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models.py
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import torch.nn as nn
from models_base import UAEBase, BetaVAEBase, View
#
# UAEModels
#
class UAEModel_256_nf32_8x8kern_fc256(UAEBase):
def __init__(self, log_step=1, dataset_name='Dataset', **kwargs):
super().__init__(log_step, dataset_name)
self.model_name = self.__class__.__name__
def init_model(self):
nf = 32
self.network = nn.Sequential( # 1, 256, 256 (input)
nn.Conv2d(1, nf, 8, 2, 3), # nf, 128, 128
nn.ReLU(True),
nn.Conv2d(nf, nf, 8, 2, 3), # nf, 64, 64
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 4, 4
nn.ReLU(True),
View((-1, nf*4*4)), # nf*16
nn.Linear(nf*4*4, 256), # 256
nn.ReLU(True),
nn.Linear(256, 256), # 256
nn.ReLU(True),
nn.Linear(256, 256), # 256
nn.ReLU(True),
nn.Linear(256, nf*4*4), # nf*16
nn.ReLU(True),
View((-1, nf, 4, 4)), # nf, 4, 4
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 64, 64
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 8, 2, 3), # nf, 128, 128
nn.ReLU(True),
nn.ConvTranspose2d(nf, 1, 8, 2, 3) # nf, 256, 256
) # 1, 256, 256 (output)
# NOTE current best UAEModel
class UAEModel_256_nf96_8x8kern_fc256(UAEBase):
def __init__(self, log_step=1, dataset_name='Dataset', **kwargs):
super().__init__(log_step, dataset_name)
self.model_name = self.__class__.__name__
def init_model(self):
nf = 96
self.network = nn.Sequential( # 1, 256, 256 (input)
nn.Conv2d(1, nf, 8, 2, 3), # nf, 128, 128
nn.ReLU(True),
nn.Conv2d(nf, nf, 8, 2, 3), # nf, 64, 64
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 4, 4
nn.ReLU(True),
View((-1, nf*4*4)), # nf*16
nn.Linear(nf*4*4, 256), # 256
nn.ReLU(True),
nn.Linear(256, 256), # 256
nn.ReLU(True),
nn.Linear(256, 256), # 256
nn.ReLU(True),
nn.Linear(256, nf*4*4), # nf*16
nn.ReLU(True),
View((-1, nf, 4, 4)), # nf, 4, 4
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 64, 64
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 8, 2, 3), # nf, 128, 128
nn.ReLU(True),
nn.ConvTranspose2d(nf, 1, 8, 2, 3) # nf, 256, 256
) # 1, 256, 256 (output)
#
# BetaVAEModels
#
# NOTE legacy best model from ICRA 2021 submission
class BetaVAEModel_128_b1_z16_nf32_4x4kern(BetaVAEBase):
def __init__(self, dataset_name='Dataset', kld_weight=1, log_step=1):
super().__init__(log_step, dataset_name)
self.kld_weight = kld_weight
self.model_name = self.__class__.__name__
def init_model(self):
self.beta = 1.0
self.z_dim = 16 # dimension of latent distribution
nf = 32
self.encoder = nn.Sequential( # 1, 128, 128 (input)
nn.Conv2d(1, nf, 4, 2, 1), # nf, 64, 64
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 4, 4
nn.ReLU(True),
View((-1, nf*4*4)), # nf*16
nn.Linear(nf*4*4, 256), # 256
nn.ReLU(True),
nn.Linear(256, 256), # 256
nn.ReLU(True),
nn.Linear(256, 2*self.z_dim) # 2 x z_dim
)
self.decoder = nn.Sequential(
nn.Linear(self.z_dim, 256), # 256
nn.ReLU(True),
nn.Linear(256, 256), # 256
nn.ReLU(True),
nn.Linear(256, nf*4*4), # nf*16
nn.ReLU(True),
View((-1, nf, 4, 4)), # nf, 4, 4
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 64, 64
nn.ReLU(True),
nn.ConvTranspose2d(nf, 1, 4, 2, 1) # nf, 128, 128
) # 1, 128, 128 (output)
# NOTE current best BetaVAEModel
class BetaVAEModel_256_b1_z32_nf64_8x8kern(BetaVAEBase):
def __init__(self, dataset_name='Dataset', kld_weight=1, log_step=1):
super().__init__(log_step, dataset_name)
self.kld_weight = kld_weight
self.model_name = self.__class__.__name__
def init_model(self):
self.beta = 1.0
self.z_dim = 32 # dimension of latent distribution
nf = 64
self.encoder = nn.Sequential( # 1, 256, 256 (input)
nn.Conv2d(1, nf, 8, 2, 3), # nf, 128, 128
nn.ReLU(True),
nn.Conv2d(nf, nf, 8, 2, 3), # nf, 64, 64
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.ReLU(True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 4, 4
nn.ReLU(True),
View((-1, nf*4*4)), # nf*16
nn.Linear(nf*4*4, 256), # 256
nn.ReLU(True),
nn.Linear(256, 256), # 256
nn.ReLU(True),
nn.Linear(256, 2*self.z_dim) # 2 x z_dim
)
self.decoder = nn.Sequential(
nn.Linear(self.z_dim, 256), # 256
nn.ReLU(True),
nn.Linear(256, 256), # 256
nn.ReLU(True),
nn.Linear(256, nf*4*4), # nf*16
nn.ReLU(True),
View((-1, nf, 4, 4)), # nf, 4, 4
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 64, 64
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 8, 2, 3), # nf, 128, 128
nn.ReLU(True),
nn.ConvTranspose2d(nf, 1, 8, 2, 3) # nf, 256, 256
) # 1, 256, 256 (output)
#
# ConvAEModels
#
class ConvAEModel_nf128_8x8kern(UAEBase):
def __init__(self, log_step=1, dataset_name='Dataset', **kwargs):
super().__init__(log_step, dataset_name)
self.model_name = self.__class__.__name__
def init_model(self):
nf = 128
self.network = nn.Sequential( # 1, 128, 128 (input)
nn.Conv2d(1, nf, 8, 2, 3), # nf, 64, 64
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 8, 2, 3), # nf, 32, 32
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 4, 4
nn.LeakyReLU(0.2, True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 8, 2, 3), # nf, 64, 64
nn.ReLU(True),
nn.ConvTranspose2d(nf, 1, 8, 2, 3), # nf, 128, 128
) # 1, 128, 128 (output)
class ConvAEModel_px256_nf128_8x8kern(UAEBase):
def __init__(self, log_step=1, dataset_name='Dataset', **kwargs):
super().__init__(log_step, dataset_name)
self.model_name = self.__class__.__name__
def init_model(self):
nf = 128
self.network = nn.Sequential( # 1, 256, 256 (input)
nn.Conv2d(1, nf, 8, 2, 3), # nf, 128, 128
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 8, 2, 3), # nf, 64, 64
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 4, 4
nn.LeakyReLU(0.2, True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 64, 64
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 8, 2, 3), # nf, 128, 128
nn.ReLU(True),
nn.ConvTranspose2d(nf, 1, 8, 2, 3), # nf, 256, 256
) # 1, 256, 256 (output)
class ConvAEModel_px256_nf192_8x8kern(UAEBase):
def __init__(self, log_step=1, dataset_name='Dataset', **kwargs):
super().__init__(log_step, dataset_name)
self.model_name = self.__class__.__name__
def init_model(self):
nf = 192
self.network = nn.Sequential( # 1, 256, 256 (input)
nn.Conv2d(1, nf, 8, 2, 3), # nf, 128, 128
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 8, 2, 3), # nf, 64, 64
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.LeakyReLU(0.2, True),
nn.Conv2d(nf, nf, 4, 2, 1), # nf, 4, 4
nn.LeakyReLU(0.2, True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 4, 2, 1), # nf, 64, 64
nn.ReLU(True),
nn.ConvTranspose2d(nf, nf, 8, 2, 3), # nf, 128, 128
nn.ReLU(True),
nn.ConvTranspose2d(nf, 1, 8, 2, 3), # nf, 256, 256
)