Evgeny Lazarev
c8dd831fc3
* Added transformation config to support automl efficientdet-4 model * Added configuration file to convert Automl EfficientDet model * Updated unit test for Pack * Added instruction on how to convert EfficientDet Tensorflow model * Updated documentation on how to convert EfficientDet model * Updated a documentation with instruction on how to convert Automl EfficientDet.
141 lines
7.7 KiB
Python
141 lines
7.7 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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from extensions.front.Pack import Pack
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from extensions.front.TransposeOrderNormalizer import TransposeOrderNormalizer
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from extensions.front.eltwise_n import EltwiseNReplacement
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from extensions.front.tf.pad_tf_to_pad import PadTFToPad
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from extensions.ops.DetectionOutput import DetectionOutput
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from extensions.ops.activation_ops import Sigmoid
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from extensions.ops.priorbox_clustered import PriorBoxClusteredOp
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from mo.front.common.partial_infer.utils import int64_array
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from mo.front.tf.replacement import FrontReplacementFromConfigFileGeneral
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from mo.graph.graph import Graph, Node
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from mo.middle.passes.convert_data_type import data_type_str_to_np
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from mo.ops.concat import Concat
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from mo.ops.const import Const
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from mo.ops.reshape import Reshape
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from mo.ops.result import Result
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class EfficientDet(FrontReplacementFromConfigFileGeneral):
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replacement_id = 'AutomlEfficientDet'
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def run_before(self):
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from extensions.front.ExpandDimsToUnsqueeze import ExpandDimsToUnsqueeze
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return [ExpandDimsToUnsqueeze, Pack, TransposeOrderNormalizer, PadTFToPad, EltwiseNReplacement]
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class AnchorGenerator:
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def __init__(self, min_level, aspect_ratios, num_scales, anchor_scale):
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self.min_level = min_level
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self.aspect_ratios = aspect_ratios
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self.anchor_scale = anchor_scale
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self.scales = [2 ** (float(s) / num_scales) for s in range(num_scales)]
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def get(self, layer_id):
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widths = []
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heights = []
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for s in self.scales:
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for a in self.aspect_ratios:
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base_anchor_size = 2 ** (self.min_level + layer_id) * self.anchor_scale
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heights.append(base_anchor_size * s * a[1])
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widths.append(base_anchor_size * s * a[0])
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return widths, heights
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def transform_graph(self, graph: Graph, replacement_descriptions: dict):
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parameter_node = graph.get_op_nodes(op='Parameter')[0]
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parameter_node['data_type'] = data_type_str_to_np(parameter_node.graph.graph['cmd_params'].data_type)
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parameter_node.out_port(0).disconnect()
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# remove existing Result operations to remove unsupported sub-graph
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graph.remove_nodes_from([node.id for node in graph.get_op_nodes(op='Result')] + ['detections'])
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# determine if the op which is a input/final result of mean value and scale applying to the input tensor
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# then connect it to the input of the first convolution of the model, so we remove the image pre-processing
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# which includes padding and resizing from the model
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preprocessing_input_node_id = replacement_descriptions['preprocessing_input_node']
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assert preprocessing_input_node_id in graph.nodes, 'The node with name "{}" is not found in the graph. This ' \
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'node should provide scaled image output and is specified' \
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' in the json file.'.format(preprocessing_input_node_id)
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preprocessing_input_node = Node(graph, preprocessing_input_node_id)
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preprocessing_input_node.in_port(0).get_connection().set_source(parameter_node.out_port(0))
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preprocessing_output_node_id = replacement_descriptions['preprocessing_output_node']
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assert preprocessing_output_node_id in graph.nodes, 'The node with name "{}" is not found in the graph. This ' \
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'node should provide scaled image output and is specified' \
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' in the json file.'.format(preprocessing_output_node_id)
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preprocessing_output_node = Node(graph, preprocessing_output_node_id)
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preprocessing_output_node.out_port(0).disconnect()
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convolution_nodes = [n for n in graph.pseudo_topological_sort() if n.soft_get('type') == 'Convolution']
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convolution_nodes[0].in_port(0).get_connection().set_source(preprocessing_output_node.out_port(0))
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# create prior boxes (anchors) generator
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aspect_ratios = replacement_descriptions['aspect_ratios']
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assert len(aspect_ratios) % 2 == 0
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aspect_ratios = list(zip(aspect_ratios[::2], aspect_ratios[1::2]))
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priors_generator = self.AnchorGenerator(min_level=int(replacement_descriptions['min_level']),
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aspect_ratios=aspect_ratios,
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num_scales=int(replacement_descriptions['num_scales']),
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anchor_scale=replacement_descriptions['anchor_scale'])
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prior_boxes = []
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for i in range(100):
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inp_name = 'box_net/box-predict{}/BiasAdd'.format('_%d' % i if i else '')
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if inp_name not in graph:
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break
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widths, heights = priors_generator.get(i)
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prior_box_op = PriorBoxClusteredOp(graph, {'width': np.array(widths),
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'height': np.array(heights),
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'clip': 0, 'flip': 0,
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'variance': replacement_descriptions['variance'],
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'offset': 0.5})
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prior_boxes.append(prior_box_op.create_node([Node(graph, inp_name), parameter_node]))
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# concatenate prior box operations
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concat_prior_boxes = Concat(graph, {'axis': -1}).create_node()
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for idx, node in enumerate(prior_boxes):
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concat_prior_boxes.add_input_port(idx)
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concat_prior_boxes.in_port(idx).connect(node.out_port(0))
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conf = Sigmoid(graph, dict(name='concat/sigmoid')).create_node([Node(graph, 'concat')])
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reshape_size_node = Const(graph, {'value': int64_array([0, -1])}).create_node([])
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logits = Reshape(graph, dict(name=conf.name + '/Flatten')).create_node([conf, reshape_size_node])
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deltas = Reshape(graph, dict(name='concat_1/Flatten')).create_node([Node(graph, 'concat_1'), reshape_size_node])
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# revert convolution boxes prediction weights from yxYX to xyXY (convolutions share weights and bias)
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weights = Node(graph, 'box_net/box-predict/pointwise_kernel')
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weights.value = weights.value.reshape(-1, 4)[:, [1, 0, 3, 2]].reshape(weights.shape)
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bias = Node(graph, 'box_net/box-predict/bias')
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bias.value = bias.value.reshape(-1, 4)[:, [1, 0, 3, 2]].reshape(bias.shape)
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detection_output_node = DetectionOutput(graph, dict(
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name='detections',
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num_classes=int(replacement_descriptions['num_classes']),
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share_location=1,
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background_label_id=int(replacement_descriptions['num_classes']) + 1,
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nms_threshold=replacement_descriptions['nms_threshold'],
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confidence_threshold=replacement_descriptions['confidence_threshold'],
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top_k=100,
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keep_top_k=100,
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code_type='caffe.PriorBoxParameter.CENTER_SIZE',
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)).create_node([deltas, logits, concat_prior_boxes])
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output_op = Result(graph, dict(name='output'))
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output_op.create_node([detection_output_node])
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