def UpdateModels(self):
loader = model_loader.ModelLoader(DEVICE_MODEL_DATA)
added, updated = loader.Update()
if added or updated:
memcache.delete(memcache_keys.INDEX_INFO)
memcache.delete(memcache_keys.PRODUCT_COUNT_KEY)
memcache.delete(memcache_keys.MANUFACTURER_MODEL_COUNTS)
memcache.delete(memcache_keys.CATEGORY_MODEL_COUNTS)
memcache.delete(memcache_keys.TAG_MODEL_COUNTS)
UpdateModificationTime(timestamp_keys.DEVICES)
return ('Models:\nAdded: %s\nUpdated: %s' %
(', '.join(added), ', '.join(updated)))
import sys
import tensorflow as tf
from os import getcwd
MODULE_DIR = getcwd() + '/tfmodules'
sys.path.insert(0, MODULE_DIR)
import path_manager
import model_loader as ld
import trainer as tr
import train_config as tr_config
sys.path.insert(0, path_manager.EXPORT_DIR)
model_export_dir = '/runtrain-20180613-yglee'
import_meta_filename = model_export_dir + '/net.ckpt.meta'
export_pb_filename = 'net.pb'
model_loader = ld.ModelLoader(subdir_and_filename=import_meta_filename)
model_graph_def = model_loader.load_model(clear_devices=True)
# converting to pb / pbdef files
train_config = tr_config.TrainConfig()
trainer = tr.Trainer(model_graph=model_graph_def, config=train_config)
trainer.export_graphdef_as_pb(subdir=model_export_dir,
filename=export_pb_filename)
stepvalues = [3000, 5000]
base_lr = 0.000002
rate_decay = 0.1
snapshot = 1000
display = 20
save_summary = 10
dataset_root = '/home/yfji/benchmark/Keypoint/fashionAI_key_points_train_20180227/train/'
with tf.device('/gpu:2'):
g = tf.Graph()
with g.as_default():
loader = loader.DataLoader(csv_path=op.join(dataset_root,
'Annotations/train.csv'),
dataset_root=dataset_root)
model_loader = ml.ModelLoader(load_mode='ckpt',
model_path='./models/fashion.ckpt-25000')
datashape, labelshape = loader.get_shape()
tensor_in = tf.placeholder(shape=datashape, dtype=tf.float32)
label = tf.placeholder(shape=labelshape, dtype=tf.float32)
tensor_out = network.make_cpm(tensor_in)
diff2_stage1 = tf.square(tf.subtract(tensor_out[0], label))
diff2_stage2 = tf.square(tf.subtract(tensor_out[1], label))
# with tf.name_scope('loss1'):
loss_stage1 = 0.5 * tf.reduce_mean(
tf.reduce_sum(tf.reduce_sum(tf.reduce_sum(diff2_stage1, axis=1),
axis=1),
dataset_root = '/home/yfji/Workspace/Python/FashionAI/test_new/test/'
iteration = 20000
with tf.Graph().as_default():
tensor_in = tf.placeholder(shape=[1, input_size, input_size, 3],
dtype=tf.float32)
tensor_out = network.make_cpm(tensor_in)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model_loader = ml.ModelLoader(load_mode='ckpt',
model_path='models/fashion.ckpt-%d' %
iteration)
model_loader.load_model(session=sess)
with open(
'/home/yfji/benchmark/Keypoint/fashionAI_key_points_train_20180227/train/Annotations/train.csv',
'r') as tf:
reader = csv.reader(tf)
header = None
for row in reader:
header = list(row)
break
print(header)
csv_name = 'pred_iter_%d_0.85.csv' % iteration
rows = []
with open(op.join(dataset_root, 'test.csv'), 'r') as f: