68badf5165
* added IteratorGetNextCut, some improvements in graph.py * added allowed types check * reused new graph API for ports * returned back old API 'out_nodes', removed soft-getting name from base class, changed run_after -> [] * correctly used new port API * corrected IteratorGetNext message
108 lines
5.4 KiB
Python
108 lines
5.4 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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from mo.graph.graph import Graph
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from mo.utils.model_analysis import AnalyzeAction
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class InputsAnalysis(AnalyzeAction):
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"""
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The analyser gets information about model inputs and their default values if any.
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"""
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@classmethod
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def fifo_queue_analysis(cls, graph: Graph, inputs_desc: dict):
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"""
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The FIFOQueue with QueueDeque has a separate input that specifies the size of batch to extract from queue. This
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input is redundant and should be remove from the model analysis output.
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"""
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inputs_to_ignore = set()
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for fifo_queue in graph.get_op_nodes(op='FIFOQueueV2'):
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if len(fifo_queue.get_outputs({'out': 0})) != 1:
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log.debug('The FIFOQueue operation "{}" has more than 1 consumers'.format(fifo_queue.id))
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continue
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queue_deque = fifo_queue.out_node(0)
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if queue_deque.op in ['QueueDequeueMany', 'QueueDequeueManyV2', 'QueueDequeueUpTo', 'QueueDequeueUpToV2']:
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queue_deque_input_1 = queue_deque.in_node(1)
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if queue_deque_input_1.op in ['Parameter', 'PlaceholderWithDefault']:
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log.debug('Adding node "{}" to placeholder ignore list'.format(queue_deque_input_1.id))
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inputs_to_ignore.add(queue_deque_input_1.id)
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# create input per each QueueDeque output port
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for port_ind in range(len(queue_deque.out_nodes())):
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inputs_desc["{}:{}".format(queue_deque.id, port_ind)] = {'shape': fifo_queue.shapes[port_ind].tolist(),
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'value': None,
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'data_type': fifo_queue.types[port_ind]}
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return inputs_to_ignore
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@classmethod
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def ignore_mxnet_softmax_inputs(cls, graph: Graph):
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"""
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MxNet Softmax layers may have additional inputs which should be ignored. Refer to the
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extensions/front/mxnet/check_softmax_node_inputs.py.
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"""
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inputs_to_ignore = set()
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softmax_nodes = []
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[softmax_nodes.extend(graph.get_op_nodes(op=op)) for op in ('SoftMax', 'SoftmaxActivation', 'SoftmaxOutput')]
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for softmax_node in softmax_nodes:
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for i in range(1, len(softmax_node.in_nodes())):
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if softmax_node.in_node(i).has_valid('op') and softmax_node.in_node(i).op == 'Parameter':
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inputs_to_ignore.add(softmax_node.in_node(i).id)
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return inputs_to_ignore
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@classmethod
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def iterator_get_next_analysis(cls, graph: Graph, inputs_desc: dict):
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message = None
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op_nodes = graph.get_op_nodes(op='IteratorGetNext')
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params = ''
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for iter_get_next in op_nodes:
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for port in iter_get_next.out_nodes().keys():
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inputs_desc['{}:{}'.format(iter_get_next.soft_get('name', iter_get_next.id), port)] = {
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'shape': iter_get_next.shapes[port].tolist(),
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'value': None,
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'data_type': iter_get_next.types[port]
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}
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if params != '':
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params = params + ','
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shape = str(iter_get_next.shapes[port].tolist()).replace(',', '')
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params = params + '{}:{}{}'.format(iter_get_next.soft_get('name', iter_get_next.id), port, shape)
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if len(op_nodes):
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message = 'It looks like there is IteratorGetNext as input\n' \
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'Run the Model Optimizer without --input option \n' \
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'Otherwise, try to run the Model Optimizer with:\n\t\t--input "{}"\n'.format(params)
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return message
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def analyze(self, graph: Graph):
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inputs_desc = dict()
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message = InputsAnalysis.iterator_get_next_analysis(graph, inputs_desc)
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inputs_to_ignore = InputsAnalysis.fifo_queue_analysis(graph, inputs_desc)
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if graph.graph['fw'] == 'mxnet':
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inputs_to_ignore.update(InputsAnalysis.ignore_mxnet_softmax_inputs(graph))
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inputs = graph.get_op_nodes(op='Parameter')
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for input in inputs:
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inputs_desc[input.name] = {'shape': input.soft_get('shape', None),
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'data_type': input.soft_get('data_type', None),
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'value': None,
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}
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placeholders_with_default = graph.get_op_nodes(op='PlaceholderWithDefault')
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for input in placeholders_with_default:
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inputs_desc[input.name] = {'shape': input.soft_get('shape', None),
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'data_type': input.soft_get('data_type', None),
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'value': input.in_node(0).value if 0 in input.in_nodes() and
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input.in_node(0).has_valid('value') else None}
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for input_to_ignore in inputs_to_ignore:
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del inputs_desc[input_to_ignore]
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# workaround for the ONNX models case where input shape is specified as string value like: "width", "height".
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# In this case the string value is converted to 0, but in fact it is an arbitrary value so should be -1
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if graph.graph['fw'] == 'onnx':
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for inp in inputs_desc.values():
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inp['shape'] = [-1 if item == 0 else item for item in inp['shape']]
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return {'inputs': inputs_desc}, message
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