from typing import Optional import torch from torch import nn from torch import nn, Tensor from torch.nn.modules.transformer import _get_activation_fn def add_ml_decoder_head(model): if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # resnet50 model.global_pool = nn.Identity() del model.fc num_classes = model.num_classes num_features = model.num_features model.fc = MLDecoder(num_classes=num_classes, initial_num_features=num_features) elif hasattr(model, 'head'): # tresnet del model.head num_classes = model.num_classes num_features = model.num_features model.head = MLDecoder(num_classes=num_classes, initial_num_features=num_features) else: print("model is not suited for ml-decoder") exit(-1) return model class TransformerDecoderLayerOptimal(nn.Module): def __init__(self, d_model, nhead=8, dim_feedforward=2048, dropout=0.1, activation="relu", layer_norm_eps=1e-5) -> None: super(TransformerDecoderLayerOptimal, self).__init__() self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps) self.dropout = nn.Dropout(dropout) self.dropout1 = nn.Dropout(dropout) self.dropout2 = nn.Dropout(dropout) self.dropout3 = nn.Dropout(dropout) self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) # Implementation of Feedforward model self.linear1 = nn.Linear(d_model, dim_feedforward) self.linear2 = nn.Linear(dim_feedforward, d_model) self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps) self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps) self.activation = _get_activation_fn(activation) def __setstate__(self, state): if 'activation' not in state: state['activation'] = torch.nn.functional.relu super(TransformerDecoderLayerOptimal, self).__setstate__(state) def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None, memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None) -> Tensor: tgt = tgt + self.dropout1(tgt) tgt = self.norm1(tgt) tgt2 = self.multihead_attn(tgt, memory, memory)[0] tgt = tgt + self.dropout2(tgt2) tgt = self.norm2(tgt) tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) tgt = tgt + self.dropout3(tgt2) tgt = self.norm3(tgt) return tgt # @torch.jit.script # class ExtrapClasses(object): # def __init__(self, num_queries: int, group_size: int): # self.num_queries = num_queries # self.group_size = group_size # # def __call__(self, h: torch.Tensor, class_embed_w: torch.Tensor, class_embed_b: torch.Tensor, out_extrap: # torch.Tensor): # # h = h.unsqueeze(-1).expand(-1, -1, -1, self.group_size) # h = h[..., None].repeat(1, 1, 1, self.group_size) # torch.Size([bs, 5, 768, groups]) # w = class_embed_w.view((self.num_queries, h.shape[2], self.group_size)) # out = (h * w).sum(dim=2) + class_embed_b # out = out.view((h.shape[0], self.group_size * self.num_queries)) # return out @torch.jit.script class GroupFC(object): def __init__(self, embed_len_decoder: int): self.embed_len_decoder = embed_len_decoder def __call__(self, h: torch.Tensor, duplicate_pooling: torch.Tensor, out_extrap: torch.Tensor): for i in range(self.embed_len_decoder): h_i = h[:, i, :] w_i = duplicate_pooling[i, :, :] out_extrap[:, i, :] = torch.matmul(h_i, w_i) class MLDecoder(nn.Module): def __init__(self, num_classes, num_of_groups=-1, decoder_embedding=768, initial_num_features=2048): super(MLDecoder, self).__init__() embed_len_decoder = 100 if num_of_groups < 0 else num_of_groups if embed_len_decoder > num_classes: embed_len_decoder = num_classes # switching to 768 initial embeddings decoder_embedding = 768 if decoder_embedding < 0 else decoder_embedding self.embed_standart = nn.Linear(initial_num_features, decoder_embedding) # decoder decoder_dropout = 0.1 num_layers_decoder = 1 dim_feedforward = 2048 layer_decode = TransformerDecoderLayerOptimal(d_model=decoder_embedding, dim_feedforward=dim_feedforward, dropout=decoder_dropout) self.decoder = nn.TransformerDecoder(layer_decode, num_layers=num_layers_decoder) # non-learnable queries self.query_embed = nn.Embedding(embed_len_decoder, decoder_embedding) self.query_embed.requires_grad_(False) # group fully-connected self.num_classes = num_classes self.duplicate_factor = int(num_classes / embed_len_decoder + 0.999) self.duplicate_pooling = torch.nn.Parameter( torch.Tensor(embed_len_decoder, decoder_embedding, self.duplicate_factor)) self.duplicate_pooling_bias = torch.nn.Parameter(torch.Tensor(num_classes)) torch.nn.init.xavier_normal_(self.duplicate_pooling) torch.nn.init.constant_(self.duplicate_pooling_bias, 0) self.group_fc = GroupFC(embed_len_decoder) def forward(self, x): if len(x.shape) == 4: # [bs,2048, 7,7] embedding_spatial = x.flatten(2).transpose(1, 2) else: # [bs, 197,468] embedding_spatial = x embedding_spatial_786 = self.embed_standart(embedding_spatial) embedding_spatial_786 = torch.nn.functional.relu(embedding_spatial_786, inplace=True) bs = embedding_spatial_786.shape[0] query_embed = self.query_embed.weight # tgt = query_embed.unsqueeze(1).repeat(1, bs, 1) tgt = query_embed.unsqueeze(1).expand(-1, bs, -1) # no allocation of memory with expand h = self.decoder(tgt, embedding_spatial_786.transpose(0, 1)) # [embed_len_decoder, batch, 768] h = h.transpose(0, 1) out_extrap = torch.zeros(h.shape[0], h.shape[1], self.duplicate_factor, device=h.device, dtype=h.dtype) self.group_fc(h, self.duplicate_pooling, out_extrap) h_out = out_extrap.flatten(1)[:, :self.num_classes] h_out += self.duplicate_pooling_bias logits = h_out return logits