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| import math |
| import torch |
| import torch.nn as nn |
| from timm.models.layers import trunc_normal_ as __call_trunc_normal_ |
| from timm.models.registry import register_model |
|
|
| from torchscale.model.BEiT3 import BEiT3 |
| from torchscale.architecture.config import EncoderConfig |
|
|
|
|
| def trunc_normal_(tensor, mean=0., std=1.): |
| __call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std) |
|
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|
|
| def _get_base_config( |
| img_size=224, patch_size=16, drop_path_rate=0, |
| checkpoint_activations=None, mlp_ratio=4, vocab_size=64010, **kwargs |
| ): |
| return EncoderConfig( |
| img_size=img_size, patch_size=patch_size, vocab_size=vocab_size, multiway=True, |
| layernorm_embedding=False, normalize_output=True, no_output_layer=True, |
| drop_path_rate=drop_path_rate, encoder_embed_dim=768, encoder_attention_heads=12, |
| encoder_ffn_embed_dim=int(768 * mlp_ratio), encoder_layers=12, |
| checkpoint_activations=checkpoint_activations, |
| ) |
|
|
|
|
| def _get_large_config( |
| img_size=224, patch_size=16, drop_path_rate=0, |
| checkpoint_activations=None, mlp_ratio=4, vocab_size=64010, **kwargs |
| ): |
| return EncoderConfig( |
| img_size=img_size, patch_size=patch_size, vocab_size=vocab_size, multiway=True, |
| layernorm_embedding=False, normalize_output=True, no_output_layer=True, |
| drop_path_rate=drop_path_rate, encoder_embed_dim=1024, encoder_attention_heads=16, |
| encoder_ffn_embed_dim=int(1024 * mlp_ratio), encoder_layers=24, |
| checkpoint_activations=checkpoint_activations, |
| ) |
|
|
|
|
| class BEiT3Wrapper(nn.Module): |
| def __init__(self, args, **kwargs): |
| super().__init__() |
| self.args = args |
| self.beit3 = BEiT3(args) |
| self.apply(self._init_weights) |
| self.mim_head = nn.Linear(1024, 8192) |
| self.num_img_patches = self.beit3.vision_embed.num_position_embeddings() |
| self.hidden_size = args.encoder_embed_dim |
|
|
| def fix_init_weight(self): |
| def rescale(param, layer_id): |
| param.div_(math.sqrt(2.0 * layer_id)) |
|
|
| for layer_id, layer in enumerate(self.blocks): |
| rescale(layer.attn.proj.weight.data, layer_id + 1) |
| rescale(layer.mlp.fc2.weight.data, layer_id + 1) |
|
|
| def get_num_layers(self): |
| return self.beit3.encoder.num_layers |
|
|
| @torch.jit.ignore |
| def no_weight_decay(self): |
| return {'pos_embed', 'cls_token', 'beit3.encoder.embed_positions.A.weight', 'beit3.vision_embed.cls_token', 'logit_scale'} |
|
|
| def _init_weights(self, m): |
| if isinstance(m, nn.Linear): |
| trunc_normal_(m.weight, std=.02) |
| if isinstance(m, nn.Linear) and m.bias is not None: |
| nn.init.constant_(m.bias, 0) |
| elif isinstance(m, nn.LayerNorm): |
| nn.init.constant_(m.bias, 0) |
| nn.init.constant_(m.weight, 1.0) |
|
|
| def forward(self, pixel_values, query_embed=None): |
| B = pixel_values.size(0) |
| dtype = self.beit3.vision_embed.proj.weight.dtype |
| pixel_values = pixel_values.to(dtype) |
| token_embeddings = self.beit3.vision_embed(pixel_values) |
| multiway_split_position = -1 |
| if query_embed is not None: |
| query_embed = torch.stack([query_embed] * B) |
| multiway_split_position = token_embeddings.size(1) |
| token_embeddings = torch.cat([token_embeddings, query_embed], dim=1) |
|
|
| outputs = self.beit3.encoder( |
| src_tokens=None, |
| token_embeddings=token_embeddings, |
| multiway_split_position=multiway_split_position |
| ) |
| vision_hidden_states = outputs["encoder_out"] |
| if query_embed is not None: |
| vision_hidden_states = vision_hidden_states[:, self.num_img_patches:] |
| return vision_hidden_states |
|
|
|
|
| @register_model |
| def beit3_large_patch16_224(pretrained=False, **kwargs): |
| args = _get_large_config(img_size=224, **kwargs) |
| model = BEiT3Wrapper(args, **kwargs) |
| return model |
|
|
