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