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Run entire SSDs from TensorFlow using Intel's Inference Engine

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This commit is contained in:
Dmitry Kurtaev
2018-07-19 17:05:56 +03:00
parent 6c4f618db5
commit c213a3823e
3 changed files with 49 additions and 20 deletions
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22
samples/dnn/tf_text_graph_ssd.py
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@@ -208,12 +208,18 @@ for label in ['ClassPredictor', 'BoxEncodingPredictor']:
graph_def.node.extend([flatten])
addConcatNode('%s/concat' % label, concatInputs, 'concat/axis_flatten')
idx = 0
for node in graph_def.node:
if node.name == ('BoxPredictor_%d/BoxEncodingPredictor/Conv2D' % idx):
text_format.Merge('b: true', node.attr["loc_pred_transposed"])
idx += 1
assert(idx == args.num_layers)
# Add layers that generate anchors (bounding boxes proposals).
scales = [args.min_scale + (args.max_scale - args.min_scale) * i / (args.num_layers - 1)
for i in range(args.num_layers)] + [1.0]
priorBoxes = []
addConstNode('reshape_prior_boxes_to_4d', [1, 2, -1, 1])
for i in range(args.num_layers):
priorBox = NodeDef()
priorBox.name = 'PriorBox_%d' % i
@@ -240,18 +246,9 @@ for i in range(args.num_layers):
text_format.Merge(tensorMsg([0.1, 0.1, 0.2, 0.2]), priorBox.attr["variance"])
graph_def.node.extend([priorBox])
priorBoxes.append(priorBox.name)
# Reshape from 1x2xN to 1x2xNx1
reshape = NodeDef()
reshape.name = priorBox.name + '/4d'
reshape.op = 'Reshape'
reshape.input.append(priorBox.name)
reshape.input.append('reshape_prior_boxes_to_4d')
graph_def.node.extend([reshape])
priorBoxes.append(reshape.name)
addConcatNode('PriorBox/concat', priorBoxes, 'PriorBox/concat/axis')
addConcatNode('PriorBox/concat', priorBoxes, 'concat/axis_flatten')
# Sigmoid for classes predictions and DetectionOutput layer
sigmoid = NodeDef()
@@ -276,7 +273,6 @@ text_format.Merge('i: 100', detectionOut.attr['top_k'])
text_format.Merge('s: "CENTER_SIZE"', detectionOut.attr['code_type'])
text_format.Merge('i: 100', detectionOut.attr['keep_top_k'])
text_format.Merge('f: 0.01', detectionOut.attr['confidence_threshold'])
text_format.Merge('b: true', detectionOut.attr['loc_pred_transposed'])
graph_def.node.extend([detectionOut])