

In [1]:

    
from keras.optimizers import SGD

import numpy as np

import models
from vggnet.upgrade_weights import upgrade_weights
from vggnet.vgg16_keras import VGG_16

models = reload(models)
solver = SGD()
rgb_shape = (6, 224, 224)
flow_shape = (2, 224, 224)
regressor_outputs = 6
init = 'glorot_normal'
huge = models.vggnet16_joint_reg_class_flow_leftright({
    'images': (6, 224, 224),
    'flow': (2, 224, 224),
    'leftright': (16,),
    'head': (16,),
    'class': (4,)
}, solver, init)
flow_seq = huge.nodes['flow_conv']
rgb_seq = huge.nodes['rgb_conv']
ilsvrc_weights_path = './vggnet/vgg16_weights.h5'
ilsvrc_model = VGG_16(ilsvrc_weights_path)
upgrade_weights(flow_seq.layers, ilsvrc_model.layers)
upgrade_weights(rgb_seq.layers, ilsvrc_model.layers)
front_layers = len(flow_seq.layers)
back_ilsvrc_layers = ilsvrc_model.layers[front_layers:]
back_seq = huge.nodes['shared_layers']
upgrade_weights(back_seq.layers, back_ilsvrc_layers)
huge.save_weights('vggnet/vgg16-2stream-3pose-lr-reg-clas.h5')









    



Using gpu device 1: Tesla K80 (CNMeM is enabled)
/home/sam/repos/joint-regressor/keras/env/local/lib/python2.7/site-packages/theano/tensor/signal/downsample.py:5: UserWarning: downsample module has been moved to the pool module.
  warnings.warn("downsample module has been moved to the pool module.")






    



Using Theano backend.
Concatenating old L1 dimensions to change shape, (64, 3, 3, 3)->(64, 2, 3, 3)
First layer done
WARNING: layer count differs (17 new v. 37 old)
Changing ZeroPadding2D
Changing Convolution2D
Changing MaxPooling2D
Changing ZeroPadding2D
Changing Convolution2D
Changing ZeroPadding2D
Changing Convolution2D
Changing MaxPooling2D
Changing ZeroPadding2D
Changing Convolution2D
Changing ZeroPadding2D
Changing Convolution2D
Changing ZeroPadding2D
Changing Convolution2D
Changing MaxPooling2D
Concatenating old L1 dimensions to change shape, (64, 3, 3, 3)->(64, 6, 3, 3)
First layer done
WARNING: layer count differs (17 new v. 37 old)
Changing ZeroPadding2D
Changing Convolution2D
Changing MaxPooling2D
Changing ZeroPadding2D
Changing Convolution2D
Changing ZeroPadding2D
Changing Convolution2D
Changing MaxPooling2D
Changing ZeroPadding2D
Changing Convolution2D
Changing ZeroPadding2D
Changing Convolution2D
Changing ZeroPadding2D
Changing Convolution2D
Changing MaxPooling2D
Concatenating old L1 dimensions to change shape, (512, 256, 3, 3)->(512, 512, 3, 3)
First layer done
WARNING: layer count differs (19 new v. 20 old)
Changing ZeroPadding2D
Changing Convolution2D
Changing ZeroPadding2D
Changing Convolution2D
Changing MaxPooling2D
Changing ZeroPadding2D
Changing Convolution2D
Changing ZeroPadding2D
Changing Convolution2D
Changing ZeroPadding2D
Changing Convolution2D
Changing MaxPooling2D
Changing Flatten
Changing Dense
Changing Dropout
Changing Dense
Changing Dropout
[WARNING] vggnet/vgg16-2stream-3pose-lr-reg-clas.h5 already exists - overwrite? [y/n]y
[TIP] Next time specify overwrite=True in save_weights!

leftright experiment

`leftright` experiment