def model(x, H, reuse, is_training=True):
if H['slim_basename'] == 'resnet_v1_101':
with slim.arg_scope(resnet.resnet_arg_scope()):
_, T = resnet.resnet_v1_101(x,
is_training=is_training,
num_classes=1000,
reuse=reuse)
elif H['slim_basename'] == 'InceptionV1':
with slim.arg_scope(inception.inception_v1_arg_scope()):
_, T = inception.inception_v1(x,
is_training=is_training,
num_classes=1001,
spatial_squeeze=False,
reuse=reuse)
elif H['slim_basename'] == 'InceptionResnetV4':
with slim.arg_scope(in_resnet.inception_resnet_v2_arg_scope()):
_, T = in_resnet.inception_resnet_v2(x,
is_training=is_training,
num_classes=1001,
reuse=reuse)
#print '\n'.join(map(str, [(k, v.op.outputs[0].get_shape()) for k, v in T.iteritems()]))
coarse_feat = T[H['slim_top_lname']][:, :, :, :H['later_feat_channels']]
assert coarse_feat.op.outputs[0].get_shape()[3] == H['later_feat_channels']
# fine feat can be used to reinspect input
attention_lname = H.get('slim_attention_lname', 'Mixed_3b')
early_feat = T[attention_lname]
return coarse_feat, early_feat
def model(x, H, reuse, is_training=True):
if H['slim_basename'] == 'resnet_v1_101':
with slim.arg_scope(resnet.resnet_arg_scope()):
_, T = resnet.resnet_v1_101(x,
is_training=is_training,
num_classes=1000,
reuse=reuse)
elif H['slim_basename'] == 'InceptionV1':
with slim.arg_scope(inception.inception_v1_arg_scope()):
_, T = inception.inception_v1(x,
is_training=is_training,
num_classes=1001,
spatial_squeeze=False,
reuse=reuse)
#print '\n'.join(map(str, [(k, v.op.outputs[0].get_shape()) for k, v in T.iteritems()]))
coarse_feat = T[H['slim_top_lname']][:, :, :, :H['later_feat_channels']]
assert coarse_feat.op.outputs[0].get_shape()[3] == H['later_feat_channels']
# fine feat can be used to reinspect input
attention_lname = H.get('slim_attention_lname', 'Mixed_3b')
early_feat = T[attention_lname]
return coarse_feat, early_feat