# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import os import pickle import shutil import unittest import numpy as np import paddle from paddle.static import InputSpec import paddle.fluid as fluid from paddle.fluid.layers.utils import flatten from paddle.fluid.dygraph import Linear from paddle.fluid.dygraph import declarative, ProgramTranslator from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX, INFER_PARAMS_INFO_SUFFIX from paddle.fluid import unique_name BATCH_SIZE = 32 BATCH_NUM = 10 SEED = 10 def random_batch_reader(input_size, label_size): def _get_random_inputs_and_labels(input_size, label_size): np.random.seed(SEED) input = np.random.random(size=input_size).astype('float32') label = np.random.random(size=label_size).astype('int64') return input, label def __reader__(): for _ in range(BATCH_NUM): batch_input, batch_label = _get_random_inputs_and_labels( [BATCH_SIZE, input_size], [BATCH_SIZE, label_size]) yield batch_input, batch_label return __reader__ class LinearNet(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super(LinearNet, self).__init__() self._linear = Linear(in_size, out_size) @declarative def forward(self, x): return self._linear(x) class LinearNetWithInputSpec(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super(LinearNetWithInputSpec, self).__init__() self._linear = Linear(in_size, out_size) @declarative(input_spec=[InputSpec(shape=[None, 784], dtype='float32')]) def forward(self, x): return self._linear(x) class LinearNetNotDeclarative(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super(LinearNetNotDeclarative, self).__init__() self._linear = Linear(in_size, out_size) def forward(self, x): return self._linear(x) class LinerNetWithLabel(paddle.nn.Layer): def __init__(self, in_size, out_size): super(LinerNetWithLabel, self).__init__() self._linear = Linear(in_size, out_size) @declarative(input_spec=[ InputSpec( shape=[None, 784], dtype='float32', name="image"), InputSpec( shape=[None, 1], dtype='int64', name="label") ]) def forward(self, x, label): out = self._linear(x) loss = fluid.layers.cross_entropy(out, label) avg_loss = fluid.layers.mean(loss) return out, avg_loss class LinerNetWithPruneInput(paddle.nn.Layer): def __init__(self, in_size, out_size): super(LinerNetWithPruneInput, self).__init__() self._linear = Linear(in_size, out_size) @declarative(input_spec=[ InputSpec( shape=[None, 784], dtype='float32', name="image"), InputSpec( shape=[None, 1], dtype='int64', name="label") ]) def forward(self, x, label): out = self._linear(x) loss = fluid.layers.cross_entropy(out, label) avg_loss = fluid.layers.mean(loss) return out class LinerNetWithUselessInput(paddle.nn.Layer): def __init__(self, in_size, out_size): super(LinerNetWithUselessInput, self).__init__() self._linear = Linear(in_size, out_size) @declarative(input_spec=[ InputSpec( shape=[None, 784], dtype='float32', name="image"), InputSpec( shape=[None, 1], dtype='int64', name="label") ]) def forward(self, x, label): out = self._linear(x) return out class LinearNetReturnLoss(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super(LinearNetReturnLoss, self).__init__() self._linear = Linear(in_size, out_size) @declarative def forward(self, x): y = self._linear(x) z = self._linear(y) loss = fluid.layers.mean(z) return z, loss class LinearNetMultiInput(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super(LinearNetMultiInput, self).__init__() self._linear1 = Linear(in_size, out_size) self._linear2 = Linear(in_size, out_size) @declarative(input_spec=[ InputSpec( [None, 8], dtype='float32'), InputSpec( [None, 8], dtype='float32') ]) def forward(self, x, y): x_out = self._linear1(x) y_out = self._linear2(y) loss = fluid.layers.mean(x_out + y_out) return x_out, y_out, loss class LinearNetMultiInput1(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super(LinearNetMultiInput1, self).__init__() self._linear1 = Linear(in_size, out_size) self._linear2 = Linear(in_size, out_size) @declarative(input_spec=(InputSpec( [None, 8], dtype='float32'), InputSpec( [None, 8], dtype='float32'))) def forward(self, x, y): x_out = self._linear1(x) y_out = self._linear2(y) loss = fluid.layers.mean(x_out + y_out) return x_out, y_out, loss class MultiLoadingLinearNet(fluid.dygraph.Layer): def __init__(self, size, model_path): super(MultiLoadingLinearNet, self).__init__() self._linear = Linear(size, size) self._load_linear1 = paddle.jit.load(model_path) self._load_linear2 = paddle.jit.load(model_path) @declarative def forward(self, x): tmp1 = self._linear(x) tmp2 = self._load_linear1(tmp1) tmp3 = self._load_linear2(tmp2) y = self._linear(tmp3) return y class LinearNetReturnHidden(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super(LinearNetReturnHidden, self).__init__() self._linear_1 = Linear(in_size, out_size) self._linear_2 = Linear(in_size, out_size) @declarative def forward(self, x): y = self._linear_1(x) z = self._linear_2(y) loss = fluid.layers.mean(z) return y, loss class LinearNetWithNestOut(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super(LinearNetWithNestOut, self).__init__() self._linear_1 = Linear(in_size, out_size) self._linear_2 = Linear(in_size, out_size) @declarative def forward(self, x): y = self._linear_1(x) z = self._linear_2(y) out = y + z loss = fluid.layers.mean(out) return y, [(z, loss), out] class LinearNetWithDictInput(paddle.nn.Layer): def __init__(self, in_size, out_size): super(LinearNetWithDictInput, self).__init__() self._linear = Linear(in_size, out_size) @paddle.jit.to_static(input_spec=[{ 'img': InputSpec( shape=[None, 8], dtype='float32', name='img') }, { 'label': InputSpec( shape=[None, 1], dtype='int64', name='label') }]) def forward(self, img, label): out = self._linear(img['img']) # not return loss to avoid prune output loss = paddle.nn.functional.cross_entropy(out, label['label']) return out class LinearNetWithDictInputNoPrune(paddle.nn.Layer): def __init__(self, in_size, out_size): super(LinearNetWithDictInputNoPrune, self).__init__() self._linear = Linear(in_size, out_size) def forward(self, img): out = self._linear(img['img'] + img['img2']) return out class EmptyLayer(paddle.nn.Layer): def __init__(self): super(EmptyLayer, self).__init__() @paddle.jit.to_static def forward(self, x): return x class NoParamLayer(paddle.nn.Layer): def __init__(self): super(NoParamLayer, self).__init__() @paddle.jit.to_static def forward(self, x, y): return x + y class LinearNetWithMultiStaticFunc(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super(LinearNetWithMultiStaticFunc, self).__init__() self._linear_0 = Linear(in_size, out_size) self._linear_1 = Linear(in_size, out_size) self._scale = paddle.to_tensor(9.9) @paddle.jit.to_static def forward(self, x): return self._linear_0(x) @paddle.jit.to_static def forward_no_param(self, x): return x @paddle.jit.to_static def forward_general(self, x): return self._linear_0(x) + self._linear_1(x) * self._scale def train(layer, input_size=784, label_size=1): # create optimizer sgd = fluid.optimizer.SGDOptimizer( learning_rate=0.01, parameter_list=layer.parameters()) # create data loader train_loader = fluid.io.DataLoader.from_generator(capacity=5) train_loader.set_batch_generator( random_batch_reader(input_size, label_size)) # train for data in train_loader(): img, label = data label.stop_gradient = True cost = layer(img) loss = fluid.layers.cross_entropy(cost, label) avg_loss = fluid.layers.mean(loss) avg_loss.backward() sgd.minimize(avg_loss) layer.clear_gradients() return [img], layer, avg_loss def train_with_label(layer, input_size=784, label_size=1): # create optimizer sgd = fluid.optimizer.SGDOptimizer( learning_rate=0.01, parameter_list=layer.parameters()) # create data loader train_loader = fluid.io.DataLoader.from_generator(capacity=5) train_loader.set_batch_generator( random_batch_reader(input_size, label_size)) # train for data in train_loader(): img, label = data label.stop_gradient = True out, avg_loss = layer(img, label) avg_loss.backward() sgd.minimize(avg_loss) layer.clear_gradients() return out class TestJitSaveLoad(unittest.TestCase): def setUp(self): self.model_path = "test_jit_save_load/model" # enable dygraph mode fluid.enable_dygraph() # config seed paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) def train_and_save_model(self, model_path=None): layer = LinearNet(784, 1) example_inputs, layer, _ = train(layer) final_model_path = model_path if model_path else self.model_path orig_input_types = [type(x) for x in example_inputs] paddle.jit.save( layer=layer, path=final_model_path, input_spec=example_inputs) new_input_types = [type(x) for x in example_inputs] self.assertEqual(orig_input_types, new_input_types) return layer def test_save_load(self): # train and save model train_layer = self.train_and_save_model() # load model loaded_layer = paddle.jit.load(self.model_path) self.load_and_inference(train_layer, loaded_layer) self.load_dygraph_state_dict(train_layer) self.load_and_finetune(train_layer, loaded_layer) def load_and_inference(self, train_layer, infer_layer): train_layer.eval() infer_layer.eval() # inference & compare x = fluid.dygraph.to_variable( np.random.random((1, 784)).astype('float32')) self.assertTrue( np.array_equal(train_layer(x).numpy(), infer_layer(x).numpy())) def load_and_finetune(self, train_layer, load_train_layer): train_layer.train() load_train_layer.train() # train & compare img0, _, train_loss = train(train_layer) img1, _, load_train_loss = train(load_train_layer) self.assertTrue( np.array_equal(train_loss.numpy(), load_train_loss.numpy())) def load_dygraph_state_dict(self, train_layer): train_layer.eval() # construct new model new_layer = LinearNet(784, 1) orig_state_dict = new_layer.state_dict() load_state_dict = paddle.load(self.model_path) for structured_name in orig_state_dict: self.assertTrue(structured_name in load_state_dict) new_layer.set_state_dict(load_state_dict) new_layer.eval() # inference & compare x = fluid.dygraph.to_variable( np.random.random((1, 784)).astype('float32')) self.assertTrue( np.array_equal(train_layer(x).numpy(), new_layer(x).numpy())) def test_load_dygraph_no_path(self): model_path = "test_jit_save_load.no_path/model_path" with self.assertRaises(ValueError): model_dict, _ = fluid.dygraph.load_dygraph(model_path) def test_jit_load_model_incomplete(self): model_path = "test_jit_save_load.remove_variables/model" self.train_and_save_model(model_path) # remove `.pdiparams` var_path = model_path + INFER_PARAMS_SUFFIX os.remove(var_path) with self.assertRaises(ValueError): paddle.jit.load(model_path) def test_jit_load_no_path(self): path = "test_jit_save_load.no_path/model_path" with self.assertRaises(ValueError): loaded_layer = paddle.jit.load(path) class TestSaveLoadWithNestOut(unittest.TestCase): def setUp(self): # enable dygraph mode fluid.enable_dygraph() def test_nest_output(self): x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32')) net = LinearNetWithNestOut(8, 8) dy_outs = flatten(net(x)) net = declarative(net, input_spec=[InputSpec([None, 8], name='x')]) model_path = "net_with_nest_out/model" paddle.jit.save(net, model_path) load_net = paddle.jit.load(model_path) load_outs = flatten(load_net(x)) self.assertTrue(len(dy_outs) == 4) for dy_out, load_out in zip(dy_outs, load_outs): self.assertTrue(np.allclose(dy_out.numpy(), load_out.numpy())) class TestSaveLoadWithDictInput(unittest.TestCase): def test_dict_input(self): # NOTE: This net cannot be executed, it is just # a special case for exporting models in model validation # We DO NOT recommend this writing way of Layer net = LinearNetWithDictInput(8, 8) # net.forward.concrete_program.inputs: # (<__main__.LinearNetWithDictInput object at 0x7f2655298a98>, # {'img': var img : fluid.VarType.LOD_TENSOR.shape(-1, 8).astype(VarType.FP32)}, # {'label': var label : fluid.VarType.LOD_TENSOR.shape(-1, 1).astype(VarType.INT64)}) self.assertEqual(len(net.forward.concrete_program.inputs), 3) path = "test_jit_save_load_with_dict_input/model" # prune inputs paddle.jit.save( layer=net, path=path, input_spec=[{ 'img': InputSpec( shape=[None, 8], dtype='float32', name='img') }]) img = paddle.randn(shape=[4, 8], dtype='float32') loaded_net = paddle.jit.load(path) loaded_out = loaded_net(img) # loaded_net._input_spec(): # [InputSpec(shape=(-1, 8), dtype=VarType.FP32, name=img)] self.assertEqual(len(loaded_net._input_spec()), 1) class TestSaveLoadWithDictInputNoPrune(unittest.TestCase): def test_dict_input(self): net = LinearNetWithDictInputNoPrune(8, 8) path = "test_jit_save_load_with_dict_input_no_prune/model" # prune inputs paddle.jit.save( layer=net, path=path, input_spec=[{ 'img': InputSpec( shape=[None, 8], dtype='float32', name='img'), 'img2': InputSpec( shape=[None, 8], dtype='float32', name='img2') }]) img = paddle.randn(shape=[4, 8], dtype='float32') img2 = paddle.randn(shape=[4, 8], dtype='float32') loaded_net = paddle.jit.load(path) loaded_out = loaded_net(img, img2) self.assertEqual(len(loaded_net._input_spec()), 2) class TestSaveLoadWithInputSpec(unittest.TestCase): def setUp(self): # enable dygraph mode fluid.enable_dygraph() def test_with_input_spec(self): net = LinearNetReturnLoss(8, 8) # set x.shape = [None, 8] net.forward = declarative( net.forward, input_spec=[InputSpec( [None, 8], name='x')]) model_path = "input_spec.output_spec/model" # check inputs and outputs self.assertTrue(len(net.forward.inputs) == 1) input_x = net.forward.inputs[0] self.assertTrue(input_x.shape == (-1, 8)) self.assertTrue(input_x.name == 'x') # 1. prune loss output_spec = net.forward.outputs[:1] paddle.jit.save(net, model_path, output_spec=output_spec) # 2. load to infer infer_layer = paddle.jit.load(model_path) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32')) pred = infer_layer(x) def test_multi_in_out(self): net = LinearNetMultiInput(8, 8) model_path = "multi_inout.output_spec1/model" # 1. check inputs and outputs self.assertTrue(len(net.forward.inputs) == 2) input_x = net.forward.inputs[0] input_y = net.forward.inputs[1] self.assertTrue(input_x.shape == (-1, 8)) self.assertTrue(input_y.shape == (-1, 8)) # 2. prune loss output_spec = net.forward.outputs[:2] paddle.jit.save(net, model_path, output_spec=output_spec) # 3. load to infer infer_layer = paddle.jit.load(model_path) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32')) y = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32')) # 4. predict pred_x, pred_y = infer_layer(x, y) # 1. prune y and loss model_path = "multi_inout.output_spec2/model" output_spec = net.forward.outputs[:1] paddle.jit.save(net, model_path, [input_x], output_spec=output_spec) # 2. load again infer_layer2 = paddle.jit.load(model_path) # 3. predict pred_xx = infer_layer2(x) # 4. assert pred_x == pred_xx self.assertTrue(np.allclose(pred_x.numpy(), pred_xx.numpy())) def test_multi_in_out1(self): net = LinearNetMultiInput1(8, 8) model_path = "multi_inout1.output_spec1/model" # 1. check inputs and outputs self.assertTrue(len(net.forward.inputs) == 2) input_x = net.forward.inputs[0] input_y = net.forward.inputs[1] self.assertTrue(input_x.shape == (-1, 8)) self.assertTrue(input_y.shape == (-1, 8)) # 2. prune loss output_spec = net.forward.outputs[:2] paddle.jit.save(net, model_path, output_spec=output_spec) # 3. load to infer infer_layer = paddle.jit.load(model_path) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32')) y = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32')) # 4. predict pred_x, pred_y = infer_layer(x, y) # 1. prune y and loss model_path = "multi_inout1.output_spec2/model" output_spec = net.forward.outputs[:1] paddle.jit.save(net, model_path, (input_x, ), output_spec=output_spec) # 2. load again infer_layer2 = paddle.jit.load(model_path) # 3. predict pred_xx = infer_layer2(x) # 4. assert pred_x == pred_xx self.assertTrue(np.allclose(pred_x.numpy(), pred_xx.numpy())) class TestJitSaveLoadConfig(unittest.TestCase): def setUp(self): # enable dygraph mode fluid.enable_dygraph() # config seed paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) def test_output_spec(self): train_layer = LinearNetReturnLoss(8, 8) adam = fluid.optimizer.AdamOptimizer( learning_rate=0.1, parameter_list=train_layer.parameters()) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32')) for i in range(10): out, loss = train_layer(x) loss.backward() adam.minimize(loss) train_layer.clear_gradients() model_path = "save_load_config.output_spec" output_spec = [out] paddle.jit.save( layer=train_layer, path=model_path, input_spec=[x], output_spec=output_spec) train_layer.eval() infer_layer = paddle.jit.load(model_path) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32')) self.assertTrue( np.array_equal(train_layer(x)[0].numpy(), infer_layer(x).numpy())) def test_save_no_support_config_error(self): layer = LinearNet(784, 1) path = "no_support_config_test" with self.assertRaises(ValueError): paddle.jit.save(layer=layer, path=path, model_filename="") def test_load_empty_model_filename_error(self): path = "error_model_filename_test" with self.assertRaises(ValueError): paddle.jit.load(path, model_filename="") def test_load_empty_params_filename_error(self): path = "error_params_filename_test" with self.assertRaises(ValueError): paddle.jit.load(path, params_filename="") def test_load_with_no_support_config(self): path = "no_support_config_test" with self.assertRaises(ValueError): paddle.jit.load(path, separate_params=True) class TestJitMultipleLoading(unittest.TestCase): def setUp(self): self.linear_size = 4 self.model_path = "jit_multi_load/model" # enable dygraph mode fluid.enable_dygraph() # config seed paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) # train and save base model self.train_and_save_orig_model() def train_and_save_orig_model(self): layer = LinearNet(self.linear_size, self.linear_size) example_inputs, layer, _ = train(layer, self.linear_size, 1) paddle.jit.save( layer=layer, path=self.model_path, input_spec=example_inputs) def test_load_model_retransform_inference(self): multi_loaded_layer = MultiLoadingLinearNet(self.linear_size, self.model_path) state_dict = multi_loaded_layer.state_dict() name_set = set() for _, var in state_dict.items(): self.assertTrue(var.name not in name_set) name_set.add(var.name) class TestJitPruneModelAndLoad(unittest.TestCase): def setUp(self): self.linear_size = 4 self.model_path = "jit_prune_model_and_load/model" # enable dygraph mode fluid.enable_dygraph() # config seed paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) def train_and_save(self): train_layer = LinearNetReturnHidden(8, 8) adam = fluid.optimizer.AdamOptimizer( learning_rate=0.1, parameter_list=train_layer.parameters()) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32')) for i in range(10): hidden, loss = train_layer(x) loss.backward() adam.minimize(loss) train_layer.clear_gradients() output_spec = [hidden] paddle.jit.save( layer=train_layer, path=self.model_path, input_spec=[x], output_spec=output_spec) return train_layer def test_load_pruned_model(self): train_layer = self.train_and_save() train_layer.eval() infer_layer = paddle.jit.load(self.model_path) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32')) self.assertTrue( np.array_equal(train_layer(x)[0].numpy(), infer_layer(x).numpy())) def test_load_var_not_in_extra_var_info(self): self.train_and_save() # chage extra var info var_info_path = self.model_path + INFER_PARAMS_INFO_SUFFIX with open(var_info_path, 'rb') as f: extra_var_info = pickle.load(f) extra_var_info.clear() with open(var_info_path, 'wb') as f: pickle.dump(extra_var_info, f, protocol=2) with self.assertRaises(RuntimeError): paddle.jit.load(self.model_path) class TestJitSaveMultiCases(unittest.TestCase): def setUp(self): # enable dygraph mode fluid.enable_dygraph() # config seed paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) def verify_inference_correctness(self, layer, model_path, with_label_and_loss=False, with_label=False): layer.eval() loaded_layer = paddle.jit.load(model_path) loaded_layer.eval() # inference & compare x = paddle.to_tensor(np.random.random((1, 784)).astype('float32')) if with_label_and_loss: y = paddle.to_tensor(np.random.random((1, 1)).astype('int64')) pred, _ = layer(x, y) pred = pred.numpy() elif with_label: y = paddle.to_tensor(np.random.random((1, 1)).astype('int64')) pred = layer(x, y) pred = pred.numpy() else: pred = layer(x).numpy() loaded_pred = loaded_layer(x).numpy() self.assertTrue( np.array_equal(pred, loaded_pred), msg="Result diff when load and inference:\nlayer result:\n{}\n" \ "loaded layer result:\n{}".format(pred, loaded_pred)) def test_no_prune_to_static_after_train(self): layer = LinearNet(784, 1) train(layer) model_path = "test_no_prune_to_static_after_train/model" paddle.jit.save(layer, model_path) self.verify_inference_correctness(layer, model_path) def test_no_prune_to_static_no_train(self): layer = LinearNetWithInputSpec(784, 1) model_path = "test_no_prune_to_static_no_train/model" paddle.jit.save(layer, model_path) self.verify_inference_correctness(layer, model_path) def test_no_prune_no_to_static_after_train(self): layer = LinearNetNotDeclarative(784, 1) train(layer) model_path = "test_no_prune_no_to_static_after_train/model" paddle.jit.save( layer, model_path, input_spec=[InputSpec( shape=[None, 784], dtype='float32')]) self.verify_inference_correctness(layer, model_path) def test_no_prune_no_to_static_after_train_with_examples(self): layer = LinearNetNotDeclarative(784, 1) example_inputs, _, _ = train(layer) model_path = "test_no_prune_no_to_static_after_train_with_examples/model" paddle.jit.save(layer=layer, path=model_path, input_spec=example_inputs) self.verify_inference_correctness(layer, model_path) def test_no_prune_no_to_static_no_train(self): layer = LinearNetNotDeclarative(784, 1) model_path = "test_no_prune_no_to_static_no_train/model" paddle.jit.save( layer, model_path, input_spec=[InputSpec( shape=[None, 784], dtype='float32')]) self.verify_inference_correctness(layer, model_path) def test_prune_to_static_after_train(self): layer = LinerNetWithLabel(784, 1) out = train_with_label(layer) model_path = "test_prune_to_static_after_train/model" paddle.jit.save( layer, model_path, input_spec=[ InputSpec( shape=[None, 784], dtype='float32', name="image") ], output_spec=[out]) self.verify_inference_correctness( layer, model_path, with_label_and_loss=True) def test_prune_to_static_no_train(self): layer = LinerNetWithLabel(784, 1) model_path = "test_prune_to_static_no_train/model" # TODO: no train, cannot get output_spec var here # now only can use index output_spec = layer.forward.outputs[:1] paddle.jit.save( layer, model_path, input_spec=[ InputSpec( shape=[None, 784], dtype='float32', name="image") ], output_spec=output_spec) self.verify_inference_correctness( layer, model_path, with_label_and_loss=True) def test_prune_input_to_static_no_train(self): layer = LinerNetWithPruneInput(784, 1) model_path = "test_prune_input_to_static_no_train/model" paddle.jit.save( layer, model_path, input_spec=[ InputSpec( shape=[None, 784], dtype='float32', name="image") ]) self.verify_inference_correctness(layer, model_path, with_label=True) def test_prune_useless_input_to_static_no_train(self): layer = LinerNetWithUselessInput(784, 1) model_path = "test_prune_useless_input_to_static_no_train/model" paddle.jit.save( layer, model_path, input_spec=[ InputSpec( shape=[None, 784], dtype='float32', name="image") ]) self.verify_inference_correctness(layer, model_path, with_label=True) def test_no_prune_input_spec_name_warning(self): layer = LinearNetWithInputSpec(784, 1) train(layer) model_path = "test_no_prune_input_spec_name_warning/model" paddle.jit.save( layer, model_path, input_spec=[InputSpec( shape=[None, 784], dtype='float32')]) paddle.jit.save( layer, model_path, input_spec=[ InputSpec( shape=[None, 784], dtype='float32', name='feed_input') ]) self.verify_inference_correctness(layer, model_path) def test_not_prune_output_spec_name_warning(self): layer = LinearNet(784, 1) train(layer) model_path = "test_not_prune_output_spec_name_warning/model" out = paddle.to_tensor(np.random.random((1, 1)).astype('float')) paddle.jit.save(layer, model_path, output_spec=[out]) self.verify_inference_correctness(layer, model_path) def test_prune_input_spec_name_error(self): layer = LinerNetWithLabel(784, 1) model_path = "test_prune_input_spec_name_error/model" with self.assertRaises(ValueError): paddle.jit.save( layer, model_path, input_spec=[InputSpec( shape=[None, 784], dtype='float32')]) with self.assertRaises(ValueError): paddle.jit.save( layer, model_path, input_spec=[ InputSpec( shape=[None, 784], dtype='float32', name='feed_input') ]) def test_prune_output_spec_name_error(self): layer = LinerNetWithLabel(784, 1) train_with_label(layer) model_path = "test_prune_to_static_after_train/model" out = paddle.to_tensor(np.random.random((1, 1)).astype('float')) with self.assertRaises(ValueError): paddle.jit.save( layer, model_path, input_spec=[ InputSpec( shape=[None, 784], dtype='float32', name="image") ], output_spec=[out]) class TestJitSaveLoadEmptyLayer(unittest.TestCase): def setUp(self): self.model_path = "jit_save_load_empty_layer/model" # enable dygraph mode paddle.disable_static() def test_save_load_empty_layer(self): layer = EmptyLayer() x = paddle.to_tensor(np.random.random((10)).astype('float32')) out = layer(x) paddle.jit.save(layer, self.model_path) load_layer = paddle.jit.load(self.model_path) load_out = load_layer(x) self.assertTrue(np.array_equal(out, load_out)) class TestJitSaveLoadNoParamLayer(unittest.TestCase): def setUp(self): self.model_path = "jit_save_load_no_param_layer/model" # enable dygraph mode paddle.disable_static() def test_save_load_no_param_layer(self): layer = NoParamLayer() x = paddle.to_tensor(np.random.random((5)).astype('float32')) y = paddle.to_tensor(np.random.random((5)).astype('float32')) out = layer(x, y) paddle.jit.save(layer, self.model_path) load_layer = paddle.jit.load(self.model_path) load_out = load_layer(x, y) self.assertTrue(np.array_equal(out, load_out)) class TestJitSaveLoadMultiMethods(unittest.TestCase): def setUp(self): # enable dygraph mode paddle.disable_static() def test_jit_save_load_inference(self): model_path_inference = "jit_save_load_multi_methods/model" IMAGE_SIZE = 224 layer = LinearNetWithMultiStaticFunc(IMAGE_SIZE, 10) inps = paddle.randn([1, IMAGE_SIZE]) result_origin = {} for func in dir(layer): if func.startswith('forward'): result_origin[func] = getattr(layer, func, None)(inps) paddle.jit.save(layer, model_path_inference) load_net = paddle.jit.load(model_path_inference) for func, result in result_origin.items(): self.assertTrue( float((result - getattr(load_net, func, None)(inps)).abs().max( )) < 1e-5) def test_jit_save_load_multi_methods_inputspec(self): model_path = 'jit_save_load_multi_methods/model' layer = LinearNetWithMultiStaticFunc(784, 1) with self.assertRaises(ValueError): paddle.jit.save( layer, model_path, input_spec=[InputSpec(shape=[None, 784])]) def test_parse_name(self): model_path_inference = "jit_save_load_parse_name/model" IMAGE_SIZE = 224 layer = LinearNet(IMAGE_SIZE, 1) inps = paddle.randn([1, IMAGE_SIZE]) layer(inps) paddle.jit.save(layer, model_path_inference) paddle.jit.save(layer, model_path_inference + '_v2') load_net = paddle.jit.load(model_path_inference) self.assertFalse(hasattr(load_net, 'v2')) class LayerSaved(paddle.nn.Layer): def __init__(self, in_size, out_size): super(LayerSaved, self).__init__() self.hidden = 100 self._linear_0 = Linear(in_size, self.hidden) self._linear_1_0 = Linear(self.hidden, self.hidden) self._linear_1_1 = Linear(self.hidden, self.hidden) self._linear_2 = Linear(self.hidden, out_size) self._scale = paddle.to_tensor(9.9) @paddle.jit.to_static def forward(self, x): y = self._linear_0(x) # Multiple blocks if x.shape[0] == 1: y = self._linear_1_0(y) else: y += self._linear_1_1(y + self._scale) return self._linear_2(y) class LayerLoadFinetune(paddle.nn.Layer): def __init__(self, in_size, out_size, load_path): super(LayerLoadFinetune, self).__init__() # Test duplicate name self._linear_0 = Linear(in_size, in_size) self._linear_1_0 = Linear(out_size, in_size) self._linear_1_1 = Linear(out_size, in_size) self._linear_2 = Linear(out_size, out_size) self._scale = paddle.to_tensor(9.9) # Load multiple times self._load_l1 = paddle.jit.load(load_path) self._load_l2 = paddle.jit.load(load_path) @paddle.jit.to_static def forward(self, x): y = self._linear_0(x) y = self._load_l1(y) # Multiple blocks if x.shape[0] == 1: y = self._linear_1_0(y) y = self._load_l1(y) else: y += self._linear_1_1(x + self._scale) y = self._load_l2(y) y = self._linear_1_0(y) y = self._load_l1(y) y = self._linear_1_0(y) # Use the same layer multiple times. y = self._load_l1(y) return y class TestJitSaveLoadSaveWithoutRunning(unittest.TestCase): def setUp(self): # enable dygraph mode paddle.disable_static() def test_save_load_finetune_load(self): model_path = "test_jit_save_load_save_without_running/model" IMAGE_SIZE = 224 inps0 = paddle.randn([1, IMAGE_SIZE]) inps1 = paddle.randn([2, IMAGE_SIZE]) # Use new namespace with unique_name.guard(): layer_save = LayerSaved(IMAGE_SIZE, IMAGE_SIZE) #save paddle.jit.save( layer_save, model_path, input_spec=[ paddle.static.InputSpec( shape=[None, IMAGE_SIZE], dtype='float32') ]) result_00 = layer_save(inps0) result_01 = layer_save(inps1) #load and save without running with unique_name.guard(): layer_load = paddle.jit.load(model_path) paddle.jit.save( layer_load, model_path, input_spec=[ paddle.static.InputSpec( shape=[None, IMAGE_SIZE], dtype='float32') ]) #reload layer_reload = paddle.jit.load(model_path) result_10 = layer_reload(inps0) result_11 = layer_reload(inps1) self.assertTrue(float((result_00 - result_10).abs().max()) < 1e-5) self.assertTrue(float((result_01 - result_11).abs().max()) < 1e-5) class TestJitSaveLoadFinetuneLoad(unittest.TestCase): def setUp(self): # enable dygraph mode paddle.disable_static() def test_save_load_finetune_load(self): model_path = "test_jit_save_load_finetune_load/model" IMAGE_SIZE = 224 inps0 = paddle.randn([1, IMAGE_SIZE]) inps1 = paddle.randn([2, IMAGE_SIZE]) # Use new namespace with unique_name.guard(): layer_save = LayerSaved(IMAGE_SIZE, IMAGE_SIZE) layer_save(inps0) #save paddle.jit.save(layer_save, model_path) #load with unique_name.guard(): layer_load = LayerLoadFinetune(IMAGE_SIZE, IMAGE_SIZE, model_path) #train train(layer_load, input_size=IMAGE_SIZE) result_00 = layer_load(inps0) result_01 = layer_load(inps1) #save paddle.jit.save(layer_load, model_path) #load layer_finetune = paddle.jit.load(model_path) result_10 = layer_finetune(inps0) result_11 = layer_finetune(inps1) self.assertTrue(float((result_00 - result_10).abs().max()) < 1e-5) self.assertTrue(float(((result_01 - result_11)).abs().max()) < 1e-5) class TestJitSaveLoadDataParallel(unittest.TestCase): def verify_inference_correctness(self, layer, path): layer.eval() loaded_layer = paddle.jit.load(path) loaded_layer.eval() # inference & compare x = paddle.to_tensor(np.random.random((1, 784)).astype('float32')) pred = layer(x).numpy() loaded_pred = loaded_layer(x).numpy() self.assertTrue( np.array_equal(pred, loaded_pred), msg="Result diff when load and inference:\nlayer result:\n{}\n" \ "loaded layer result:\n{}".format(pred, loaded_pred)) def test_jit_save_data_parallel_with_inputspec(self): layer = LinearNetNotDeclarative(784, 1) layer = paddle.DataParallel(layer) path = "jit_save_data_parallel_with_inputspec/model" paddle.jit.save( layer=layer, path=path, input_spec=[InputSpec(shape=[None, 784])]) self.verify_inference_correctness(layer, path) def test_jit_save_data_parallel_with_to_static(self): layer = LinearNetWithInputSpec(784, 1) layer = paddle.DataParallel(layer) path = "jit_save_data_parallel_with_to_static/model" paddle.jit.save(layer, path) self.verify_inference_correctness(layer, path) class InputSepcLayer(paddle.nn.Layer): ''' A layer with InputSpec to test InputSpec compatibility ''' @paddle.jit.to_static(input_spec=[ InputSpec( shape=[None, 8], dtype='float32', name='x'), InputSpec( shape=[None, 1], dtype='float64', name='y') ]) def forward(self, x, y): return x, y class TestInputSpecCompatibility(unittest.TestCase): def _assert_input_spec_layer_return(self, expect_layer, test_layer): input_x = paddle.uniform([8, 8], dtype='float32') input_y = paddle.uniform([8, 1], dtype='float64') expected_result = expect_layer(input_x, input_y) test_result = test_layer(input_x, input_y) np.testing.assert_allclose(expected_result[0].numpy(), test_result[0].numpy()) np.testing.assert_allclose(expected_result[1].numpy(), test_result[1].numpy()) def test_jit_save_compatible_input_sepc(self): layer = InputSepcLayer() save_dir = "jit_save_compatible_input_spec" path = save_dir + "/model" paddle.jit.save(layer=layer, path=path) no_input_spec_layer = paddle.jit.load(path) self._assert_input_spec_layer_return(layer, no_input_spec_layer) shutil.rmtree(save_dir) paddle.jit.save( layer=layer, path=path, input_spec=[ InputSpec( shape=[None, 8], dtype='float32', name='x'), InputSpec( shape=[None, 1], dtype='float64', name='y') ]) same_input_spec_layer = paddle.jit.load(path) self._assert_input_spec_layer_return(layer, same_input_spec_layer) shutil.rmtree(save_dir) paddle.jit.save( layer=layer, path=path, input_spec=[ InputSpec( shape=[8, 8], dtype='float32'), InputSpec( shape=[8, -1], dtype='float64') ]) compatible_input_spec_layer = paddle.jit.load(path) self._assert_input_spec_layer_return(layer, compatible_input_spec_layer) shutil.rmtree(save_dir) def test_jit_save_incompatible_input_sepc(self): layer = InputSepcLayer() save_dir = "jit_save_compatible_input_spec" path = save_dir + "/model" with self.assertRaises(ValueError): # type mismatch paddle.jit.save( layer=layer, path=path, input_spec=[ InputSpec( shape=[None, 8], dtype='float64'), InputSpec( shape=[None, 1], dtype='float64') ]) with self.assertRaises(ValueError): # shape len mismatch paddle.jit.save( layer=layer, path=path, input_spec=[ InputSpec( shape=[None, 8, 1], dtype='float32'), InputSpec( shape=[None, 1], dtype='float64') ]) with self.assertRaises(ValueError): # shape mismatch paddle.jit.save( layer=layer, path=path, input_spec=[ InputSpec( shape=[None, 8], dtype='float32'), InputSpec( shape=[None, 2], dtype='float64') ]) if os.path.exists(save_dir): shutil.rmtree(save_dir) if __name__ == '__main__': unittest.main()