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Merge pull request #12125 from dkurt:dnn_mobilenet_ppn

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Vadim Pisarevsky
2018-08-06 14:40:50 +00:00
parent 70b893333d 4fb086d6c3
commit 7c8ab271fc
3 changed files with 103 additions and 46 deletions
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samples/dnn/tf_text_graph_ssd.py
+28 -6
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@@ -30,6 +30,11 @@ parser.add_argument('--aspect_ratios', default=[1.0, 2.0, 0.5, 3.0, 0.333], type
help='Hyper-parameter of ssd_anchor_generator from config file.')
parser.add_argument('--image_width', default=300, type=int, help='Training images width.')
parser.add_argument('--image_height', default=300, type=int, help='Training images height.')
parser.add_argument('--not_reduce_boxes_in_lowest_layer', default=False, action='store_true',
help='A boolean to indicate whether the fixed 3 boxes per '
'location is used in the lowest achors generation layer.')
parser.add_argument('--box_predictor', default='convolutional', type=str,
choices=['convolutional', 'weight_shared_convolutional'])
args = parser.parse_args()
# Nodes that should be kept.
@@ -173,12 +178,18 @@ def addConcatNode(name, inputs, axisNodeName):
addConstNode('concat/axis_flatten', [-1])
addConstNode('PriorBox/concat/axis', [-2])
for label in ['ClassPredictor', 'BoxEncodingPredictor']:
for label in ['ClassPredictor', 'BoxEncodingPredictor' if args.box_predictor is 'convolutional' else 'BoxPredictor']:
concatInputs = []
for i in range(args.num_layers):
# Flatten predictions
flatten = NodeDef()
inpName = 'BoxPredictor_%d/%s/BiasAdd' % (i, label)
if args.box_predictor is 'convolutional':
inpName = 'BoxPredictor_%d/%s/BiasAdd' % (i, label)
else:
if i == 0:
inpName = 'WeightSharedConvolutionalBoxPredictor/%s/BiasAdd' % label
else:
inpName = 'WeightSharedConvolutionalBoxPredictor_%d/%s/BiasAdd' % (i, label)
flatten.input.append(inpName)
flatten.name = inpName + '/Flatten'
flatten.op = 'Flatten'
@@ -189,7 +200,9 @@ for label in ['ClassPredictor', 'BoxEncodingPredictor']:
idx = 0
for node in graph_def.node:
if node.name == ('BoxPredictor_%d/BoxEncodingPredictor/Conv2D' % idx):
if node.name == ('BoxPredictor_%d/BoxEncodingPredictor/Conv2D' % idx) or \
node.name == ('WeightSharedConvolutionalBoxPredictor_%d/BoxPredictor/Conv2D' % idx) or \
node.name == 'WeightSharedConvolutionalBoxPredictor/BoxPredictor/Conv2D':
text_format.Merge('b: true', node.attr["loc_pred_transposed"])
idx += 1
assert(idx == args.num_layers)
@@ -203,13 +216,19 @@ for i in range(args.num_layers):
priorBox = NodeDef()
priorBox.name = 'PriorBox_%d' % i
priorBox.op = 'PriorBox'
priorBox.input.append('BoxPredictor_%d/BoxEncodingPredictor/BiasAdd' % i)
if args.box_predictor is 'convolutional':
priorBox.input.append('BoxPredictor_%d/BoxEncodingPredictor/BiasAdd' % i)
else:
if i == 0:
priorBox.input.append('WeightSharedConvolutionalBoxPredictor/BoxPredictor/Conv2D')
else:
priorBox.input.append('WeightSharedConvolutionalBoxPredictor_%d/BoxPredictor/BiasAdd' % i)
priorBox.input.append(graph_def.node[0].name) # image_tensor
text_format.Merge('b: false', priorBox.attr["flip"])
text_format.Merge('b: false', priorBox.attr["clip"])
if i == 0:
if i == 0 and not args.not_reduce_boxes_in_lowest_layer:
widths = [0.1, args.min_scale * sqrt(2.0), args.min_scale * sqrt(0.5)]
heights = [0.1, args.min_scale / sqrt(2.0), args.min_scale / sqrt(0.5)]
else:
@@ -240,7 +259,10 @@ detectionOut = NodeDef()
detectionOut.name = 'detection_out'
detectionOut.op = 'DetectionOutput'
detectionOut.input.append('BoxEncodingPredictor/concat')
if args.box_predictor == 'convolutional':
detectionOut.input.append('BoxEncodingPredictor/concat')
else:
detectionOut.input.append('BoxPredictor/concat')
detectionOut.input.append(sigmoid.name)
detectionOut.input.append('PriorBox/concat')