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Fix fully convolutional model support. #100

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Fix fully convolutional model support. #100

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3 changes: 1 addition & 2 deletions yad2k.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,8 +38,7 @@
parser.add_argument(
'-flcl',
'--fully_convolutional',
help='Model is fully convolutional so set input shape to (None, None, 3). '
'WARNING: This experimental option does not work properly for YOLO_v2.',
help='Model is fully convolutional so set input shape to (None, None, 3). ',
action='store_true')


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6 changes: 3 additions & 3 deletions yad2k/models/keras_yolo.py
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Expand Up @@ -96,7 +96,7 @@ def yolo_head(feats, anchors, num_classes):
# conv_dims = K.variable([conv_width, conv_height])

# Dynamic implementation of conv dims for fully convolutional model.
conv_dims = K.shape(feats)[1:3] # assuming channels last
conv_dims = K.gather(K.shape(feats), [2, 1]) # assuming channels last; [width, height]
# In YOLO the height index is the inner most iteration.
conv_height_index = K.arange(0, stop=conv_dims[0])
conv_width_index = K.arange(0, stop=conv_dims[1])
Expand All @@ -108,11 +108,11 @@ def yolo_head(feats, anchors, num_classes):
K.expand_dims(conv_width_index, 0), [conv_dims[0], 1])
conv_width_index = K.flatten(K.transpose(conv_width_index))
conv_index = K.transpose(K.stack([conv_height_index, conv_width_index]))
conv_index = K.reshape(conv_index, [1, conv_dims[0], conv_dims[1], 1, 2])
conv_index = K.reshape(conv_index, [1, conv_dims[1], conv_dims[0], 1, 2])
conv_index = K.cast(conv_index, K.dtype(feats))

feats = K.reshape(
feats, [-1, conv_dims[0], conv_dims[1], num_anchors, num_classes + 5])
feats, [-1, conv_dims[1], conv_dims[0], num_anchors, num_classes + 5])
conv_dims = K.cast(K.reshape(conv_dims, [1, 1, 1, 1, 2]), K.dtype(feats))

# Static generation of conv_index:
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