Update.

diff --git a/ffutils.py b/ffutils.py
deleted file mode 100755 (executable)
index 45f44d8..0000000
--- a/ffutils.py
+++ /dev/null
@@ -1,108 +0,0 @@
-#!/usr/bin/env python
-
-# Any copyright is dedicated to the Public Domain.
-# https://creativecommons.org/publicdomain/zero/1.0/
-
-# Written by Francois Fleuret <francois@fleuret.org>
-
-import torch
-import sys, contextlib
-
-import torch
-from torch import Tensor
-
-######################################################################
-
-
-@contextlib.contextmanager
-def evaluation(*models):
-    with torch.inference_mode():
-        t = [(m, m.training) for m in models]
-        for m in models:
-            m.train(False)
-        yield
-        for m, u in t:
-            m.train(u)
-
-
-######################################################################
-
-from torch.utils._python_dispatch import TorchDispatchMode
-
-
-def hasNaN(x):
-    if torch.is_tensor(x):
-        return x.isnan().max()
-    else:
-        try:
-            return any([hasNaN(y) for y in x])
-        except TypeError:
-            return False
-
-
-class NaNDetect(TorchDispatchMode):
-    def __torch_dispatch__(self, func, types, args, kwargs=None):
-        kwargs = kwargs or {}
-        res = func(*args, **kwargs)
-
-        if hasNaN(res):
-            raise RuntimeError(
-                f"Function {func}(*{args}, **{kwargs}) " "returned a NaN"
-            )
-        return res
-
-
-######################################################################
-
-
-def exception_hook(exc_type, exc_value, tb):
-    r"""Hacks the call stack message to show all the local variables
-    in case of relevant error, and prints tensors as shape, dtype and
-    device.
-
-    """
-
-    repr_orig = Tensor.__repr__
-    Tensor.__repr__ = lambda x: f"{x.size()}:{x.dtype}:{x.device}"
-
-    while tb:
-        print("--------------------------------------------------\n")
-        filename = tb.tb_frame.f_code.co_filename
-        name = tb.tb_frame.f_code.co_name
-        line_no = tb.tb_lineno
-        print(f'  File "{filename}", line {line_no}, in {name}')
-        print(open(filename, "r").readlines()[line_no - 1])
-
-        if exc_type in {RuntimeError, ValueError, IndexError, TypeError}:
-            for n, v in tb.tb_frame.f_locals.items():
-                print(f"  {n} -> {v}")
-
-        print()
-        tb = tb.tb_next
-
-    Tensor.__repr__ = repr_orig
-
-    print(f"{exc_type.__name__}: {exc_value}")
-
-
-def activate_tensorstack():
-    sys.excepthook = exception_hook
-
-
-######################################################################
-
-if __name__ == "__main__":
-    import torch
-
-    def dummy(a, b):
-        print(a @ b)
-
-    def blah(a, b):
-        c = b + b
-        dummy(a, c)
-
-    mmm = torch.randn(2, 3)
-    xxx = torch.randn(3)
-    # print(xxx@mmm)
-    blah(mmm, xxx)
-    blah(xxx, mmm)

diff --git a/main.py b/main.py
index 9e8589a..ed43b33 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -11,7 +11,7 @@ import torch, torchvision
 from torch import nn
 from torch.nn import functional as F
 
-import ffutils, grids, attae
+import grids, attae
 
 import threading, subprocess
 
@@ -1009,6 +1009,11 @@ for n_epoch in range(current_epoch, args.nb_epochs):
 
     ranked_models = sorted(models, key=lambda m: float(m.test_accuracy))
     weakest_models = ranked_models[: len(gpus)]
+    weakest_models = list(
+        filter(
+            lambda m: m.test_accuracy < args.accuracy_to_make_c_quizzes, weakest_models
+        )
+    )
 
     log_string(
         f"weakest_accuracies {[model.test_accuracy for model in weakest_models]}"