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03be07d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | from collections import OrderedDict
from dataclasses import dataclass
from typing import Optional
import torch
import torch.nn as nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutput
from transformers.utils import ModelOutput, logging
from .configuration_msp_visual import MSPVisualConfig
from .modeling_avhubert import AVHubertModel
logger = logging.get_logger(__name__)
@dataclass
class MSPVisualOutput(ModelOutput):
last_hidden_state: Optional[torch.Tensor] = None
padding_mask_videos: Optional[torch.Tensor] = None
hidden_states: Optional[torch.Tensor] = None
attentions: Optional[torch.Tensor] = None
class MSPVisualPreTrainedModel(PreTrainedModel):
config_class = MSPVisualConfig
base_model_prefix = "msp_visual"
main_input_name = "pixel_values_videos"
input_modalities = "video"
supports_gradient_checkpointing = False
all_tied_weights_keys = OrderedDict()
def _init_weights(self, module):
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=0.02)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
class MSPVisualModel(MSPVisualPreTrainedModel, AVHubertModel):
def __init__(self, config: MSPVisualConfig):
super().__init__(config.visual_config)
self.config = config.visual_config
self.feature_extractor_audio.requires_grad_(False)
@property
def dummy_inputs(self) -> dict:
return {
"pixel_values_videos": torch.zeros(1, 1, 10, 88, 88, dtype=torch.float32),
"padding_mask_videos": torch.ones(1, 10, dtype=torch.long),
}
def forward(
self,
pixel_values_videos: torch.Tensor | None = None,
padding_mask_videos: torch.Tensor | None = None,
**kwargs,
) -> MSPVisualOutput:
feature, padding_mask = self.extract_finetune(
source={
"video": pixel_values_videos, # shape [batch_size, num_channels=1, num_frames, height, width]
"audio": None,
},
padding_mask=padding_mask_videos, # shape [batch_size, num_frames]
)
return MSPVisualOutput(
last_hidden_state=feature, # shape [batch_size, num_frames, hidden_size]
padding_mask_videos=padding_mask, # shape [batch_size, num_frames]
hidden_states=None,
attentions=None,
)
class MSPVisualForCTC(MSPVisualPreTrainedModel):
def __init__(self, config: MSPVisualConfig):
super().__init__(config)
if config.vocab_size is None:
raise ValueError(
"vocab_size must be set in MSPVisualConfig to instantiate MSPVisualForCTC."
)
self.msp_visual = MSPVisualModel(config)
for param in self.msp_visual.feature_extractor_audio.parameters():
param.requires_grad = False
self.dropout = nn.Dropout(config.final_dropout)
output_hidden_size = (
config.visual_config.adim
if hasattr(config.visual_config, "adim")
else config.visual_config.hidden_size
)
self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
@property
def dummy_inputs(self) -> dict:
return {
"pixel_values_videos": torch.zeros(1, 1, 10, 88, 88, dtype=torch.float32),
"padding_mask_videos": torch.ones(1, 10, dtype=torch.long),
}
def freeze_feature_encoder(self) -> None:
for param in self.msp_visual.feature_extractor_video.parameters():
param.requires_grad = False
for param in self.msp_visual.feature_extractor_audio.parameters():
param.requires_grad = False
def freeze_base_model(self) -> None:
for param in self.msp_visual.parameters():
param.requires_grad = False
def forward(
self,
pixel_values_videos: torch.Tensor,
padding_mask_videos: torch.Tensor | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
labels: torch.Tensor | None = None,
**kwargs,
) -> CausalLMOutput:
if labels is not None and labels.max() >= self.config.vocab_size:
raise ValueError(
f"Label value {labels.max()} >= vocab_size={self.config.vocab_size}."
)
outputs = self.msp_visual(
pixel_values_videos=pixel_values_videos,
padding_mask_videos=padding_mask_videos,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
hidden_states = self.dropout(outputs.last_hidden_state)
padding_mask_videos = outputs.padding_mask_videos
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
if padding_mask_videos is not None:
input_lengths = (
padding_mask_videos.sum(-1)
.to(torch.long)
.to(pixel_values_videos.device)
)
else:
input_lengths = torch.full(
(pixel_values_videos.shape[0],),
pixel_values_videos.shape[2],
dtype=torch.long,
device=pixel_values_videos.device,
)
labels_mask = labels >= 0
target_lengths = labels_mask.sum(-1)
flattened_targets = labels.masked_select(labels_mask)
# ctc_loss doesn't support fp16
log_probs = nn.functional.log_softmax(
logits, dim=-1, dtype=torch.float32
).transpose(0, 1)
with torch.backends.cudnn.flags(enabled=False):
loss = nn.functional.ctc_loss(
log_probs,
flattened_targets,
input_lengths,
target_lengths,
blank=self.config.pad_token_id,
reduction=self.config.ctc_loss_reduction,
zero_infinity=self.config.ctc_zero_infinity,
)
return CausalLMOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
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