Spaces:
Running
on
Zero
Running
on
Zero
File size: 10,698 Bytes
55866f4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 |
"""
This is just a wrapper around the various baselines implemented in the
Chefer et. al. Transformer Explainability repository.
Implements
- CheferLRPSegmentationModel
- CheferRolloutSegmentationModel
- CheferLastLayerAttentionSegmentationModel
- CheferAttentionGradCAMSegmentationModel
- CheferTransformerAttributionSegmentationModel
- CheferFullLRPSegmentationModel
- CheferLastLayerLRPSegmentationModel
"""
# # segmentation test for the rollout baseline
# if args.method == 'rollout':
# Res = baselines.generate_rollout(image.cuda(), start_layer=1).reshape(batch_size, 1, 14, 14)
# # segmentation test for the LRP baseline (this is full LRP, not partial)
# elif args.method == 'full_lrp':
# Res = orig_lrp.generate_LRP(image.cuda(), method="full").reshape(batch_size, 1, 224, 224)
# # segmentation test for our method
# elif args.method == 'transformer_attribution':
# Res = lrp.generate_LRP(image.cuda(), start_layer=1, method="transformer_attribution").reshape(batch_size, 1, 14, 14)
# # segmentation test for the partial LRP baseline (last attn layer)
# elif args.method == 'lrp_last_layer':
# Res = orig_lrp.generate_LRP(image.cuda(), method="last_layer", is_ablation=args.is_ablation)\
# .reshape(batch_size, 1, 14, 14)
# # segmentation test for the raw attention baseline (last attn layer)
# elif args.method == 'attn_last_layer':
# Res = orig_lrp.generate_LRP(image.cuda(), method="last_layer_attn", is_ablation=args.is_ablation)\
# .reshape(batch_size, 1, 14, 14)
# # segmentation test for the GradCam baseline (last attn layer)
# elif args.method == 'attn_gradcam':
# Res = baselines.generate_cam_attn(image.cuda()).reshape(batch_size, 1, 14, 14)
# if args.method != 'full_lrp':
# # interpolate to full image size (224,224)
# Res = torch.nn.functional.interpolate(Res, scale_factor=16, mode='bilinear').cuda()
import torch
import PIL
from concept_attention.binary_segmentation_baselines.chefer_vit_explainability.ViT_explanation_generator import LRP
from concept_attention.segmentation import SegmentationAbstractClass
from concept_attention.binary_segmentation_baselines.chefer_vit_explainability.ViT_explanation_generator import Baselines, LRP
from concept_attention.binary_segmentation_baselines.chefer_vit_explainability.ViT_new import vit_base_patch16_224
from concept_attention.binary_segmentation_baselines.chefer_vit_explainability.ViT_LRP import vit_base_patch16_224 as vit_LRP
from concept_attention.binary_segmentation_baselines.chefer_vit_explainability.ViT_orig_LRP import vit_base_patch16_224 as vit_orig_LRP
# # Model
# model = vit_base_patch16_224(pretrained=True).cuda()
# baselines = Baselines(model)
# # LRP
# model_LRP = vit_LRP(pretrained=True).cuda()
# model_LRP.eval()
# lrp = LRP(model_LRP)
# # orig LRP
# model_orig_LRP = vit_orig_LRP(pretrained=True).cuda()
# model_orig_LRP.eval()
# orig_lrp = LRP(model_orig_LRP)
# model.eval()
class CheferLRPSegmentationModel(SegmentationAbstractClass):
def __init__(
self,
device: str = "cuda",
width: int = 224,
height: int = 224,
):
"""
Initialize the segmentation model.
"""
super(CheferLRPSegmentationModel, self).__init__()
self.width = width
self.height = height
self.device = device
# Load the LRP model
model_orig_LRP = vit_orig_LRP(pretrained=True).to(self.device)
model_orig_LRP.eval()
self.orig_lrp = LRP(model_orig_LRP)
def segment_individual_image(self, image: torch.Tensor, concepts: list[str], caption: str, **kwargs):
"""
Takes a real image and generates a concept segmentation map
it by adding noise and running the DiT on it.
"""
if len(image.shape) == 3:
image = image.unsqueeze(0)
prediction_map = self.orig_lrp.generate_LRP(
image.to(self.device),
method="full"
)
prediction_map = prediction_map.unsqueeze(0)
# Rescale the prediction map to 64x64
prediction_map = torch.nn.functional.interpolate(
prediction_map,
size=(self.width, self.height),
mode="nearest"
).reshape(1, self.width, self.height)
return prediction_map, None
class CheferRolloutSegmentationModel(SegmentationAbstractClass):
def __init__(self, device: str = "cuda", width: int = 224, height: int = 224):
super(CheferRolloutSegmentationModel, self).__init__()
self.width = width
self.height = height
self.device = device
model = vit_base_patch16_224(pretrained=True).to(device)
self.baselines = Baselines(model)
def segment_individual_image(self, image: torch.Tensor, concepts: list[str], caption: str, **kwargs):
if len(image.shape) == 3:
image = image.unsqueeze(0)
prediction_map = self.baselines.generate_rollout(
image.to(self.device), start_layer=1
).reshape(1, 1, 14, 14)
# Rescale the prediction map to 64x64
prediction_map = torch.nn.functional.interpolate(
prediction_map,
size=(self.width, self.height),
mode="nearest"
).reshape(1, self.width, self.height)
return prediction_map, None
class CheferLastLayerAttentionSegmentationModel(SegmentationAbstractClass):
def __init__(self, device: str = "cuda", width: int = 224, height: int = 224):
super(CheferLastLayerAttentionSegmentationModel, self).__init__()
self.width = width
self.height = height
self.device = device
model_orig_LRP = vit_orig_LRP(pretrained=True).to(device)
model_orig_LRP.eval()
self.orig_lrp = LRP(model_orig_LRP)
def segment_individual_image(self, image: torch.Tensor, concepts: list[str], caption: str, **kwargs):
if len(image.shape) == 3:
image = image.unsqueeze(0)
prediction_map = self.orig_lrp.generate_LRP(
image.to(self.device), method="last_layer_attn"
).reshape(1, 1, 14, 14)
# Rescale the prediction map to 64x64
prediction_map = torch.nn.functional.interpolate(
prediction_map,
size=(self.width, self.height),
mode="nearest"
).reshape(1, self.width, self.height)
return prediction_map, None
class CheferAttentionGradCAMSegmentationModel(SegmentationAbstractClass):
def __init__(self, device: str = "cuda", width: int = 224, height: int = 224):
super(CheferAttentionGradCAMSegmentationModel, self).__init__()
self.width = width
self.height = height
self.device = device
model = vit_base_patch16_224(pretrained=True).to(device)
self.baselines = Baselines(model)
def segment_individual_image(self, image: torch.Tensor, concepts: list[str], caption: str, **kwargs):
if len(image.shape) == 3:
image = image.unsqueeze(0)
prediction_map = self.baselines.generate_cam_attn(
image.to(self.device)
).reshape(1, 1, 14, 14)
# Rescale the prediction map to 64x64
prediction_map = torch.nn.functional.interpolate(
prediction_map,
size=(self.width, self.height),
mode="nearest"
).reshape(1, self.width, self.height)
return prediction_map, None
class CheferTransformerAttributionSegmentationModel(SegmentationAbstractClass):
def __init__(self, device: str = "cuda", width: int = 224, height: int = 224):
super(CheferTransformerAttributionSegmentationModel, self).__init__()
self.width = width
self.height = height
self.device = device
model_LRP = vit_LRP(pretrained=True).to(device)
model_LRP.eval()
self.lrp = LRP(model_LRP)
def segment_individual_image(self, image: torch.Tensor, concepts: list[str], caption: str, **kwargs):
if len(image.shape) == 3:
image = image.unsqueeze(0)
prediction_map = self.lrp.generate_LRP(
image.to(self.device), start_layer=1, method="transformer_attribution"
).reshape(1, 1, 14, 14)
# Rescale the prediction map to 64x64
prediction_map = torch.nn.functional.interpolate(
prediction_map,
size=(self.width, self.height),
mode="nearest"
).reshape(1, self.width, self.height)
return prediction_map, None
class CheferFullLRPSegmentationModel(SegmentationAbstractClass):
def __init__(self, device: str = "cuda", width: int = 224, height: int = 224):
super(CheferFullLRPSegmentationModel, self).__init__()
self.width = width
self.height = height
self.device = device
model_LRP = vit_LRP(pretrained=True).to(device)
model_LRP.eval()
self.lrp = LRP(model_LRP)
def segment_individual_image(self, image: torch.Tensor, concepts: list[str], caption: str, **kwargs):
if len(image.shape) == 3:
image = image.unsqueeze(0)
prediction_map = self.lrp.generate_LRP(
image.to(self.device), method="full"
).reshape(1, 1, 224, 224)
# Rescale the prediction map to 64x64
prediction_map = torch.nn.functional.interpolate(
prediction_map,
size=(self.width, self.height),
mode="nearest"
).reshape(1, self.width, self.height)
return prediction_map, None
class CheferLastLayerLRPSegmentationModel(SegmentationAbstractClass):
def __init__(self, device: str = "cuda", width: int = 224, height: int = 224):
super(CheferLastLayerLRPSegmentationModel, self).__init__()
self.width = width
self.height = height
self.device = device
model_LRP = vit_LRP(pretrained=True).to(device)
model_LRP.eval()
self.lrp = LRP(model_LRP)
def segment_individual_image(self, image: torch.Tensor, concepts: list[str], caption: str, **kwargs):
if len(image.shape) == 3:
image = image.unsqueeze(0)
prediction_map = self.lrp.generate_LRP(
image.to(self.device), method="last_layer"
).reshape(1, 1, 14, 14)
# Rescale the prediction map to 64x64
prediction_map = torch.nn.functional.interpolate(
prediction_map,
size=(self.width, self.height),
mode="nearest"
).reshape(1, self.width, self.height)
return prediction_map, None |