def testMicroAddElementsFail(self):
data = [m(3, 3), m(3, 3)]
test_func = self.testAddElements
args = list()
###############
x = [
pkb.placeholder(shape=t.shape) for t in data
if isinstance(t, np.ndarray)
]
xv = [
pkb.variable(t, dtype=floatx()) for t in data
if isinstance(t, np.ndarray)
]
par = [t for t in data if not isinstance(t, np.ndarray)]
grad_funcs = test_func(pkb, *(x + par + list(args)))
funcs = test_func(pkb, *(xv + par + list(args)))
#for gf, f in zip(grad_funcs, funcs):
gf = grad_funcs[0]
f = funcs[0]
df = pkb.gradients(pkb.mean(gf), x)
gfn = pkb.function(x, df, updates=[])
fr = f.eval()
gr = gfn([t for t in data if isinstance(t, np.ndarray)])
if args.verbose:
print(pkb, fr, gr)
results.append((fr, gr))
return results
def testRecompileWithChangingProgramCacheSize(self):
# This test is thanks to iperov,
# who reported https://github.com/plaidml/plaidml/issues/274,
# demonstrating a case where exceeding certain number of ops
# causes recompiling of kernels (the encoder is slightly modified from
# his example for reproduciblilty)
shape = (64, 64, 3)
LeakyReLU = keras.layers.LeakyReLU
def encflow(x):
x = LeakyReLU()(keras.layers.Conv2D(128,
5,
strides=2,
padding="same")(x))
x = keras.layers.Conv2D(128, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(256, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(256, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(256, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(512, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(512, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(1024, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(1024, 5, strides=2, padding="same")(x)
x = keras.layers.Conv2D(1024, 5, strides=2, padding="same")(x)
x = keras.layers.Dense(64)(keras.layers.Flatten()(x))
x = keras.layers.Dense(4 * 4 * 1024)(x)
x = keras.layers.Reshape((4, 4, 1024))(x)
x = keras.layers.Conv2DTranspose(512, 3, strides=2,
padding="same")(x)
return x
def decflow(x):
x = x[0]
x = LeakyReLU()(keras.layers.Conv2DTranspose(512,
3,
strides=2,
padding="same")(x))
x = keras.layers.Conv2DTranspose(256, 3, strides=2,
padding="same")(x)
x = keras.layers.Conv2DTranspose(128, 3, strides=2,
padding="same")(x)
x = keras.layers.Conv2D(3, 5, strides=1, padding="same")(x)
return x
def modelify(model_functor):
def func(tensor):
return keras.models.Model(tensor, model_functor(tensor))
return func
encoder = modelify(encflow)(keras.Input(shape))
decoder1 = modelify(decflow)(
[keras.Input(pkb.int_shape(x)[1:]) for x in encoder.outputs])
decoder2 = modelify(decflow)(
[keras.Input(pkb.int_shape(x)[1:]) for x in encoder.outputs])
inp = x = keras.Input(shape)
code = encoder(x)
x1 = decoder1(code)
x2 = decoder2(code)
loss = pkb.mean(pkb.square(inp - x1)) + pkb.mean(pkb.square(inp - x2))
train_func = pkb.function(
[inp], [loss],
keras.optimizers.Adam().get_updates(
loss, encoder.trainable_weights + decoder1.trainable_weights +
decoder2.trainable_weights))
view_func1 = pkb.function([inp], [x1])
view_func2 = pkb.function([inp], [x2])
for i in range(5):
print("Loop %i" % i, flush=True)
data = np.zeros((1, 64, 64, 3))
train_func([data])
view_func1([data])
view_func2([data])
print("Saving weights", flush=True)
encoder.save_weights(r"testweights.h5")
decoder1.save_weights(r"testweights1.h5")
decoder2.save_weights(r"testweights2.h5")
