def test_torch_input(self):
model = fe.build(model_fn=MultiLayerTorchModel, optimizer_fn="adam")
weights_before = deepcopy(model.fc1.weight.data.numpy())
op = UpdateOp(model=model, loss_name='loss')
pred = fe.backend.feed_forward(model, self.torch_input_data)
loss = fe.backend.mean_squared_error(y_pred=pred, y_true=self.torch_y)
op.forward(data=loss, state=self.state)
weights_after = model.fc1.weight.data.numpy()
self.assertFalse(is_equal(weights_before, weights_after))
def test_tf_input(self):
model = fe.build(one_layer_tf_model, optimizer_fn="adam")
op = UpdateOp(model=model, loss_name='loss')
weights_before = model.layers[1].get_weights()
with tf.GradientTape(persistent=True) as tape:
self.state['tape'] = tape
pred = fe.backend.feed_forward(model, self.tf_input_data)
loss = fe.backend.mean_squared_error(y_pred=pred, y_true=self.tf_y)
op.forward(data=loss, state=self.state)
weights_after = model.layers[1].get_weights()
self.assertFalse(is_equal(weights_before, weights_after))
def test_torch_model(self):
op = UpdateOp(model=self.torch_model, loss_name='loss')
pred = fe.backend.feed_forward(self.torch_model, self.torch_input_data)
loss = fe.backend.mean_squared_error(y_pred=pred, y_true=self.torch_y)
op.forward(data=loss, state=self.state)
bms = BestModelSaver(model=self.torch_model, save_dir=self.save_dir)
bms.on_epoch_end(data=self.data)
m2 = fe.build(model_fn=MultiLayerTorchModelWithoutWeights,
optimizer_fn='adam')
fe.backend.load_model(
m2, os.path.join(self.save_dir, 'torch_best_loss.pt'))
self.assertTrue(
is_equal(list(m2.parameters()),
list(self.torch_model.parameters())))
def test_tf_model(self):
op = UpdateOp(model=self.tf_model, loss_name='loss')
with tf.GradientTape(persistent=True) as tape:
self.state['tape'] = tape
pred = fe.backend.feed_forward(self.tf_model, self.tf_input_data)
loss = fe.backend.mean_squared_error(y_pred=pred, y_true=self.tf_y)
op.forward(data=loss, state=self.state)
bms = BestModelSaver(model=self.tf_model, save_dir=self.save_dir)
bms.on_epoch_end(data=self.data)
m2 = fe.build(model_fn=one_layer_model_without_weights,
optimizer_fn='adam')
fe.backend.load_model(m2, os.path.join(self.save_dir,
'tf_best_loss.h5'))
self.assertTrue(
is_equal(m2.trainable_variables,
self.tf_model.trainable_variables))
