forked from huawei/mindspore2022
clean code
fix sample count bug change file permission close fd parens add lixiahohui33 as approver
This commit is contained in:
unknown
parent
5bcaae94a7
commit
38f87a86f3
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@ -3,3 +3,4 @@ approvers:
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- wangyue01
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- wenkai_dist
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- lilongfei15
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- lixiaohui33
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@ -59,8 +59,8 @@ def _np_to_image(img_np, mode):
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return Image.fromarray(np.uint8(img_np * 255), mode=mode)
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class _VerifyFlag:
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"""Verification flags of dataset and settings of ImageClassificationRunner."""
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class _Verifier:
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"""Verification of dataset and settings of ImageClassificationRunner."""
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ALL = 0xFFFFFFFF
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REGISTRATION = 1
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DATA_N_NETWORK = 1 << 1
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@ -68,8 +68,168 @@ class _VerifyFlag:
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HOC = 1 << 3
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ENVIRONMENT = 1 << 4
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def _verify(self, flags):
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"""
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Verify datasets and settings.
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class ImageClassificationRunner:
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Args:
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flags (int): Verification flags, use bitwise or '|' to combine multiple flags.
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Possible bitwise flags are shown as follow.
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- ALL: Verify everything.
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- REGISTRATION: Verify explainer module registration.
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- DATA_N_NETWORK: Verify dataset and network.
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- SALIENCY: Verify saliency related settings.
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- HOC: Verify HOC related settings.
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- ENVIRONMENT: Verify the runtime environment.
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Raises:
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ValueError: Be raised for any data or settings' value problem.
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TypeError: Be raised for any data or settings' type problem.
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RuntimeError: Be raised for any runtime problem.
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"""
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if flags & self.ENVIRONMENT:
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device_target = context.get_context('device_target')
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if device_target not in ("Ascend", "GPU"):
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raise RuntimeError(f"Unsupported device_target: '{device_target}', "
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f"only 'Ascend' or 'GPU' is supported. "
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f"Please call context.set_context(device_target='Ascend') or "
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f"context.set_context(device_target='GPU').")
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if flags & (self.ENVIRONMENT | self.SALIENCY):
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if self._is_saliency_registered:
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mode = context.get_context('mode')
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if mode != context.PYNATIVE_MODE:
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raise RuntimeError("Context mode: GRAPH_MODE is not supported, "
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"please call context.set_context(mode=context.PYNATIVE_MODE).")
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if flags & self.REGISTRATION:
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if self._is_uncertainty_registered and not self._is_saliency_registered:
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raise ValueError("Function register_uncertainty() is called but register_saliency() is not.")
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if not self._is_saliency_registered and not self._is_hoc_registered:
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raise ValueError(
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"No explanation module was registered, user should at least call register_saliency() "
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"or register_hierarchical_occlusion() once with proper arguments.")
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if flags & (self.DATA_N_NETWORK | self.SALIENCY | self.HOC):
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self._verify_data()
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if flags & self.DATA_N_NETWORK:
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self._verify_network()
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if flags & self.SALIENCY:
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self._verify_saliency()
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def _verify_labels(self):
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"""Verify labels."""
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label_set = set()
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if not self._labels:
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raise ValueError(f"The label list provided is empty.")
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for i, label in enumerate(self._labels):
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if label.strip() == "":
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raise ValueError(f"Label [{i}] is all whitespaces or empty. Please make sure there is "
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f"no empty label.")
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if label in label_set:
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raise ValueError(f"Duplicated label:{label}! Please make sure all labels are unique.")
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label_set.add(label)
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def _verify_ds_inputs_shape(self, sample, inputs, labels):
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"""Verify a dataset sample's input shape."""
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if len(inputs.shape) > 4 or len(inputs.shape) < 3 or inputs.shape[-3] not in [1, 3, 4]:
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raise ValueError(
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"Image shape {} is unrecognizable: the dimension of image can only be CHW or NCHW.".format(
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inputs.shape))
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if len(inputs.shape) == 3:
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log.warning(
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"Image shape {} is 3-dimensional. All the data will be automatically unsqueezed at the 0-th"
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" dimension as batch data.".format(inputs.shape))
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if len(sample) > 1:
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if len(labels.shape) > 2 and (np.array(labels.shape[1:]) > 1).sum() > 1:
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raise ValueError(
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"Labels shape {} is unrecognizable: outputs should not have more than two dimensions"
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" with length greater than 1.".format(labels.shape))
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if self._is_hoc_registered:
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if inputs.shape[-3] != 3:
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raise ValueError(
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"Hierarchical occlusion is registered, images must be in 3 channels format, but "
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"{} channel(s) is(are) encountered.".format(inputs.shape[-3]))
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short_side = min(inputs.shape[-2:])
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if short_side < hoc.AUTO_IMAGE_SHORT_SIDE_MIN:
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raise ValueError(
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"Hierarchical occlusion is registered, images' short side must be equals to or greater then "
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"{}, but {} is encountered.".format(hoc.AUTO_IMAGE_SHORT_SIDE_MIN, short_side))
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def _verify_ds_sample(self, sample):
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"""Verify a dataset sample."""
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if len(sample) not in [1, 2, 3]:
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raise ValueError("The dataset should provide [images] or [images, labels], [images, labels, bboxes]"
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" as columns.")
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if len(sample) == 3:
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inputs, labels, bboxes = sample
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if bboxes.shape[-1] != 4:
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raise ValueError("The third element of dataset should be bounding boxes with shape of "
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"[batch_size, num_ground_truth, 4].")
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else:
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if self._benchmarkers is not None:
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if any([isinstance(bench, Localization) for bench in self._benchmarkers]):
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raise ValueError("The dataset must provide bboxes if Localization is to be computed.")
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if len(sample) == 2:
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inputs, labels = sample
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if len(sample) == 1:
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inputs = sample[0]
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self._verify_ds_inputs_shape(sample, inputs, labels)
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def _verify_data(self):
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"""Verify dataset and labels."""
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self._verify_labels()
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try:
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sample = next(self._dataset.create_tuple_iterator())
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except StopIteration:
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raise ValueError("The dataset provided is empty.")
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self._verify_ds_sample(sample)
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def _verify_network(self):
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"""Verify the network."""
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next_element = next(self._dataset.create_tuple_iterator())
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inputs, _, _ = self._unpack_next_element(next_element)
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prop_test = self._full_network(inputs)
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check_value_type("output of network in explainer", prop_test, ms.Tensor)
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if prop_test.shape[1] != len(self._labels):
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raise ValueError("The dimension of network output does not match the no. of classes. Please "
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"check labels or the network in the explainer again.")
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def _verify_saliency(self):
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"""Verify the saliency settings."""
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if self._explainers:
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explainer_classes = []
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for explainer in self._explainers:
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if explainer.__class__ in explainer_classes:
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raise ValueError(f"Repeated {explainer.__class__.__name__} explainer! "
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"Please make sure all explainers' class is distinct.")
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if explainer.network is not self._network:
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raise ValueError(f"The network of {explainer.__class__.__name__} explainer is different "
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"instance from network of runner. Please make sure they are the same "
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"instance.")
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explainer_classes.append(explainer.__class__)
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if self._benchmarkers:
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benchmarker_classes = []
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for benchmarker in self._benchmarkers:
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if benchmarker.__class__ in benchmarker_classes:
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raise ValueError(f"Repeated {benchmarker.__class__.__name__} benchmarker! "
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"Please make sure all benchmarkers' class is distinct.")
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if isinstance(benchmarker, LabelSensitiveMetric) and benchmarker.num_labels != len(self._labels):
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raise ValueError(f"The num_labels of {benchmarker.__class__.__name__} benchmarker is different "
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"from no. of labels of runner. Please make them are the same.")
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benchmarker_classes.append(benchmarker.__class__)
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class ImageClassificationRunner(_Verifier):
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"""
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A high-level API for users to generate and store results of the explanation methods and the evaluation methods.
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@ -132,9 +292,9 @@ class ImageClassificationRunner:
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# printing spacer
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_SPACER = "{:120}\r"
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# datafile directory's permission
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_DIR_MODE = 0o750
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_DIR_MODE = 0o700
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# datafile's permission
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_FILE_MODE = 0o600
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_FILE_MODE = 0o400
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def __init__(self,
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summary_dir,
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@ -169,7 +329,7 @@ class ImageClassificationRunner:
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self._full_network = SequentialCell([self._network, activation_fn])
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self._full_network.set_train(False)
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self._verify(_VerifyFlag.DATA_N_NETWORK | _VerifyFlag.ENVIRONMENT)
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self._verify(_Verifier.DATA_N_NETWORK | _Verifier.ENVIRONMENT)
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def register_saliency(self,
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explainers,
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@ -211,7 +371,7 @@ class ImageClassificationRunner:
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self._benchmarkers = benchmarkers
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try:
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self._verify(_VerifyFlag.SALIENCY | _VerifyFlag.ENVIRONMENT)
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self._verify(_Verifier.SALIENCY | _Verifier.ENVIRONMENT)
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except (ValueError, TypeError):
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self._explainers = None
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self._benchmarkers = None
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@ -235,7 +395,7 @@ class ImageClassificationRunner:
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self._hoc_searcher = hoc.Searcher(self._full_network)
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try:
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self._verify(_VerifyFlag.HOC | _VerifyFlag.ENVIRONMENT)
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self._verify(_Verifier.HOC | _Verifier.ENVIRONMENT)
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except ValueError:
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self._hoc_searcher = None
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raise
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@ -274,7 +434,7 @@ class ImageClassificationRunner:
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TypeError: Be raised for any data or settings' type problem.
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RuntimeError: Be raised for any runtime problem.
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"""
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self._verify(_VerifyFlag.ALL)
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self._verify(_Verifier.ALL)
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self._manifest = {"saliency_map": False,
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"benchmark": False,
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"uncertainty": False,
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@ -358,25 +518,24 @@ class ImageClassificationRunner:
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sample_id_labels = {}
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self._sample_index = 0
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ds.config.set_seed(self._DATASET_SEED)
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for j, next_element in enumerate(self._dataset):
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for j, batch in enumerate(self._dataset):
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now = time()
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self._run_sample(summary, next_element, sample_id_labels, threshold)
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self._sample_index += 1
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self._infer_batch(summary, batch, sample_id_labels, threshold)
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self._spaced_print("Finish running and writing {}-th batch inference data."
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" Time elapsed: {:.3f} s".format(j, time() - now))
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return sample_id_labels
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def _run_sample(self, summary, next_element, sample_id_labels, threshold):
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def _infer_batch(self, summary, batch, sample_id_labels, threshold):
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"""
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Run inference for a sample.
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Infer a batch.
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Args:
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summary (SummaryRecord): The summary object to store the data.
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next_element (tuple): The next dataset sample.
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batch (tuple): The next dataset sample.
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sample_id_labels (dict): The sample id to labels dictionary.
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threshold (float): The threshold for prediction.
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"""
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inputs, labels, _ = self._unpack_next_element(next_element)
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inputs, labels, _ = self._unpack_next_element(batch)
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prob = self._full_network(inputs).asnumpy()
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if self._uncertainty is not None:
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@ -436,6 +595,8 @@ class ImageClassificationRunner:
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if self._is_hoc_registered:
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self._run_hoc(summary, self._sample_index, inputs[idx], prob[idx])
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self._sample_index += 1
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def _run_explainer(self, summary, sample_id_labels, explainer):
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"""
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Run the explainer.
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@ -689,166 +850,6 @@ class ImageClassificationRunner:
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sds[i] = 0
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return itl_lows, itl_his, sds
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def _verify(self, flags):
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"""
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Verify datasets and settings.
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Args:
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flags (int): Verification flags, use bitwise or '|' to combine multiple flags.
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Possible bitwise flags are shown as follow.
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- _VerifyFlag.ALL: Verify everything.
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- _VerifyFlag.REGISTRATION: Verify explainer module registration.
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- _VerifyFlag.DATA_N_NETWORK: Verify dataset and network.
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- _VerifyFlag.SALIENCY: Verify saliency related settings.
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- _VerifyFlag.HOC: Verify HOC related settings.
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- _VerifyFlag.ENVIRONMENT: Verify the runtime environment.
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Raises:
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ValueError: Be raised for any data or settings' value problem.
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TypeError: Be raised for any data or settings' type problem.
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RuntimeError: Be raised for any runtime problem.
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"""
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if flags & _VerifyFlag.ENVIRONMENT:
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device_target = context.get_context('device_target')
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if device_target not in ("Ascend", "GPU"):
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raise RuntimeError(f"Unsupported device_target: '{device_target}', "
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f"only 'Ascend' or 'GPU' is supported. "
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f"Please call context.set_context(device_target='Ascend') or "
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f"context.set_context(device_target='GPU').")
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if flags & (_VerifyFlag.ENVIRONMENT | _VerifyFlag.SALIENCY):
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if self._is_saliency_registered:
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mode = context.get_context('mode')
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if mode != context.PYNATIVE_MODE:
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raise RuntimeError("Context mode: GRAPH_MODE is not supported, "
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"please call context.set_context(mode=context.PYNATIVE_MODE).")
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if flags & _VerifyFlag.REGISTRATION:
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if self._is_uncertainty_registered and not self._is_saliency_registered:
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raise ValueError("Function register_uncertainty() is called but register_saliency() is not.")
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if not self._is_saliency_registered and not self._is_hoc_registered:
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raise ValueError(
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"No explanation module was registered, user should at least call register_saliency() "
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"or register_hierarchical_occlusion() once with proper arguments.")
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if flags & (_VerifyFlag.DATA_N_NETWORK | _VerifyFlag.SALIENCY | _VerifyFlag.HOC):
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self._verify_data()
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if flags & _VerifyFlag.DATA_N_NETWORK:
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self._verify_network()
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if flags & _VerifyFlag.SALIENCY:
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self._verify_saliency()
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def _verify_labels(self):
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"""Verify labels."""
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label_set = set()
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if not self._labels:
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raise ValueError(f"The label list provided is empty.")
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for i, label in enumerate(self._labels):
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if label.strip() == "":
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raise ValueError(f"Label [{i}] is all whitespaces or empty. Please make sure there is "
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f"no empty label.")
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if label in label_set:
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raise ValueError(f"Duplicated label:{label}! Please make sure all labels are unique.")
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label_set.add(label)
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def _verify_ds_inputs_shape(self, sample, inputs, labels):
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"""Verify a dataset sample's input shape."""
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if len(inputs.shape) > 4 or len(inputs.shape) < 3 or inputs.shape[-3] not in [1, 3, 4]:
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raise ValueError(
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"Image shape {} is unrecognizable: the dimension of image can only be CHW or NCHW.".format(
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inputs.shape))
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if len(inputs.shape) == 3:
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log.warning(
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"Image shape {} is 3-dimensional. All the data will be automatically unsqueezed at the 0-th"
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" dimension as batch data.".format(inputs.shape))
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if len(sample) > 1:
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if len(labels.shape) > 2 and (np.array(labels.shape[1:]) > 1).sum() > 1:
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raise ValueError(
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"Labels shape {} is unrecognizable: outputs should not have more than two dimensions"
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" with length greater than 1.".format(labels.shape))
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if self._is_hoc_registered:
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if inputs.shape[-3] != 3:
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raise ValueError(
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"Hierarchical occlusion is registered, images must be in 3 channels format, but "
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"{} channel(s) is(are) encountered.".format(inputs.shape[-3]))
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short_side = min(inputs.shape[-2:])
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if short_side < hoc.AUTO_IMAGE_SHORT_SIDE_MIN:
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raise ValueError(
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"Hierarchical occlusion is registered, images' short side must be equals to or greater then "
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"{}, but {} is encountered.".format(hoc.AUTO_IMAGE_SHORT_SIDE_MIN, short_side))
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def _verify_ds_sample(self, sample):
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"""Verify a dataset sample."""
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if len(sample) not in [1, 2, 3]:
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raise ValueError("The dataset should provide [images] or [images, labels], [images, labels, bboxes]"
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" as columns.")
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if len(sample) == 3:
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inputs, labels, bboxes = sample
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if bboxes.shape[-1] != 4:
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raise ValueError("The third element of dataset should be bounding boxes with shape of "
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"[batch_size, num_ground_truth, 4].")
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else:
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if self._benchmarkers is not None:
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if any([isinstance(bench, Localization) for bench in self._benchmarkers]):
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raise ValueError("The dataset must provide bboxes if Localization is to be computed.")
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if len(sample) == 2:
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inputs, labels = sample
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if len(sample) == 1:
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inputs = sample[0]
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self._verify_ds_inputs_shape(sample, inputs, labels)
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def _verify_data(self):
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"""Verify dataset and labels."""
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self._verify_labels()
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try:
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sample = next(self._dataset.create_tuple_iterator())
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except StopIteration:
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raise ValueError("The dataset provided is empty.")
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self._verify_ds_sample(sample)
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def _verify_network(self):
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"""Verify the network."""
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next_element = next(self._dataset.create_tuple_iterator())
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inputs, _, _ = self._unpack_next_element(next_element)
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prop_test = self._full_network(inputs)
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check_value_type("output of network in explainer", prop_test, ms.Tensor)
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if prop_test.shape[1] != len(self._labels):
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raise ValueError("The dimension of network output does not match the no. of classes. Please "
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"check labels or the network in the explainer again.")
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def _verify_saliency(self):
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"""Verify the saliency settings."""
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if self._explainers:
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explainer_classes = []
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for explainer in self._explainers:
|
||||
if explainer.__class__ in explainer_classes:
|
||||
raise ValueError(f"Repeated {explainer.__class__.__name__} explainer! "
|
||||
"Please make sure all explainers' class is distinct.")
|
||||
if explainer.network is not self._network:
|
||||
raise ValueError(f"The network of {explainer.__class__.__name__} explainer is different "
|
||||
"instance from network of runner. Please make sure they are the same "
|
||||
"instance.")
|
||||
explainer_classes.append(explainer.__class__)
|
||||
if self._benchmarkers:
|
||||
benchmarker_classes = []
|
||||
for benchmarker in self._benchmarkers:
|
||||
if benchmarker.__class__ in benchmarker_classes:
|
||||
raise ValueError(f"Repeated {benchmarker.__class__.__name__} benchmarker! "
|
||||
"Please make sure all benchmarkers' class is distinct.")
|
||||
if isinstance(benchmarker, LabelSensitiveMetric) and benchmarker.num_labels != len(self._labels):
|
||||
raise ValueError(f"The num_labels of {benchmarker.__class__.__name__} benchmarker is different "
|
||||
"from no. of labels of runner. Please make them are the same.")
|
||||
benchmarker_classes.append(benchmarker.__class__)
|
||||
|
||||
def _transform_bboxes(self, inputs, labels, bboxes, ifbbox):
|
||||
"""
|
||||
Transform the bounding boxes.
|
||||
|
|
@ -863,26 +864,28 @@ class ImageClassificationRunner:
|
|||
bboxes (Union[list[dict], None, Tensor]): the bounding boxes
|
||||
"""
|
||||
input_len = len(inputs)
|
||||
if bboxes is not None and ifbbox:
|
||||
bboxes = ms.Tensor(bboxes, ms.int32)
|
||||
masks_lst = []
|
||||
labels = labels.asnumpy().reshape([input_len, -1])
|
||||
bboxes = bboxes.asnumpy().reshape([input_len, -1, 4])
|
||||
for idx, label in enumerate(labels):
|
||||
height, width = inputs[idx].shape[-2], inputs[idx].shape[-1]
|
||||
masks = {}
|
||||
for j, label_item in enumerate(label):
|
||||
target = int(label_item)
|
||||
if -1 < target < len(self._labels):
|
||||
if target not in masks:
|
||||
mask = np.zeros((1, 1, height, width))
|
||||
else:
|
||||
mask = masks[target]
|
||||
x_min, y_min, x_len, y_len = bboxes[idx][j].astype(int)
|
||||
mask[:, :, x_min:x_min + x_len, y_min:y_min + y_len] = 1
|
||||
masks[target] = mask
|
||||
masks_lst.append(masks)
|
||||
bboxes = masks_lst
|
||||
if bboxes is None or not ifbbox:
|
||||
return bboxes
|
||||
bboxes = ms.Tensor(bboxes, ms.int32)
|
||||
masks_lst = []
|
||||
labels = labels.asnumpy().reshape([input_len, -1])
|
||||
bboxes = bboxes.asnumpy().reshape([input_len, -1, 4])
|
||||
for idx, label in enumerate(labels):
|
||||
height, width = inputs[idx].shape[-2], inputs[idx].shape[-1]
|
||||
masks = {}
|
||||
for j, label_item in enumerate(label):
|
||||
target = int(label_item)
|
||||
if not -1 < target < len(self._labels):
|
||||
continue
|
||||
if target not in masks:
|
||||
mask = np.zeros((1, 1, height, width))
|
||||
else:
|
||||
mask = masks[target]
|
||||
x_min, y_min, x_len, y_len = bboxes[idx][j].astype(int)
|
||||
mask[:, :, x_min:x_min + x_len, y_min:y_min + y_len] = 1
|
||||
masks[target] = mask
|
||||
masks_lst.append(masks)
|
||||
bboxes = masks_lst
|
||||
return bboxes
|
||||
|
||||
def _transform_data(self, inputs, labels, bboxes, ifbbox):
|
||||
|
|
@ -976,7 +979,7 @@ class ImageClassificationRunner:
|
|||
self._DATAFILE_DIRNAME_PREFIX + str(self._summary_timestamp)]
|
||||
abs_dir_path = self._create_subdir(*path_tokens)
|
||||
save_path = os.path.join(abs_dir_path, self._MANIFEST_FILENAME)
|
||||
fd = os.open(save_path, os.O_WRONLY | os.O_CREAT)
|
||||
fd = os.open(save_path, os.O_WRONLY | os.O_CREAT, mode=self._FILE_MODE)
|
||||
file = os.fdopen(fd, "w")
|
||||
try:
|
||||
json.dump(self._manifest, file, indent=4)
|
||||
|
|
@ -984,7 +987,7 @@ class ImageClassificationRunner:
|
|||
log.error(f"Failed to save manifest as {save_path}!")
|
||||
raise
|
||||
finally:
|
||||
file.close()
|
||||
os.close(fd)
|
||||
os.chmod(save_path, self._FILE_MODE)
|
||||
|
||||
def _save_original_image(self, sample_id, image):
|
||||
|
|
|
|||
|
|
@ -608,20 +608,21 @@ class Searcher:
|
|||
job.parent_step.add_child(step)
|
||||
job_queue.extend(sub_job_queue)
|
||||
|
||||
def _process_job(self, job, sample_input, job_queue):
|
||||
def _prepare_job(self, job, sample_input):
|
||||
"""
|
||||
Process a job.
|
||||
Prepare a job for process.
|
||||
|
||||
Args:
|
||||
job (_SearchJob): Search job to be processed.
|
||||
sample_input (numpy.ndarray): Image tensor in NCHW(N=1) format.
|
||||
job_queue (list[_SearchJob]): Job queue.
|
||||
|
||||
Returns:
|
||||
tuple[list[EditStep], _StopReason], result edit stop and the stop reason.
|
||||
"""
|
||||
edit_steps = []
|
||||
numpy.ndarray, the image tensor workpiece.
|
||||
|
||||
Raise:
|
||||
OriginalOutputError: Be raised if network output of the original image is not strictly larger than the
|
||||
threshold.
|
||||
"""
|
||||
# make the network output with the original image is strictly larger than the threshold
|
||||
if job.layer == 0:
|
||||
original_output = self._network(Tensor(sample_input))[0, job.class_idx].asnumpy().item()
|
||||
|
|
@ -644,9 +645,29 @@ class Searcher:
|
|||
self._by_masking)
|
||||
|
||||
job.on_start(sample_input, workpiece, self._compiled_mask, self._network)
|
||||
return workpiece
|
||||
|
||||
def _process_job(self, job, sample_input, job_queue):
|
||||
"""
|
||||
Process a job.
|
||||
|
||||
Args:
|
||||
job (_SearchJob): Search job to be processed.
|
||||
sample_input (numpy.ndarray): Image tensor in NCHW(N=1) format.
|
||||
job_queue (list[_SearchJob]): Job queue.
|
||||
|
||||
Returns:
|
||||
tuple[list[EditStep], _StopReason], result edit stop and the stop reason.
|
||||
|
||||
Raise:
|
||||
OriginalOutputError: Be raised if network output of the original image is not strictly larger than the
|
||||
threshold.
|
||||
"""
|
||||
workpiece = self._prepare_job(job, sample_input)
|
||||
|
||||
start_output = self._network(Tensor(workpiece))[0, job.class_idx].asnumpy().item()
|
||||
last_output = start_output
|
||||
|
||||
edit_steps = []
|
||||
# greedy search loop
|
||||
while True:
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue