File size: 2,343 Bytes
a3f6fc6 | 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 | import torch
import einops
import torch.nn as nn
from transformers import AutoModel, AutoVideoProcessor
import einops
from transformers.video_utils import VideoMetadata
class VJEPA2Encoder(nn.Module):
def __init__(self, name, feature_key, output_dim=None, postprocess=None, n_patches=256):
super().__init__()
print("Encoder feature_key:", feature_key)
self.name = name
repo = "facebook/vjepa2-vitl-fpc64-256"
self.base_model = AutoModel.from_pretrained(
repo,
# use_auth_token=True,
trust_remote_code=True,
)
self.processor = AutoVideoProcessor.from_pretrained(
repo,
trust_remote_code=True,
)
self.feature_key = feature_key
self.emb_dim = self.base_model.config.hidden_size
self.output_dim = self.emb_dim # for compatibility
self.latent_ndim = 2
if feature_key == "x_norm_patchtokens":
self.latent_ndim = 2
else:
raise ValueError(f"Invalid feature key: {feature_key}")
self.patch_size = self.base_model.config.patch_size
self.postprocess = postprocess
if postprocess is not None:
if postprocess == 'avg_pool':
self.latent_ndim = 1
def forward(self, x):
# Accept arbitrary number of leading dimensions before (C, H, W)
# and preserve them on return.
# Example: input shape (...prefix, C, H, W)
prefix_shape = x.shape[:-3]
c, h, w = x.shape[-3:]
# Collapse all leading dims into a single batch dimension for the base model
prod_prefix = 1
for d in prefix_shape:
prod_prefix *= d
x = x.reshape(prod_prefix, c, h, w)
inputs = self.processor(x, return_tensors="pt", do_rescale=False)["pixel_values_videos"]
T = 2
video_batch = einops.rearrange(inputs, "t b c h w -> b t c h w").repeat(1, T, 1, 1, 1)
outputs = self.base_model.get_vision_features(video_batch)
emb = outputs.reshape(*prefix_shape, *outputs.shape[1:])
if self.postprocess == 'avg_pool':
emb = torch.mean(emb, dim=-2) # (...prefix, e)
if self.latent_ndim == 1:
emb = emb.unsqueeze(len(prefix_shape)) # dummy patch dim, b v 1 e
return emb
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