models/fflip.py

"""
@file fflip.py
@brief This file contains the code for the multimodal model. It is a modified version of the CLIP model from the huggingface transformers library.
@author yutangli
"""


import torch
import torch.nn as nn
from transformers.modeling_outputs import BaseModelOutputWithPooling
from transformers.utils import logging
from typing import Optional, Union, Tuple
from torch import Tensor, device, dtype, nn
from transformers import BertTokenizer

import os
from urllib.parse import urlparse
from timm.models.hub import download_cached_file

from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    BaseModelOutputWithPoolingAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    MaskedLMOutput,
    MultipleChoiceModelOutput,
    NextSentencePredictorOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)

from models.mm import (
    VisionTrainedModel, 
    BertEmbeddings,
    BertEncoder,
    BertPreTrainedModel,
    BertPooler,
    BertConfig,
    VisionConfig,  
    VisionTransformer)

logger = logging.get_logger(__name__)


def init_tokenizer():
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    tokenizer.add_special_tokens({'bos_token':'[DEC]'})
    tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})       
    tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]  
    return tokenizer


class VisionModel(VisionTrainedModel):
    config_class = VisionConfig
    main_input_name = "pixel_values"

    def __init__(self, config: VisionConfig):
        super().__init__(config)
        self.vision_model = VisionTransformer(config)
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self) -> nn.Module:
        return self.vision_model.embeddings.patch_embedding

    @staticmethod
    def get_output_channel(model_type):
        if model_type == 'base':
            return 768
        if model_type == 'large':
            return 1024
        if model_type == 'huge':
            return 1280

    @staticmethod
    def get_default_output_indices(model_type):
        if model_type == 'base':
            return [3, 5, 7, 11]
        if model_type == 'large':
            return [7, 11, 15, 23]
        if model_type == 'huge':
            return [8, 14, 20, 31]

    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        intermediate_hidden_state: Optional[bool] = None
    ) -> Union[Tuple, BaseModelOutputWithPooling]:
        r"""
        Returns:

        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, CLIPVisionModel

        >>> model = CLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32")
        >>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = processor(images=image, return_tensors="pt")

        >>> outputs = model(**inputs)
        >>> last_hidden_state = outputs.last_hidden_state
        >>> pooled_output = outputs.pooler_output  # pooled CLS states
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        return self.vision_model(
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            intermediate_hidden_state=intermediate_hidden_state
        )


class BertModel(BertPreTrainedModel):
    """
    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
    input to the forward pass.
    """

    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config

        self.embeddings = BertEmbeddings(config)
        
        self.encoder = BertEncoder(config)

        self.pooler = BertPooler(config) if add_pooling_layer else None

        self.init_weights()
 

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    
    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
        """
        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

        Arguments:
            attention_mask (:obj:`torch.Tensor`):
                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
            input_shape (:obj:`Tuple[int]`):
                The shape of the input to the model.
            device: (:obj:`torch.device`):
                The device of the input to the model.

        Returns:
            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
        """
        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        if attention_mask.dim() == 3:
            extended_attention_mask = attention_mask[:, None, :, :]
        elif attention_mask.dim() == 2:
            # Provided a padding mask of dimensions [batch_size, seq_length]
            # - if the model is a decoder, apply a causal mask in addition to the padding mask
            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
            if is_decoder:
                batch_size, seq_length = input_shape

                seq_ids = torch.arange(seq_length, device=device)
                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
                # causal and attention masks must have same type with pytorch version < 1.3
                causal_mask = causal_mask.to(attention_mask.dtype)
   
                if causal_mask.shape[1] < attention_mask.shape[1]:
                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
                    causal_mask = torch.cat(
                        [
                            torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
                            causal_mask,
                        ],
                        axis=-1,
                    )                     

                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
            else:
                extended_attention_mask = attention_mask[:, None, None, :]
        else:
            raise ValueError(
                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
                    input_shape, attention_mask.shape
                )
            )

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
        # positions we want to attend and -10000.0 for masked positions.
        # Since we are adding it to the raw scores before the softmax, this is
        # effectively the same as removing these entirely.
        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
        return extended_attention_mask
    
    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        is_decoder=False,
        mode='multimodal',
    ):
        r"""
        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
        use_cache (:obj:`bool`, `optional`):
            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
            decoding (see :obj:`past_key_values`).
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache
        else:
            use_cache = False

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            batch_size, seq_length = input_shape
            device = input_ids.device
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            batch_size, seq_length = input_shape
            device = inputs_embeds.device
        elif encoder_embeds is not None:    
            input_shape = encoder_embeds.size()[:-1]
            batch_size, seq_length = input_shape 
            device = encoder_embeds.device
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")

        # past_key_values_length
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

        if attention_mask is None:
            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
            
        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, 
                                                                                 device, is_decoder)

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if encoder_hidden_states is not None:
            if type(encoder_hidden_states) == list:
                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
            else:
                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            
            if type(encoder_attention_mask) == list:
                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
            elif encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
            else:    
                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        
        if encoder_embeds is None:
            embedding_output = self.embeddings(
                input_ids=input_ids,
                position_ids=position_ids,
                inputs_embeds=inputs_embeds,
                past_key_values_length=past_key_values_length,
            )
        else:
            embedding_output = encoder_embeds
            
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            mode=mode,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_attentions,
        )
    

class MMSEG_UPerHead(nn.Module):
    """Wraps the UPerHead from mmseg for segmentation.
    """

    def __init__(self, num_classes: int,
                 in_channels: list = [384, 384, 384, 384], channels: int = 512):
        super().__init__()

        from mmseg.models.decode_heads import UPerHead
        self.head = UPerHead(
            in_channels=in_channels,
            in_index=[0, 1, 2, 3],
            pool_scales=(1, 2, 3, 6),
            channels=channels,
            dropout_ratio=0.1,
            num_classes=num_classes,
            norm_cfg=dict(type='SyncBN', requires_grad=True),
            align_corners=False,
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))

    def forward(self, inputs):
        return self.head(inputs)


def _make_fpns(vision_patch_size: int, output_channels: int):
    if vision_patch_size in {16, 14}:
        fpn1 = nn.Sequential(
            nn.ConvTranspose2d(output_channels, output_channels,
                               kernel_size=2, stride=2),
            nn.SyncBatchNorm(output_channels),
            nn.GELU(),
            nn.ConvTranspose2d(output_channels, output_channels, kernel_size=2, stride=2))

        fpn2 = nn.ConvTranspose2d(
            output_channels, output_channels, kernel_size=2, stride=2)
        fpn3 = nn.Identity()
        fpn4 = nn.MaxPool2d(kernel_size=2, stride=2)
        return nn.ModuleList([fpn1, fpn2, fpn3, fpn4])
    
    elif vision_patch_size == 8:
        fpn1 = nn.Sequential(nn.ConvTranspose2d(
            output_channels, output_channels, kernel_size=2, stride=2))
        fpn2 = nn.Identity()
        fpn3 = nn.MaxPool2d(kernel_size=2, stride=2)
        fpn4 = nn.MaxPool2d(kernel_size=4, stride=4)
        return nn.ModuleList([fpn1, fpn2, fpn3, fpn4])
    else:
        raise NotImplementedError()


def is_url(url_or_filename):
    parsed = urlparse(url_or_filename)
    return parsed.scheme in ("http", "https")


def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):        
    # interpolate position embedding
    embedding_size = pos_embed_checkpoint.shape[-1]
    num_patches = visual_encoder.vision_model.embeddings.num_patches
    num_extra_tokens = visual_encoder.vision_model.embeddings.position_embedding.weight.shape[-2] - num_patches
    # height (== width) for the checkpoint position embedding
    orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
    # height (== width) for the new position embedding
    new_size = int(num_patches ** 0.5)

    if orig_size!=new_size:
        # class_token and dist_token are kept unchanged
        extra_tokens = pos_embed_checkpoint[:num_extra_tokens, :]
        # only the position tokens are interpolated
        pos_tokens = pos_embed_checkpoint[num_extra_tokens:, :]
        pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
        pos_tokens = torch.nn.functional.interpolate(
            pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
        pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2).squeeze(0)
        new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=0)
        print('reshape position embedding from %d to %d'%(orig_size ** 2, new_size ** 2))
        return new_pos_embed
    else:
        return pos_embed_checkpoint


def load_checkpoint(model,url_or_filename):
    if is_url(url_or_filename):
        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
        checkpoint = torch.load(cached_file, map_location='cpu') 
    elif os.path.isfile(url_or_filename):        
        checkpoint = torch.load(url_or_filename, map_location='cpu') 
    else:
        raise RuntimeError('checkpoint url or path is invalid')
        
    state_dict = checkpoint['model']
    
    state_dict['visual_encoder.vision_model.embeddings.position_embedding.weight'] = interpolate_pos_embed(state_dict['visual_encoder.vision_model.embeddings.position_embedding.weight'], model.visual_encoder) 
    if hasattr(model, " visual_encoder_m") and 'visual_encoder.vision_model.embeddings.position_embedding.weight' in model.state_dict().keys():
        state_dict['visual_encoder.vision_model.embeddings.position_embedding.weight'] = interpolate_pos_embed(state_dict['visual_encoder.vision_model.embeddings.position_embedding.weight'],
                                                                         model.visual_encoder_m)
    for key in model.state_dict().keys():
        if key in state_dict.keys():
            if state_dict[key].shape!=model.state_dict()[key].shape:
                del state_dict[key]
    
    msg = model.load_state_dict(state_dict,strict=False)
    print('load checkpoint from %s'%url_or_filename)  
    return model, msg