prior-fitting/encoders.py

import math

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import TransformerEncoder, TransformerEncoderLayer

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)


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)

    @property
    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)

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 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)


Positional = lambda _, emsize: _PositionalEncoding(d_model=emsize)


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):
        x = super().forward(x)
        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)