fix code_emb
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@ -210,8 +210,8 @@ class TransformerDiffusion(nn.Module):
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def timestep_independent(self, prior, expected_seq_len):
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if self.new_code_expansion:
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code_emb = F.interpolate(prior.permute(0,2,1), size=expected_seq_len, mode='linear').permute(0,2,1)
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code_emb = self.ar_input(code_emb) if self.ar_prior else self.input_converter(code_emb)
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prior = F.interpolate(prior.permute(0,2,1), size=expected_seq_len, mode='linear').permute(0,2,1)
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code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior)
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code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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@ -732,14 +732,14 @@ def test_cheater_model():
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# For music:
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model = TransformerDiffusionWithCheaterLatent(in_channels=256, out_channels=512,
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model_channels=1024, contraction_dim=512,
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prenet_channels=1024, num_heads=8,
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input_vec_dim=256, num_layers=12, prenet_layers=6,
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model_channels=1536, contraction_dim=768,
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prenet_channels=1024, num_heads=12,
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input_vec_dim=256, num_layers=20, prenet_layers=6,
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dropout=.1, new_code_expansion=True,
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)
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diff_weights = torch.load('extracted_diff.pth')
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model.diff.load_state_dict(diff_weights, strict=False)
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cheater_ar_weights = torch.load('X:\\dlas\\experiments\\train_music_gpt_cheater\\models\\19500_generator_ema.pth')
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#diff_weights = torch.load('extracted_diff.pth')
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#model.diff.load_state_dict(diff_weights, strict=False)
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cheater_ar_weights = torch.load('X:\\dlas\\experiments\\train_music_gpt_cheater\\models\\60000_generator_ema.pth')
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cheater_ar = GptMusicLower(dim=1024, encoder_out_dim=256, layers=16, fp16=False, num_target_vectors=8192, num_vaes=4,
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vqargs= {'positional_dims': 1, 'channels': 64,
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'hidden_dim': 512, 'num_resnet_blocks': 3, 'codebook_dim': 512, 'num_tokens': 8192,
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