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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
from src.cocktails.representation_learning.simple_model import SimpleNet
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 MultiHeadModel(nn.Module):
def __init__(self, encoder, auxiliaries_dict, activation, hidden_dims_decoder):
super(MultiHeadModel, self).__init__()
self.encoder = encoder
self.latent_dim = self.encoder.output_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 = SimpleNet(input_dim=self.latent_dim, hidden_dims=[], output_dim=auxiliaries_dict[aux_str]['dim_output'],
activation=activation, dropout=0.0, final_activ=auxiliaries_dict[aux_str]['final_activ'])
else:
self.auxiliaries_str.append(aux_str)
if aux_str == 'ingredients_quantities':
hd = hidden_dims_decoder
else:
hd = []
self.auxiliaries.append(SimpleNet(input_dim=self.latent_dim, hidden_dims=hd, output_dim=auxiliaries_dict[aux_str]['dim_output'],
activation=activation, dropout=0.0, final_activ=auxiliaries_dict[aux_str]['final_activ']))
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(self, x, aux_str=None):
z = self.encoder(x)
if aux_str is not None:
return z, self.get_auxiliary(z, aux_str), [aux_str]
else:
return z, self.get_all_auxiliaries(z), self.auxiliaries_str
def get_multihead_model(input_dim, activation, hidden_dims_cocktail, latent_dim, dropout, auxiliaries_dict, hidden_dims_decoder):
encoder = SimpleNet(input_dim, hidden_dims_cocktail, latent_dim, activation, dropout)
model = MultiHeadModel(encoder, auxiliaries_dict, activation, hidden_dims_decoder)
return model |