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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel, AutoFeatureExtractor
import numpy as np
import math
import warnings
warnings.filterwarnings("ignore")
device = torch.device("cpu")
vision_model_name = "google/vit-base-patch16-224-in21k"
language_model_name = "vinai/phobert-base"
def generate_padding_mask(sequences, padding_idx):
if sequences is None:
return None
if len(sequences.shape) == 2:
__seq = sequences.unsqueeze(dim=-1)
else:
__seq = sequences
mask = (torch.sum(__seq, dim=-1) == (padding_idx*__seq.shape[-1])).long() * -10e4
return mask.unsqueeze(1).unsqueeze(1)
class ScaledDotProduct(nn.Module):
def __init__(self, d_model = 512, h = 8, d_k = 64, d_v = 64):
super().__init__()
self.fc_q = nn.Linear(d_model, h * d_k)
self.fc_k = nn.Linear(d_model, h * d_k)
self.fc_v = nn.Linear(d_model, h * d_v)
self.fc_o = nn.Linear(h * d_v, d_model)
self.d_model = d_model
self.d_k = d_k
self.d_v = d_v
self.h = h
self.init_weights()
def init_weights(self):
nn.init.xavier_uniform_(self.fc_q.weight)
nn.init.xavier_uniform_(self.fc_k.weight)
nn.init.xavier_uniform_(self.fc_v.weight)
nn.init.xavier_uniform_(self.fc_o.weight)
nn.init.constant_(self.fc_q.bias, 0)
nn.init.constant_(self.fc_k.bias, 0)
nn.init.constant_(self.fc_v.bias, 0)
nn.init.constant_(self.fc_o.bias, 0)
def forward(self, queries, keys, values, attention_mask=None, **kwargs):
b_s, nq = queries.shape[:2]
nk = keys.shape[1]
q = self.fc_q(queries).view(b_s, nq, self.h, self.d_k).permute(0, 2, 1, 3) # (b_s, h, nq, d_k)
k = self.fc_k(keys).view(b_s, nk, self.h, self.d_k).permute(0, 2, 3, 1) # (b_s, h, d_k, nk)
v = self.fc_v(values).view(b_s, nk, self.h, self.d_v).permute(0, 2, 1, 3) # (b_s, h, nk, d_v)
att = torch.matmul(q, k) / np.sqrt(self.d_k) # (b_s, h, nq, nk)
if attention_mask is not None:
att += attention_mask
att = torch.softmax(att, dim=-1)
out = torch.matmul(att, v).permute(0, 2, 1, 3).contiguous().view(b_s, nq, self.h * self.d_v) # (b_s, nq, h*d_v)
out = self.fc_o(out) # (b_s, nq, d_model)
return out, att
class MultiheadAttention(nn.Module):
def __init__(self, d_model = 512, dropout = 0.1, use_aoa = True):
super().__init__()
self.d_model = d_model
self.use_aoa = use_aoa
self.attention = ScaledDotProduct()
self.norm = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
if self.use_aoa:
self.infomative_attention = nn.Linear(2 * self.d_model, self.d_model)
self.gated_attention = nn.Linear(2 * self.d_model, self.d_model)
def forward(self, q, k, v, mask = None):
out, _ = self.attention(q, k, v, mask)
if self.use_aoa:
aoa_input = torch.cat([q, out], dim = -1)
i = self.infomative_attention(aoa_input)
g = torch.sigmoid(self.gated_attention(aoa_input))
out = i * g
return out
class PositionWiseFeedForward(nn.Module):
def __init__(self, d_model = 512, d_ff = 2048, dropout = 0.1):
super().__init__()
self.fc1 = nn.Linear(d_model, d_ff)
self.fc2 = nn.Linear(d_ff, d_model)
self.relu = nn.ReLU()
def forward(self, input):
out = self.fc1(input)
out = self.fc2(self.relu(out))
return out
class AddNorm(nn.Module):
def __init__(self, dim = 512, dropout = 0.1):
super().__init__()
self.dropout = nn.Dropout(dropout)
self.norm = nn.LayerNorm(dim)
def forward(self, x, y):
return self.norm(x + self.dropout(y))
class SinusoidPositionalEmbedding(nn.Module):
def __init__(self, num_pos_feats=512, temperature=10000, normalize=False, scale=None):
super().__init__()
self.num_pos_feats = num_pos_feats
self.temperature = temperature
self.normalize = normalize
if scale is not None and normalize is False:
raise ValueError("normalize should be True if scale is passed")
if scale is None:
scale = 2 * math.pi
self.scale = scale
def forward(self, x, mask=None):
if mask is None:
mask = torch.zeros(x.shape[:-1], dtype=torch.bool, device=x.device)
not_mask = (mask == False)
embed = not_mask.cumsum(1, dtype=torch.float32)
if self.normalize:
eps = 1e-6
embed = embed / (embed[:, -1:] + eps) * self.scale
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
dim_t = self.temperature ** (2 * (torch.div(dim_t, 2, rounding_mode="floor")) / self.num_pos_feats)
pos = embed[:, :, None] / dim_t
pos = torch.stack((pos[:, :, 0::2].sin(), pos[:, :, 1::2].cos()), dim=-1).flatten(-2)
return pos
class GuidedEncoderLayer(nn.Module):
def __init__(self):
super().__init__()
self.self_mhatt = MultiheadAttention()
self.guided_mhatt = MultiheadAttention()
self.pwff = PositionWiseFeedForward()
self.first_norm = AddNorm()
self.second_norm = AddNorm()
self.third_norm = AddNorm()
def forward(self, q, k, v, self_mask, guided_mask):
self_att = self.self_mhatt(q, q, q, self_mask)
self_att = self.first_norm(self_att, q)
guided_att = self.guided_mhatt(self_att, k, v, guided_mask)
guided_att = self.second_norm(guided_att, self_att)
out = self.pwff(guided_att)
out = self.third_norm(out, guided_att)
return out
class GuidedAttentionEncoder(nn.Module):
def __init__(self, num_layers = 2, d_model = 512):
super().__init__()
self.pos_embedding = SinusoidPositionalEmbedding()
self.layer_norm = nn.LayerNorm(d_model)
self.guided_layers = nn.ModuleList([GuidedEncoderLayer() for _ in range(num_layers)])
self.language_layers = nn.ModuleList(GuidedEncoderLayer() for _ in range(num_layers))
def forward(self, vision_features, vision_mask, language_features, language_mask):
vision_features = self.layer_norm(vision_features) + self.pos_embedding(vision_features)
language_features = self.layer_norm(language_features) + self.pos_embedding(language_features)
for layers in zip(self.guided_layers, self.language_layers):
guided_layer, language_layer = layers
vision_features = guided_layer(q = vision_features,
k = language_features,
v = language_features,
self_mask = vision_mask,
guided_mask = language_mask)
language_features = language_layer(q = language_features,
k = vision_features,
v = vision_features,
self_mask = language_mask,
guided_mask = vision_mask)
return vision_features, language_features
class VisionEmbedding(nn.Module):
def __init__(self, out_dim = 768, hidden_dim = 512, dropout = 0.1):
super().__init__()
self.prep = AutoFeatureExtractor.from_pretrained(vision_model_name)
self.backbone = AutoModel.from_pretrained(vision_model_name)
for param in self.backbone.parameters():
param.requires_grad = False
self.proj = nn.Linear(out_dim, hidden_dim)
self.dropout = nn.Dropout(dropout)
self.gelu = nn.GELU()
def forward(self, images):
inputs = self.prep(images = images, return_tensors = "pt").to(device)
with torch.no_grad():
outputs = self.backbone(**inputs)
features = outputs.last_hidden_state
vision_mask = generate_padding_mask(features, padding_idx = 0)
out = self.proj(features)
out = self.gelu(out)
out = self.dropout(out)
return out, vision_mask
class LanguageEmbedding(nn.Module):
def __init__(self, out_dim = 768, hidden_dim = 512, dropout = 0.1):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(language_model_name)
self.embeding = AutoModel.from_pretrained(language_model_name)
for param in self.embeding.parameters():
param.requires_grad = False
self.proj = nn.Linear(out_dim, hidden_dim)
self.dropout = nn.Dropout(dropout)
self.gelu = nn.GELU()
def forward(self, questions):
inputs = self.tokenizer(questions,
padding = 'max_length',
max_length = 30,
truncation = True,
return_tensors = 'pt',
return_token_type_ids = True,
return_attention_mask = True).to(device)
features = self.embeding(**inputs).last_hidden_state
language_mask = generate_padding_mask(inputs.input_ids, padding_idx=self.tokenizer.pad_token_id)
out = self.proj(features)
out = self.gelu(out)
out = self.dropout(out)
return out, language_mask
class BaseModel(nn.Module):
def __init__(self, num_classes = 353, d_model = 512):
super().__init__()
self.vision_embedding = VisionEmbedding()
self.language_embedding = LanguageEmbedding()
self.encoder = GuidedAttentionEncoder()
self.fusion = nn.Sequential(nn.Linear(2 * d_model, d_model),
nn.ReLU(),
nn.Dropout(0.2))
self.classify = nn.Linear(d_model, num_classes)
self.attention_weights = nn.Linear(d_model, 1)
def forward(self, images, questions):
embedded_text, text_mask = self.language_embedding(questions)
embedded_vision, vison_mask = self.vision_embedding(images)
encoded_image, encoded_text = self.encoder(embedded_vision, vison_mask,embedded_text, text_mask)
text_attended = self.attention_weights(torch.tanh(encoded_text))
image_attended = self.attention_weights(torch.tanh(encoded_image))
attention_weights = torch.softmax(torch.cat([text_attended, image_attended], dim=1), dim=1)
attended_text = torch.sum(attention_weights[:, 0].unsqueeze(-1) * encoded_text, dim=1)
attended_image = torch.sum(attention_weights[:, 1].unsqueeze(-1) * encoded_image, dim=1)
fused_output = self.fusion(torch.cat([attended_text, attended_image], dim=1))
logits = self.classify(fused_output)
logits = F.log_softmax(logits, dim=-1)
return logits
if __name__ == "__main__":
model = BaseModel().to(device)
print(model.eval)
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