python中lt方法_Python torch.lt方法代碼示例
本文整理匯總了Python中torch.lt方法的典型用法代碼示例。如果您正苦於以下問題：Python torch.lt方法的具體用法？Python torch.lt怎麽用？Python torch.lt使用的例子？那麽恭喜您, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在模塊torch的用法示例。
在下文中一共展示了torch.lt方法的30個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於我們的係統推薦出更棒的Python代碼示例。
示例1: test_train
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def test_train(self):
self._metric.train()
calls = [[torch.FloatTensor([0.0]), torch.LongTensor([0])],
[torch.FloatTensor([0.0, 0.1, 0.2, 0.3]), torch.LongTensor([0, 1, 2, 3])]]
for i in range(len(self._states)):
self._metric.process(self._states[i])
self.assertEqual(2, len(self._metric_function.call_args_list))
for i in range(len(self._metric_function.call_args_list)):
self.assertTrue(torch.eq(self._metric_function.call_args_list[i][0][0], calls[i][0]).all)
self.assertTrue(torch.lt(torch.abs(torch.add(self._metric_function.call_args_list[i][0][1], -calls[i][1])), 1e-12).all)
self._metric_function.reset_mock()
self._metric.process_final({})
self.assertEqual(self._metric_function.call_count, 1)
self.assertTrue(torch.eq(self._metric_function.call_args_list[0][0][1], torch.LongTensor([0, 1, 2, 3, 4])).all)
self.assertTrue(torch.lt(torch.abs(torch.add(self._metric_function.call_args_list[0][0][0], -torch.FloatTensor([0.0, 0.1, 0.2, 0.3, 0.4]))), 1e-12).all)
開發者ID:pytorchbearer，項目名稱:torchbearer，代碼行數:18，
示例2: regenerate_cache
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def regenerate_cache(self):
"""
Resamples the big matrix and resets the counter of the total
number of elements in the returned masks.
"""
low_size = int(self.resolution * self.max_size)
low_pattern = self.rng.uniform(0, 1, size=(low_size, low_size)) * 255
low_pattern = torch.from_numpy(low_pattern.astype('float32'))
pattern = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize(self.max_size, Image.BICUBIC),
transforms.ToTensor(),
])(low_pattern[None])[0]
pattern = torch.lt(pattern, self.density).byte()
self.pattern = pattern.byte()
self.points_used = 0
開發者ID:tigvarts，項目名稱:vaeac，代碼行數:18，
示例3: test_terner_connect_sto_forward
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def test_terner_connect_sto_forward():
x = torch.Tensor([1,0,0.45,-1,-0.9]).view(1,-1)
results = list()
for i in range(1000):
temp_result = TernaryConnectStochastic.apply(x)
# Tensor must have only -1 , 0 , 1 values
assert not torch.any(torch.lt(torch.abs(temp_result-1),1e-8)*torch.lt(torch.abs(temp_result),1e-8))
results.append(temp_result)
result = torch.cat(results,0 )
result = torch.sum(result, 0)/1000
assert equals(
result,
torch.Tensor([1,0,0.45,-1,-0.9]).view(1,-1),
5e-2)
開發者ID:Enderdead，項目名稱:Pytorch_Quantize_impls，代碼行數:19，
示例4: intersectionAndUnion
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def intersectionAndUnion(batch_data, pred, numClass):
(imgs, segs, infos) = batch_data
_, preds = torch.max(pred.data.cpu(), dim=1)
# compute area intersection
intersect = preds.clone()
intersect[torch.ne(preds, segs)] = -1
area_intersect = torch.histc(intersect.float(),
bins=numClass,
min=0,
max=numClass - 1)
# compute area union:
preds[torch.lt(segs, 0)] = -1
area_pred = torch.histc(preds.float(),
bins=numClass,
min=0,
max=numClass - 1)
area_lab = torch.histc(segs.float(),
bins=numClass,
min=0,
max=numClass - 1)
area_union = area_pred + area_lab - area_intersect
return area_intersect, area_union
開發者ID:soeaver，項目名稱:pytorch-priv，代碼行數:27，
示例5: _jit_linear_cg_updates
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def _jit_linear_cg_updates(
result, alpha, residual_inner_prod, eps, beta, residual, precond_residual, mul_storage, is_zero, curr_conjugate_vec
):
# # Update result
# # result_{k} = result_{k-1} + alpha_{k} p_vec_{k-1}
result = torch.addcmul(result, alpha, curr_conjugate_vec, out=result)
# beta_{k} = (precon_residual{k}^T r_vec_{k}) / (precon_residual{k-1}^T r_vec_{k-1})
beta.resize_as_(residual_inner_prod).copy_(residual_inner_prod)
torch.mul(residual, precond_residual, out=mul_storage)
torch.sum(mul_storage, -2, keepdim=True, out=residual_inner_prod)
# Do a safe division here
torch.lt(beta, eps, out=is_zero)
beta.masked_fill_(is_zero, 1)
torch.div(residual_inner_prod, beta, out=beta)
beta.masked_fill_(is_zero, 0)
# Update curr_conjugate_vec
# curr_conjugate_vec_{k} = precon_residual{k} + beta_{k} curr_conjugate_vec_{k-1}
curr_conjugate_vec.mul_(beta).add_(precond_residual)
開發者ID:cornellius-gp，項目名稱:gpytorch，代碼行數:23，
示例6: check_monitor_top_k
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def check_monitor_top_k(self, current):
less_than_k_models = len(self.best_k_models) < self.save_top_k
if less_than_k_models:
return True
if not isinstance(current, torch.Tensor):
rank_zero_warn(
f'{current} is supposed to be a `torch.Tensor`. Saving checkpoint may not work correctly.'
f' HINT: check the value of {self.monitor} in your validation loop', RuntimeWarning
)
current = torch.tensor(current)
monitor_op = {
"min": torch.lt,
"max": torch.gt,
}[self.mode]
return monitor_op(current, self.best_k_models[self.kth_best_model_path])
開發者ID:PyTorchLightning，項目名稱:pytorch-lightning，代碼行數:20，
示例7: reward_ddpg_A
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def reward_ddpg_A(solution, use_cuda):
"""
Count number of consecutively correctly sorted for each sample in minibatch
starting from beginning
Very hard to randomly find enough non-zero samples - reward is very sparse!
solution is FloatTensor of dim [batch,n]
"""
(batch_size, n, m) = solution.size()
n_correctly_sorted = Variable(torch.zeros(batch_size, 1), requires_grad=False)
mask = Variable(torch.ones(batch_size, 1).byte(), requires_grad=False)
if use_cuda:
n_correctly_sorted = n_correctly_sorted.cuda()
mask = mask.cuda()
for i in range(1, m):
res = torch.lt(solution[:,:,i-1], solution[:,:,i])
mask.data &= res.data
n_correctly_sorted[mask] += 1
return torch.div(n_correctly_sorted, m - 1)
開發者ID:pemami4911，項目名稱:sinkhorn-policy-gradient.pytorch，代碼行數:24，
示例8: reward_ddpg_B
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def reward_ddpg_B(solution, use_cuda):
"""
Count number of (nonconsecutively) correctly sorted starting from beginning
Tends to converge to [0,2,4,6,8,1,3,5,7,9]
solution is FloatTensor of dim [batch,n]
"""
(batch_size, n, m) = solution.size()
n_correctly_sorted = Variable(torch.zeros(batch_size, 1), requires_grad=False)
if use_cuda:
n_correctly_sorted = n_correctly_sorted.cuda()
for i in range(1, m):
res = torch.lt(solution[:,:,i-1], solution[:,:,i])
n_correctly_sorted += res.float()
return torch.div(n_correctly_sorted, m - 1)
開發者ID:pemami4911，項目名稱:sinkhorn-policy-gradient.pytorch，代碼行數:21，
示例9: get_morph
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def get_morph(batch):
#Not very nice but we do not have access to value comming from opt.gpuid command line parameter here.
use_cuda = batch.src[0].is_cuda
# morph_index = batch.morph.data.transpose(0, 1) # [ seqLen x batch_size ] ==> [ batch_size x seqLen ]
# morph_voc = batch.dataset.fields['morph'].vocab.stoi
morph_index = batch.morph.view((batch.src[0].data.size()[0], 6, batch.src[0].data.size()[1]))
morph_index = morph_index.permute(2, 0, 1).contiguous()
# morph_index = torch.LongTensor(morph_index)
morph_mask = torch.lt(torch.eq(morph_index, 1), 1).float()
# morph_index = autograd.Variable(morph_index)
# morph_mask = autograd.Variable(torch.FloatTensor(morph_mask), requires_grad=False)
if use_cuda:
morph_index = morph_index.cuda()
morph_mask = morph_mask.cuda()
return morph_index, morph_mask
開發者ID:diegma，項目名稱:graph-2-text，代碼行數:25，代碼來源:IO.py
示例10: wrapper_gmask
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def wrapper_gmask(opt):
# batchsize should be 1 for mask_global
mask_global = torch.ByteTensor(1, 1, \
opt.fineSize, opt.fineSize)
res = 0.06 # the lower it is, the more continuous the output will be. 0.01 is too small and 0.1 is too large
density = 0.25
MAX_SIZE = 350
maxPartition = 30
low_pattern = torch.rand(1, 1, int(res * MAX_SIZE), int(res * MAX_SIZE)).mul(255)
pattern = F.interpolate(low_pattern, (MAX_SIZE, MAX_SIZE), mode='bilinear').detach()
low_pattern = None
pattern.div_(255)
pattern = torch.lt(pattern, density).byte() # 25% 1s and 75% 0s
pattern = torch.squeeze(pattern).byte()
gMask_opts = {}
gMask_opts['pattern'] = pattern
gMask_opts['MAX_SIZE'] = MAX_SIZE
gMask_opts['fineSize'] = opt.fineSize
gMask_opts['maxPartition'] = maxPartition
gMask_opts['mask_global'] = mask_global
return create_gMask(gMask_opts) # create an initial random mask.
開發者ID:Zhaoyi-Yan，項目名稱:Shift-Net_pytorch，代碼行數:25，
示例11: forward
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def forward(self, words, frequent_tuning=False):
if frequent_tuning and self.training:
padding_mask = words.eq(0).long()
# Fine-tuning - N the most frequent
fine_tune_mask = torch.lt(words, self.threshold_index) * padding_mask.eq(
0
) # < threshold_index
fine_tune_words = words * fine_tune_mask.long()
fine_tune_embedded = self.fine_tune_word_embedding(fine_tune_words)
fine_tune_embedded = f.masked_zero(fine_tune_embedded, fine_tune_mask)
# Fixed - under N frequent
fixed_mask = torch.ge(words, self.threshold_index) # >= threshold_index
fixed_embedeed = self.fixed_word_embedding(words).detach() # Fixed
fixed_embedeed = f.masked_zero(fixed_embedeed, fixed_mask)
embedded_words = fine_tune_embedded + fixed_embedeed
else:
embedded_words = self.fixed_word_embedding(words)
return self.dropout(embedded_words)
開發者ID:naver，項目名稱:claf，代碼行數:27，
示例12: maxOfTwo
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def maxOfTwo(x, y):
z = x.clone()
maskYLarger = torch.lt(x, y)
z[maskYLarger.detach()] = y[maskYLarger.detach()]
return z
開發者ID:JunjH，項目名稱:Visualizing-CNNs-for-monocular-depth-estimation，代碼行數:7，
示例13: forward
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def forward(self, estDisp, gtDisp):
if not torch.is_tensor(gtDisp):
raise TypeError('ground truth disparity map is expected to be tensor, got {}'.format(type(gtDisp)))
if not torch.is_tensor(estDisp):
raise TypeError('estimated disparity map is expected to be tensor, got {}'.format(type(estDisp)))
assert estDisp.shape == gtDisp.shape
if gtDisp.dim() == 2: # single image H x W
h, w = gtDisp.size(0), gtDisp.size(1)
gtDisp = gtDisp.view(1, 1, h, w)
estDisp = estDisp.view(1, 1, h, w)
if gtDisp.dim() == 3: # multi image B x H x W
b, h, w = gtDisp.size(0), gtDisp.size(1), gtDisp.size(2)
gtDisp = gtDisp.view(b, 1, h, w)
estDisp = estDisp.view(b, 1, h, w)
if gtDisp.dim() == 4:
if gtDisp.size(1) == 1: # mult image B x 1 x H x W
self.gtDisp = gtDisp
self.estDisp = estDisp
else:
raise ValueError('2nd dimension size should be 1, got {}'.format(gtDisp.size(1)))
confidence_gt_label = torch.lt(torch.abs(self.estDisp - self.gtDisp), self.theta).type_as(self.gtDisp)
return confidence_gt_label
開發者ID:DeepMotionAIResearch，項目名稱:DenseMatchingBenchmark，代碼行數:31，
示例14: getProb
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def getProb(self):
# |d - d{gt}| < variance, [BatchSize, maxDisp, Height, Width]
probability = torch.lt(torch.abs(self.disp_sample - self.gtDisp), self.variance).type_as(self.gtDisp)
return probability
開發者ID:DeepMotionAIResearch，項目名稱:DenseMatchingBenchmark，代碼行數:7，
示例15: test_validate
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def test_validate(self):
self._metric.eval()
for i in range(len(self._states)):
self._metric.process(self._states[i])
self._metric_function.assert_not_called()
self._metric.process_final_validate({})
self.assertEqual(self._metric_function.call_count, 1)
self.assertTrue(torch.eq(self._metric_function.call_args_list[0][0][1], torch.LongTensor([0, 1, 2, 3, 4])).all)
self.assertTrue(torch.lt(torch.abs(torch.add(self._metric_function.call_args_list[0][0][0], -torch.FloatTensor([0.0, 0.1, 0.2, 0.3, 0.4]))), 1e-12).all)
開發者ID:pytorchbearer，項目名稱:torchbearer，代碼行數:12，
示例16: log1mexp
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def log1mexp(U, eps=1e-3):
"""
Compute log(1 - exp(u)) for u <= 0.
"""
res = torch.log1p(-torch.exp(U))
# |U| << 1 requires care for numerical stability:
# 1 - exp(U) = -U + o(U)
small = torch.lt(U.abs(), eps)
res[small] = torch.log(-U[small])
return res
開發者ID:oval-group，項目名稱:smooth-topk，代碼行數:14，
示例17: prune_vanilla_elementwise
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def prune_vanilla_elementwise(param, sparsity, fn_importance=lambda x: x.abs()):
"""
element-wise vanilla pruning
:param param: torch.(cuda.)Tensor, weight of conv/fc layer
:param sparsity: float, pruning sparsity
:param fn_importance: function, inputs 'param' and returns the importance of
each position in 'param',
default=lambda x: x.abs()
:return:
torch.(cuda.)ByteTensor, mask for zeros
"""
sparsity = min(max(0.0, sparsity), 1.0)
if sparsity == 1.0:
return torch.zeros_like(param).byte()
num_el = param.numel()
importance = fn_importance(param)
num_pruned = int(math.ceil(num_el * sparsity))
num_stayed = num_el - num_pruned
if sparsity <= 0.5:
_, topk_indices = torch.topk(importance.view(num_el), k=num_pruned,
dim=0, largest=False, sorted=False)
mask = torch.zeros_like(param).byte()
param.view(num_el).index_fill_(0, topk_indices, 0)
mask.view(num_el).index_fill_(0, topk_indices, 1)
else:
thr = torch.min(torch.topk(importance.view(num_el), k=num_stayed,
dim=0, largest=True, sorted=False)[0])
mask = torch.lt(importance, thr)
param.masked_fill_(mask, 0)
return mask
開發者ID:synxlin，項目名稱:nn-compression，代碼行數:32，
示例18: forward
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def forward(self, pos_context, pos_path, neg_context, neg_path):
pos_context_embedding = torch.sum(self.word_embeddings(pos_context), dim=1, keepdim=True)
pos_path_embedding = self.context_embeddings(pos_path)
pos_score = torch.bmm(pos_context_embedding, pos_path_embedding.transpose(2, 1)).squeeze()
neg_context_embedding = torch.sum(self.word_embeddings(neg_context), dim=1, keepdim=True)
neg_path_embedding = self.context_embeddings(neg_path)
neg_score = torch.bmm(neg_context_embedding, neg_path_embedding.transpose(2, 1)).squeeze()
pos_sigmoid_score = torch.lt(torch.sigmoid(pos_score), 0.5)
neg_sigmoid_score = torch.gt(torch.sigmoid(neg_score), 0.5)
sigmoid_score = torch.cat((pos_sigmoid_score, neg_sigmoid_score))
sigmoid_score = torch.sum(sigmoid_score, dim=0).item() / sigmoid_score.size(0)
return sigmoid_score
開發者ID:smilelight，項目名稱:lightNLP，代碼行數:14，
示例19: forward
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def forward(self, pos_target, pos_path, neg_target, neg_path):
pos_target_embedding = torch.sum(self.word_embeddings(pos_target), dim=1, keepdim=True)
pos_path_embedding = self.context_embeddings(pos_path)
pos_score = torch.bmm(pos_target_embedding, pos_path_embedding.transpose(2, 1)).squeeze()
neg_target_embedding = torch.sum(self.word_embeddings(neg_target), dim=1, keepdim=True)
neg_path_embedding = self.context_embeddings(neg_path)
neg_score = torch.bmm(neg_target_embedding, neg_path_embedding.transpose(2, 1)).squeeze()
pos_sigmoid_score = torch.lt(torch.sigmoid(pos_score), 0.5)
neg_sigmoid_score = torch.gt(torch.sigmoid(neg_score), 0.5)
sigmoid_score = torch.cat((pos_sigmoid_score, neg_sigmoid_score))
sigmoid_score = torch.sum(sigmoid_score, dim=0).item() / sigmoid_score.size(0)
return sigmoid_score
開發者ID:smilelight，項目名稱:lightNLP，代碼行數:14，
示例20: forward
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def forward(self, x, routing_weights):
x_orig = x
B, C, H, W = x.shape
weight = torch.matmul(routing_weights, self.weight) # (Expert x out x in x 3x3) --> (B x out x in x 3x3)
new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
weight = weight.view(new_weight_shape) # (B*out x in x 3 x 3)
bias = None
if self.bias is not None:
bias = torch.matmul(routing_weights, self.bias)
bias = bias.view(B * self.out_channels)
# move batch elements with channels so each batch element can be efficiently convolved with separate kernel
x = x.view(1, B * C, H, W)
if self.dynamic_padding:
out = conv2d_same(
x, weight, bias, stride=self.stride, padding=self.padding,
dilation=self.dilation, groups=self.groups * B)
else:
out = F.conv2d(
x, weight, bias, stride=self.stride, padding=self.padding,
dilation=self.dilation, groups=self.groups * B)
# out : (1 x B*out x ...)
out = out.permute([1, 0, 2, 3]).view(B, self.out_channels, out.shape[-2], out.shape[-1])
# out2 = self.forward_legacy(x_orig, routing_weights)
# lt = torch.lt(torch.abs(torch.add(out, -out2)), 1e-8)
# assert torch.all(lt), torch.abs(torch.add(out, -out2))[lt]
# print('checked')
return out
開發者ID:kakaobrain，項目名稱:fast-autoaugment，代碼行數:31，
示例21: logits_mask
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def logits_mask(coords, logits, num_points_per_object):
"""
Use logits to sample points
:param coords: coords of points, FloatTensor[B, 3, N]
:param logits: binary classification logits, FloatTensor[B, 2, N]
:param num_points_per_object: M, #points per object after masking, int
:return:
selected_coords: FloatTensor[B, 3, M]
masked_coords_mean: mean coords of selected points, FloatTensor[B, 3]
mask: mask to select points, BoolTensor[B, N]
"""
batch_size, _, num_points = coords.shape
mask = torch.lt(logits[:, 0, :], logits[:, 1, :]) # [B, N]
num_candidates = torch.sum(mask, dim=-1, keepdim=True) # [B, 1]
masked_coords = coords * mask.view(batch_size, 1, num_points) # [B, C, N]
masked_coords_mean = torch.sum(masked_coords, dim=-1) / torch.max(num_candidates,
torch.ones_like(num_candidates)).float() # [B, C]
selected_indices = torch.zeros((batch_size, num_points_per_object), device=coords.device, dtype=torch.int32)
for i in range(batch_size):
current_mask = mask[i] # [N]
current_candidates = current_mask.nonzero().view(-1)
current_num_candidates = current_candidates.numel()
if current_num_candidates >= num_points_per_object:
choices = np.random.choice(current_num_candidates, num_points_per_object, replace=False)
selected_indices[i] = current_candidates[choices]
elif current_num_candidates > 0:
choices = np.concatenate([
np.arange(current_num_candidates).repeat(num_points_per_object // current_num_candidates),
np.random.choice(current_num_candidates, num_points_per_object % current_num_candidates, replace=False)
])
np.random.shuffle(choices)
selected_indices[i] = current_candidates[choices]
selected_coords = gather(masked_coords - masked_coords_mean.view(batch_size, -1, 1), selected_indices)
return selected_coords, masked_coords_mean, mask
開發者ID:mit-han-lab，項目名稱:pvcnn，代碼行數:36，
示例22: equals
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def equals(a, b, epsilon=1e-12):
return torch.all(torch.lt( torch.abs(a-b), epsilon ))
開發者ID:Enderdead，項目名稱:Pytorch_Quantize_impls，代碼行數:4，
示例23: sequence_mask
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def sequence_mask(sequence_length, max_len=None):
if max_len is None:
max_len = sequence_length.data.max()
batch_size = sequence_length.size(0)
seq_range = torch.arange(0, max_len).long()
seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
seq_range_expand = seq_range_expand.clone().detach()
if sequence_length.is_cuda:
seq_range_expand = seq_range_expand.cuda()
seq_length_expand = (sequence_length.unsqueeze(1)
.expand_as(seq_range_expand))
return torch.lt(seq_range_expand, seq_length_expand)
開發者ID:Great-Li-Xin，項目名稱:dumb-chatbot，代碼行數:14，
示例24: check_monitor_top_k
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def check_monitor_top_k(self, current):
# If we don't have enough models
if len(self.best_k_models) < self.save_top_k:
return True
# Convert to torch if necessary
if not isinstance(current, torch.Tensor):
current = torch.tensor(current)
# Get monitoring operation
monitor_op = {
"min": torch.lt,
"max": torch.gt,
}[self.mode]
# Compare and return
return monitor_op(current, self.best_k_models[self.kth_best_model])
開發者ID:TRI-ML，項目名稱:packnet-sfm，代碼行數:16，
示例25: get_unsupervised_loss
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def get_unsupervised_loss(self, examples):
samples, sample_scores, enc_states = self.infer(examples)
reconstruction_scores = self.decoder.score(samples)
# compute prior probability
prior_scores = self.prior([e.tgt_code for e in samples])
if isinstance(self.prior, UniformPrior):
prior_scores = Variable(sample_scores.data.new(prior_scores))
kl_term = self.args.alpha * (sample_scores - prior_scores)
raw_learning_signal = reconstruction_scores - kl_term
baseline = self.baseline(samples, enc_states)
learning_signal = raw_learning_signal.detach() - baseline
# clip learning signal
if self.args.clip_learning_signal is not None:
mask = torch.lt(learning_signal, self.args.clip_learning_signal).float()
clipped_learning_signal = learning_signal * (1. - mask) + mask * self.args.clip_learning_signal
else:
clipped_learning_signal = learning_signal
encoder_loss = -clipped_learning_signal.detach() * sample_scores
decoder_loss = -reconstruction_scores
# compute baseline loss
baseline_loss = learning_signal ** 2
meta_data = {'samples': samples,
'reconstruction_scores': reconstruction_scores,
'encoding_scores': sample_scores,
'raw_learning_signal': raw_learning_signal,
'learning_signal': learning_signal,
'baseline': baseline,
'kl_term': kl_term,
'prior': prior_scores}
return encoder_loss, decoder_loss, baseline_loss, meta_data
開發者ID:pcyin，項目名稱:tranX，代碼行數:40，
示例26: assertTensorAlmostEqual
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def assertTensorAlmostEqual(self, expected, actual):
diff = torch.all(
torch.lt(torch.abs(torch.add(expected, -actual)), 1e-4))
if not diff:
self.fail("Tensors didn't match but were supposed to {} vs"
" {}".format(expected, actual))
開發者ID:joeynmt，項目名稱:joeynmt，代碼行數:8，
示例27: dist_acc
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def dist_acc(dists, thr=0.5):
"""
Return percentage below threshold while ignoring values with a -1
"""
dist_cal = torch.ne(dists, -1)
num_dist_cal = dist_cal.sum()
if num_dist_cal > 0:
return torch.lt(dists[dist_cal], thr).float().sum() / num_dist_cal
else:
return -1
開發者ID:stefanopini，項目名稱:simple-HRNet，代碼行數:12，
示例28: lt
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def lt(t1, t2):
"""
Element-wise rich less than comparison between values from operand t1 with respect to values of
operand t2 (i.e. t1 < t2), not commutative.
Takes the first and second operand (scalar or tensor) whose elements are to be compared as argument.
Parameters
----------
t1: tensor or scalar
The first operand to be compared less than second operand
t2: tensor or scalar
The second operand to be compared greater than first operand
Returns
-------
result: ht.DNDarray
A uint8-tensor holding 1 for all elements in which values of t1 are less than values of t2,
0 for all other elements
Examples
-------
>>> import heat as ht
>>> T1 = ht.float32([[1, 2],[3, 4]])
>>> ht.lt(T1, 3.0)
tensor([[1, 1],
[0, 0]], dtype=torch.uint8)
>>> T2 = ht.float32([[2, 2], [2, 2]])
>>> ht.lt(T1, T2)
tensor([[1, 0],
[0, 0]], dtype=torch.uint8)
"""
return operations.__binary_op(torch.lt, t1, t2)
開發者ID:helmholtz-analytics，項目名稱:heat，代碼行數:35，
示例29: compute
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def compute(self, left, right) -> torch.Tensor:
return torch.lt(left, right)
開發者ID:Heerozh，項目名稱:spectre，代碼行數:4，
示例30: __init__
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import lt [as 別名]
def __init__(self, monitor: str = 'val_loss', min_delta: float = 0.0, patience: int = 3,
verbose: bool = False, mode: str = 'auto', strict: bool = True):
super().__init__()
self.monitor = monitor
self.patience = patience
self.verbose = verbose
self.strict = strict
self.min_delta = min_delta
self.wait_count = 0
self.stopped_epoch = 0
self.mode = mode
if mode not in self.mode_dict:
if self.verbose > 0:
log.info(f'EarlyStopping mode {mode} is unknown, fallback to auto mode.')
self.mode = 'auto'
if self.mode == 'auto':
if self.monitor == 'acc':
self.mode = 'max'
else:
self.mode = 'min'
if self.verbose > 0:
log.info(f'EarlyStopping mode set to {self.mode} for monitoring {self.monitor}.')
self.min_delta *= 1 if self.monitor_op == torch.gt else -1
self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf
開發者ID:PyTorchLightning，項目名稱:pytorch-lightning，代碼行數:29，
注：本文中的torch.lt方法示例整理自Github/MSDocs等源碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。