def __init__(self,
save_checkpoint_steps=1,
save_checkpoint_seconds=0,
keep_checkpoint_max=5,
keep_checkpoint_per_n_minutes=0,
integrated_save=True):
if not save_checkpoint_steps and not save_checkpoint_seconds and \
not keep_checkpoint_max and not keep_checkpoint_per_n_minutes:
raise ValueError("The input_param can't be all None or 0")
if save_checkpoint_steps:
save_checkpoint_steps = check_int_non_negative(save_checkpoint_steps)
if save_checkpoint_seconds:
save_checkpoint_seconds = check_int_non_negative(save_checkpoint_seconds)
if keep_checkpoint_max:
keep_checkpoint_max = check_int_non_negative(keep_checkpoint_max)
if keep_checkpoint_per_n_minutes:
keep_checkpoint_per_n_minutes = check_int_non_negative(keep_checkpoint_per_n_minutes)
self._save_checkpoint_steps = save_checkpoint_steps
self._save_checkpoint_seconds = save_checkpoint_seconds
if self._save_checkpoint_steps and self._save_checkpoint_steps > 0:
self._save_checkpoint_seconds = None
self._keep_checkpoint_max = keep_checkpoint_max
self._keep_checkpoint_per_n_minutes = keep_checkpoint_per_n_minutes
if self._keep_checkpoint_max and self._keep_checkpoint_max > 0:
self._keep_checkpoint_per_n_minutes = None
else:
if not self._keep_checkpoint_per_n_minutes or self._keep_checkpoint_per_n_minutes == 0:
self._keep_checkpoint_max = 1
self._integrated_save = check_bool(integrated_save)
def __init__(self,
save_checkpoint_steps=1,
save_checkpoint_seconds=0,
keep_checkpoint_max=5,
keep_checkpoint_per_n_minutes=0,
integrated_save=True,
model_type="normal"):
if not save_checkpoint_steps and not save_checkpoint_seconds and \
not keep_checkpoint_max and not keep_checkpoint_per_n_minutes:
raise ValueError("The input_param can't be all None or 0")
if save_checkpoint_steps:
save_checkpoint_steps = check_int_non_negative(
save_checkpoint_steps)
if save_checkpoint_seconds:
save_checkpoint_seconds = check_int_non_negative(
save_checkpoint_seconds)
if keep_checkpoint_max:
keep_checkpoint_max = check_int_non_negative(keep_checkpoint_max)
if keep_checkpoint_per_n_minutes:
keep_checkpoint_per_n_minutes = check_int_non_negative(
keep_checkpoint_per_n_minutes)
if model_type:
model_type = check_string(model_type,
["normal", "fusion", "quant"])
self._save_checkpoint_steps = save_checkpoint_steps
self._save_checkpoint_seconds = save_checkpoint_seconds
if self._save_checkpoint_steps and self._save_checkpoint_steps > 0:
self._save_checkpoint_seconds = None
self._keep_checkpoint_max = keep_checkpoint_max
self._keep_checkpoint_per_n_minutes = keep_checkpoint_per_n_minutes
if self._keep_checkpoint_max and self._keep_checkpoint_max > 0:
self._keep_checkpoint_per_n_minutes = None
else:
if not self._keep_checkpoint_per_n_minutes or self._keep_checkpoint_per_n_minutes == 0:
self._keep_checkpoint_max = 1
self._model_type = model_type
self._integrated_save = check_bool(integrated_save)
def __init__(self, in_channels, out_channels, kernel_size, stride,
pad_mode, padding, dilation, group, has_bias, weight_init,
bias_init):
super(_Conv, self).__init__()
self.in_channels = check_int_positive(in_channels)
self.out_channels = check_int_positive(out_channels)
self.kernel_size = kernel_size
self.stride = stride
self.pad_mode = pad_mode
self.padding = check_int_non_negative(padding)
self.dilation = dilation
self.group = check_int_positive(group)
self.has_bias = has_bias
if (not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
kernel_size[0] < 1 or kernel_size[1] < 1:
raise ValueError(
"Attr 'kernel_size' of 'Conv2D' Op passed " +
str(self.kernel_size) +
", should be a int or tuple and equal to or greater than 1.")
if (not isinstance(stride[0], int)) or (not isinstance(
stride[1], int)) or stride[0] < 1 or stride[1] < 1:
raise ValueError(
"Attr 'stride' of 'Conv2D' Op passed " + str(self.stride) +
", should be a int or tuple and equal to or greater than 1.")
if (not isinstance(dilation[0], int)) or (not isinstance(dilation[1], int)) or \
dilation[0] < 1 or dilation[1] < 1:
raise ValueError("Attr 'dilation' of 'Conv2D' Op passed " +
str(self.dilation) +
", should equal to or greater than 1.")
if in_channels % group != 0:
raise ValueError(
"Attr 'in_channels' of 'Conv2D' Op must be divisible by "
"attr 'group' of 'Conv2D' Op.")
if out_channels % group != 0:
raise ValueError(
"Attr 'out_channels' of 'Conv2D' Op must be divisible by "
"attr 'group' of 'Conv2D' Op.")
self.weight = Parameter(initializer(
weight_init, [out_channels, in_channels // group, *kernel_size]),
name='weight')
if check_bool(has_bias):
self.bias = Parameter(initializer(bias_init, [out_channels]),
name='bias')
else:
if bias_init != 'zeros':
logger.warning(
"Value of 'has_bias' is False, value of 'bias_init' will be ignored."
)
self.bias = None
