Datasets:

Goeschel
/

sub-005_task-C_ube-inputs

"""Predict the 50 test patterns with an encoder trained by the original UBE code.
For checkpoints saved by train_transfer/train_encoder_transfer_custom.py in
https://github.com/WeizmannVision/universal-brain-encoder-code (a whole pickled model).
Run from the root of that repo, which the pickle needs in order to import the model code.
Image preprocessing and the model call follow that repo's inference/predict_fmri_for_images.py.
    python predict_ube.py --model results/saved_models/transfer/<name>.pth \
        --images sub-005/test/images.npy --out pred.npy
The output (50, 2792) goes straight into score_retrieval.py. Models that are not UBE only
need to produce the same array: one row per test image, columns in the order of the fMRI.
"""
import argparse
import os
import sys

import numpy as np
import torch

sys.path.append(os.getcwd())    # as in the original repo's scripts, so the pickle finds models/

ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--model", required=True, help="checkpoint from train_encoder_transfer_custom.py")
ap.add_argument("--images", required=True, help="test images .npy, (50, 224, 224, 3) uint8")
ap.add_argument("--out", required=True, help="where to save the predictions .npy")
args = ap.parse_args()

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# The pickled model contains a DINOv2 backbone whose code comes from torch hub, so it has to
# be loaded once before torch.load can unpickle the model (same two lines as the original repo).
torch.hub.set_dir("data/external_models/torch_hub/")
torch.hub.load("facebookresearch/dinov2", "dinov2_vits14_reg")
model = torch.load(args.model, map_location=device, weights_only=False).eval().to(device)

# A transfer model has one voxel embedding per training voxel; predict all of them, in order,
# so prediction column k is the same voxel as column k of the fMRI arrays.
n_voxels = model.voxel_embed.shape[0]
vox_ind = torch.arange(n_voxels, device=device).unsqueeze(0)

# ImageNet normalisation, as in the original repo: scale to [0, 1], normalise, and reorder
# height x width x RGB to RGB x height x width. No random shift at test time.
MEAN = np.array([0.485, 0.456, 0.406]).reshape([1, 1, 3])
STD = np.array([0.229, 0.224, 0.225]).reshape([1, 1, 3])

images = np.load(args.images)
pred = np.zeros((len(images), n_voxels), dtype=np.float32)
with torch.no_grad():
    for i, img in enumerate(images):
        x = ((img / 255.0 - MEAN) / STD).transpose([2, 0, 1])
        x = torch.from_numpy(x.astype(np.float32)).unsqueeze(0).to(device)
        pred[i] = model(x, vox_ind).cpu().numpy()

np.save(args.out, pred)
print(f"saved {pred.shape} predictions -> {args.out}")

	"""Predict the 50 test patterns with an encoder trained by the original UBE code.

	For checkpoints saved by train_transfer/train_encoder_transfer_custom.py in
	https://github.com/WeizmannVision/universal-brain-encoder-code (a whole pickled model).
	Run from the root of that repo, which the pickle needs in order to import the model code.
	Image preprocessing and the model call follow that repo's inference/predict_fmri_for_images.py.

	python predict_ube.py --model results/saved_models/transfer/<name>.pth \
	--images sub-005/test/images.npy --out pred.npy

	The output (50, 2792) goes straight into score_retrieval.py. Models that are not UBE only
	need to produce the same array: one row per test image, columns in the order of the fMRI.
	"""
	import argparse
	import os
	import sys

	import numpy as np
	import torch

	sys.path.append(os.getcwd()) # as in the original repo's scripts, so the pickle finds models/

	ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
	ap.add_argument("--model", required=True, help="checkpoint from train_encoder_transfer_custom.py")
	ap.add_argument("--images", required=True, help="test images .npy, (50, 224, 224, 3) uint8")
	ap.add_argument("--out", required=True, help="where to save the predictions .npy")
	args = ap.parse_args()

	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

	# The pickled model contains a DINOv2 backbone whose code comes from torch hub, so it has to
	# be loaded once before torch.load can unpickle the model (same two lines as the original repo).
	torch.hub.set_dir("data/external_models/torch_hub/")
	torch.hub.load("facebookresearch/dinov2", "dinov2_vits14_reg")
	model = torch.load(args.model, map_location=device, weights_only=False).eval().to(device)

	# A transfer model has one voxel embedding per training voxel; predict all of them, in order,
	# so prediction column k is the same voxel as column k of the fMRI arrays.
	n_voxels = model.voxel_embed.shape[0]
	vox_ind = torch.arange(n_voxels, device=device).unsqueeze(0)

	# ImageNet normalisation, as in the original repo: scale to [0, 1], normalise, and reorder
	# height x width x RGB to RGB x height x width. No random shift at test time.
	MEAN = np.array([0.485, 0.456, 0.406]).reshape([1, 1, 3])
	STD = np.array([0.229, 0.224, 0.225]).reshape([1, 1, 3])

	images = np.load(args.images)
	pred = np.zeros((len(images), n_voxels), dtype=np.float32)
	with torch.no_grad():
	for i, img in enumerate(images):
	x = ((img / 255.0 - MEAN) / STD).transpose([2, 0, 1])
	x = torch.from_numpy(x.astype(np.float32)).unsqueeze(0).to(device)
	pred[i] = model(x, vox_ind).cpu().numpy()

	np.save(args.out, pred)
	print(f"saved {pred.shape} predictions -> {args.out}")