if i == 26:
break
if k == 'conv1_1_W':
sess.run(en_parameters[i].assign(np.concatenate([weights[k],np.zeros([3,3,1,64])],axis = 2)))
else:
sess.run(en_parameters[i].assign(weights[k]))
print('finish loading vgg16 model')
#load train data
while epoch_num < max_epochs:
print('epoch %d' % epoch_num)
while batch_num < batchs_per_epoch:
print('batch %d, loading batch data...' % batch_num)
batch_index = sess.run(index_dequeue_op)
batch_RGB_paths = paths_RGB[batch_index]
batch_alpha_paths = paths_alpha[batch_index]
batch_FG_paths = paths_FG[batch_index]
batch_BG_paths = paths_BG[batch_index]
print('finish loading path')
batch_RGBs,batch_trimaps,batch_alphas,batch_FGs,batch_BGs = load_data(sess,batch_RGB_paths,batch_alpha_paths,batch_FG_paths,batch_BG_paths)
# feed = {image_batch:batch_RGBs.eval(), GT_matte_batch:batch_alphas.eval(),GT_trimap:batch_trimaps, GTBG_batch:batch_BGs.eval(), GTFG_batch:batch_FGs.eval(),is_train:True}
feed = {image_batch:batch_RGBs, GT_matte_batch:batch_alphas,GT_trimap:batch_trimaps, GTBG_batch:batch_BGs, GTFG_batch:batch_FGs,is_train:True}
_,loss,summary_str,step,p_mattes = sess.run([train_op,total_loss,summary_op,global_step,pred_mattes],feed_dict = feed)
misc.imsave('./predict/alpha.jpg',p_mattes[0,:,:,0])
summary_writer.add_summary(summary_str,global_step = step)
print('loss is %f' %loss)
batch_num += 1
epoch_num += 1
else:
sess.run(en_parameters[i].assign(weights[k]))
print('finish loading vgg16 model')
else:
print('Restoring pretrained model...')
saver.restore(sess, tf.train.latest_checkpoint('./model'))
sess.graph.finalize()
while epoch_num < max_epochs:
while batch_num < batchs_per_epoch:
batch_index = sess.run(index_dequeue_op)
batch_alpha_paths = paths_alpha[batch_index]
batch_eps_paths = paths_eps[batch_index]
batch_BG_paths = paths_BG[batch_index]
batch_RGBs, batch_trimaps, batch_alphas, batch_BGs, batch_FGs, RGBs_with_mean = load_data(
batch_alpha_paths, batch_eps_paths, batch_BG_paths)
feed = {
image_batch: batch_RGBs,
GT_matte_batch: batch_alphas,
GT_trimap: batch_trimaps,
GTBG_batch: batch_BGs,
GTFG_batch: batch_FGs,
raw_RGBs: RGBs_with_mean,
training: True
}
_, loss, summary_str, step = sess.run(
[train_op, total_loss, summary_op, global_step],
feed_dict=feed)
print('epoch %d batch %d loss is %f' %
nummm = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
]
fff = -1
while epoch_num < max_epochs:
while batch_num < batchs_per_epoch:
fff = fff + 1
batch_index = sess.run(index_dequeue_op)
batch_alpha_paths = paths_alpha[batch_index]
batch_eps_paths = paths_eps[batch_index]
batch_BG_paths = paths_BG[batch_index]
batch_dir_paths = paths_dir[batch_index]
# batch_RGBs,batch_trimaps,batch_alphas,batch_BGs,batch_FGs,RGBs_with_mean = load_data(batch_alpha_paths,batch_eps_paths,batch_BG_paths)
batch_Gradients, batch_masks, batch_alphas = load_data(
batch_alpha_paths, batch_eps_paths, batch_dir_paths)
# feed = {image_batch:batch_RGBs, GT_matte_batch:batch_alphas,GT_trimap:batch_trimaps, GTBG_batch:batch_BGs, GTFG_batch:batch_FGs,raw_RGBs:RGBs_with_mean,training:True}
feed = {
image_batch: batch_Gradients,
GT_matte_batch: batch_alphas,
GT_mask: batch_masks,
training: True
}
_, loss, summary_str, step = sess.run(
[train_op, total_loss, summary_op, global_step],
feed_dict=feed)
# trimap=b_trimap.eval(feed_dict=feed)
mask_ = b_mask.eval(feed_dict=feed)
alpha_ = pred_final.eval(feed_dict=feed)