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| from torch import nn | |
| import torch.nn.functional as nnf | |
| from transformers import GPT2Tokenizer, GPT2LMHeadModel | |
| import torch | |
| from typing import Tuple, List, Union, Optional | |
| import numpy as np | |
| N = type(None) | |
| V = np.array | |
| ARRAY = np.ndarray | |
| ARRAYS = Union[Tuple[ARRAY, ...], List[ARRAY]] | |
| VS = Union[Tuple[V, ...], List[V]] | |
| VN = Union[V, N] | |
| VNS = Union[VS, N] | |
| T = torch.Tensor | |
| TS = Union[Tuple[T, ...], List[T]] | |
| TN = Optional[T] | |
| TNS = Union[Tuple[TN, ...], List[TN]] | |
| TSN = Optional[TS] | |
| TA = Union[T, ARRAY] | |
| class ClipCaptionModel(nn.Module): | |
| def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor: | |
| return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device) | |
| def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None, | |
| labels: Optional[torch.Tensor] = None): | |
| embedding_text = self.gpt.transformer.wte(tokens) | |
| prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size) | |
| embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1) | |
| if labels is not None: | |
| dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) | |
| labels = torch.cat((dummy_token, tokens), dim=1) | |
| out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) | |
| return out | |
| def __init__(self): | |
| super(ClipCaptionModel, self).__init__() | |
| self.prefix_length = 40 | |
| self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') | |
| self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] | |
| self.clip_project = TransformerMapper(640, self.gpt_embedding_size, 40, | |
| 40, 8) | |
| class MLP(nn.Module): | |
| def forward(self, x: T) -> T: | |
| return self.model(x) | |
| def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh): | |
| super(MLP, self).__init__() | |
| layers = [] | |
| for i in range(len(sizes) -1): | |
| layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias)) | |
| if i < len(sizes) - 2: | |
| layers.append(act()) | |
| self.model = nn.Sequential(*layers) | |
| class ClipCaptionPrefix(ClipCaptionModel): | |
| def parameters(self, recurse: bool = True): | |
| return self.clip_project.parameters() | |
| def train(self, mode: bool = True): | |
| super(ClipCaptionPrefix, self).train(mode) | |
| self.gpt.eval() | |
| return self | |
| class MlpTransformer(nn.Module): | |
| def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.): | |
| super().__init__() | |
| out_d = out_d if out_d is not None else in_dim | |
| self.fc1 = nn.Linear(in_dim, h_dim) | |
| self.act = act | |
| self.fc2 = nn.Linear(h_dim, out_d) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| x = self.fc1(x) | |
| x = self.act(x) | |
| x = self.dropout(x) | |
| x = self.fc2(x) | |
| x = self.dropout(x) | |
| return x | |
| class MultiHeadAttention(nn.Module): | |
| def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.): | |
| super().__init__() | |
| self.num_heads = num_heads | |
| head_dim = dim_self // num_heads | |
| self.scale = head_dim ** -0.5 | |
| self.to_queries = nn.Linear(dim_self, dim_self, bias=bias) | |
| self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias) | |
| self.project = nn.Linear(dim_self, dim_self) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x, y=None, mask=None): | |
| y = y if y is not None else x | |
| b, n, c = x.shape | |
| _, m, d = y.shape | |
| # b n h dh | |
| queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads) | |
| # b m 2 h dh | |
| keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads) | |
| keys, values = keys_values[:, :, 0], keys_values[:, :, 1] | |
| attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale | |
| if mask is not None: | |
| if mask.dim() == 2: | |
| mask = mask.unsqueeze(1) | |
| attention = attention.masked_fill(mask.unsqueeze(3), float("-inf")) | |
| attention = attention.softmax(dim=2) | |
| out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c) | |
| out = self.project(out) | |
| return out, attention | |
| class TransformerLayer(nn.Module): | |
| def forward_with_attention(self, x, y=None, mask=None): | |
| x_, attention = self.attn(self.norm1(x), y, mask) | |
| x = x + x_ | |
| x = x + self.mlp(self.norm2(x)) | |
| return x, attention | |
| def forward(self, x, y=None, mask=None): | |
| x = x + self.attn(self.norm1(x), y, mask)[0] | |
| x = x + self.mlp(self.norm2(x)) | |
| return x | |
| def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu, | |
| norm_layer: nn.Module = nn.LayerNorm): | |
| super().__init__() | |
| self.norm1 = norm_layer(dim_self) | |
| self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout) | |
| self.norm2 = norm_layer(dim_self) | |
| self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout) | |
| class Transformer(nn.Module): | |
| def forward_with_attention(self, x, y=None, mask=None): | |
| attentions = [] | |
| for layer in self.layers: | |
| x, att = layer.forward_with_attention(x, y, mask) | |
| attentions.append(att) | |
| return x, attentions | |
| def forward(self, x, y=None, mask=None): | |
| for i, layer in enumerate(self.layers): | |
| if i % 2 == 0 and self.enc_dec: # cross | |
| x = layer(x, y) | |
| elif self.enc_dec: # self | |
| x = layer(x, x, mask) | |
| else: # self or cross | |
| x = layer(x, y, mask) | |
| return x | |
| def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None, | |
| mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False): | |
| super(Transformer, self).__init__() | |
| dim_ref = dim_ref if dim_ref is not None else dim_self | |
| self.enc_dec = enc_dec | |
| if enc_dec: | |
| num_layers = num_layers * 2 | |
| layers = [] | |
| for i in range(num_layers): | |
| if i % 2 == 0 and enc_dec: # cross | |
| layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) | |
| elif enc_dec: # self | |
| layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) | |
| else: # self or cross | |
| layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) | |
| self.layers = nn.ModuleList(layers) | |
| class TransformerMapper(nn.Module): | |
| def forward(self, x): | |
| x = self.linear(x).view(x.shape[0], self.clip_length, -1) | |
| prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape) | |
| prefix = torch.cat((x, prefix), dim=1) | |
| out = self.transformer(prefix)[:, self.clip_length:] | |
| return out | |
| def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8): | |
| super(TransformerMapper, self).__init__() | |
| self.clip_length = clip_length | |
| self.transformer = Transformer(dim_embedding, 8, num_layers) | |
| self.linear = nn.Linear(dim_clip, clip_length * dim_embedding) | |
| self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True) |