def _make_dataloaders(train_set, valid_set, test_set, train_size, valid_size, batch_size):
# Split training into train and validation
indices = torch.randperm(len(train_set))
train_indices = indices[:len(indices)-valid_size][:train_size or None]
valid_indices = indices[len(indices)-valid_size:] if valid_size else None
train_loader = torch.utils.data.DataLoader(train_set, pin_memory=True, batch_size=batch_size,
sampler=SubsetRandomSampler(train_indices))
test_loader = torch.utils.data.DataLoader(test_set, pin_memory=True, batch_size=batch_size)
if valid_size:
valid_loader = torch.utils.data.DataLoader(valid_set, pin_memory=True, batch_size=batch_size,
sampler=SubsetRandomSampler(valid_indices))
else:
valid_loader = None
return train_loader, valid_loader, test_loader
python类randperm()的实例源码
def train(self, dataset):
self.model.train()
self.optimizer.zero_grad()
total_loss = 0.0
indices = torch.randperm(len(dataset))
for idx in tqdm(range(len(dataset)),desc='Training epoch ' + str(self.epoch + 1) + ''):
ltree, lsent, rtree, rsent, label = dataset[indices[idx]]
linput, rinput = Var(lsent), Var(rsent)
target = Var(map_label_to_target(label, dataset.num_classes))
if self.args.cuda:
linput, rinput = linput.cuda(), rinput.cuda()
target = target.cuda()
output = self.model(ltree, linput, rtree, rinput)
loss = self.criterion(output, target)
total_loss += loss.data[0]
loss.backward()
if idx % self.args.batchsize == 0 and idx > 0:
self.optimizer.step()
self.optimizer.zero_grad()
self.epoch += 1
return total_loss / len(dataset)
# helper function for testing
def random(nin, nout, nto):
nker = nto * nout
tbl = torch.Tensor(nker, 2)
fi = torch.randperm(nin)
frcntr = 0
nfi = math.floor(nin / nto) # number of distinct nto chunks
totbl = tbl.select(1, 1)
frtbl = tbl.select(1, 0)
fitbl = fi.narrow(0, 0, (nfi * nto)) # part of fi that covers distinct chunks
ufrtbl = frtbl.unfold(0, nto, nto)
utotbl = totbl.unfold(0, nto, nto)
ufitbl = fitbl.unfold(0, nto, nto)
# start fill_ing frtbl
for i in range(nout): # fro each unit in target map
ufrtbl.select(0, i).copy_(ufitbl.select(0, frcntr))
frcntr += 1
if frcntr-1 == nfi: # reset fi
fi.copy_(torch.randperm(nin))
frcntr = 1
for tocntr in range(utotbl.size(0)):
utotbl.select(0, tocntr).fill_(tocntr)
return tbl
def test_index_copy(self):
num_copy, num_dest = 3, 20
dest = torch.randn(num_dest, 4, 5)
src = torch.randn(num_copy, 4, 5)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy).long()
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]].copy_(src[i])
self.assertEqual(dest, dest2, 0)
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy).long()
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = src[i]
self.assertEqual(dest, dest2, 0)
def test_index_add(self):
num_copy, num_dest = 3, 3
dest = torch.randn(num_dest, 4, 5)
src = torch.randn(num_copy, 4, 5)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy).long()
dest2 = dest.clone()
dest.index_add_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]].add_(src[i])
self.assertEqual(dest, dest2)
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy).long()
dest2 = dest.clone()
dest.index_add_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = dest2[idx[i]] + src[i]
self.assertEqual(dest, dest2)
# Fill idx with valid indices.
def resample(self, seed=None):
"""Resample the dataset.
Args:
seed (int, optional): Seed for resampling. By default no seed is
used.
"""
if seed is not None:
gen = torch.manual_seed(seed)
else:
gen = torch.default_generator
if self.replacement:
self.perm = torch.LongTensor(len(self)).random_(
len(self.dataset), generator=gen)
else:
self.perm = torch.randperm(
len(self.dataset), generator=gen).narrow(0, 0, len(self))
def random(nin, nout, nto):
nker = nto * nout
tbl = torch.Tensor(nker, 2)
fi = torch.randperm(nin)
frcntr = 0
nfi = math.floor(nin / nto) # number of distinct nto chunks
totbl = tbl.select(1, 1)
frtbl = tbl.select(1, 0)
fitbl = fi.narrow(0, 0, (nfi * nto)) # part of fi that covers distinct chunks
ufrtbl = frtbl.unfold(0, nto, nto)
utotbl = totbl.unfold(0, nto, nto)
ufitbl = fitbl.unfold(0, nto, nto)
# start fill_ing frtbl
for i in range(nout): # fro each unit in target map
ufrtbl.select(0, i).copy_(ufitbl.select(0, frcntr))
frcntr += 1
if frcntr - 1 == nfi: # reset fi
fi.copy_(torch.randperm(nin))
frcntr = 1
for tocntr in range(utotbl.size(0)):
utotbl.select(0, tocntr).fill_(tocntr)
return tbl
def test_index_copy(self):
num_copy, num_dest = 3, 20
dest = torch.randn(num_dest, 4, 5)
src = torch.randn(num_copy, 4, 5)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]].copy_(src[i])
self.assertEqual(dest, dest2, 0)
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = src[i]
self.assertEqual(dest, dest2, 0)
def test_index_add(self):
num_copy, num_dest = 3, 3
dest = torch.randn(num_dest, 4, 5)
src = torch.randn(num_copy, 4, 5)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_add_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]].add_(src[i])
self.assertEqual(dest, dest2)
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_add_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = dest2[idx[i]] + src[i]
self.assertEqual(dest, dest2)
# Fill idx with valid indices.
def random(nin, nout, nto):
nker = nto * nout
tbl = torch.Tensor(nker, 2)
fi = torch.randperm(nin)
frcntr = 0
nfi = math.floor(nin / nto) # number of distinct nto chunks
totbl = tbl.select(1, 1)
frtbl = tbl.select(1, 0)
fitbl = fi.narrow(0, 0, (nfi * nto)) # part of fi that covers distinct chunks
ufrtbl = frtbl.unfold(0, nto, nto)
utotbl = totbl.unfold(0, nto, nto)
ufitbl = fitbl.unfold(0, nto, nto)
# start fill_ing frtbl
for i in range(nout): # fro each unit in target map
ufrtbl.select(0, i).copy_(ufitbl.select(0, frcntr))
frcntr += 1
if frcntr - 1 == nfi: # reset fi
fi.copy_(torch.randperm(nin))
frcntr = 1
for tocntr in range(utotbl.size(0)):
utotbl.select(0, tocntr).fill_(tocntr)
return tbl
def test_index_copy(self):
num_copy, num_dest = 3, 20
dest = torch.randn(num_dest, 4, 5)
src = torch.randn(num_copy, 4, 5)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]].copy_(src[i])
self.assertEqual(dest, dest2, 0)
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = src[i]
self.assertEqual(dest, dest2, 0)
def test_index_add(self):
num_copy, num_dest = 3, 3
dest = torch.randn(num_dest, 4, 5)
src = torch.randn(num_copy, 4, 5)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_add_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]].add_(src[i])
self.assertEqual(dest, dest2)
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_add_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = dest2[idx[i]] + src[i]
self.assertEqual(dest, dest2)
# Fill idx with valid indices.
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = list(torch.randperm(len(self.dataset), generator=g))
# add extra samples to make it evenly divisible
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
offset = self.num_samples * self.rank
indices = indices[offset:offset + self.num_samples]
assert len(indices) == self.num_samples
return iter(indices)
def random(nin, nout, nto):
nker = nto * nout
tbl = torch.Tensor(nker, 2)
fi = torch.randperm(nin)
frcntr = 0
nfi = math.floor(nin / nto) # number of distinct nto chunks
totbl = tbl.select(1, 1)
frtbl = tbl.select(1, 0)
fitbl = fi.narrow(0, 0, (nfi * nto)) # part of fi that covers distinct chunks
ufrtbl = frtbl.unfold(0, nto, nto)
utotbl = totbl.unfold(0, nto, nto)
ufitbl = fitbl.unfold(0, nto, nto)
# start fill_ing frtbl
for i in range(nout): # fro each unit in target map
ufrtbl.select(0, i).copy_(ufitbl.select(0, frcntr))
frcntr += 1
if frcntr - 1 == nfi: # reset fi
fi.copy_(torch.randperm(nin))
frcntr = 1
for tocntr in range(utotbl.size(0)):
utotbl.select(0, tocntr).fill_(tocntr)
return tbl
def test_index_copy(self):
num_copy, num_dest = 3, 20
dest = torch.randn(num_dest, 4, 5)
src = torch.randn(num_copy, 4, 5)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]].copy_(src[i])
self.assertEqual(dest, dest2, 0)
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = src[i]
self.assertEqual(dest, dest2, 0)
def train():
for epoch in xrange(args.num_epoch):
all_indices = torch.randperm(tensor_tr.size(0)).split(args.batch_size)
loss_epoch = 0.0
model.train() # switch to training mode
for batch_indices in all_indices:
if not args.nogpu: batch_indices = batch_indices.cuda()
input = Variable(tensor_tr[batch_indices])
recon, loss = model(input, compute_loss=True)
# optimize
optimizer.zero_grad() # clear previous gradients
loss.backward() # backprop
optimizer.step() # update parameters
# report
loss_epoch += loss.data[0] # add loss to loss_epoch
if epoch % 5 == 0:
print('Epoch {}, loss={}'.format(epoch, loss_epoch / len(all_indices)))
def train(self, dataset):
self.model.train()
self.optimizer.zero_grad()
loss, k = 0.0, 0
indices = torch.randperm(len(dataset))
for idx in tqdm(range(len(dataset)), desc='Training epoch ' + str(self.epoch + 1) + ''):
ltree, lsent, rtree, rsent, label = dataset[indices[idx]]
linput, rinput = Var(lsent), Var(rsent)
target = Var(map_label_to_target(label, dataset.num_classes))
if self.args.cuda:
linput, rinput = linput.cuda(), rinput.cuda()
target = target.cuda()
output = self.model(ltree, linput, rtree, rinput)
err = self.criterion(output, target)
loss += err.data[0]
err.backward()
k += 1
if k % self.args.batchsize == 0:
self.optimizer.step()
self.optimizer.zero_grad()
self.epoch += 1
return loss / len(dataset)
# helper function for testing
def __call__(self, *inputs):
outputs = []
for idx, _input in enumerate(inputs):
size = _input.size()
img_height = size[1]
img_width = size[2]
x_blocks = int(img_height/self.blocksize) # number of x blocks
y_blocks = int(img_width/self.blocksize)
ind = th.randperm(x_blocks*y_blocks)
new = th.zeros(_input.size())
count = 0
for i in range(x_blocks):
for j in range (y_blocks):
row = int(ind[count] / x_blocks)
column = ind[count] % x_blocks
new[:, i*self.blocksize:(i+1)*self.blocksize, j*self.blocksize:(j+1)*self.blocksize] = \
_input[:, row*self.blocksize:(row+1)*self.blocksize, column*self.blocksize:(column+1)*self.blocksize]
count += 1
outputs.append(new)
return outputs if idx > 1 else outputs[0]
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = list(torch.randperm(len(self.dataset), generator=g))
# add extra samples to make it evenly divisible
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
offset = self.num_samples * self.rank
indices = indices[offset:offset + self.num_samples]
assert len(indices) == self.num_samples
return iter(indices)
def random(nin, nout, nto):
nker = nto * nout
tbl = torch.Tensor(nker, 2)
fi = torch.randperm(nin)
frcntr = 0
nfi = math.floor(nin / nto) # number of distinct nto chunks
totbl = tbl.select(1, 1)
frtbl = tbl.select(1, 0)
fitbl = fi.narrow(0, 0, (nfi * nto)) # part of fi that covers distinct chunks
ufrtbl = frtbl.unfold(0, nto, nto)
utotbl = totbl.unfold(0, nto, nto)
ufitbl = fitbl.unfold(0, nto, nto)
# start fill_ing frtbl
for i in range(nout): # fro each unit in target map
ufrtbl.select(0, i).copy_(ufitbl.select(0, frcntr))
frcntr += 1
if frcntr - 1 == nfi: # reset fi
fi.copy_(torch.randperm(nin))
frcntr = 1
for tocntr in range(utotbl.size(0)):
utotbl.select(0, tocntr).fill_(tocntr)
return tbl
def test_index_copy(self):
num_copy, num_dest = 3, 20
dest = torch.randn(num_dest, 4, 5)
src = torch.randn(num_copy, 4, 5)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]].copy_(src[i])
self.assertEqual(dest, dest2, 0)
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = src[i]
self.assertEqual(dest, dest2, 0)
def test_index_add(self):
num_copy, num_dest = 3, 3
dest = torch.randn(num_dest, 4, 5)
src = torch.randn(num_copy, 4, 5)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_add_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]].add_(src[i])
self.assertEqual(dest, dest2)
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_add_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = dest2[idx[i]] + src[i]
self.assertEqual(dest, dest2)
def cosine_ranking_loss(input_data, ctx, margin=0.1):
"""
:param input_data: [batch_size, 300] tensor of predictions
:param ctx: [batch_size, 300] tensor of ground truths
:param margin: Difference between them
:return:
"""
normed = _normalize(input_data)
ctx_normed = _normalize(ctx)
shuff_inds = torch.randperm(normed.size(0))
if ctx.is_cuda:
shuff_inds = shuff_inds.cuda()
shuff = ctx_normed[shuff_inds]
correct_contrib = torch.sum(normed * ctx_normed, 1).squeeze()
incorrect_contrib = torch.sum(normed * shuff, 1).squeeze()
# similarity = torch.mm(normed, ctx_normed.t()) #[predictions, gts]
# correct_contrib = similarity.diag()
# incorrect_contrib = incorrect_contrib.sum(1).squeeze()/(incorrect_contrib.size(1)-1.0)
#
cost = (0.1 + incorrect_contrib-correct_contrib).clamp(min=0)
return cost, correct_contrib, incorrect_contrib
def train(self, dataset):
self.model.train()
self.optimizer.zero_grad()
loss, k = 0.0, 0
indices = torch.randperm(len(dataset))
for idx in tqdm(xrange(len(dataset)),desc='Training epoch '+str(self.epoch+1)+''):
ltree,lsent,rtree,rsent,label = dataset[indices[idx]]
linput, rinput = Var(lsent), Var(rsent)
target = Var(map_label_to_target(label,dataset.num_classes))
if self.args.cuda:
linput, rinput = linput.cuda(), rinput.cuda()
target = target.cuda()
output = self.model(ltree,linput,rtree,rinput)
err = self.criterion(output, target)
loss += err.data[0]
err.backward()
k += 1
if k%self.args.batchsize==0:
self.optimizer.step()
self.optimizer.zero_grad()
self.epoch += 1
return loss/len(dataset)
# helper function for testing
def __call__(self, img):
if self.transforms is None:
return img
order = torch.randperm(len(self.transforms))
for i in order:
img = self.transforms[i](img)
return img
def __iter__(self):
return iter(torch.randperm(self.num_samples).long())
def __iter__(self):
for i in range(10):
yield torch.randn(2, 10), torch.randperm(10)[:2]
def test_len(self):
source = TensorDataset(torch.randn(15, 10, 2, 3, 4, 5), torch.randperm(15))
self.assertEqual(len(source), 15)
def setUp(self):
self.data = torch.randn(100, 2, 3, 5)
self.labels = torch.randperm(50).repeat(2)
self.dataset = TensorDataset(self.data, self.labels)
def _fill_indices(self, idx, dim, dim_size, elems_per_row, m, n, o):
for i in range(1 if dim == 0 else m):
for j in range(1 if dim == 1 else n):
for k in range(1 if dim == 2 else o):
ii = [i, j, k]
ii[dim] = slice(0, idx.size(dim)+1)
idx[tuple(ii)] = torch.randperm(dim_size)[0:elems_per_row]
