Add multiple pkgs to import_attr
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@ -68,14 +68,14 @@ class FieldDataset(Dataset):
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if in_norms is not None:
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assert len(in_patterns) == len(in_norms), \
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'numbers of input normalization functions and fields do not match'
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in_norms = [import_attr(norm, norms.__name__, callback_at)
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in_norms = [import_attr(norm, norms, callback_at=callback_at)
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for norm in in_norms]
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self.in_norms = in_norms
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if tgt_norms is not None:
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assert len(tgt_patterns) == len(tgt_norms), \
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'numbers of target normalization functions and fields do not match'
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tgt_norms = [import_attr(norm, norms.__name__, callback_at)
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tgt_norms = [import_attr(norm, norms, callback_at=callback_at)
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for norm in tgt_norms]
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self.tgt_norms = tgt_norms
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@ -40,9 +40,9 @@ def test(args):
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in_chan, out_chan = test_dataset.in_chan, test_dataset.tgt_chan
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model = import_attr(args.model, models.__name__, args.callback_at)
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model = import_attr(args.model, models, callback_at=args.callback_at)
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model = model(sum(in_chan), sum(out_chan), scale_factor=args.scale_factor)
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criterion = import_attr(args.criterion, torch.nn.__name__, args.callback_at)
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criterion = import_attr(args.criterion, torch.nn, callback_at=args.callback_at)
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criterion = criterion()
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device = torch.device('cpu')
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@ -119,18 +119,19 @@ def gpu_worker(local_rank, node, args):
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args.in_chan, args.out_chan = train_dataset.in_chan, train_dataset.tgt_chan
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model = import_attr(args.model, models.__name__, args.callback_at)
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model = import_attr(args.model, models, callback_at=args.callback_at)
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model = model(sum(args.in_chan), sum(args.out_chan),
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scale_factor=args.scale_factor)
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model.to(device)
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model = DistributedDataParallel(model, device_ids=[device],
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process_group=dist.new_group())
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criterion = import_attr(args.criterion, nn.__name__, args.callback_at)
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criterion = import_attr(args.criterion, nn, models,
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callback_at=args.callback_at)
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criterion = criterion()
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criterion.to(device)
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optimizer = import_attr(args.optimizer, optim.__name__, args.callback_at)
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optimizer = import_attr(args.optimizer, optim, callback_at=args.callback_at)
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optimizer = optimizer(
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model.parameters(),
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lr=args.lr,
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@ -2,37 +2,52 @@ import os
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import importlib
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def import_attr(name, pkg, callback_at=None):
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def import_attr(name, *pkgs, callback_at=None):
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"""Import attribute. Try package first and then callback directory.
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To use a callback, `name` must contain a module, formatted as 'mod.attr'.
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Examples
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--------
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>>> import_attr('attr', 'pkg1.pkg2')
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>>> import_attr('attr', pkg1.pkg2)
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tries to import attr from pkg1.pkg2.
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>>> import_attr('mod.attr', 'pkg1.pkg2', 'path/to/cb_dir')
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>>> import_attr('mod.attr', pkg1.pkg2, pkg3, callback_at='path/to/cb_dir')
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first tries to import attr from pkg1.pkg2.mod, then from
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'path/to/cb_dir/mod.py'.
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first tries to import attr from pkg1.pkg2.mod, then from pkg3.mod, finally
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from 'path/to/cb_dir/mod.py'.
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"""
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if name.count('.') == 0:
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attr = name
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return getattr(importlib.import_module(pkg), attr)
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errors = []
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for pkg in pkgs:
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try:
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return getattr(importlib.import_module(pkg.__name__), attr)
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except (ModuleNotFoundError, AttributeError) as e:
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errors.append(e)
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raise Exception(errors)
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else:
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mod, attr = name.rsplit('.', 1)
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try:
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return getattr(importlib.import_module(pkg + '.' + mod), attr)
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except (ModuleNotFoundError, AttributeError):
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if callback_at is None:
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raise
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errors = []
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for pkg in pkgs:
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try:
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return getattr(
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importlib.import_module(pkg.__name__ + '.' + mod), attr)
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except (ModuleNotFoundError, AttributeError):
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errors.append(e)
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if callback_at is None:
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raise Exception(errors)
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callback_at = os.path.join(callback_at, mod + '.py')
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assert os.path.isfile(callback_at), 'callback file not found'
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if not os.path.isfile(callback_at):
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raise FileNotFoundError('callback file not found')
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spec = importlib.util.spec_from_file_location(mod, callback_at)
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module = importlib.util.module_from_spec(spec)
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