code stringlengths 3 6.57k |
|---|
is_apex_available() |
the (Sync) |
regularized (weight_decay=0) |
regularized_params.append(module.weight) |
unregularized_params.append(module.weight) |
regularized_params.append(module.bias) |
unregularized_params.append(module.bias) |
_filter_trainable(param_list: List[Any]) |
list(filter(lambda x: x.requires_grad, param_list) |
unregularized_param_list (pre-existing list of parameters not to regularize) |
enumerate(parameters_to_unregularize) |
enumerate(regularized_param_list) |
indices_to_remove_from_regularized.append(reg_param_ind) |
indices_to_remove_from_new_unregularized.append(unreg_param_ind) |
indices_to_remove_from_regularized.sort(reverse=True) |
indices_to_remove_from_new_unregularized.sort(reverse=True) |
param_groups (List[Dict]) |
model.parameters() |
list(model.parameters() |
model.named_modules() |
isinstance(module, nn.Linear) |
isinstance(module, _CONV_TYPES) |
hasattr(module, "weight_g") |
head_regularized_params.append(module.weight_g) |
head_regularized_params.append(module.weight_v) |
head_regularized_params.append(module.weight) |
head_regularized_params.append(module.bias) |
head_unregularized_params.append(module.bias) |
isinstance(module, _NORM_TYPES) |
isinstance(module, nn.Linear) |
isinstance(module, _CONV_TYPES) |
hasattr(module, "weight_g") |
trunk_regularized_params.append(module.weight_g) |
trunk_regularized_params.append(module.weight_v) |
trunk_regularized_params.append(module.weight) |
trunk_regularized_params.append(module.bias) |
trunk_unregularized_params.append(module.bias) |
isinstance(module, _NORM_TYPES) |
len(list(module.children() |
them (set recurse=False) |
module.parameters(recurse=False) |
regularized_params.append(params) |
model.named_parameters() |
any(hits) |
unregularized_params.append(param) |
model.named_parameters() |
non_trainable_params.append(param) |
_filter_trainable(model.parameters() |
_filter_trainable(trunk_regularized_params) |
_filter_trainable(trunk_unregularized_params) |
_filter_trainable(head_regularized_params) |
_filter_trainable(head_unregularized_params) |
_filter_trainable(regularized_params) |
len(trainable_params) |
len(non_trainable_params) |
len(trunk_regularized_params) |
len(trunk_unregularized_params) |
len(head_regularized_params) |
len(head_unregularized_params) |
len(regularized_params) |
len(unregularized_params) |
len(regularized_params) |
len(unregularized_params) |
Copyright (C) |
upgrade() |
sa.Date() |
db.session.query(models.Workflow) |
date.today() |
all() |
if ((not all(tasks_start_days) |
all(tasks_end_days) |
or
(not tasks_start_days and not tasks_end_days) |
db.session.add(workflow) |
db.session.query(models.Workflow) |
date.today() |
all() |
if ((not all(tasks_start_days) |
all(tasks_end_days) |
or
(not tasks_start_days and not tasks_end_days) |
db.session.add(workflow) |
max([c.start_date for c in workflow.cycles]) |
base_date.today() |
max(base_date, workflow.next_cycle_start_date) |
get_cycle_calculator(workflow, base_date=base_date) |
calculator.reified_tasks.values() |
calculator.reified_tasks.values() |
calculator.adjust_date(nancsd_date) |
calculator.adjust_date(nancsd_date) |
calculator.adjust_date(nancsd_date) |
db.session.add(workflow) |
db.session.commit() |
downgrade() |
op.drop_column('workflows', 'non_adjusted_next_cycle_start_date') |
Copyright (c) |
diele_func_to_coeff(freq, real, imag) |
return (2 * sqrt(2) |
sqrt(sqrt(real ** 2 + imag ** 2) |
DieleFuncData(MSONable, ToJsonFileMixIn) |
ave_absorption_coeff(self) |
sum(self.diele_func_real[i][:3]) |
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