Python torch 模块，numel() 实例源码
我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用torch.numel()。
def grad_sparsity(self):
global_state = self._global_state
if self._iter == 0:
global_state["sparsity_avg"] = 0.0
non_zero_cnt = 0.0
all_entry_cnt = 0.0
for group in self._optimizer.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
grad_non_zero = grad.nonzero()
if grad_non_zero.dim() > 0:
non_zero_cnt += grad_non_zero.size()[0]
all_entry_cnt += torch.numel(grad)
beta = self._beta
global_state["sparsity_avg"] = beta * global_state["sparsity_avg"] \
+ (1 - beta) * non_zero_cnt / float(all_entry_cnt)
self._sparsity_avg = \
global_state["sparsity_avg"] / self.zero_debias_factor()
if self._verbose:
logging.debug("sparsity %f, sparsity avg %f", non_zero_cnt / float(all_entry_cnt), self._sparsity_avg)
return
def grad_sparsity(self):
global_state = self._global_state
if self._iter == 0:
global_state["sparsity_avg"] = 0.0
non_zero_cnt = 0.0
all_entry_cnt = 0.0
for group in self._optimizer.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
grad_non_zero = grad.nonzero()
if grad_non_zero.dim() > 0:
non_zero_cnt += grad_non_zero.size()[0]
all_entry_cnt += torch.numel(grad)
beta = self._beta
global_state["sparsity_avg"] = beta * global_state["sparsity_avg"] \
+ (1 - beta) * non_zero_cnt / float(all_entry_cnt)
self._sparsity_avg = \
global_state["sparsity_avg"] / self.zero_debias_factor()
if DEBUG:
logging.debug("sparsity %f, sparsity avg %f", non_zero_cnt / float(all_entry_cnt), self._sparsity_avg)
return
def split_ps(point_set):
#print point_set.size()
num_points = point_set.size()[0]/2
diff = point_set.max(dim=0)[0] - point_set.min(dim=0)[0]
dim = torch.max(diff, dim = 1)[1][0,0]
cut = torch.median(point_set[:,dim])[0][0]
left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut))
right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut))
middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut))
if torch.numel(left_idx) < num_points:
left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0)
if torch.numel(right_idx) < num_points:
right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0)
left_ps = torch.index_select(point_set, dim = 0, index = left_idx)
right_ps = torch.index_select(point_set, dim = 0, index = right_idx)
return left_ps, right_ps, dim
def split_ps(point_set):
#print point_set.size()
num_points = point_set.size()[0]/2
diff = point_set.max(dim=0)[0] - point_set.min(dim=0)[0]
dim = torch.max(diff, dim = 1)[1][0,0]
cut = torch.median(point_set[:,dim])[0][0]
left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut))
right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut))
middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut))
if torch.numel(left_idx) < num_points:
left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0)
if torch.numel(right_idx) < num_points:
right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0)
left_ps = torch.index_select(point_set, dim = 0, index = left_idx)
right_ps = torch.index_select(point_set, dim = 0, index = right_idx)
return left_ps, right_ps, dim
def split_ps(point_set):
#print point_set.size()
num_points = point_set.size()[0]/2
diff = point_set.max(dim=0, keepdim = True)[0] - point_set.min(dim=0, keepdim = True)[0]
dim = torch.max(diff, dim = 1, keepdim = True)[1][0,0]
cut = torch.median(point_set[:,dim], keepdim = True)[0][0]
left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut))
right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut))
middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut))
if torch.numel(left_idx) < num_points:
left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0)
if torch.numel(right_idx) < num_points:
right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0)
left_ps = torch.index_select(point_set, dim = 0, index = left_idx)
right_ps = torch.index_select(point_set, dim = 0, index = right_idx)
return left_ps, right_ps, dim
def split_ps(point_set):
#print point_set.size()
num_points = point_set.size()[0]/2
diff = point_set.max(dim=0)[0] - point_set.min(dim=0)[0]
diff = diff[:3]
dim = torch.max(diff, dim = 1)[1][0,0]
cut = torch.median(point_set[:,dim])[0][0]
left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut))
right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut))
middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut))
if torch.numel(left_idx) < num_points:
left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0)
if torch.numel(right_idx) < num_points:
right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0)
left_ps = torch.index_select(point_set, dim = 0, index = left_idx)
right_ps = torch.index_select(point_set, dim = 0, index = right_idx)
return left_ps, right_ps, dim
def split_ps(point_set):
#print point_set.size()
num_points = point_set.size()[0]/2
diff = point_set.max(dim=0)[0] - point_set.min(dim=0)[0]
dim = torch.max(diff, dim = 1)[1][0,0]
cut = torch.median(point_set[:,dim])[0][0]
left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut))
right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut))
middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut))
if torch.numel(left_idx) < num_points:
left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0)
if torch.numel(right_idx) < num_points:
right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0)
left_ps = torch.index_select(point_set, dim = 0, index = left_idx)
right_ps = torch.index_select(point_set, dim = 0, index = right_idx)
return left_ps, right_ps, dim
def grad_sparsity(self):
global_state = self._global_state
if self._iter == 0:
global_state["sparsity_avg"] = 0.0
non_zero_cnt = 0.0
all_entry_cnt = 0.0
for group in self._optimizer.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
grad_non_zero = grad.nonzero()
if grad_non_zero.dim() > 0:
non_zero_cnt += grad_non_zero.size()[0]
all_entry_cnt += torch.numel(grad)
beta = self._beta
global_state["sparsity_avg"] = beta * global_state["sparsity_avg"] \
+ (1 - beta) * non_zero_cnt / float(all_entry_cnt)
self._sparsity_avg = \
global_state["sparsity_avg"] / self.zero_debias_factor()
return
def to_float(val):
""" Check that val is one of the following:
- pytorch autograd Variable with one element
- pytorch tensor with one element
- numpy array with one element
- any type supporting float() operation
And convert val to float
"""
if isinstance(val, np.ndarray):
assert val.size == 1, \
"val should have one element (got {})".format(val.size)
return float(val.squeeze()[0])
if torch is not None:
if isinstance(val, torch_autograd.Variable):
val = val.data
if torch.is_tensor(val):
assert torch.numel(val) == 1, \
"val should have one element (got {})".format(torch.numel(val))
return float(val.squeeze()[0])
try:
return float(val)
except:
raise TypeError("Unsupported type for val ({})".format(type(val)))
def split_ps_reuse(point_set, level, pos, tree, cutdim):
sz = point_set.size()
num_points = np.array(sz)[0]/2
max_value = point_set.max(dim=0)[0]
min_value = -(-point_set).max(dim=0)[0]
diff = max_value - min_value
dim = torch.max(diff, dim = 1)[1][0,0]
cut = torch.median(point_set[:,dim])[0][0]
left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut))
right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut))
middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut))
if torch.numel(left_idx) < num_points:
left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0)
if torch.numel(right_idx) < num_points:
right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0)
left_ps = torch.index_select(point_set, dim = 0, index = left_idx)
right_ps = torch.index_select(point_set, dim = 0, index = right_idx)
tree[level+1][pos * 2] = left_ps
tree[level+1][pos * 2 + 1] = right_ps
cutdim[level][pos * 2] = dim
cutdim[level][pos * 2 + 1] = dim
return
def split_ps_reuse(point_set, level, pos, tree, cutdim):
sz = point_set.size()
num_points = np.array(sz)[0]/2
max_value = point_set.max(dim=0)[0]
min_value = -(-point_set).max(dim=0)[0]
diff = max_value - min_value
dim = torch.max(diff, dim = 1)[1][0,0]
cut = torch.median(point_set[:,dim])[0][0]
left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut))
right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut))
middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut))
if torch.numel(left_idx) < num_points:
left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0)
if torch.numel(right_idx) < num_points:
right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0)
left_ps = torch.index_select(point_set, dim = 0, index = left_idx)
right_ps = torch.index_select(point_set, dim = 0, index = right_idx)
tree[level+1][pos * 2] = left_ps
tree[level+1][pos * 2 + 1] = right_ps
cutdim[level][pos * 2] = dim
cutdim[level][pos * 2 + 1] = dim
return
def split_ps_reuse(point_set, level, pos, tree, cutdim):
sz = point_set.size()
num_points = np.array(sz)[0]/2
max_value = point_set.max(dim=0)[0]
min_value = -(-point_set).max(dim=0)[0]
diff = max_value - min_value
diff = diff[:,:3]
dim = torch.max(diff, dim = 1)[1][0,0]
cut = torch.median(point_set[:,dim])[0][0]
left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut))
right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut))
middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut))
if torch.numel(left_idx) < num_points:
left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0)
if torch.numel(right_idx) < num_points:
right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0)
left_ps = torch.index_select(point_set, dim = 0, index = left_idx)
right_ps = torch.index_select(point_set, dim = 0, index = right_idx)
tree[level+1][pos * 2] = left_ps
tree[level+1][pos * 2 + 1] = right_ps
cutdim[level][pos * 2] = dim
cutdim[level][pos * 2 + 1] = dim
return
def split_ps_reuse(point_set, level, pos, tree, cutdim):
sz = point_set.size()
num_points = np.array(sz)[0]/2
max_value = point_set.max(dim=0)[0]
min_value = -(-point_set).max(dim=0)[0]
diff = max_value - min_value
dim = torch.max(diff, dim = 1)[1][0,0]
cut = torch.median(point_set[:,dim])[0][0]
left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut))
right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut))
middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut))
if torch.numel(left_idx) < num_points:
left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0)
if torch.numel(right_idx) < num_points:
right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0)
left_ps = torch.index_select(point_set, dim = 0, index = left_idx)
right_ps = torch.index_select(point_set, dim = 0, index = right_idx)
tree[level+1][pos * 2] = left_ps
tree[level+1][pos * 2 + 1] = right_ps
cutdim[level][pos * 2] = dim
cutdim[level][pos * 2 + 1] = dim
return