#!/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 math, sys, tqdm
import torch, torchvision
else:
return s
+
class DiscreteSampler2d(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
- s = (x >= x.max(-3,keepdim=True).values).float()
+ s = (x >= x.max(-3, keepdim=True).values).float()
if self.training:
u = x.softmax(dim=-3)
logger=None,
device=torch.device("cpu"),
):
-
mu, std = train_input.float().mean(), train_input.float().std()
def encoder_core(depth, dim):
frame2seq,
seq2frame,
) = create_data_and_processors(
- 25000, 1000,
+ 25000,
+ 1000,
nb_epochs=5,
mode="first_last",
nb_steps=20,