def model(x, H, reuse, is_training=True):
if H['inception'] == 1:
with slim.arg_scope(inception_v1.inception_v1_arg_scope()):
_, T = inception_v1.inception_v1(
x,
is_training=is_training,
num_classes=1001,
dropout_keep_prob=H['dense_dropout'],
input_dropout=H['input_dropout'],
spatial_squeeze=False,
reuse=reuse)
coarse_feat = T[H['coarse_feat']]
# fine feat can be used to reinspect input
early_feat = T[H['early_feat']]
early_feat_channels = 480
elif H['inception'] == 3:
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
_, T = inception_v3.inception_v3(x,
is_training=is_training,
num_classes=1001,
dropout_keep_prob=0.8,
spatial_squeeze=False,
reuse=reuse)
coarse_feat = T['Mixed_5b']
# fine feat can be used to reinspect input
attention_lname = H.get('attention_lname', 'Mixed_3b')
early_feat = T[attention_lname]
early_feat_channels = 480
return coarse_feat, early_feat, early_feat_channels
def testModelHasExpectedNumberOfParameters(self):
batch_size = 5
height, width = 224, 224
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope(inception_v1.inception_v1_arg_scope()):
inception_v1.inception_v1_base(inputs)
total_params, _ = slim.model_analyzer.analyze_vars(
slim.get_model_variables())
self.assertAlmostEqual(5607184, total_params)
def model(x, H, reuse, is_training=True):
with slim.arg_scope(inception.inception_v1_arg_scope()):
_, T = inception.inception_v1(x,
is_training=is_training,
num_classes=1001,
spatial_squeeze=False,
reuse=reuse)
coarse_feat = T['Mixed_5b']
# fine feat can be used to reinspect input
attention_lname = H.get('attention_lname', 'Mixed_3b')
early_feat = T[attention_lname]
early_feat_channels = 480
return coarse_feat, early_feat, early_feat_channels
def model(x, H, reuse, is_training=True):
with slim.arg_scope(inception.inception_v1_arg_scope()):
_, T = inception.inception_v1(x,
is_training=is_training,
num_classes=1001,
spatial_squeeze=False,
reuse=reuse)
coarse_feat = T['Mixed_5b']
# fine feat can be used to reinspect input
attention_lname = H.get('attention_lname', 'Mixed_3b')
early_feat = T[attention_lname]
early_feat_channels = 480
return coarse_feat, early_feat, early_feat_channels
def model(x, H, reuse, is_training=True):
if H['slim_basename'] == 'resnet_v1_101':
with slim.arg_scope(resnet.resnet_arg_scope()):
_, T = resnet.resnet_v1_101(x,
is_training=is_training,
num_classes=1000,
reuse=reuse)
elif H['slim_basename'] == 'InceptionV1':
with slim.arg_scope(inception.inception_v1_arg_scope()):
_, T = inception.inception_v1(x,
is_training=is_training,
num_classes=1001,
spatial_squeeze=False,
reuse=reuse)
#print '\n'.join(map(str, [(k, v.op.outputs[0].get_shape()) for k, v in T.iteritems()]))
coarse_feat = T[H['slim_top_lname']][:, :, :, :H['later_feat_channels']]
assert coarse_feat.op.outputs[0].get_shape()[3] == H['later_feat_channels']
# fine feat can be used to reinspect input
attention_lname = H.get('slim_attention_lname', 'Mixed_3b')
early_feat = T[attention_lname]
return coarse_feat, early_feat
tf.FixedLenFeature([], tf.int64),
"image":
tf.FixedLenFeature([], tf.string)
})
img = tf.decode_raw(features["image"], tf.uint8)
img = tf.reshape(img, [image_pixels, image_pixels, 3])
img = tf.cast(img, tf.float32)
label = tf.cast(features["label"], tf.int32)
return img, label
images = tf.placeholder(tf.float32, [None, image_pixels, image_pixels, 3],
name="input/x_input")
labels = tf.placeholder(tf.int64, [None], name="input/y_input")
with slim.arg_scope(inception_v1_arg_scope()):
logits, end_points = inception_v1(images,
num_classes=classes,
is_training=False)
correct_prediction = tf.equal(labels,
tf.argmax(end_points['Predictions'], 1),
name="correct_prediction")
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32),
name="accuracy")
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state("ckpt")
if ckpt:
print(ckpt.model_checkpoint_path)
tf.train.Saver().restore(sess, ckpt.model_checkpoint_path)