def restore_params(network, alpha, path='models'):
logging.info("Restore pre-trained parameters")
#maybe_download_and_extract(
# 'mobilenet.npz', path, 'https://github.com/tensorlayer/pretrained-models/raw/master/models/',
# expected_bytes=25600116
#) # ls -al
filename = "mbnetv1_" + str(alpha) + ".npz"
params = load_npz(name=os.path.join(path, filename))
for idx, net_weight in enumerate(network.all_weights):
if 'batchnorm' in net_weight.name:
params[idx] = params[idx].reshape(1, 1, 1, -1)
# exchange batchnorm's beta and gmma (TL and keras is different)
idx = 0
while idx < len(network.all_weights):
net_weight = network.all_weights[idx]
if ('batchnorm' in net_weight.name) and ('beta' in net_weight.name):
tmp = params[idx]
params[idx] = params[idx + 1]
params[idx + 1] = tmp
idx += 2
else:
idx += 1
assign_weights(params[:len(network.all_weights)], network)
del params
def restore_params(self, sess, path='models'):
logging.info("Restore pre-trained parameters")
maybe_download_and_extract(
'mobilenet.npz', path, 'https://github.com/tensorlayer/pretrained-models/raw/master/models/',
expected_bytes=25600116
) # ls -al
params = load_npz(name=os.path.join(path, 'mobilenet.npz'))
assign_params(sess, params[:len(self.net.all_params)], self.net)
del params
def restore_params(network, path='models'):
logging.info("Restore pre-trained parameters")
maybe_download_and_extract(
'squeezenet.npz', path, 'https://github.com/tensorlayer/pretrained-models/raw/master/models/',
expected_bytes=7405613
) # ls -al
params = load_npz(name=os.path.join(path, 'squeezenet.npz'))
assign_weights(params[:len(network.all_weights)], network)
del params
def restore_params(network, path='models'):
logging.info("Restore pre-trained parameters")
maybe_download_and_extract(
'squeezenet.npz', path, 'https://github.com/tensorlayer/pretrained-models/raw/master/models/',
expected_bytes=7405613
) # ls -al
params = load_npz(name=os.path.join(path, 'squeezenet.npz'))
assign_weights(params[:len(network.weights)], network)
del params
def restore_params(network, path='models'):
logging.info("Restore pre-trained parameters")
maybe_download_and_extract(
'mobilenet.npz',
path,
'https://github.com/tensorlayer/pretrained-models/raw/master/models/',
expected_bytes=25600116) # ls -al
params = load_npz(name=os.path.join(path, 'mobilenet.npz'))
for idx, net_weight in enumerate(network.all_weights):
if 'batchnorm' in net_weight.name:
params[idx] = params[idx].reshape(1, 1, 1, -1)
assign_weights(params[:len(network.all_weights)], network)
del params
