def account_one_arch(arch_index, arch_str, checkpoints, datasets,
dataloader_dict):
information = ArchResults(arch_index, arch_str)
for checkpoint_path in checkpoints:
checkpoint = torch.load(checkpoint_path, map_location='cpu')
used_seed = checkpoint_path.name.split('-')[-1].split('.')[0]
for dataset in datasets:
assert dataset in checkpoint, 'Can not find {:} in arch-{:} from {:}'.format(
dataset, arch_index, checkpoint_path)
results = checkpoint[dataset]
assert results[
'finish-train'], 'This {:} arch seed={:} does not finish train on {:} ::: {:}'.format(
arch_index, used_seed, dataset, checkpoint_path)
arch_config = {
'channel': results['channel'],
'num_cells': results['num_cells'],
'arch_str': arch_str,
'class_num': results['config']['class_num']
}
xresult = create_result_count(used_seed, dataset, arch_config,
results, dataloader_dict)
information.update(dataset, int(used_seed), xresult)
return information
def account_one_arch(arch_index, arch_str, checkpoints, datasets,
dataloader_dict):
information = ArchResults(arch_index, arch_str)
for checkpoint_path in checkpoints:
checkpoint = torch.load(checkpoint_path, map_location="cpu")
used_seed = checkpoint_path.name.split("-")[-1].split(".")[0]
for dataset in datasets:
assert (
dataset
in checkpoint), "Can not find {:} in arch-{:} from {:}".format(
dataset, arch_index, checkpoint_path)
results = checkpoint[dataset]
assert results[
"finish-train"], "This {:} arch seed={:} does not finish train on {:} ::: {:}".format(
arch_index, used_seed, dataset, checkpoint_path)
arch_config = {
"channel": results["channel"],
"num_cells": results["num_cells"],
"arch_str": arch_str,
"class_num": results["config"]["class_num"],
}
xresult = create_result_count(used_seed, dataset, arch_config,
results, dataloader_dict)
information.update(dataset, int(used_seed), xresult)
return information
def account_one_arch(arch_index: int, arch_str: Text, checkpoints: List[Text],
datasets: List[Text],
dataloader_dict: Dict[Text, Any]) -> ArchResults:
information = ArchResults(arch_index, arch_str)
for checkpoint_path in checkpoints:
checkpoint = torch.load(checkpoint_path, map_location='cpu')
used_seed = checkpoint_path.name.split('-')[-1].split('.')[0]
ok_dataset = 0
for dataset in datasets:
if dataset not in checkpoint:
print('Can not find {:} in arch-{:} from {:}'.format(
dataset, arch_index, checkpoint_path))
continue
else:
ok_dataset += 1
results = checkpoint[dataset]
assert results[
'finish-train'], 'This {:} arch seed={:} does not finish train on {:} ::: {:}'.format(
arch_index, used_seed, dataset, checkpoint_path)
arch_config = {
'channel': results['channel'],
'num_cells': results['num_cells'],
'arch_str': arch_str,
'class_num': results['config']['class_num']
}
xresult = create_result_count(used_seed, dataset, arch_config,
results, dataloader_dict)
information.update(dataset, int(used_seed), xresult)
if ok_dataset == 0:
raise ValueError(
'{:} does not find any data'.format(checkpoint_path))
return information
def account_one_arch(arch_index: int, arch_str: Text, checkpoints: List[Text],
datasets: List[Text]) -> ArchResults:
information = ArchResults(arch_index, arch_str)
for checkpoint_path in checkpoints:
try:
checkpoint = torch.load(checkpoint_path, map_location='cpu')
except:
raise ValueError(
'This checkpoint failed to be loaded : {:}'.format(
checkpoint_path))
used_seed = checkpoint_path.name.split('-')[-1].split('.')[0]
ok_dataset = 0
for dataset in datasets:
if dataset not in checkpoint:
print('Can not find {:} in arch-{:} from {:}'.format(
dataset, arch_index, checkpoint_path))
continue
else:
ok_dataset += 1
results = checkpoint[dataset]
assert results[
'finish-train'], 'This {:} arch seed={:} does not finish train on {:} ::: {:}'.format(
arch_index, used_seed, dataset, checkpoint_path)
arch_config = {
'name': 'infer.shape.tiny',
'channels': arch_str,
'arch_str': arch_str,
'genotype': results['arch_config']['genotype'],
'class_num': results['arch_config']['num_classes']
}
xresult = ResultsCount(dataset, results['net_state_dict'],
results['train_acc1es'],
results['train_losses'], results['param'],
results['flop'], arch_config, used_seed,
results['total_epoch'], None)
xresult.update_train_info(results['train_acc1es'],
results['train_acc5es'],
results['train_losses'],
results['train_times'])
xresult.update_eval(results['valid_acc1es'],
results['valid_losses'],
results['valid_times'])
information.update(dataset, int(used_seed), xresult)
if ok_dataset < len(datasets):
raise ValueError('{:} does find enought data : {:} vs {:}'.format(
checkpoint_path, ok_dataset, len(datasets)))
return information
def account_one_arch(
arch_index: int,
arch_str: Text,
checkpoints: List[Text],
datasets: List[Text],
dataloader_dict: Dict[Text, Any],
) -> ArchResults:
information = ArchResults(arch_index, arch_str)
for checkpoint_path in checkpoints:
checkpoint = torch.load(checkpoint_path, map_location="cpu")
used_seed = checkpoint_path.name.split("-")[-1].split(".")[0]
ok_dataset = 0
for dataset in datasets:
if dataset not in checkpoint:
print("Can not find {:} in arch-{:} from {:}".format(
dataset, arch_index, checkpoint_path))
continue
else:
ok_dataset += 1
results = checkpoint[dataset]
assert results[
"finish-train"], "This {:} arch seed={:} does not finish train on {:} ::: {:}".format(
arch_index, used_seed, dataset, checkpoint_path)
arch_config = {
"channel": results["channel"],
"num_cells": results["num_cells"],
"arch_str": arch_str,
"class_num": results["config"]["class_num"],
}
xresult = create_result_count(used_seed, dataset, arch_config,
results, dataloader_dict)
information.update(dataset, int(used_seed), xresult)
if ok_dataset == 0:
raise ValueError(
"{:} does not find any data".format(checkpoint_path))
return information
