def restore_params(self, sess):
# For no pre-trained model link
# logging.info("Restore pre-trained parameters")
# vgg19_npy_path = os.path.join('models', 'vgg19.npy')
# if not os.path.isfile(vgg19_npy_path):
# print("Please download vgg19.npy from : https://github.com/machrisaa/tensorflow-vgg")
# exit()
# npz = np.load(vgg19_npy_path, encoding='latin1').item()
# For existing pre-trained model link
logging.info("Restore pre-trained parameters")
maybe_download_and_extract(
'vgg19.npy',
'models',
'https://media.githubusercontent.com/media/tensorlayer/pretrained-models/master/models/',
expected_bytes=574670860)
vgg19_npy_path = os.path.join('models', 'vgg19.npy')
npz = np.load(vgg19_npy_path, encoding='latin1').item()
params = []
for val in sorted(npz.items()):
W = np.asarray(val[1][0])
b = np.asarray(val[1][1])
print(" Loading %s: %s, %s" % (val[0], W.shape, b.shape))
params.extend([W, b])
print("Restoring model from npz file")
assign_params(sess, params, self.net)
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(self, sess, weights):
from tensorlayer.files import assign_params
logging.info("Restore pre-trained parameters")
npz = np.load(weights)
params = []
for val in sorted(npz.items()):
print(" Loading params %s" % str(val[1].shape))
params.append(val[1])
if len(self.vgg.all_params) == len(params):
break
assign_params(sess, params, self.vgg)
del params
def restore_params(self, md, sess):
# we have to customize the restore param function a bit
from tensorlayer.files import assign_params
logging.info("Restore pre-trained parameters")
npz = np.load(
os.path.join('../data/pretrain_model', 'vgg16_weights.npz'))
params = []
for val in sorted(npz.items()):
print(" Loading params %s" % str(val[1].shape))
params.append(val[1])
if len(md.all_params) == len(params):
break
assign_params(sess, params, md.net)
del params
def restore_params(self, sess):
logging.info("Restore pre-trained parameters")
maybe_download_and_extract(
'vgg16_weights.npz', 'models', 'http://www.cs.toronto.edu/~frossard/vgg16/', expected_bytes=553436134
)
npz = np.load(os.path.join('models', 'vgg16_weights.npz'))
params = []
for val in sorted(npz.items()):
logging.info(" Loading params %s" % str(val[1].shape))
params.append(val[1])
if len(self.all_params) == len(params):
break
assign_params(sess, params, self.net)
del params
sess.run(tf.global_variables_initializer())
maybe_download_and_extract('vgg16_weights.npz',
'models',
'http://www.cs.toronto.edu/~frossard/vgg16/',
expected_bytes=553436134)
npz = np.load(os.path.join('models', 'vgg16_weights.npz'))
params = []
idx = 0
for val in sorted(npz.items()):
idx = idx + 1
print(" Loading params %s" % (str(val[1].shape)))
params.append(val[1])
if idx == 17:
break
assign_params(sess, params, net_work['block4'])
for epoch in range(train_epoch):
idx = 0
for batch_imgs, batch_ann in tl.iterate.minibatches(laod_imgs,
load_ann,
batch_size,
shuffle=False):
g_img, g_ann = getTrainData(batch_imgs, batch_ann)
gt_class, gt_location, gt_positives, gt_negatives = generate_groundtruth_data(
g_ann)
# print("g_img",g_img)
# print("gt_positives",np.sum(gt_positives,axis=1))
# print("gt_positives", np.sum(gt_negatives, axis=1))
# print("pre_cls", sess.run(pre_cls, feed_dict={imageinput: g_img, groundtruth_class: gt_class,
# groundtruth_location: gt_location,
# groundtruth_positives: gt_positives,
