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e8deda1 | 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 | import numpy as np
import torch
import torch.nn as nn
from timm.utils import AverageMeter
from torch.utils.data import DataLoader
from .metric import pearsonr_score
from .submission_utils import EXPECTED_SUBJECTS, EXPECTED_OOD_KEYS
@torch.no_grad()
def evaluate_stage1(
*,
epoch: int,
model: torch.nn.Module,
val_loader: DataLoader,
device: torch.device,
subjects: list,
ds_name: str = "val",
):
model.eval()
loss_m = AverageMeter()
samples = []
outputs = []
for batch_idx, batch in enumerate(val_loader):
feats = [f.to(device) for f in batch["features"]]
fmri = batch["fmri"].to(device)
batch_size = fmri.size(0)
pred, _ = model(feats)
loss = nn.MSELoss()(pred, fmri)
loss_m.update(loss.item(), batch_size)
N, S, L, C = fmri.shape
assert N, S == (1, 4)
outputs.append(pred.cpu().numpy().swapaxes(0, 1).reshape((S, N * L, C)))
samples.append(fmri.cpu().numpy().swapaxes(0, 1).reshape((S, N * L, C)))
outputs = np.concatenate(outputs, axis=1)
samples = np.concatenate(samples, axis=1)
metrics = {}
dim = samples.shape[-1]
acc = 0.0
acc_map = np.zeros(dim)
for ii, sub in enumerate(subjects):
y_true = samples[ii].reshape(-1, dim)
y_pred = outputs[ii].reshape(-1, dim)
metrics[f"accmap_sub-{sub}"] = acc_map_i = pearsonr_score(y_true, y_pred)
metrics[f"acc_sub-{sub}"] = acc_i = np.mean(acc_map_i)
acc_map += acc_map_i / len(subjects)
acc += acc_i / len(subjects)
metrics["accmap_avg"] = acc_map
metrics["acc_avg"] = acc
accs_fmt = ",".join(
f"{val:.3f}" for key, val in metrics.items() if key.startswith("acc_sub-")
)
print(
f"Evaluate Stage 1 ({ds_name}): {epoch:>3d}"
f" Loss: {loss_m.avg:#.3g}"
f" Acc: {accs_fmt} ({acc:.3f})"
)
return acc, metrics
@torch.no_grad()
def evaluate_stage2(
*,
epoch: int,
stage1_model: torch.nn.Module,
stage2_models: nn.ModuleDict,
val_loader: DataLoader,
device: torch.device,
subjects: list,
ds_name: str = "val",
n_timesteps: int = 10,
):
stage1_model.eval()
for model in stage2_models.values():
model.eval()
samples = []
outputs = []
for batch in val_loader:
feats = [f.to(device) for f in batch["features"]]
fmri = batch["fmri"].to(device)
mu_anchor, embed_anchor = stage1_model(feats)
batch_preds = []
for i, sub in enumerate(subjects):
sub_key = str(sub)
cfm = stage2_models[sub_key]
src_cond = mu_anchor[:, i].transpose(1, 2)
mu_fusion = embed_anchor.transpose(1, 2)
pred = cfm(src_cond, mu_fusion, n_timesteps=n_timesteps)
pred = pred.transpose(1, 2).unsqueeze(1)
batch_preds.append(pred)
pred_combined = torch.cat(batch_preds, dim=1)
N, S, L, C = fmri.shape
assert N, S == (1, 4)
outputs.append(
pred_combined.cpu().numpy().swapaxes(0, 1).reshape((S, N * L, C))
)
samples.append(fmri.cpu().numpy().swapaxes(0, 1).reshape((S, N * L, C)))
outputs = np.concatenate(outputs, axis=1)
samples = np.concatenate(samples, axis=1)
metrics = {}
dim = samples.shape[-1]
acc = 0.0
acc_map = np.zeros(dim)
for ii, sub in enumerate(subjects):
y_true = samples[ii].reshape(-1, dim)
y_pred = outputs[ii].reshape(-1, dim)
metrics[f"accmap_sub-{sub}"] = acc_map_i = pearsonr_score(y_true, y_pred)
metrics[f"acc_sub-{sub}"] = acc_i = np.mean(acc_map_i)
acc_map += acc_map_i / len(subjects)
acc += acc_i / len(subjects)
metrics["accmap_avg"] = acc_map
metrics["acc_avg"] = acc
accs_fmt = ",".join(
f"{val:.3f}" for key, val in metrics.items() if key.startswith("acc_sub-")
)
print(f"Evaluate Stage 2 ({ds_name}): {epoch:>3d}" f" Acc: {accs_fmt} ({acc:.3f})")
return acc, metrics
def validate_submission(
predictions: dict[str, dict[str, np.ndarray]],
test_set_name: str,
fmri_num_samples: dict[str, dict[str, int]],
):
subject_keys = set(predictions.keys())
if subject_keys != EXPECTED_SUBJECTS:
extra = subject_keys - EXPECTED_SUBJECTS
missing = EXPECTED_SUBJECTS - subject_keys
raise ValueError(
f"Subject key mismatch. Extra: {extra}, Missing: {missing}. "
f"Expected exactly: {EXPECTED_SUBJECTS}"
)
if test_set_name == "ood":
expected_episodes = EXPECTED_OOD_KEYS
elif test_set_name == "friends-s7":
all_episodes = set()
for sub_samples in fmri_num_samples.values():
all_episodes.update(sub_samples.keys())
expected_episodes = all_episodes
else:
print(f" Warning: no key validation for test set '{test_set_name}'")
return
for sub, episodes_dict in predictions.items():
episode_keys = set(episodes_dict.keys())
extra = episode_keys - expected_episodes
missing = expected_episodes - episode_keys
if extra:
raise ValueError(
f"{sub}: extra episode keys {extra} — these will cause a formatting error"
)
if missing:
raise ValueError(
f"{sub}: missing episode keys {missing} — submission is incomplete"
)
for ep, pred in episodes_dict.items():
expected_n = fmri_num_samples[sub].get(ep)
if expected_n is not None and pred.shape[0] != expected_n:
raise ValueError(
f"{sub}/{ep}: shape {pred.shape} but expected N={expected_n}"
)
if pred.shape[1] != 1000:
raise ValueError(
f"{sub}/{ep}: shape {pred.shape} but expected 1000 parcels"
)
if pred.dtype != np.float32:
raise ValueError(
f"{sub}/{ep}: dtype {pred.dtype} but expected float32"
)
print(f" Validation passed: {len(predictions)} subjects, "
f"{len(expected_episodes)} episodes each")
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