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Re-implement caffe old-style extractors with extractor extensions (#3675
) * move crop extractor * Add concat_ext.py * Add roipooling_ext.py * Add roipooling_ext * Add scale extractor * Add scale extractor * Add bn_ext.py and dropout_ext.py * Add bn_ext.py and dropout_ext.py * Add bn_ext.py and dropout_ext.py * Fix bn.ext.py * Sort fix * Fix bn_test.py * rename to batchnorm_ext * Add bn_ext * Fix batchnorm_ext.py * small fix * Small fix
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,53 @@ | ||
| """ | ||
| Copyright (C) 2018-2021 Intel Corporation | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| """ | ||
| import numpy as np | ||
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| from extensions.ops.BatchNormInference import BatchNormInference | ||
| from mo.front.caffe.extractors.utils import embed_input | ||
| from mo.front.extractor import FrontExtractorOp | ||
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| class BatchNormalizationExtractor(FrontExtractorOp): | ||
| op = 'batchnorm' | ||
| enabled = True | ||
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| @classmethod | ||
| def extract(cls, node): | ||
| eps = node.pb.batch_norm_param.eps | ||
| attrs = { | ||
| 'eps': eps | ||
| } | ||
| pb_model = None if not node.soft_get('model_pb', None) else node.model_pb | ||
| if pb_model: | ||
| blobs = pb_model.blobs | ||
| assert len(blobs) >= 2, 'BatchNorm accepts not less then two input blobs' | ||
| mean = np.array(blobs[0].data) | ||
| variance = np.array(blobs[1].data) | ||
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| if len(blobs) == 3: | ||
| scale = blobs[2].data[0] | ||
| if scale != 0: | ||
| scale = 1.0 / scale | ||
| mean *= scale | ||
| variance *= scale | ||
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| embed_input(attrs, 1, 'gamma', np.ones(mean.shape), 'gamma') | ||
| embed_input(attrs, 2, 'beta', np.zeros(variance.shape), 'beta') | ||
| embed_input(attrs, 3, 'mean', mean, 'biases') | ||
| embed_input(attrs, 4, 'variance', variance, 'weights') | ||
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| BatchNormInference.update_node_stat(node, attrs) | ||
| return cls.enabled |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,32 @@ | ||
| """ | ||
| Copyright (C) 2018-2021 Intel Corporation | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| """ | ||
|
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| from mo.front.extractor import FrontExtractorOp | ||
| from mo.ops.concat import Concat | ||
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| class ConcatFrontExtractor(FrontExtractorOp): | ||
| op = 'concat' | ||
| enabled = True | ||
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| @classmethod | ||
| def extract(cls, node): | ||
| pb = node.pb | ||
| mapping_rule = { | ||
| 'axis': pb.concat_param.axis, | ||
| } | ||
| Concat.update_node_stat(node, mapping_rule) | ||
| return cls.enabled |
2 changes: 1 addition & 1 deletion
2
...timizer/mo/front/caffe/extractors/crop.py → ...imizer/extensions/front/caffe/crop_ext.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,35 @@ | ||
| """ | ||
| Copyright (C) 2018-2021 Intel Corporation | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| """ | ||
|
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| from mo.front.extractor import FrontExtractorOp | ||
| from mo.ops.roipooling import ROIPooling | ||
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| class ROIPoolingFrontExtractor(FrontExtractorOp): | ||
| op = 'roipooling' | ||
| enabled = True | ||
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| @classmethod | ||
| def extract(cls, node): | ||
| param = node.pb.roi_pooling_param | ||
| attrs = { | ||
| 'pooled_h': param.pooled_h, | ||
| 'pooled_w': param.pooled_w, | ||
| 'spatial_scale': param.spatial_scale, | ||
| } | ||
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| ROIPooling.update_node_stat(node, attrs) | ||
| return cls.enabled |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,55 @@ | ||
| """ | ||
| Copyright (C) 2018-2021 Intel Corporation | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| """ | ||
|
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| import numpy as np | ||
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| from mo.front.caffe.extractors.utils import embed_input, weights_biases | ||
| from mo.front.common.partial_infer.elemental import copy_shape_infer | ||
| from mo.front.extractor import FrontExtractorOp | ||
| from mo.ops.scale_shift import ScaleShiftOp | ||
| from mo.utils.utils import NamedAttrsClass | ||
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| class ScaleFrontExtractor(FrontExtractorOp): | ||
| op = 'scale' | ||
| enabled = True | ||
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| @classmethod | ||
| def extract(cls, node): | ||
| pb = node.pb | ||
| model = node.model_pb | ||
| param = pb.scale_param | ||
| attrs = { | ||
| 'axis': param.axis, | ||
| } | ||
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| if model is None and len(pb.bottom) == 1: | ||
| # default weights and biases for scale layer if the caffemodel file doesn't contain them | ||
| model = NamedAttrsClass({'blobs': np.array([NamedAttrsClass({'data': np.array([1])}), | ||
| NamedAttrsClass({'data': np.array([0])})])}) | ||
| # scale with 1 input and 1 or 2 blobs | ||
| if model and len(model.blobs) != 0 and len(pb.bottom) == 1: | ||
| attrs.update(weights_biases(param.bias_term, model)) | ||
| # 2 inputs + bias | ||
| elif len(pb.bottom) == 2 and param.bias_term: | ||
| if model is None or len(model.blobs) == 0: | ||
| # default bias for scale layer with 2 inputs if the caffemodel file doesn't contain them | ||
| model = NamedAttrsClass({'blobs': np.array([NamedAttrsClass({'data': np.array([0])})])}) | ||
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| embed_input(attrs, 1, 'biases', model.blobs[0].data) | ||
| ScaleShiftOp.update_node_stat(node, attrs) | ||
| return cls.enabled | ||
|
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