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| import math | |
| import torch | |
| import torch.nn as nn | |
| from utils import normalize_data | |
| import torch.nn.functional as F | |
| from torch.nn import TransformerEncoder, TransformerEncoderLayer | |
| class StyleEncoder(nn.Module): | |
| def __init__(self, num_hyperparameters, em_size): | |
| super().__init__() | |
| self.em_size = em_size | |
| self.embedding = nn.Linear(num_hyperparameters, self.em_size) | |
| def forward(self, hyperparameters): # B x num_hps | |
| return self.embedding(hyperparameters) | |
| class StyleEmbEncoder(nn.Module): | |
| def __init__(self, num_hyperparameters, em_size, num_embeddings=100): | |
| super().__init__() | |
| assert num_hyperparameters == 1 | |
| self.em_size = em_size | |
| self.embedding = nn.Embedding(num_embeddings, self.em_size) | |
| def forward(self, hyperparameters): # B x num_hps | |
| return self.embedding(hyperparameters.squeeze(1)) | |
| class _PositionalEncoding(nn.Module): | |
| def __init__(self, d_model, dropout=0.): | |
| super().__init__() | |
| self.dropout = nn.Dropout(p=dropout) | |
| self.d_model = d_model | |
| self.device_test_tensor = nn.Parameter(torch.tensor(1.)) | |
| def forward(self, x):# T x B x num_features | |
| assert self.d_model % x.shape[-1]*2 == 0 | |
| d_per_feature = self.d_model // x.shape[-1] | |
| pe = torch.zeros(*x.shape, d_per_feature, device=self.device_test_tensor.device) | |
| #position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) | |
| interval_size = 10 | |
| div_term = (1./interval_size) * 2*math.pi*torch.exp(torch.arange(0, d_per_feature, 2, device=self.device_test_tensor.device).float()*math.log(math.sqrt(2))) | |
| #print(div_term/2/math.pi) | |
| pe[..., 0::2] = torch.sin(x.unsqueeze(-1) * div_term) | |
| pe[..., 1::2] = torch.cos(x.unsqueeze(-1) * div_term) | |
| return self.dropout(pe).view(x.shape[0],x.shape[1],self.d_model) | |
| Positional = lambda _, emsize: _PositionalEncoding(d_model=emsize) | |
| class EmbeddingEncoder(nn.Module): | |
| def __init__(self, num_features, em_size, num_embs=100): | |
| super().__init__() | |
| self.num_embs = num_embs | |
| self.embeddings = nn.Embedding(num_embs * num_features, em_size, max_norm=True) | |
| self.init_weights(.1) | |
| self.min_max = (-2,+2) | |
| def width(self): | |
| return self.min_max[1] - self.min_max[0] | |
| def init_weights(self, initrange): | |
| self.embeddings.weight.data.uniform_(-initrange, initrange) | |
| def discretize(self, x): | |
| split_size = self.width / self.num_embs | |
| return (x - self.min_max[0] // split_size).int().clamp(0, self.num_embs - 1) | |
| def forward(self, x): # T x B x num_features | |
| x_idxs = self.discretize(x) | |
| x_idxs += torch.arange(x.shape[-1], device=x.device).view(1, 1, -1) * self.num_embs | |
| # print(x_idxs,self.embeddings.weight.shape) | |
| return self.embeddings(x_idxs).mean(-2) | |
| class Normalize(nn.Module): | |
| def __init__(self, mean, std): | |
| super().__init__() | |
| self.mean = mean | |
| self.std = std | |
| def forward(self, x): | |
| return (x-self.mean)/self.std | |
| def get_normalized_uniform_encoder(encoder_creator): | |
| """ | |
| This can be used to wrap an encoder that is fed uniform samples in [0,1] and normalizes these to 0 mean and 1 std. | |
| For example, it can be used as `encoder_creator = get_normalized_uniform_encoder(encoders.Linear)`, now this can | |
| be initialized with `encoder_creator(feature_dim, in_dim)`. | |
| :param encoder: | |
| :return: | |
| """ | |
| return lambda in_dim, out_dim: nn.Sequential(Normalize(.5, math.sqrt(1/12)), encoder_creator(in_dim, out_dim)) | |
| def get_normalized_encoder(encoder_creator, data_std): | |
| return lambda in_dim, out_dim: nn.Sequential(Normalize(0., data_std), encoder_creator(in_dim, out_dim)) | |
| class ZNormalize(nn.Module): | |
| def forward(self, x): | |
| return (x-x.mean(-1,keepdim=True))/x.std(-1,keepdim=True) | |
| class AppendEmbeddingEncoder(nn.Module): | |
| def __init__(self, base_encoder, num_features, emsize): | |
| super().__init__() | |
| self.num_features = num_features | |
| self.base_encoder = base_encoder | |
| self.emb = nn.Parameter(torch.zeros(emsize)) | |
| def forward(self, x): | |
| if (x[-1] == 1.).all(): | |
| append_embedding = True | |
| else: | |
| assert (x[-1] == 0.).all(), "You need to specify as last position whether to append embedding. " \ | |
| "If you don't want this behavior, please use the wrapped encoder instead." | |
| append_embedding = False | |
| x = x[:-1] | |
| encoded_x = self.base_encoder(x) | |
| if append_embedding: | |
| encoded_x = torch.cat([encoded_x, self.emb[None, None, :].repeat(1, encoded_x.shape[1], 1)], 0) | |
| return encoded_x | |
| def get_append_embedding_encoder(encoder_creator): | |
| return lambda num_features, emsize: AppendEmbeddingEncoder(encoder_creator(num_features, emsize), num_features, emsize) | |
| class VariableNumFeaturesEncoder(nn.Module): | |
| def __init__(self, base_encoder, num_features): | |
| super().__init__() | |
| self.base_encoder = base_encoder | |
| self.num_features = num_features | |
| def forward(self, x): | |
| x = x * (self.num_features/x.shape[-1]) | |
| x = torch.cat((x, torch.zeros(*x.shape[:-1], self.num_features - x.shape[-1], device=x.device)), -1) | |
| return self.base_encoder(x) | |
| def get_variable_num_features_encoder(encoder_creator): | |
| return lambda num_features, emsize: VariableNumFeaturesEncoder(encoder_creator(num_features, emsize), num_features) | |
| class NoMeanEncoder(nn.Module): | |
| """ | |
| This can be useful for any prior that is translation invariant in x or y. | |
| A standard GP for example is translation invariant in x. | |
| That is, GP(x_test+const,x_train+const,y_train) = GP(x_test,x_train,y_train). | |
| """ | |
| def __init__(self, base_encoder): | |
| super().__init__() | |
| self.base_encoder = base_encoder | |
| def forward(self, x): | |
| return self.base_encoder(x - x.mean(0, keepdim=True)) | |
| def get_no_mean_encoder(encoder_creator): | |
| return lambda num_features, emsize: NoMeanEncoder(encoder_creator(num_features, emsize)) | |
| Linear = nn.Linear | |
| MLP = lambda num_features, emsize: nn.Sequential(nn.Linear(num_features+1,emsize*2), | |
| nn.ReLU(), | |
| nn.Linear(emsize*2,emsize)) | |
| class NanHandlingEncoder(nn.Module): | |
| def __init__(self, num_features, emsize, keep_nans=True): | |
| super().__init__() | |
| self.num_features = 2 * num_features if keep_nans else num_features | |
| self.emsize = emsize | |
| self.keep_nans = keep_nans | |
| self.layer = nn.Linear(self.num_features, self.emsize) | |
| def forward(self, x): | |
| if self.keep_nans: | |
| x = torch.cat([torch.nan_to_num(x, nan=0.0), normalize_data(torch.isnan(x) * -1 | |
| + torch.logical_and(torch.isinf(x), torch.sign(x) == 1) * 1 | |
| + torch.logical_and(torch.isinf(x), torch.sign(x) == -1) * 2 | |
| )], -1) | |
| else: | |
| x = torch.nan_to_num(x, nan=0.0) | |
| return self.layer(x) | |
| class Linear(nn.Linear): | |
| def __init__(self, num_features, emsize, replace_nan_by_zero=False): | |
| super().__init__(num_features, emsize) | |
| self.num_features = num_features | |
| self.emsize = emsize | |
| self.replace_nan_by_zero = replace_nan_by_zero | |
| def forward(self, x): | |
| if self.replace_nan_by_zero: | |
| x = torch.nan_to_num(x, nan=0.0) | |
| return super().forward(x) | |
| def __setstate__(self, state): | |
| super().__setstate__(state) | |
| self.__dict__.setdefault('replace_nan_by_zero', True) | |
| class Conv(nn.Module): | |
| def __init__(self, input_size, emsize): | |
| super().__init__() | |
| self.convs = torch.nn.ModuleList([nn.Conv2d(64 if i else 1, 64, 3) for i in range(5)]) | |
| self.linear = nn.Linear(64,emsize) | |
| def forward(self, x): | |
| size = math.isqrt(x.shape[-1]) | |
| assert size*size == x.shape[-1] | |
| x = x.reshape(*x.shape[:-1], 1, size, size) | |
| for conv in self.convs: | |
| if x.shape[-1] < 4: | |
| break | |
| x = conv(x) | |
| x.relu_() | |
| x = nn.AdaptiveAvgPool2d((1,1))(x).squeeze(-1).squeeze(-1) | |
| return self.linear(x) | |
| class CanEmb(nn.Embedding): | |
| def __init__(self, num_features, num_embeddings: int, embedding_dim: int, *args, **kwargs): | |
| assert embedding_dim % num_features == 0 | |
| embedding_dim = embedding_dim // num_features | |
| super().__init__(num_embeddings, embedding_dim, *args, **kwargs) | |
| def forward(self, x): | |
| lx = x.long() | |
| assert (lx == x).all(), "CanEmb only works with tensors of whole numbers" | |
| x = super().forward(lx) | |
| return x.view(*x.shape[:-2], -1) | |
| def get_Canonical(num_classes): | |
| return lambda num_features, emsize: CanEmb(num_features, num_classes, emsize) | |
| def get_Embedding(num_embs_per_feature=100): | |
| return lambda num_features, emsize: EmbeddingEncoder(num_features, emsize, num_embs=num_embs_per_feature) | |