Spaces:
Runtime error
Runtime error
File size: 10,237 Bytes
e775f6d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 |
import torch; torch.manual_seed(0)
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
import torch.distributions
import matplotlib.pyplot as plt; plt.rcParams['figure.dpi'] = 200
device = 'cuda' if torch.cuda.is_available() else 'cpu'
def get_activation(activation):
if activation == 'tanh':
activ = F.tanh
elif activation == 'relu':
activ = F.relu
elif activation == 'mish':
activ = F.mish
elif activation == 'sigmoid':
activ = F.sigmoid
elif activation == 'leakyrelu':
activ = F.leaky_relu
elif activation == 'exp':
activ = torch.exp
else:
raise ValueError
return activ
class IngredientEncoder(nn.Module):
def __init__(self, input_dim, deepset_latent_dim, hidden_dims, activation, dropout):
super(IngredientEncoder, self).__init__()
self.linears = nn.ModuleList()
self.dropouts = nn.ModuleList()
dims = [input_dim] + hidden_dims + [deepset_latent_dim]
for d_in, d_out in zip(dims[:-1], dims[1:]):
self.linears.append(nn.Linear(d_in, d_out))
self.dropouts.append(nn.Dropout(dropout))
self.activation = get_activation(activation)
self.n_layers = len(self.linears)
self.layer_range = range(self.n_layers)
def forward(self, x):
for i_layer, layer, dropout in zip(self.layer_range, self.linears, self.dropouts):
x = layer(x)
if i_layer != self.n_layers - 1:
x = self.activation(dropout(x))
return x # do not use dropout on last layer?
class DeepsetCocktailEncoder(nn.Module):
def __init__(self, input_dim, deepset_latent_dim, hidden_dims_ing, activation,
hidden_dims_cocktail, latent_dim, aggregation, dropout):
super(DeepsetCocktailEncoder, self).__init__()
self.input_dim = input_dim # dimension of ingredient representation + quantity
self.ingredient_encoder = IngredientEncoder(input_dim, deepset_latent_dim, hidden_dims_ing, activation, dropout) # encode each ingredient separately
self.deepset_latent_dim = deepset_latent_dim # dimension of the deepset aggregation
self.aggregation = aggregation
self.latent_dim = latent_dim
# post aggregation network
self.linears = nn.ModuleList()
self.dropouts = nn.ModuleList()
dims = [deepset_latent_dim] + hidden_dims_cocktail
for d_in, d_out in zip(dims[:-1], dims[1:]):
self.linears.append(nn.Linear(d_in, d_out))
self.dropouts.append(nn.Dropout(dropout))
self.FC_mean = nn.Linear(hidden_dims_cocktail[-1], latent_dim)
self.FC_logvar = nn.Linear(hidden_dims_cocktail[-1], latent_dim)
self.softplus = nn.Softplus()
self.activation = get_activation(activation)
self.n_layers = len(self.linears)
self.layer_range = range(self.n_layers)
def forward(self, nb_ingredients, x):
# reshape x in (batch size * nb ingredients, dim_ing_rep)
batch_size = x.shape[0]
all_ingredients = []
for i in range(batch_size):
for j in range(nb_ingredients[i]):
all_ingredients.append(x[i, self.input_dim * j: self.input_dim * (j + 1)].reshape(1, -1))
x = torch.cat(all_ingredients, dim=0)
# encode ingredients in parallel
ingredients_encodings = self.ingredient_encoder(x)
assert ingredients_encodings.shape == (torch.sum(nb_ingredients), self.deepset_latent_dim)
# aggregate
x = []
index_first = 0
for i in range(batch_size):
index_last = index_first + nb_ingredients[i]
# aggregate
if self.aggregation == 'sum':
x.append(torch.sum(ingredients_encodings[index_first:index_last], dim=0).reshape(1, -1))
elif self.aggregation == 'mean':
x.append(torch.mean(ingredients_encodings[index_first:index_last], dim=0).reshape(1, -1))
else:
raise ValueError
index_first = index_last
x = torch.cat(x, dim=0)
assert x.shape[0] == batch_size
for i_layer, layer, dropout in zip(self.layer_range, self.linears, self.dropouts):
x = self.activation(dropout(layer(x)))
mean = self.FC_mean(x)
logvar = self.FC_logvar(x)
return mean, logvar
class Decoder(nn.Module):
def __init__(self, latent_dim, hidden_dims, num_ingredients, activation, dropout, filter_output=None):
super(Decoder, self).__init__()
self.linears = nn.ModuleList()
self.dropouts = nn.ModuleList()
dims = [latent_dim] + hidden_dims + [num_ingredients]
for d_in, d_out in zip(dims[:-1], dims[1:]):
self.linears.append(nn.Linear(d_in, d_out))
self.dropouts.append(nn.Dropout(dropout))
self.activation = get_activation(activation)
self.n_layers = len(self.linears)
self.layer_range = range(self.n_layers)
self.filter = filter_output
def forward(self, x, to_filter=False):
for i_layer, layer, dropout in zip(self.layer_range, self.linears, self.dropouts):
x = layer(x)
if i_layer != self.n_layers - 1:
x = self.activation(dropout(x))
if to_filter:
x = self.filter(x)
return x
class PredictorHead(nn.Module):
def __init__(self, latent_dim, dim_output, final_activ):
super(PredictorHead, self).__init__()
self.linear = nn.Linear(latent_dim, dim_output)
if final_activ != None:
self.final_activ = get_activation(final_activ)
self.use_final_activ = True
else:
self.use_final_activ = False
def forward(self, x):
x = self.linear(x)
if self.use_final_activ: x = self.final_activ(x)
return x
class VAEModel(nn.Module):
def __init__(self, encoder, decoder, auxiliaries_dict):
super(VAEModel, self).__init__()
self.encoder = encoder
self.decoder = decoder
self.latent_dim = self.encoder.latent_dim
self.auxiliaries_str = []
self.auxiliaries = nn.ModuleList()
for aux_str in sorted(auxiliaries_dict.keys()):
if aux_str == 'taste_reps':
self.taste_reps_decoder = PredictorHead(self.latent_dim, auxiliaries_dict[aux_str]['dim_output'], auxiliaries_dict[aux_str]['final_activ'])
else:
self.auxiliaries_str.append(aux_str)
self.auxiliaries.append(PredictorHead(self.latent_dim, auxiliaries_dict[aux_str]['dim_output'], auxiliaries_dict[aux_str]['final_activ']))
def reparameterization(self, mean, logvar):
std = torch.exp(0.5 * logvar)
epsilon = torch.randn_like(std).to(device) # sampling epsilon
z = mean + std * epsilon # reparameterization trick
return z
def sample(self, n=1):
z = torch.randn(size=(n, self.latent_dim))
return self.decoder(z)
def get_all_auxiliaries(self, x):
return [aux(x) for aux in self.auxiliaries]
def get_auxiliary(self, z, aux_str):
if aux_str == 'taste_reps':
return self.taste_reps_decoder(z)
else:
index = self.auxiliaries_str.index(aux_str)
return self.auxiliaries[index](z)
def forward_direct(self, x, aux_str=None, to_filter=False):
mean, logvar = self.encoder(x)
z = self.reparameterization(mean, logvar) # takes exponential function (log var -> std)
x_hat = self.decoder(mean, to_filter=to_filter)
if aux_str is not None:
return x_hat, z, mean, logvar, self.get_auxiliary(z, aux_str), [aux_str]
else:
return x_hat, z, mean, logvar, self.get_all_auxiliaries(z), self.auxiliaries_str
def forward(self, nb_ingredients, x, aux_str=None, to_filter=False):
assert False
mean, std = self.encoder(nb_ingredients, x)
z = self.reparameterization(mean, std) # takes exponential function (log var -> std)
x_hat = self.decoder(mean, to_filter=to_filter)
if aux_str is not None:
return x_hat, z, mean, std, self.get_auxiliary(z, aux_str), [aux_str]
else:
return x_hat, z, mean, std, self.get_all_auxiliaries(z), self.auxiliaries_str
class SimpleEncoder(nn.Module):
def __init__(self, input_dim, hidden_dims, latent_dim, activation, dropout):
super(SimpleEncoder, self).__init__()
self.latent_dim = latent_dim
# post aggregation network
self.linears = nn.ModuleList()
self.dropouts = nn.ModuleList()
dims = [input_dim] + hidden_dims
for d_in, d_out in zip(dims[:-1], dims[1:]):
self.linears.append(nn.Linear(d_in, d_out))
self.dropouts.append(nn.Dropout(dropout))
self.FC_mean = nn.Linear(hidden_dims[-1], latent_dim)
self.FC_logvar = nn.Linear(hidden_dims[-1], latent_dim)
# self.softplus = nn.Softplus()
self.activation = get_activation(activation)
self.n_layers = len(self.linears)
self.layer_range = range(self.n_layers)
def forward(self, x):
for i_layer, layer, dropout in zip(self.layer_range, self.linears, self.dropouts):
x = self.activation(dropout(layer(x)))
mean = self.FC_mean(x)
logvar = self.FC_logvar(x)
return mean, logvar
def get_vae_model(input_dim, deepset_latent_dim, hidden_dims_ing, activation,
hidden_dims_cocktail, hidden_dims_decoder, num_ingredients, latent_dim, aggregation, dropout, auxiliaries_dict,
filter_decoder_output):
# encoder = DeepsetCocktailEncoder(input_dim, deepset_latent_dim, hidden_dims_ing, activation,
# hidden_dims_cocktail, latent_dim, aggregation, dropout)
encoder = SimpleEncoder(num_ingredients, hidden_dims_cocktail, latent_dim, activation, dropout)
decoder = Decoder(latent_dim, hidden_dims_decoder, num_ingredients, activation, dropout, filter_output=filter_decoder_output)
vae = VAEModel(encoder, decoder, auxiliaries_dict)
return vae |