# create batching lambda function
train_batches = bot.partial(bot.Batches,
dataset=train_set,
shuffle=True,
drop_last=True,
max_options=200)
valid_batches = bot.partial(bot.Batches,
dataset=valid_set,
shuffle=False,
drop_last=False)
print('=====> Input whitening')
# create input whitening network
Λ, V = bot.eigens(bot.patches(train_set['data'][:10000, :, 4:-4, 4:-4]))
input_whitening_net = bot.network(conv_pool_block=bot.conv_pool_block_pre,
prep_block=bot.partial(bot.whitening_block,
Λ=Λ,
V=V),
scale=1 / 16,
types={
nn.ReLU:
bot.partial(nn.CELU, 0.3),
bot.BatchNorm:
bot.partial(bot.GhostBatchNorm,
num_splits=16,
weight=False)
})
print('=====> Building model (with input whitening network)')
valid_set = bot.preprocess(
dataset_dict['valid'],
[bot.transpose, normalize, bot.to(torch.float16)]) #
print('=====> Data preprocessed (on GPU)')
# create batching lambda function
valid_batches = bot.partial(bot.Batches,
dataset=valid_set,
shuffle=False,
drop_last=False)
print('=====> Input whitening')
# create input whitening network
Λ, V = bot.eigens(bot.patches(valid_set['data'][:10000, :, 4:-4, 4:-4]))
input_whitening_net = bot.network(conv_pool_block=bot.conv_pool_block_pre,
prep_block=bot.partial(bot.whitening_block,
Λ=Λ,
V=V),
scale=1 / 16,
types={
nn.ReLU:
bot.partial(nn.CELU, 0.3),
bot.BatchNorm:
bot.partial(bot.GhostBatchNorm,
num_splits=16,
weight=False)
})
print('=====> Building model (with input whitening network)')