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class MLP(nn.Module):
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def __init__(self, input_dim, hidden_dim1, hidden_dim2, output_dim,dropout_rate=0.5):
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super(MLP, self).__init__()
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self.fc1 = nn.Linear(input_dim, hidden_dim1)
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self.relu1 = nn.ReLU()
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@@ -24,7 +24,7 @@ class MLP(nn.Module):
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return x
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class clip_lora_model(nn.Module):
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def __init__(self, input_dim, hidden_dim1, hidden_dim2, output_dim,dropout_rate=0.5,r=16,lora_alpha=8):
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super(clip_lora_model, self).__init__()
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self.output_dim=output_dim
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self.mlp = MLP(input_dim, hidden_dim1, hidden_dim2, output_dim,dropout_rate)
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@@ -45,7 +45,7 @@ class clip_lora_model(nn.Module):
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)
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self.model = get_peft_model(encoder, config)
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def forward(self, x
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model_outputs = self.model(x)
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image_embeds = model_outputs[1]
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model_outputs = self.proj(image_embeds)
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from peft import LoraConfig, get_peft_model
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class MLP(nn.Module):
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def __init__(self, input_dim=768, hidden_dim1=512, hidden_dim2=256, output_dim=8,dropout_rate=0.5):
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super(MLP, self).__init__()
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self.fc1 = nn.Linear(input_dim, hidden_dim1)
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self.relu1 = nn.ReLU()
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return x
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class clip_lora_model(nn.Module):
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def __init__(self, input_dim=768, hidden_dim1=512, hidden_dim2=256, output_dim=8,dropout_rate=0.5,r=16,lora_alpha=8):
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super(clip_lora_model, self).__init__()
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self.output_dim=output_dim
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self.mlp = MLP(input_dim, hidden_dim1, hidden_dim2, output_dim,dropout_rate)
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)
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self.model = get_peft_model(encoder, config)
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def forward(self, x):
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model_outputs = self.model(x)
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image_embeds = model_outputs[1]
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model_outputs = self.proj(image_embeds)
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