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Zero
File size: 8,255 Bytes
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import torch
import torch.nn as nn
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from egogpt.utils import rank0_print
try:
from s2wrapper import forward as multiscale_forward
except:
pass
class CLIPVisionTower(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 = args.mm_vision_select_layer
self.select_feature = getattr(args, "mm_vision_select_feature", "patch")
if not delay_load:
rank0_print(f"Loading vision tower: {vision_tower}")
self.load_model()
elif getattr(args, "unfreeze_mm_vision_tower", False):
# TODO: better detector is needed.
rank0_print(
f"The checkpoint seems to contain `vision_tower` weights: `unfreeze_mm_vision_tower`: True."
)
self.load_model()
elif (
hasattr(args, "mm_tunable_parts")
and "mm_vision_tower" in args.mm_tunable_parts
):
rank0_print(
f"The checkpoint seems to contain `vision_tower` weights: `mm_tunable_parts` contains `mm_vision_tower`."
)
self.load_model()
else:
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
def load_model(self, device_map=None):
if self.is_loaded:
rank0_print(
"{} is already loaded, `load_model` called again, skipping.".format(
self.vision_tower_name
)
)
return
self.image_processor = CLIPImageProcessor.from_pretrained(
self.vision_tower_name
)
self.vision_tower = CLIPVisionModel.from_pretrained(
self.vision_tower_name, device_map=device_map
)
self.vision_tower.requires_grad_(False)
self.is_loaded = True
def feature_select(self, image_forward_outs):
select_feature_type = self.select_feature
if self.select_feature in ["slicefour_patch", "slicefour_cls_patch"]:
select_every_k_layer = len(image_forward_outs.hidden_states) // 4
image_features = torch.cat(
[
image_forward_outs.hidden_states[i]
for i in range(
select_every_k_layer + self.select_layer,
len(image_forward_outs.hidden_states),
select_every_k_layer,
)
],
dim=-1,
)
select_feature_type = select_feature_type.replace("slicefour_", "")
elif self.select_feature in [
"slice_m25811_f6_patch",
"slice_m25811_f6_cls_patch",
]:
select_layers = [-2, -5, -8, -11, 6]
image_features = torch.cat(
[image_forward_outs.hidden_states[i] for i in select_layers], dim=-1
)
select_feature_type = select_feature_type.replace("slice_m25811_f6_", "")
else:
image_features = image_forward_outs.hidden_states[self.select_layer]
if select_feature_type == "patch":
image_features = image_features[:, 1:]
elif select_feature_type == "cls_patch":
image_features = image_features
else:
raise ValueError(f"Unexpected select feature: {select_feature_type}")
return image_features
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)
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):
_hidden_size = self.config.hidden_size
if "slicefour" in self.select_feature:
_hidden_size *= 4
if "slice_m25811_f6" in self.select_feature:
_hidden_size *= 5
return _hidden_size
@property
def num_patches_per_side(self):
return self.config.image_size // self.config.patch_size
@property
def num_patches(self):
_num_patches = (self.config.image_size // self.config.patch_size) ** 2
if "cls_patch" in self.select_feature:
_num_patches += 1
return _num_patches
@property
def image_size(self):
return self.config.image_size
class CLIPVisionTowerS2(CLIPVisionTower):
def __init__(self, vision_tower, args, delay_load=False):
self.s2_scales = getattr(args, "s2_scales", "336,672,1008")
self.s2_scales = list(map(int, self.s2_scales.split(",")))
self.s2_scales.sort()
self.s2_split_size = self.s2_scales[0]
self.s2_image_size = self.s2_scales[-1]
super().__init__(vision_tower, args, delay_load)
# change resize/crop size in preprocessing to the largest image size in s2_scale
if not delay_load or getattr(args, "unfreeze_mm_vision_tower", False):
self.image_processor.size["shortest_edge"] = self.s2_image_size
self.image_processor.crop_size["height"] = self.image_processor.crop_size[
"width"
] = self.s2_image_size
def load_model(self, device_map=None):
if self.is_loaded:
rank0_print(
"{} is already loaded, `load_model` called again, skipping.".format(
self.vision_tower_name
)
)
return
self.image_processor = CLIPImageProcessor.from_pretrained(
self.vision_tower_name
)
self.vision_tower = CLIPVisionModel.from_pretrained(
self.vision_tower_name, device_map=device_map
)
self.vision_tower.requires_grad_(False)
self.image_processor.size["shortest_edge"] = self.s2_image_size
self.image_processor.crop_size["height"] = self.image_processor.crop_size[
"width"
] = self.s2_image_size
self.is_loaded = True
def forward_feature(self, images):
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)
return image_features
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_feature = multiscale_forward(
self.forward_feature,
image.unsqueeze(0),
img_sizes=self.s2_scales,
max_split_size=self.s2_split_size,
split_forward=True,
)
image_features.append(image_feature)
else:
image_features = multiscale_forward(
self.forward_feature,
images,
img_sizes=self.s2_scales,
max_split_size=self.s2_split_size,
split_forward=True,
)
return image_features
@property
def hidden_size(self):
return self.config.hidden_size * len(self.s2_scales)
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