import math
import torch
from functools import reduce
from sys import float_info
from torch._six import inf, nan
class __PrinterOptions(object):
precision = 4
threshold = 1000
edgeitems = 3
linewidth = 80
PRINT_OPTS = __PrinterOptions()
# We could use **kwargs, but this will give better docs
[docs]def set_printoptions(
precision=None,
threshold=None,
edgeitems=None,
linewidth=None,
profile=None,
):
r"""Set options for printing. Items shamelessly taken from NumPy
Args:
precision: Number of digits of precision for floating point output
(default = 8).
threshold: Total number of array elements which trigger summarization
rather than full `repr` (default = 1000).
edgeitems: Number of array items in summary at beginning and end of
each dimension (default = 3).
linewidth: The number of characters per line for the purpose of
inserting line breaks (default = 80). Thresholded matrices will
ignore this parameter.
profile: Sane defaults for pretty printing. Can override with any of
the above options. (any one of `default`, `short`, `full`)
"""
if profile is not None:
if profile == "default":
PRINT_OPTS.precision = 4
PRINT_OPTS.threshold = 1000
PRINT_OPTS.edgeitems = 3
PRINT_OPTS.linewidth = 80
elif profile == "short":
PRINT_OPTS.precision = 2
PRINT_OPTS.threshold = 1000
PRINT_OPTS.edgeitems = 2
PRINT_OPTS.linewidth = 80
elif profile == "full":
PRINT_OPTS.precision = 4
PRINT_OPTS.threshold = inf
PRINT_OPTS.edgeitems = 3
PRINT_OPTS.linewidth = 80
if precision is not None:
PRINT_OPTS.precision = precision
if threshold is not None:
PRINT_OPTS.threshold = threshold
if edgeitems is not None:
PRINT_OPTS.edgeitems = edgeitems
if linewidth is not None:
PRINT_OPTS.linewidth = linewidth
class _Formatter(object):
def __init__(self, tensor):
self.floating_dtype = tensor.dtype.is_floating_point
self.int_mode = True
self.sci_mode = False
self.max_width = 1
if not self.floating_dtype:
copy = torch.empty(tensor.size(), dtype=torch.long).copy_(tensor).view(tensor.nelement())
for value in copy.tolist():
value_str = '{}'.format(value)
self.max_width = max(self.max_width, len(value_str))
else:
copy = torch.empty(tensor.size(), dtype=torch.float64).copy_(tensor).view(tensor.nelement())
copy_list = copy.tolist()
try:
for value in copy_list:
if value != math.ceil(value):
self.int_mode = False
break
# nonfinites will throw errors
except (ValueError, OverflowError):
self.int_mode = False
if self.int_mode:
for value in copy_list:
value_str = '{:.0f}'.format(value)
if math.isnan(value) or math.isinf(value):
self.max_width = max(self.max_width, len(value_str))
else:
# in int_mode for floats, all numbers are integers, and we append a decimal to nonfinites
# to indicate that the tensor is of floating type. add 1 to the len to account for this.
self.max_width = max(self.max_width, len(value_str) + 1)
else:
copy_abs = copy.abs()
pos_inf_mask = copy_abs.eq(inf)
neg_inf_mask = copy_abs.eq(-inf)
nan_mask = copy_abs.ne(copy)
invalid_value_mask = pos_inf_mask + neg_inf_mask + nan_mask
if invalid_value_mask.all():
example_value = 0
else:
example_value = copy_abs[invalid_value_mask.eq(0)][0]
copy_abs[invalid_value_mask] = example_value
exp_min = copy_abs.min()
if exp_min != 0:
exp_min = math.floor(math.log10(exp_min)) + 1
else:
exp_min = 1
exp_max = copy_abs.max()
if exp_max != 0:
exp_max = math.floor(math.log10(exp_max)) + 1
else:
exp_max = 1
# these conditions for using scientific notation are based on numpy
if exp_max - exp_min > PRINT_OPTS.precision or exp_max > 8 or exp_min < -4:
self.sci_mode = True
for value in copy_list:
value_str = ('{{:.{}e}}').format(PRINT_OPTS.precision).format(value)
self.max_width = max(self.max_width, len(value_str))
else:
for value in copy_list:
value_str = ('{{:.{}f}}').format(PRINT_OPTS.precision).format(value)
self.max_width = max(self.max_width, len(value_str))
def width(self):
return self.max_width
def format(self, value):
if self.floating_dtype:
if self.int_mode:
ret = '{:.0f}'.format(value)
if not (math.isinf(value) or math.isnan(value)):
ret += '.'
elif self.sci_mode:
ret = ('{{:{}.{}e}}').format(self.max_width, PRINT_OPTS.precision).format(value)
else:
ret = ('{{:.{}f}}').format(PRINT_OPTS.precision).format(value)
else:
ret = '{}'.format(value)
return (self.max_width - len(ret)) * ' ' + ret
def _scalar_str(self, formatter):
return formatter.format(self.item())
def _vector_str(self, indent, formatter, summarize):
# length includes spaces and comma between elements
element_length = formatter.width() + 2
elements_per_line = max(1, int(math.floor((PRINT_OPTS.linewidth - indent) / (element_length))))
char_per_line = element_length * elements_per_line
if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems:
data = ([formatter.format(val) for val in self[:PRINT_OPTS.edgeitems].tolist()] +
[' ...'] +
[formatter.format(val) for val in self[-PRINT_OPTS.edgeitems:].tolist()])
else:
data = [formatter.format(val) for val in self.tolist()]
data_lines = [data[i:i + elements_per_line] for i in range(0, len(data), elements_per_line)]
lines = [', '.join(line) for line in data_lines]
return '[' + (',' + '\n' + ' ' * (indent + 1)).join(lines) + ']'
def _tensor_str(self, indent, formatter, summarize):
dim = self.dim()
if dim == 0:
return _scalar_str(self, formatter)
if dim == 1:
return _vector_str(self, indent, formatter, summarize)
if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems:
slices = ([_tensor_str(self[i], indent + 1, formatter, summarize)
for i in range(0, PRINT_OPTS.edgeitems)] +
['...'] +
[_tensor_str(self[i], indent + 1, formatter, summarize)
for i in range(len(self) - PRINT_OPTS.edgeitems, len(self))])
else:
slices = [_tensor_str(self[i], indent + 1, formatter, summarize) for i in range(0, self.size(0))]
tensor_str = (',' + '\n' * (dim - 1) + ' ' * (indent + 1)).join(slices)
return '[' + tensor_str + ']'
def _maybe_wrap_suffix(suffix, indent, tensor_str):
suffix_len = len(suffix)
last_line_len = len(tensor_str) - tensor_str.rfind('\n') + 1
if suffix_len > 2 and last_line_len + suffix_len > PRINT_OPTS.linewidth:
return ',\n' + ' ' * indent + suffix[2:]
return suffix
def get_summarized_data(self):
dim = self.dim()
if dim == 0:
return self
if dim == 1:
if self.size(0) > 2 * PRINT_OPTS.edgeitems:
return torch.cat((self[:PRINT_OPTS.edgeitems], self[-PRINT_OPTS.edgeitems:]))
else:
return self
if self.size(0) > 2 * PRINT_OPTS.edgeitems:
start = [get_summarized_data(self[i]).view(-1) for i in range(0, PRINT_OPTS.edgeitems)]
end = ([get_summarized_data(self[i]).view(-1)
for i in range(len(self) - PRINT_OPTS.edgeitems, len(self))])
return torch.cat((start + end))
else:
return self
def _str(self):
if self.is_sparse:
size_str = str(tuple(self.shape)).replace(' ', '')
return '{} of size {} with indices:\n{}\nand values:\n{}'.format(
self.type(), size_str, self._indices(), self._values())
prefix = 'tensor('
indent = len(prefix)
summarize = self.numel() > PRINT_OPTS.threshold
suffix = ''
if not torch._C._is_default_type_cuda():
if self.device.type == 'cuda':
suffix += ', device=\'' + str(self.device) + '\''
else:
if self.device.type == 'cpu' or torch.cuda.current_device() != self.device.index:
suffix += ', device=\'' + str(self.device) + '\''
if self.numel() == 0:
# Explicitly print the shape if it is not (0,), to match NumPy behavior
if self.dim() != 1:
suffix += ', size=' + str(tuple(self.shape))
# In an empty tensor, there are no elements to infer if the dtype should be int64,
# so it must be shown explicitly.
if self.dtype != torch.get_default_dtype():
suffix += ', dtype=' + str(self.dtype)
tensor_str = '[]'
else:
if self.dtype != torch.get_default_dtype() and self.dtype != torch.int64:
suffix += ', dtype=' + str(self.dtype)
formatter = _Formatter(get_summarized_data(self) if summarize else self)
tensor_str = _tensor_str(self, indent, formatter, summarize)
if self.grad_fn is not None:
suffix += ', grad_fn=<{}>'.format(type(self.grad_fn).__name__)
elif self.requires_grad:
suffix += ', requires_grad=True'
suffix += ')'
suffix = _maybe_wrap_suffix(suffix, indent, tensor_str)
return prefix + tensor_str + suffix