vae_test / configuration_vae.py

End of training

372980e verified about 1 year ago

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from enum import Enum
from  transformers import PretrainedConfig

from .module_layers import Encoder, Decoder
from .module_layers_attn import Encoder as AttnEncoder, Decoder as AttnDecoder
# from .module_quantizers import VectorQuantizer


class EncoderType(Enum):
    Simple = Encoder
    Attn = AttnEncoder


class DecoderType(Enum):
    Simple = Decoder
    Attn = AttnDecoder


# class QuantizerType(Enum):
#     VQ = VectorQuantizer


class VAEConfig(PretrainedConfig):
    model_type = "vae"

    def __init__(self, **kwargs):
        # ref ./modules/__init__.py
        self.encoder_type = kwargs.get("encoder_type", EncoderType.Simple.name)
        self.decoder_type = kwargs.get("decoder_type", DecoderType.Simple.name)
        # self.quantizer_type = kwargs.get("quantizer_type", QuantizerType.VQ.name)
        # in_ch -> channels * channels_mult -> z_channels -> codebook_dim -> z_channels -> channels * channels_mult -> out_ch
        self.in_channels = kwargs.get("in_channels", 3)
        self.out_channels = kwargs.get("out_channels", 3)
        self.z_channels = kwargs.get("z_channels", 256) # embeding dim
        self.channels = kwargs.get("channels", 128)
        # features = [channels * mult for mult in channels_mult]
        # res -> res // 2**(len(channels_mult)-1)
        self.channels_mult = kwargs.get("channels_mult", [1, 1, 2, 2])
        self.codebook_dim = kwargs.get("codebook_dim", 8)
        self.codebook_size = kwargs.get("codebook_size", 1024)
        # if res = 128 and ch_mult = [1, 1, 2, 2], select any from [128/1, 128/2, 128/2**2, 128/2**3]
        # in taming-transformers use attn_resolutions = [res/2**(len(ch_mult)-1)]
        self.attn_resolutions = kwargs.get("attn_resolutions", [])
        self.num_res_blocks = kwargs.get("num_res_blocks", 2)
        self.resolution = kwargs.get("resolution", [64, 64])
        self.dropout = kwargs.get("dropout", 0.)
        # imagenet mean [0.1616, 0.1646, 0.1618], std [0.2206, 0.2233, 0.2214]
        # nusc mean [0.3814, 0.3861, 0.3778], std [0.2219, 0.2188, 0.2248]
        self.image_mean = kwargs.get('image_mean', [0.1616, 0.1646, 0.1618])
        self.image_std = kwargs.get("image_std", [0.2206, 0.2233, 0.2214])
        self.w_mse = kwargs.get("w_mse", 2)
        self.w_l1 = kwargs.get("w_l1", 0.2)
        self.w_perceptual = kwargs.get("w_perceptual", 0.1)
        self.w_commit = kwargs.get("w_commit", 1)
        self.w_dino = kwargs.get("w_dino", 0.1)
        self.w_kl = kwargs.get("w_kl", 0.1)
        super().__init__(**kwargs)

	from enum import Enum
	from transformers import PretrainedConfig

	from .module_layers import Encoder, Decoder
	from .module_layers_attn import Encoder as AttnEncoder, Decoder as AttnDecoder
	# from .module_quantizers import VectorQuantizer


	class EncoderType(Enum):
	Simple = Encoder
	Attn = AttnEncoder


	class DecoderType(Enum):
	Simple = Decoder
	Attn = AttnDecoder


	# class QuantizerType(Enum):
	# VQ = VectorQuantizer


	class VAEConfig(PretrainedConfig):
	model_type = "vae"

	def __init__(self, **kwargs):
	# ref ./modules/__init__.py
	self.encoder_type = kwargs.get("encoder_type", EncoderType.Simple.name)
	self.decoder_type = kwargs.get("decoder_type", DecoderType.Simple.name)
	# self.quantizer_type = kwargs.get("quantizer_type", QuantizerType.VQ.name)
	# in_ch -> channels * channels_mult -> z_channels -> codebook_dim -> z_channels -> channels * channels_mult -> out_ch
	self.in_channels = kwargs.get("in_channels", 3)
	self.out_channels = kwargs.get("out_channels", 3)
	self.z_channels = kwargs.get("z_channels", 256) # embeding dim
	self.channels = kwargs.get("channels", 128)
	# features = [channels * mult for mult in channels_mult]
	# res -> res // 2**(len(channels_mult)-1)
	self.channels_mult = kwargs.get("channels_mult", [1, 1, 2, 2])
	self.codebook_dim = kwargs.get("codebook_dim", 8)
	self.codebook_size = kwargs.get("codebook_size", 1024)
	# if res = 128 and ch_mult = [1, 1, 2, 2], select any from [128/1, 128/2, 128/22, 128/23]
	# in taming-transformers use attn_resolutions = [res/2**(len(ch_mult)-1)]
	self.attn_resolutions = kwargs.get("attn_resolutions", [])
	self.num_res_blocks = kwargs.get("num_res_blocks", 2)
	self.resolution = kwargs.get("resolution", [64, 64])
	self.dropout = kwargs.get("dropout", 0.)
	# imagenet mean [0.1616, 0.1646, 0.1618], std [0.2206, 0.2233, 0.2214]
	# nusc mean [0.3814, 0.3861, 0.3778], std [0.2219, 0.2188, 0.2248]
	self.image_mean = kwargs.get('image_mean', [0.1616, 0.1646, 0.1618])
	self.image_std = kwargs.get("image_std", [0.2206, 0.2233, 0.2214])
	self.w_mse = kwargs.get("w_mse", 2)
	self.w_l1 = kwargs.get("w_l1", 0.2)
	self.w_perceptual = kwargs.get("w_perceptual", 0.1)
	self.w_commit = kwargs.get("w_commit", 1)
	self.w_dino = kwargs.get("w_dino", 0.1)
	self.w_kl = kwargs.get("w_kl", 0.1)
	super().__init__(**kwargs)