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model.py
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import numpy as np
import scipy.stats
import torch
from torch import nn
from torch.nn import functional as f
from torchvision import models
from utils import init_embedding_, compute_wordsim_rho
class SkipGramModel(nn.Module):
def __init__(self, vocabulary_size, dimension):
super().__init__()
self.vocabulary_size = vocabulary_size
self.dimension = dimension
self.u_embedding = nn.Embedding(vocabulary_size, dimension)
self.v_embedding = nn.Embedding(vocabulary_size, dimension)
self.init_embeddings()
def init_embeddings(self):
init_embedding_(self.u_embedding.weight, self.dimension)
nn.init.zeros_(self.v_embedding.weight)
def forward(self, u, v, negative_v):
u_embedding = self.u_embedding(u)
v_embedding = self.v_embedding(v)
unnormalized_score = (u_embedding * v_embedding).squeeze().sum(1)
score = f.logsigmoid(unnormalized_score)
negative_v_embedding = self.v_embedding(negative_v)
unnormalized_negative_score = (
negative_v_embedding @ u_embedding.unsqueeze(2)
).squeeze()
negative_score = f.logsigmoid(-unnormalized_negative_score)
return -(torch.mean(score) + torch.mean(negative_score)) / 2
def get_wordsim_rho(self, wordsim_tuples, id_from_word, word_from_id):
embedding = self.u_embedding.weight.cpu().detach().numpy()
rho = compute_wordsim_rho(embedding, wordsim_tuples, id_from_word)
return rho
def get_embedding(self, id_from_word, word_from_id):
embedding = self.u_embedding.weight
return embedding
class NanoNet(nn.Module):
def __init__(self, dimension):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
self.conv3 = nn.Conv2d(32, 32, 3, padding=1)
self.fc = nn.Linear(32, dimension)
nn.init.kaiming_uniform_(self.conv1.weight)
nn.init.kaiming_uniform_(self.conv2.weight)
nn.init.kaiming_uniform_(self.conv3.weight)
nn.init.xavier_uniform_(self.fc.weight)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = f.adaptive_avg_pool2d(x, output_size=1).squeeze()
x = self.fc(x)
return x
class GlyphNet(nn.Module):
def __init__(self, dimension):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
self.fc = nn.Linear(32, dimension)
nn.init.kaiming_uniform_(self.conv1.weight)
nn.init.kaiming_uniform_(self.conv2.weight)
nn.init.xavier_uniform_(self.fc.weight)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = f.adaptive_avg_pool2d(x, output_size=1).squeeze()
x = self.fc(x)
return x
class TianzigeCNN(nn.Module):
def __init__(self, dimension):
super().__init__()
self.conv1 = nn.Conv2d(3, 1024, 5)
self.relu = nn.ReLU(inplace=True)
self.max_pool = nn.MaxPool2d(4)
self.conv2 = nn.Conv2d(1024, 256, 1, groups=8)
self.conv3 = nn.Conv2d(256, dimension, 2, groups=16)
nn.init.kaiming_uniform_(self.conv1.weight)
nn.init.kaiming_uniform_(self.conv2.weight)
nn.init.kaiming_uniform_(self.conv3.weight)
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.max_pool(x)
x = self.conv2(x)
x = self.conv3(x)
x = f.adaptive_avg_pool2d(x, output_size=1).squeeze()
return x
class VisualModel(nn.Module):
def __init__(self, vocabulary_size, dimension, renderer, pretrained=False):
super().__init__()
self.vocabulary_size = vocabulary_size
self.dimension = dimension
self.renderer = renderer
self.backbone = nn.Sequential(
models.resnet18(pretrained=True),
nn.Linear(1000, dimension),
)
self.u_fc = nn.Linear(dimension, dimension)
self.v_fc = nn.Linear(dimension, dimension)
nn.init.xavier_uniform_(self.u_fc.weight)
nn.init.xavier_uniform_(self.v_fc.weight)
# u, v, negative_v are arrays of word ids
def forward(self, u, v, negative_v):
rendered_u = self.renderer(u).repeat(1, 3, 1, 1)
rendered_v = self.renderer(v).repeat(1, 3, 1, 1)
rendered_negative_v = self.renderer(negative_v)
u_embedding = self.u_fc(self.backbone(rendered_u))
v_embedding = self.v_fc(self.backbone(rendered_v))
unnormalized_score = (u_embedding * v_embedding).squeeze().sum(1)
score = f.logsigmoid(unnormalized_score)
negative_v_embedding = torch.stack([
self.v_fc(self.backbone(rendered_negative_v[:, i:i + 1, :, :].repeat(1, 3, 1, 1))) for i in range(0, rendered_negative_v.size(1), 3)
], dim=1)
unnormalized_negative_score = (
negative_v_embedding @ u_embedding.unsqueeze(dim=2)
)
negative_score = f.logsigmoid(-unnormalized_negative_score)
loss = -(torch.mean(score) + torch.mean(negative_score)) / 2
if torch.isnan(loss):
pass
return loss
def get_wordsim_rho(self, wordsim_tuples, id_from_word, word_from_id):
embedding = self.get_embedding(id_from_word, word_from_id).detach().cpu().numpy()
rho = compute_wordsim_rho(embedding, wordsim_tuples, id_from_word)
return rho
def get_embedding(self, id_from_word, word_from_id):
embedding = [None for _ in word_from_id]
for word, id_ in id_from_word.items():
id_tensor = torch.tensor([id_]).cuda()
word_embedding = (
self.u_fc(self.backbone(self.renderer(id_tensor).repeat(1, 3, 1, 1)))
.squeeze().detach().cpu().numpy()
)
embedding[id_from_word[word]] = word_embedding
embedding = torch.tensor(embedding)
return embedding