47485646bb
* Revice DetectionOutput reference implementation Ticket: 37433 * fix test_create_op * fix test_dyn_attributes * apply code format * fix crash on Windows when variance_encoded_in_target == 1 * add more checks for DetectionOutput inputs * Fix single layer tests * apply code format * fix ssd_vgg16_300 inference with batch size > 1 * update types in docs * fix crash on windows * apply code style * fix python tests * fix setting output type * change False to false and True to true in docs * Allow prior boxes to have different type than box logits Some models work that way * simplify output shape calculation * fixes to docs
240 lines
8.8 KiB
Python
240 lines
8.8 KiB
Python
# ******************************************************************************
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# Copyright 2017-2020 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ******************************************************************************
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import numpy as np
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import pytest
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import ngraph as ng
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@pytest.fixture()
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def _proposal_node():
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attributes = {
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"base_size": np.uint16(1),
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"pre_nms_topn": np.uint16(20),
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"post_nms_topn": np.uint16(64),
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"nms_thresh": np.float64(0.34),
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"feat_stride": np.uint16(16),
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"min_size": np.uint16(32),
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"ratio": np.array([0.1, 1.5, 2.0, 2.5], dtype=np.float64),
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"scale": np.array([2, 3, 3, 4], dtype=np.float64),
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}
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batch_size = 7
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class_probs = ng.parameter([batch_size, 12, 34, 62], np.float64, "class_probs")
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bbox_deltas = ng.parameter([batch_size, 24, 34, 62], np.float64, "bbox_deltas")
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image_shape = ng.parameter([3], np.float64, "image_shape")
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return ng.proposal(class_probs, bbox_deltas, image_shape, attributes)
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def test_dynamic_attributes_softmax():
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axis = 2
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data = ng.parameter([1, 2, 3, 4], np.float32, "data_in")
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node = ng.softmax(data, axis)
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assert node.get_axis() == axis
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node.set_axis(3)
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assert node.get_axis() == 3
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@pytest.mark.parametrize(
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"int_dtype, fp_dtype",
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[
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(np.int8, np.float32),
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(np.int16, np.float32),
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(np.int32, np.float32),
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(np.int64, np.float32),
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(np.uint8, np.float32),
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(np.uint16, np.float32),
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(np.uint32, np.float32),
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(np.uint64, np.float32),
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(np.int32, np.float16),
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(np.int32, np.float64),
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],
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)
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def test_dynamic_get_attribute_value(int_dtype, fp_dtype):
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attributes = {
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"num_classes": int_dtype(85),
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"background_label_id": int_dtype(13),
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"top_k": int_dtype(16),
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"variance_encoded_in_target": True,
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"keep_top_k": np.array([64, 32, 16, 8], dtype=int_dtype),
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"code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
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"share_location": False,
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"nms_threshold": fp_dtype(0.645),
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"confidence_threshold": fp_dtype(0.111),
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"clip_after_nms": True,
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"clip_before_nms": False,
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"decrease_label_id": True,
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"normalized": True,
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"input_height": int_dtype(86),
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"input_width": int_dtype(79),
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"objectness_score": fp_dtype(0.77),
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}
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box_logits = ng.parameter([4, 680], fp_dtype, "box_logits")
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class_preds = ng.parameter([4, 170], fp_dtype, "class_preds")
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proposals = ng.parameter([4, 1, 8], fp_dtype, "proposals")
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aux_class_preds = ng.parameter([4, 4], fp_dtype, "aux_class_preds")
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aux_box_preds = ng.parameter([4, 680], fp_dtype, "aux_box_preds")
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node = ng.detection_output(box_logits, class_preds, proposals, attributes, aux_class_preds, aux_box_preds)
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assert node.get_num_classes() == int_dtype(85)
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assert node.get_background_label_id() == int_dtype(13)
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assert node.get_top_k() == int_dtype(16)
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assert node.get_variance_encoded_in_target()
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assert np.all(np.equal(node.get_keep_top_k(), np.array([64, 32, 16, 8], dtype=int_dtype)))
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assert node.get_code_type() == "caffe.PriorBoxParameter.CENTER_SIZE"
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assert not node.get_share_location()
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assert np.isclose(node.get_nms_threshold(), fp_dtype(0.645))
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assert np.isclose(node.get_confidence_threshold(), fp_dtype(0.111))
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assert node.get_clip_after_nms()
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assert not node.get_clip_before_nms()
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assert node.get_decrease_label_id()
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assert node.get_normalized()
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assert node.get_input_height() == int_dtype(86)
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assert node.get_input_width() == int_dtype(79)
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assert np.isclose(node.get_objectness_score(), fp_dtype(0.77))
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assert node.get_num_classes() == int_dtype(85)
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@pytest.mark.parametrize(
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"int_dtype, fp_dtype",
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[
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(np.uint8, np.float32),
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(np.uint16, np.float32),
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(np.uint32, np.float32),
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(np.uint64, np.float32),
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(np.uint32, np.float16),
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(np.uint32, np.float64),
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],
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)
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def test_dynamic_set_attribute_value(int_dtype, fp_dtype):
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attributes = {
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"base_size": int_dtype(1),
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"pre_nms_topn": int_dtype(20),
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"post_nms_topn": int_dtype(64),
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"nms_thresh": fp_dtype(0.34),
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"feat_stride": int_dtype(16),
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"min_size": int_dtype(32),
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"ratio": np.array([0.1, 1.5, 2.0, 2.5], dtype=fp_dtype),
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"scale": np.array([2, 3, 3, 4], dtype=fp_dtype),
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}
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batch_size = 7
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class_probs = ng.parameter([batch_size, 12, 34, 62], fp_dtype, "class_probs")
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bbox_deltas = ng.parameter([batch_size, 24, 34, 62], fp_dtype, "bbox_deltas")
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image_shape = ng.parameter([3], fp_dtype, "image_shape")
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node = ng.proposal(class_probs, bbox_deltas, image_shape, attributes)
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node.set_base_size(int_dtype(15))
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node.set_pre_nms_topn(int_dtype(7))
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node.set_post_nms_topn(int_dtype(33))
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node.set_nms_thresh(fp_dtype(1.55))
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node.set_feat_stride(int_dtype(8))
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node.set_min_size(int_dtype(123))
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node.set_ratio(np.array([1.1, 2.5, 3.0, 4.5], dtype=fp_dtype))
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node.set_scale(np.array([2.1, 3.2, 3.3, 4.4], dtype=fp_dtype))
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node.set_clip_before_nms(True)
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node.set_clip_after_nms(True)
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node.set_normalize(True)
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node.set_box_size_scale(fp_dtype(1.34))
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node.set_box_coordinate_scale(fp_dtype(0.88))
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node.set_framework("OpenVINO")
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assert node.get_base_size() == int_dtype(15)
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assert node.get_pre_nms_topn() == int_dtype(7)
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assert node.get_post_nms_topn() == int_dtype(33)
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assert np.isclose(node.get_nms_thresh(), fp_dtype(1.55))
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assert node.get_feat_stride() == int_dtype(8)
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assert node.get_min_size() == int_dtype(123)
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assert np.allclose(node.get_ratio(), np.array([1.1, 2.5, 3.0, 4.5], dtype=fp_dtype))
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assert np.allclose(node.get_scale(), np.array([2.1, 3.2, 3.3, 4.4], dtype=fp_dtype))
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assert node.get_clip_before_nms()
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assert node.get_clip_after_nms()
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assert node.get_normalize()
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assert np.isclose(node.get_box_size_scale(), fp_dtype(1.34))
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assert np.isclose(node.get_box_coordinate_scale(), fp_dtype(0.88))
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assert node.get_framework() == "OpenVINO"
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def test_dynamic_attr_cache(_proposal_node):
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node = _proposal_node
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assert not node._attr_cache_valid
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node.set_nms_thresh(1.3453678102)
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assert not node._attr_cache_valid
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assert np.isclose(node.get_nms_thresh(), np.float64(1.3453678102))
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assert node._attr_cache_valid
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def test_dynamic_attr_transitivity(_proposal_node):
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node = _proposal_node
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node2 = node
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node.set_ratio(np.array([1.1, 2.5, 3.0, 4.5], dtype=np.float64))
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assert np.allclose(node.get_ratio(), np.array([1.1, 2.5, 3.0, 4.5], dtype=np.float64))
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assert np.allclose(node2.get_ratio(), np.array([1.1, 2.5, 3.0, 4.5], dtype=np.float64))
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node2.set_scale(np.array([2.1, 3.2, 3.3, 4.4], dtype=np.float64))
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assert np.allclose(node2.get_scale(), np.array([2.1, 3.2, 3.3, 4.4], dtype=np.float64))
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assert np.allclose(node.get_scale(), np.array([2.1, 3.2, 3.3, 4.4], dtype=np.float64))
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def test_dynamic_attributes_simple():
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batch_size = 1
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input_size = 16
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hidden_size = 128
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X_shape = [batch_size, input_size]
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H_t_shape = [batch_size, hidden_size]
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W_shape = [3 * hidden_size, input_size]
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R_shape = [3 * hidden_size, hidden_size]
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B_shape = [4 * hidden_size]
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parameter_X = ng.parameter(X_shape, name="X", dtype=np.float32)
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parameter_H_t = ng.parameter(H_t_shape, name="H_t", dtype=np.float32)
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parameter_W = ng.parameter(W_shape, name="W", dtype=np.float32)
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parameter_R = ng.parameter(R_shape, name="R", dtype=np.float32)
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parameter_B = ng.parameter(B_shape, name="B", dtype=np.float32)
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activations = ["tanh", "relu"]
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activations_alpha = [1.0, 2.0]
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activations_beta = [1.0, 2.0]
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clip = 0.5
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linear_before_reset = True
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node = ng.gru_cell(
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parameter_X,
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parameter_H_t,
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parameter_W,
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parameter_R,
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parameter_B,
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hidden_size,
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activations,
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activations_alpha,
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activations_beta,
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clip,
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linear_before_reset,
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)
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assert node.get_hidden_size() == hidden_size
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assert all(map(lambda x, y: x == y, node.get_activations(), activations))
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assert all(np.equal(node.get_activations_alpha(), activations_alpha))
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assert all(np.equal(node.get_activations_beta(), activations_beta))
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assert node.get_linear_before_reset() == linear_before_reset
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assert np.isclose(node.get_clip(), clip)
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