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157 lines
6.8 KiB
157 lines
6.8 KiB
from typing import Optional
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import torch
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from torch import nn
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from torch import nn, Tensor
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from torch.nn.modules.transformer import _get_activation_fn
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def add_ml_decoder_head(model):
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if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # most CNN models, like Resnet50
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model.global_pool = nn.Identity()
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del model.fc
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num_classes = model.num_classes
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num_features = model.num_features
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model.fc = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
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elif hasattr(model, 'global_pool') and hasattr(model, 'classifier'): # EfficientNet
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model.global_pool = nn.Identity()
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del model.classifier
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num_classes = model.num_classes
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num_features = model.num_features
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model.classifier = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
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elif 'RegNet' in model._get_name() or 'TResNet' in model._get_name(): # hasattr(model, 'head')
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del model.head
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num_classes = model.num_classes
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num_features = model.num_features
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model.head = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
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else:
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print("Model code-writing is not aligned currently with ml-decoder")
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exit(-1)
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if hasattr(model, 'drop_rate'): # Ml-Decoder has inner dropout
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model.drop_rate = 0
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return model
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class TransformerDecoderLayerOptimal(nn.Module):
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def __init__(self, d_model, nhead=8, dim_feedforward=2048, dropout=0.1, activation="relu",
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layer_norm_eps=1e-5) -> None:
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super(TransformerDecoderLayerOptimal, self).__init__()
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self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
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self.dropout = nn.Dropout(dropout)
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self.dropout1 = nn.Dropout(dropout)
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self.dropout2 = nn.Dropout(dropout)
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self.dropout3 = nn.Dropout(dropout)
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self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
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# Implementation of Feedforward model
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self.linear1 = nn.Linear(d_model, dim_feedforward)
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self.linear2 = nn.Linear(dim_feedforward, d_model)
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self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
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self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps)
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self.activation = _get_activation_fn(activation)
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def __setstate__(self, state):
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if 'activation' not in state:
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state['activation'] = torch.nn.functional.relu
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super(TransformerDecoderLayerOptimal, self).__setstate__(state)
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def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None,
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memory_mask: Optional[Tensor] = None,
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tgt_key_padding_mask: Optional[Tensor] = None,
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memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
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tgt = tgt + self.dropout1(tgt)
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tgt = self.norm1(tgt)
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tgt2 = self.multihead_attn(tgt, memory, memory)[0]
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tgt = tgt + self.dropout2(tgt2)
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tgt = self.norm2(tgt)
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
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tgt = tgt + self.dropout3(tgt2)
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tgt = self.norm3(tgt)
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return tgt
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# @torch.jit.script
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# class ExtrapClasses(object):
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# def __init__(self, num_queries: int, group_size: int):
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# self.num_queries = num_queries
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# self.group_size = group_size
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#
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# def __call__(self, h: torch.Tensor, class_embed_w: torch.Tensor, class_embed_b: torch.Tensor, out_extrap:
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# torch.Tensor):
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# # h = h.unsqueeze(-1).expand(-1, -1, -1, self.group_size)
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# h = h[..., None].repeat(1, 1, 1, self.group_size) # torch.Size([bs, 5, 768, groups])
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# w = class_embed_w.view((self.num_queries, h.shape[2], self.group_size))
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# out = (h * w).sum(dim=2) + class_embed_b
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# out = out.view((h.shape[0], self.group_size * self.num_queries))
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# return out
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@torch.jit.script
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class GroupFC(object):
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def __init__(self, embed_len_decoder: int):
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self.embed_len_decoder = embed_len_decoder
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def __call__(self, h: torch.Tensor, duplicate_pooling: torch.Tensor, out_extrap: torch.Tensor):
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for i in range(self.embed_len_decoder):
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h_i = h[:, i, :]
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w_i = duplicate_pooling[i, :, :]
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out_extrap[:, i, :] = torch.matmul(h_i, w_i)
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class MLDecoder(nn.Module):
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def __init__(self, num_classes, num_of_groups=-1, decoder_embedding=768, initial_num_features=2048):
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super(MLDecoder, self).__init__()
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embed_len_decoder = 100 if num_of_groups < 0 else num_of_groups
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if embed_len_decoder > num_classes:
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embed_len_decoder = num_classes
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# switching to 768 initial embeddings
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decoder_embedding = 768 if decoder_embedding < 0 else decoder_embedding
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self.embed_standart = nn.Linear(initial_num_features, decoder_embedding)
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# decoder
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decoder_dropout = 0.1
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num_layers_decoder = 1
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dim_feedforward = 2048
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layer_decode = TransformerDecoderLayerOptimal(d_model=decoder_embedding,
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dim_feedforward=dim_feedforward, dropout=decoder_dropout)
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self.decoder = nn.TransformerDecoder(layer_decode, num_layers=num_layers_decoder)
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# non-learnable queries
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self.query_embed = nn.Embedding(embed_len_decoder, decoder_embedding)
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self.query_embed.requires_grad_(False)
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# group fully-connected
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self.num_classes = num_classes
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self.duplicate_factor = int(num_classes / embed_len_decoder + 0.999)
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self.duplicate_pooling = torch.nn.Parameter(
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torch.Tensor(embed_len_decoder, decoder_embedding, self.duplicate_factor))
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self.duplicate_pooling_bias = torch.nn.Parameter(torch.Tensor(num_classes))
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torch.nn.init.xavier_normal_(self.duplicate_pooling)
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torch.nn.init.constant_(self.duplicate_pooling_bias, 0)
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self.group_fc = GroupFC(embed_len_decoder)
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def forward(self, x):
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if len(x.shape) == 4: # [bs,2048, 7,7]
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embedding_spatial = x.flatten(2).transpose(1, 2)
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else: # [bs, 197,468]
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embedding_spatial = x
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embedding_spatial_786 = self.embed_standart(embedding_spatial)
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embedding_spatial_786 = torch.nn.functional.relu(embedding_spatial_786, inplace=True)
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bs = embedding_spatial_786.shape[0]
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query_embed = self.query_embed.weight
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# tgt = query_embed.unsqueeze(1).repeat(1, bs, 1)
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tgt = query_embed.unsqueeze(1).expand(-1, bs, -1) # no allocation of memory with expand
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h = self.decoder(tgt, embedding_spatial_786.transpose(0, 1)) # [embed_len_decoder, batch, 768]
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h = h.transpose(0, 1)
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out_extrap = torch.zeros(h.shape[0], h.shape[1], self.duplicate_factor, device=h.device, dtype=h.dtype)
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self.group_fc(h, self.duplicate_pooling, out_extrap)
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h_out = out_extrap.flatten(1)[:, :self.num_classes]
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h_out += self.duplicate_pooling_bias
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logits = h_out
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return logits
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