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@@ -18,8 +18,9 @@ from chainer_addons.models import PrepareType
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from chainer_addons.training import optimizer
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from chainer_addons.training import optimizer_hooks
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-from cvdatasets.annotations import AnnotationType
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+from cvdatasets import AnnotationType
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from cvdatasets.utils import new_iterator
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+from cvdatasets.utils import pretty_print_dict
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from functools import partial
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from os.path import join
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@@ -30,10 +31,6 @@ from bdb import BdbQuit
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def check_param_for_decay(param):
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return param.name != "alpha"
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-
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-def _format_kwargs(kwargs):
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- return " ".join([f"{key}={value}" for key, value in kwargs.items()])
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-
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class _ModelMixin(abc.ABC):
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"""This mixin is responsible for optimizer creation, model creation,
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model wrapping around a classifier and model weights loading.
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@@ -56,7 +53,7 @@ class _ModelMixin(abc.ABC):
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logging.info(" ".join([
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f"Wrapped the model around {clf_class.__name__}",
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- f"with kwargs: {_format_kwargs(kwargs)}",
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+ f"with kwargs: {pretty_print_dict(kwargs)}",
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]))
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def _loss_func(self, opts):
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@@ -184,7 +181,7 @@ class _DatasetMixin(abc.ABC):
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@property
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def n_classes(self):
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- return self.part_info.n_classes + self.dataset_cls.label_shift
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+ return self.ds_info.n_classes + self.dataset_cls.label_shift
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def new_dataset(self, opts, size, subset, augment):
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"""Creates a dataset for a specific subset and certain options"""
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@@ -221,12 +218,12 @@ class _DatasetMixin(abc.ABC):
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def init_annotations(self, opts):
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"""Reads annotations and creates annotation instance, which holds important infos about the dataset"""
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- annot_cls = AnnotationType.get(opts.dataset).value
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- self.annot = annot_cls(root_or_infofile=opts.data, parts=opts.parts, load_strict=False)
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+ self.annot = AnnotationType.new_annotation(opts, load_strict=False)
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self.data_info = self.annot.info
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self.model_info = self.data_info.MODELS[opts.model_type]
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- self.part_info = self.data_info.PARTS[opts.parts]
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+ self.ds_info = self.data_info.DATASETS[opts.dataset]
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+ # self.part_info = self.data_info.PART_TYPES[opts.parts]
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if getattr(opts, "only_head", False):
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self.annot.feature_model = opts.model_type
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@@ -298,7 +295,7 @@ class _TrainerMixin(abc.ABC):
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logging.info(" ".join([
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f"Using single GPU: {self.device}.",
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f"{self.updater_cls.__name__} is initialized",
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- f"with following kwargs: {_format_kwargs(self.updater_kwargs)}"
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+ f"with following kwargs: {pretty_print_dict(self.updater_kwargs)}"
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])
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)
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