def test_restore_block(self):
definition = {
'foo': [1],
'bar/conv_/w': np.random.randn(3, 3, 1, 3),
'bar2/conv_/w': np.random.randn(3, 3, 1, 3),
'foo3/conv_/w': np.random.randn(3, 3, 1, 3),
'bar/bing/boffin': [2]
}
checkpoint_name = self.make_checkpoint('chk1', definition)
tf.reset_default_graph()
block1 = ConvolutionalLayer(3, 3, with_bn=False, name='foo')
b1 = block1(tf.ones([1., 5., 5., 1.]))
tf.add_to_collection(RESTORABLE, ('foo', checkpoint_name, 'bar'))
block2 = ConvolutionalLayer(4,
3,
name='bar',
with_bn=False,
w_initializer=tf.constant_initializer(1.))
b2 = block2(tf.ones([1., 5., 5., 1.]))
block3 = ConvolutionalLayer(3, 3, with_bn=False, name='foo2')
block3.restore_from_checkpoint(checkpoint_name, 'bar2')
b3 = block3(tf.ones([1., 5., 5., 1.]))
block4 = ConvolutionalLayer(3, 3, with_bn=False, name='foo3')
block4.restore_from_checkpoint(checkpoint_name)
b4 = block4(tf.ones([1., 5., 5., 1.]))
tf.add_to_collection(RESTORABLE, ('foo', checkpoint_name, 'bar'))
init_op = global_vars_init_or_restore()
all_vars = tf.global_variables()
with self.test_session() as sess:
sess.run(init_op)
getvar = lambda x: [v for v in all_vars if v.name == x][0]
foo_w_var = getvar(block1.layer_scope().name + '/conv_/w:0')
bar_w_var = getvar(block2.layer_scope().name + '/conv_/w:0')
foo2_w_var = getvar(block3.layer_scope().name + '/conv_/w:0')
foo3_w_var = getvar(block4.layer_scope().name + '/conv_/w:0')
vars = [foo_w_var, bar_w_var, foo2_w_var, foo3_w_var]
[foo_w, bar_w, foo2_w, foo3_w] = sess.run(vars)
self.assertAllClose(foo_w, definition['bar/conv_/w'])
self.assertAllClose(bar_w, np.ones([3, 3, 1, 4]))
self.assertAllClose(foo2_w, definition['bar2/conv_/w'])
self.assertAllClose(foo3_w, definition['foo3/conv_/w'])
def test_restore_block(self):
definition = {'foo': [1], 'bar/conv_/w': np.random.randn(3, 3, 1, 3),
'bar2/conv_/w': np.random.randn(3, 3, 1, 3),
'foo3/conv_/w': np.random.randn(3, 3, 1, 3),
'bar/bing/boffin': [2]}
checkpoint_name = self.make_checkpoint('chk1', definition)
tf.reset_default_graph()
block1 = ConvolutionalLayer(3, 3, with_bn=False, name='foo')
b1 = block1(tf.ones([1., 5., 5., 1.]))
tf.add_to_collection(RESTORABLE,
('foo', checkpoint_name, 'bar'))
block2 = ConvolutionalLayer(4, 3, name='bar', with_bn=False,
w_initializer=tf.constant_initializer(1.))
b2 = block2(tf.ones([1., 5., 5., 1.]))
block3 = ConvolutionalLayer(3, 3, with_bn=False, name='foo2')
block3.restore_from_checkpoint(checkpoint_name, 'bar2')
b3 = block3(tf.ones([1., 5., 5., 1.]))
block4 = ConvolutionalLayer(3, 3, with_bn=False, name='foo3')
block4.restore_from_checkpoint(checkpoint_name)
b4 = block4(tf.ones([1., 5., 5., 1.]))
tf.add_to_collection(RESTORABLE,
('foo', checkpoint_name, 'bar'))
init_op = global_vars_init_or_restore()
all_vars = tf.global_variables()
with self.test_session() as sess:
sess.run(init_op)
def getvar(x):
return [v for v in all_vars if v.name == x][0]
foo_w_var = getvar(block1.layer_scope().name + '/conv_/w:0')
bar_w_var = getvar(block2.layer_scope().name + '/conv_/w:0')
foo2_w_var = getvar(block3.layer_scope().name + '/conv_/w:0')
foo3_w_var = getvar(block4.layer_scope().name + '/conv_/w:0')
vars = [foo_w_var, bar_w_var, foo2_w_var, foo3_w_var]
[foo_w, bar_w, foo2_w, foo3_w] = sess.run(vars)
self.assertAllClose(foo_w, definition['bar/conv_/w'])
self.assertAllClose(bar_w, np.ones([3, 3, 1, 4]))
self.assertAllClose(foo2_w, definition['bar2/conv_/w'])
self.assertAllClose(foo3_w, definition['foo3/conv_/w'])
def test_no_restores(self):
tf.reset_default_graph()
block1 = ConvolutionalLayer(4, 3, name='bar', with_bn=False,
w_initializer=tf.constant_initializer(1.))
b2 = block1(tf.ones([1., 5., 5., 1.]))
init_op = global_vars_init_or_restore()
all_vars = tf.global_variables()
with self.test_session() as sess:
sess.run(init_op)
def getvar(x):
return [v for v in all_vars if v.name == x][0]
bar_w_var = getvar(block1.layer_scope().name + '/conv_/w:0')
[bar_w] = sess.run([bar_w_var])
self.assertAllClose(bar_w, np.ones([3, 3, 1, 4]))
