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79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
import torch
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class SDTextEncoder(torch.nn.Module):
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def __init__(
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self,
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hidden_size=768,
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intermediate_size=3072,
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num_hidden_layers=12,
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num_attention_heads=12,
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max_position_embeddings=77,
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vocab_size=49408,
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layer_norm_eps=1e-05,
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hidden_act="quick_gelu",
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initializer_factor=1.0,
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initializer_range=0.02,
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bos_token_id=0,
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eos_token_id=2,
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pad_token_id=1,
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projection_dim=768,
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):
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super().__init__()
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from transformers import CLIPConfig, CLIPTextModel
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config = CLIPConfig(
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text_config={
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"hidden_size": hidden_size,
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"intermediate_size": intermediate_size,
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"num_hidden_layers": num_hidden_layers,
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"num_attention_heads": num_attention_heads,
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"max_position_embeddings": max_position_embeddings,
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"vocab_size": vocab_size,
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"layer_norm_eps": layer_norm_eps,
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"hidden_act": hidden_act,
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"initializer_factor": initializer_factor,
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"initializer_range": initializer_range,
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"bos_token_id": bos_token_id,
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"eos_token_id": eos_token_id,
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"pad_token_id": pad_token_id,
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"projection_dim": projection_dim,
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"dropout": 0.0,
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},
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vision_config={
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"hidden_size": hidden_size,
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"intermediate_size": intermediate_size,
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"num_hidden_layers": num_hidden_layers,
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"num_attention_heads": num_attention_heads,
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"max_position_embeddings": max_position_embeddings,
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"layer_norm_eps": layer_norm_eps,
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"hidden_act": hidden_act,
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"initializer_factor": initializer_factor,
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"initializer_range": initializer_range,
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"projection_dim": projection_dim,
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},
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projection_dim=projection_dim,
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)
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self.model = CLIPTextModel(config.text_config)
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self.config = config
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def forward(
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self,
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input_ids=None,
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attention_mask=None,
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position_ids=None,
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output_hidden_states=True,
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**kwargs,
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):
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outputs = self.model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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output_hidden_states=output_hidden_states,
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return_dict=True,
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**kwargs,
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)
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if output_hidden_states:
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return outputs.last_hidden_state, outputs.hidden_states
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return outputs.last_hidden_state
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