import numpy as np
import nin
import utils
import input
import time
ISOTIMEFORMAT = '%Y-%m-%d %X'
tmp = '/ais/gobi4/fashion/data/Category-Attribute-Prediction/'
with tf.device('/gpu:0'):
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
images = tf.placeholder(tf.float32, [None, 227, 227, 3])
true_out = tf.placeholder(tf.float32, [None, 50])
train_mode = tf.placeholder(tf.bool)
nin = nin.NIN()
nin.build(images, train_mode)
# print number of variables used
print nin.get_var_count()
#sess.run(tf.initialize_all_variables())
# test classification
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(nin.final, true_out))
#loss = tf.reduce_mean(-tf.reduce_sum(true_out*tf.log(nin.prob),[1]))
#loss = tf.reduce_mean(tf.reduce_sum((nin.prob-true_out)**2,[1]))
#train = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# train = tf.train.AdamOptimizer(1e-4).minimize(loss)
global_step = tf.Variable(0)
bottom = model.insize + top
right = model.insize + left
image = image[:, top:bottom, left:right].astype(np.float32)
image -= mean_image[:, top:bottom, left:right]
image /= 255
if flip and random.randint(0, 1) == 0:
return image[:, :, ::-1]
else:
return image
import nin
mean_image = pickle.load(open(args.mean, 'rb'))
model = nin.NIN()
serializers.load_npz('tl.model', model)
cropwidth = 256 - model.insize
model.to_cpu()
def predict(net, x):
h = F.max_pooling_2d(F.relu(net.conv1(x)), 3, stride=2)
h = F.max_pooling_2d(F.relu(net.conv2(h)), 3, stride=2)
h = F.max_pooling_2d(F.relu(net.conv3(h)), 3, stride=2)
h = net.conv4(F.dropout(h, train=True))
h = F.reshape(F.average_pooling_2d(h, 6), (x.data.shape[0], 1024))
return F.softmax(h)
#setattr(model, 'predict', predict)
def __init__(self):
model_path = '../based_imagenet/model_recent'
print('Load model:', model_path, file=sys.stderr)
model_obj = nin.NIN()
chainer.serializers.load_npz(model_path, model_obj)
self.core = simple_classifier.classifier(model_obj, None)
