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import argparse
# from models.vgg import vgg19
import glob
import os
import numpy as np
import torch
from dataset.expl_hdf5 import ImagenetResults
from tqdm import tqdm
# Import saliency methods and models
from ViT_explanation_generator import Baselines
from ViT_new import vit_base_patch16_224
def normalize(tensor, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
dtype = tensor.dtype
mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)
std = torch.as_tensor(std, dtype=dtype, device=tensor.device)
tensor.sub_(mean[None, :, None, None]).div_(std[None, :, None, None])
return tensor
def eval(args):
num_samples = 0
num_correct_model = np.zeros(
(
len(
imagenet_ds,
)
)
)
dissimilarity_model = np.zeros(
(
len(
imagenet_ds,
)
)
)
model_index = 0
if args.scale == "per":
base_size = 224 * 224
perturbation_steps = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
elif args.scale == "100":
base_size = 100
perturbation_steps = [5, 10, 15, 20, 25, 30, 35, 40, 45]
else:
raise Exception("scale not valid")
num_correct_pertub = np.zeros((9, len(imagenet_ds)))
dissimilarity_pertub = np.zeros((9, len(imagenet_ds)))
logit_diff_pertub = np.zeros((9, len(imagenet_ds)))
prob_diff_pertub = np.zeros((9, len(imagenet_ds)))
perturb_index = 0
for batch_idx, (data, vis, target) in enumerate(tqdm(sample_loader)):
# Update the number of samples
num_samples += len(data)
data = data.to(device)
vis = vis.to(device)
target = target.to(device)
norm_data = normalize(data.clone())
# Compute model accuracy
pred = model(norm_data)
pred_probabilities = torch.softmax(pred, dim=1)
pred_org_logit = pred.data.max(1, keepdim=True)[0].squeeze(1)
pred_org_prob = pred_probabilities.data.max(1, keepdim=True)[0].squeeze(1)
pred_class = pred.data.max(1, keepdim=True)[1].squeeze(1)
tgt_pred = (target == pred_class).type(target.type()).data.cpu().numpy()
num_correct_model[model_index : model_index + len(tgt_pred)] = tgt_pred
probs = torch.softmax(pred, dim=1)
target_probs = torch.gather(probs, 1, target[:, None])[:, 0]
second_probs = probs.data.topk(2, dim=1)[0][:, 1]
temp = torch.log(target_probs / second_probs).data.cpu().numpy()
dissimilarity_model[model_index : model_index + len(temp)] = temp
if args.wrong:
wid = np.argwhere(tgt_pred == 0).flatten()
if len(wid) == 0:
continue
wid = torch.from_numpy(wid).to(vis.device)
vis = vis.index_select(0, wid)
data = data.index_select(0, wid)
target = target.index_select(0, wid)
# Save original shape
org_shape = data.shape
if args.neg:
vis = -vis
vis = vis.reshape(org_shape[0], -1)
for i in range(len(perturbation_steps)):
_data = data.clone()
_, idx = torch.topk(vis, int(base_size * perturbation_steps[i]), dim=-1)
idx = idx.unsqueeze(1).repeat(1, org_shape[1], 1)
_data = _data.reshape(org_shape[0], org_shape[1], -1)
_data = _data.scatter_(-1, idx, 0)
_data = _data.reshape(*org_shape)
_norm_data = normalize(_data)
out = model(_norm_data)
pred_probabilities = torch.softmax(out, dim=1)
pred_prob = pred_probabilities.data.max(1, keepdim=True)[0].squeeze(1)
diff = (pred_prob - pred_org_prob).data.cpu().numpy()
prob_diff_pertub[i, perturb_index : perturb_index + len(diff)] = diff
pred_logit = out.data.max(1, keepdim=True)[0].squeeze(1)
diff = (pred_logit - pred_org_logit).data.cpu().numpy()
logit_diff_pertub[i, perturb_index : perturb_index + len(diff)] = diff
target_class = out.data.max(1, keepdim=True)[1].squeeze(1)
temp = (target == target_class).type(target.type()).data.cpu().numpy()
num_correct_pertub[i, perturb_index : perturb_index + len(temp)] = temp
probs_pertub = torch.softmax(out, dim=1)
target_probs = torch.gather(probs_pertub, 1, target[:, None])[:, 0]
second_probs = probs_pertub.data.topk(2, dim=1)[0][:, 1]
temp = torch.log(target_probs / second_probs).data.cpu().numpy()
dissimilarity_pertub[i, perturb_index : perturb_index + len(temp)] = temp
model_index += len(target)
perturb_index += len(target)
np.save(os.path.join(args.experiment_dir, "model_hits.npy"), num_correct_model)
np.save(
os.path.join(args.experiment_dir, "model_dissimilarities.npy"),
dissimilarity_model,
)
np.save(
os.path.join(args.experiment_dir, "perturbations_hits.npy"),
num_correct_pertub[:, :perturb_index],
)
np.save(
os.path.join(args.experiment_dir, "perturbations_dissimilarities.npy"),
dissimilarity_pertub[:, :perturb_index],
)
np.save(
os.path.join(args.experiment_dir, "perturbations_logit_diff.npy"),
logit_diff_pertub[:, :perturb_index],
)
np.save(
os.path.join(args.experiment_dir, "perturbations_prob_diff.npy"),
prob_diff_pertub[:, :perturb_index],
)
print(np.mean(num_correct_model), np.std(num_correct_model))
print(np.mean(dissimilarity_model), np.std(dissimilarity_model))
print(perturbation_steps)
print(np.mean(num_correct_pertub, axis=1), np.std(num_correct_pertub, axis=1))
print(np.mean(dissimilarity_pertub, axis=1), np.std(dissimilarity_pertub, axis=1))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Train a segmentation")
parser.add_argument("--batch-size", type=int, default=16, help="")
parser.add_argument("--neg", type=bool, default=True, help="")
parser.add_argument("--value", action="store_true", default=False, help="")
parser.add_argument(
"--scale", type=str, default="per", choices=["per", "100"], help=""
)
parser.add_argument(
"--method",
type=str,
default="grad_rollout",
choices=[
"rollout",
"lrp",
"transformer_attribution",
"full_lrp",
"v_gradcam",
"lrp_last_layer",
"lrp_second_layer",
"gradcam",
"attn_last_layer",
"attn_gradcam",
"input_grads",
],
help="",
)
parser.add_argument(
"--vis-class",
type=str,
default="top",
choices=["top", "target", "index"],
help="",
)
parser.add_argument("--wrong", action="store_true", default=False, help="")
parser.add_argument("--class-id", type=int, default=0, help="")
parser.add_argument("--is-ablation", type=bool, default=False, help="")
args = parser.parse_args()
torch.multiprocessing.set_start_method("spawn")
# PATH variables
PATH = os.path.dirname(os.path.abspath(__file__)) + "/"
dataset = PATH + "dataset/"
os.makedirs(os.path.join(PATH, "experiments"), exist_ok=True)
os.makedirs(os.path.join(PATH, "experiments/perturbations"), exist_ok=True)
exp_name = args.method
exp_name += "_neg" if args.neg else "_pos"
print(exp_name)
if args.vis_class == "index":
args.runs_dir = os.path.join(
PATH,
"experiments/perturbations/{}/{}_{}".format(
exp_name, args.vis_class, args.class_id
),
)
else:
ablation_fold = "ablation" if args.is_ablation else "not_ablation"
args.runs_dir = os.path.join(
PATH,
"experiments/perturbations/{}/{}/{}".format(
exp_name, args.vis_class, ablation_fold
),
)
# args.runs_dir = os.path.join(PATH, 'experiments/perturbations/{}/{}'.format(exp_name,
# args.vis_class))
if args.wrong:
args.runs_dir += "_wrong"
experiments = sorted(glob.glob(os.path.join(args.runs_dir, "experiment_*")))
experiment_id = int(experiments[-1].split("_")[-1]) + 1 if experiments else 0
args.experiment_dir = os.path.join(
args.runs_dir, "experiment_{}".format(str(experiment_id))
)
os.makedirs(args.experiment_dir, exist_ok=True)
cuda = torch.cuda.is_available()
device = torch.device("cuda" if cuda else "cpu")
if args.vis_class == "index":
vis_method_dir = os.path.join(
PATH,
"visualizations/{}/{}_{}".format(
args.method, args.vis_class, args.class_id
),
)
else:
ablation_fold = "ablation" if args.is_ablation else "not_ablation"
vis_method_dir = os.path.join(
PATH,
"visualizations/{}/{}/{}".format(
args.method, args.vis_class, ablation_fold
),
)
# vis_method_dir = os.path.join(PATH, 'visualizations/{}/{}'.format(args.method,
# args.vis_class))
# imagenet_ds = ImagenetResults('visualizations/{}'.format(args.method))
imagenet_ds = ImagenetResults(vis_method_dir)
# Model
model = vit_base_patch16_224(pretrained=True).cuda()
model.eval()
save_path = PATH + "results/"
sample_loader = torch.utils.data.DataLoader(
imagenet_ds, batch_size=args.batch_size, num_workers=2, shuffle=False
)
eval(args)
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