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Running
on
Zero
File size: 3,618 Bytes
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import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModel, CLIPImageProcessor
from .modeling_intern_vit import InternVisionModel
class InternViTVisionTower(nn.Module):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_tower_name = vision_tower
self.select_layer = -1
self.scale_pix_shuffle = 0.5
if not delay_load:
self.load_model()
else:
self.cfg_only = AutoConfig.from_pretrained(
self.vision_tower_name, trust_remote_code=True
)
def load_model(self):
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower = InternVisionModel.from_pretrained(
self.vision_tower_name, trust_remote_code=True
)
self.vision_tower.requires_grad_(False)
self.is_loaded = True
def feature_select(self, image_forward_outs):
image_features = image_forward_outs.hidden_states[self.select_layer]
image_features = image_features[:, 1:]
return image_features
def pixel_shuffle(self, x, scale_factor=0.5):
n, w, h, c = x.size()
# N, W, H, C --> N, W, H * scale, C // scale
x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
# N, W, H * scale, C // scale --> N, H * scale, W, C // scale
x = x.permute(0, 2, 1, 3).contiguous()
# N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
x = x.view(
n, int(h * scale_factor), int(w * scale_factor), int(c / (scale_factor * scale_factor))
)
x = x.permute(0, 2, 1, 3).contiguous()
return x
#@torch.no_grad()
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_tower(
image.to(device=self.device, dtype=self.dtype).unsqueeze(0),
output_hidden_states=True,
)
image_feature = self.feature_select(image_forward_out).to(image.dtype)
image_features.append(image_feature)
else:
image_forward_outs = self.vision_tower(
images.to(device=self.device, dtype=self.dtype), output_hidden_states=True
)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
h = w = int(image_features.shape[1] ** 0.5)
assert image_features.shape[1] == h * w
image_features = image_features.reshape(image_features.shape[0], h, w, -1)
image_features = self.pixel_shuffle(image_features * self.scale_pix_shuffle)
image_features = image_features.reshape(
image_features.shape[0], -1, image_features.shape[-1]
)
return image_features
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
return self.vision_tower.dtype
@property
def device(self):
return self.vision_tower.device
@property
def config(self):
if self.is_loaded:
return self.vision_tower.config
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size * (int(1 / self.scale_pix_shuffle) ** 2)
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2
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