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import torch
import pickle
import random
import torch.nn as nn
import torch.nn.functional as F
from operator import or_
from typing import List
from functools import reduce
from enum import IntFlag, auto
from src.model.base import ODBinaryMetricClassifier
from src.model.clip import VideoAttrExtractor
from src.utility.loss import focal_loss
class PromptMode(IntFlag):
DEEP = auto() # spatial
SHALLOW = auto() # temporal
class PromptedVideoAttrExtractor(VideoAttrExtractor):
def __init__(
self,
# VideoAttrExtractor
architecture,
text_embed,
pretrain=None,
store_attrs=[],
attn_record=False,
# visual prompting
num_prompts=1,
prompt_mode=PromptMode.DEEP
):
super(PromptedVideoAttrExtractor, self).__init__(
architecture=architecture,
text_embed=text_embed,
store_attrs=store_attrs,
attn_record=attn_record,
pretrain=pretrain
)
self.num_prompts = num_prompts
self.prompt_mode = prompt_mode
if (prompt_mode == PromptMode.DEEP):
# shape = batch,frames,layers,prompts,dim
self.visual_prompts = nn.Parameter(torch.zeros(1, 1, self.n_layers, num_prompts, self.feat_dim), requires_grad=True)
elif (prompt_mode == PromptMode.SHALLOW):
# shape = batch,frames,layers,prompts,dim
self.visual_prompts = nn.Parameter(torch.zeros(1, 1, 1, num_prompts, self.feat_dim), requires_grad=True)
nn.init.normal_(self.visual_prompts, std=0.001)
def forward(self, x):
# first, we prepare the encoder before the transformer layers.
x = self.model._prepare(x)
x = torch.cat([x, self.visual_prompts[:, :, 0].repeat(x.shape[0], x.shape[1], 1, 1)], dim=-2)
# now, we alternate between synoptic and encoder layers
for i, enc_blk in enumerate(self.model.transformer.resblocks):
if i > 0 and self.prompt_mode == PromptMode.DEEP:
x[:, :, :self.num_prompts] = (
x[:, :, :self.num_prompts] +
self.visual_prompts[:, :, i].repeat(x.shape[0], x.shape[1], 1, 1)
)
data = enc_blk(x)
x = data["emb"]
layer_attrs = []
for enc_blk in self.model.transformer.resblocks:
layer_attrs.append(
{
**enc_blk.pop_attr()
}
)
embeds = x[:, :, :self.num_prompts].mean(dim=2)
return dict(
layer_attrs=layer_attrs,
embeds=embeds
)
def train(self, mode=True):
super().train(mode)
if (mode):
self.model.eval()
return self
class BinaryLinearClassifier(nn.Module):
def __init__(
self,
*args,
**kargs,
):
super().__init__()
self.encoder = PromptedVideoAttrExtractor(
*args,
**kargs
)
self.projs = self.make_linear(self.encoder.embed_dim)
def make_linear(self, embed_dim):
linear = nn.Linear(
embed_dim,
2
)
nn.init.normal_(linear.weight, std=0.001)
nn.init.normal_(linear.bias, std=0.001)
return linear
@property
def transform(self):
return self.encoder.transform
@property
def n_px(self):
return self.encoder.model.input_resolution
def forward(self, x, *args, **kargs):
results = self.encoder(x)
embeds = results["embeds"]
logits = self.projs(embeds.mean(1))
return dict(
logits=logits,
** results
)
class PromptedLinearVideoLearner(ODBinaryMetricClassifier):
def __init__(
self,
architecture: str = 'ViT-B/16',
text_embed: bool = False,
attn_record: bool = False,
pretrain: str = None,
label_weights: List[float] = [1, 1],
cls_weight: float = 10.0,
store_attrs: List[str] = [],
num_prompts=1,
prompt_mode=PromptMode.DEEP
):
super().__init__()
self.save_hyperparameters()
params = dict(
architecture=architecture,
text_embed=text_embed,
attn_record=attn_record,
pretrain=pretrain,
store_attrs=store_attrs,
num_prompts=num_prompts,
prompt_mode=PromptMode[prompt_mode]
)
self.model = BinaryLinearClassifier(**params)
self.label_weights = torch.tensor(label_weights)
self.cls_weight = cls_weight
@property
def transform(self):
return self.model.transform
@property
def n_px(self):
return self.model.n_px
def shared_step(self, batch, stage):
x, y, z = batch["xyz"]
indices = batch["indices"]
dts_name = batch["dts_name"]
names = batch["names"]
output = self(x, **z)
logits = output["logits"]
loss = 0
# classification loss
if (stage == "train"):
cls_loss = focal_loss(
logits,
y,
gamma=4,
weight=(
self.label_weights.to(y.device)
if stage == "train" else
None
)
)
loss += cls_loss.mean() * self.cls_weight
self.log(
f"{stage}/{dts_name}/loss",
cls_loss.mean(),
batch_size=logits.shape[0]
)
else:
# classification loss
cls_loss = nn.functional.cross_entropy(
logits,
y,
reduction="none",
weight=(
self.label_weights.to(y.device)
if stage == "train" else
None
)
)
loss += cls_loss.mean()
return {
"logits": logits,
"labels": y,
"loss": loss,
"dts_name": dts_name,
"indices": indices,
"output": output
}
if __name__ == "__main__":
frames = 5
model = BinaryLinearClassifier(
architecture="ViT-L/14",
attn_record=False,
text_embed=False,
num_prompts=4,
prompt_mode=PromptMode.SHALLOW
)
model.to("cuda")
result = model(torch.randn(5, frames, 3, 224, 224).to("cuda"))
logit = result["logits"]
logit.sum().backward()
print([m for m, v in model.named_parameters() if v.requires_grad])
print("done")
|