| import torch |
| import torch.nn as nn |
| from torch.nn import BCEWithLogitsLoss,CrossEntropyLoss, Dropout, Softmax, Linear, Conv2d, LayerNorm |
| import models.configs as configs |
| import math |
|
|
| class Attention(nn.Module): |
| def __init__(self, config, vis, mm=True): |
| super(Attention, self).__init__() |
| self.vis = vis |
| self.num_attention_heads = config.transformer["num_heads"] |
| self.attention_head_size = int(config.hidden_size / self.num_attention_heads) |
| self.all_head_size = self.num_attention_heads * self.attention_head_size |
|
|
| self.query = Linear(config.hidden_size, self.all_head_size) |
| self.key = Linear(config.hidden_size, self.all_head_size) |
| self.value = Linear(config.hidden_size, self.all_head_size) |
|
|
| if mm: |
| self.query_text = Linear(config.hidden_size, self.all_head_size) |
| self.key_text = Linear(config.hidden_size, self.all_head_size) |
| self.value_text = Linear(config.hidden_size, self.all_head_size) |
| self.out_text = Linear(config.hidden_size, config.hidden_size) |
| self.attn_dropout_text = Dropout(config.transformer["attention_dropout_rate"]) |
| self.attn_dropout_it = Dropout(config.transformer["attention_dropout_rate"]) |
| self.attn_dropout_ti = Dropout(config.transformer["attention_dropout_rate"]) |
| self.proj_dropout_text = Dropout(config.transformer["attention_dropout_rate"]) |
|
|
| self.out = Linear(config.hidden_size, config.hidden_size) |
| self.attn_dropout = Dropout(config.transformer["attention_dropout_rate"]) |
| self.proj_dropout = Dropout(config.transformer["attention_dropout_rate"]) |
|
|
| self.softmax = Softmax(dim=-1) |
|
|
| def transpose_for_scores(self, x): |
| new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) |
| x = x.view(*new_x_shape) |
| return x.permute(0, 2, 1, 3) |
|
|
| def forward(self, hidden_states, text=None): |
| mixed_query_layer = self.query(hidden_states) |
| mixed_key_layer = self.key(hidden_states) |
| mixed_value_layer = self.value(hidden_states) |
| if text is not None: |
| text_q = self.query_text(text) |
| text_k = self.key_text(text) |
| text_v = self.value_text(text) |
| query_layer = self.transpose_for_scores(mixed_query_layer) |
| key_layer = self.transpose_for_scores(mixed_key_layer) |
| value_layer = self.transpose_for_scores(mixed_value_layer) |
| if text is not None: |
| query_layer_img = query_layer |
| key_layer_img = key_layer |
| value_layer_img = value_layer |
| query_layer_text = self.transpose_for_scores(text_q) |
| key_layer_text = self.transpose_for_scores(text_k) |
| value_layer_text = self.transpose_for_scores(text_v) |
|
|
| if text is None: |
| attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) |
| attention_scores = attention_scores / math.sqrt(self.attention_head_size) |
| attention_probs = self.softmax(attention_scores) |
| weights = attention_probs if self.vis else None |
| attention_probs = self.attn_dropout(attention_probs) |
|
|
| context_layer = torch.matmul(attention_probs, value_layer) |
| context_layer = context_layer.permute(0, 2, 1, 3).contiguous() |
| new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) |
| context_layer = context_layer.view(*new_context_layer_shape) |
| attention_output = self.out(context_layer) |
| attention_output = self.proj_dropout(attention_output) |
|
|
| return attention_output,None, weights |
|
|
| else: |
| attention_scores_img = torch.matmul(query_layer, key_layer.transpose(-1, -2)) |
| attention_scores_text = torch.matmul(query_layer_text, key_layer_text.transpose(-1, -2)) |
| attention_scores_it = torch.matmul(query_layer_img, key_layer_text.transpose(-1, -2)) |
| attention_scores_ti = torch.matmul(query_layer_text, key_layer_img.transpose(-1, -2)) |
| attention_scores_img = attention_scores_img / math.sqrt(self.attention_head_size) |
| |
| attention_probs_img = self.softmax(attention_scores_img) |
| weights_img = attention_probs_img if self.vis else None |
|
|
| attention_probs_img = self.attn_dropout(attention_probs_img) |
|
|
| attention_scores_text = attention_scores_text / math.sqrt(self.attention_head_size) |
| attention_probs_text = self.softmax(attention_scores_text) |
|
|
| text_per_weights = attention_probs_text.mean(dim=-1) |
|
|
| text_per_weights = self.softmax(text_per_weights) |
|
|
| weights_text = attention_probs_text if self.vis else None |
|
|
| attention_probs_text = self.attn_dropout_text(attention_probs_text) |
|
|
| attention_scores_it = attention_scores_it / math.sqrt(self.attention_head_size) |
| attention_probs_it = self.softmax(attention_scores_it) |
|
|
| attention_probs_it = self.attn_dropout_it(attention_probs_it) |
|
|
| attention_scores_ti = attention_scores_ti / math.sqrt(self.attention_head_size) |
| attention_probs_ti = self.softmax(attention_scores_ti) |
| attention_probs_ti = self.attn_dropout_ti(attention_probs_ti) |
|
|
| context_layer_img = torch.matmul(attention_probs_img, value_layer_img) |
| context_layer_img = context_layer_img.permute(0, 2, 1, 3).contiguous() |
| context_layer_text = torch.matmul(attention_probs_text, value_layer_text) |
| context_layer_text = context_layer_text.permute(0, 2, 1, 3).contiguous() |
| context_layer_it = torch.matmul(attention_probs_it, value_layer_text) |
| context_layer_it = context_layer_it.permute(0, 2, 1, 3).contiguous() |
| context_layer_ti = torch.matmul(attention_probs_ti, value_layer_img) |
| context_layer_ti = context_layer_ti.permute(0, 2, 1, 3).contiguous() |
| new_context_layer_shape = context_layer_img.size()[:-2] + (self.all_head_size,) |
| context_layer_img = context_layer_img.view(*new_context_layer_shape) |
| new_context_layer_shape = context_layer_text.size()[:-2] + (self.all_head_size,) |
| context_layer_text = context_layer_text.view(*new_context_layer_shape) |
| new_context_layer_shape = context_layer_it.size()[:-2] + (self.all_head_size,) |
| context_layer_it = context_layer_it.view(*new_context_layer_shape) |
| new_context_layer_shape = context_layer_ti.size()[:-2] + (self.all_head_size,) |
| context_layer_ti = context_layer_ti.view(*new_context_layer_shape) |
| attention_output_img = self.out((context_layer_img + context_layer_it)/2) |
| attention_output_text = self.out((context_layer_text + context_layer_ti)/2) |
| attention_output_img = self.proj_dropout(attention_output_img) |
| attention_output_text = self.proj_dropout_text(attention_output_text) |
| |
| return attention_output_img, attention_output_text |
