# Copyright 2020 Huawei Technologies Co., Ltd # # 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. # ============================================================================ """ test_training """ import logging import numpy as np import pytest import mindspore.nn as nn from mindspore import Model, context from mindspore import Tensor from mindspore.nn.optim import Momentum from mindspore.train.callback import SummaryStep from ..ut_filter import non_graph_engine from ....dataset_mock import MindData class Net(nn.Cell): """ Net definition """ def __init__(self): super(Net, self).__init__() self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal', pad_mode='valid') self.bn = nn.BatchNorm2d(64) self.relu = nn.ReLU() self.flatten = nn.Flatten() self.fc = nn.Dense(64 * 222 * 222, 3) # padding=0 def construct(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) x = self.flatten(x) out = self.fc(x) return out class LossNet(nn.Cell): """ LossNet definition """ def __init__(self): super(LossNet, self).__init__() self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal', pad_mode='valid') self.bn = nn.BatchNorm2d(64) self.relu = nn.ReLU() self.flatten = nn.Flatten() self.fc = nn.Dense(64 * 222 * 222, 3) # padding=0 self.loss = nn.SoftmaxCrossEntropyWithLogits() def construct(self, x, y): x = self.conv(x) x = self.bn(x) x = self.relu(x) x = self.flatten(x) x = self.fc(x) out = self.loss(x, y) return out def get_model(metrics=None): """ get_model """ net = Net() loss = nn.SoftmaxCrossEntropyWithLogits() optim = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9) model = Model(net, loss_fn=loss, optimizer=optim, metrics=metrics) return model def get_dataset(): """ get_dataset """ dataset_types = (np.float32, np.float32) dataset_shapes = ((32, 3, 224, 224), (32, 3)) dataset = MindData(size=2, batch_size=32, np_types=dataset_types, output_shapes=dataset_shapes, input_indexs=(0, 1)) return dataset @non_graph_engine def test_single_input(): """ test_single_input """ input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32)) context.set_context(mode=context.GRAPH_MODE) model = Model(Net()) out = model.predict(input_data) assert out is not None @non_graph_engine def test_multiple_argument(): """ test_multiple_argument """ input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]).astype(np.float32)) input_label = Tensor(np.random.randint(0, 3, [1, 3]).astype(np.float32)) context.set_context(mode=context.GRAPH_MODE) model = Model(LossNet()) out = model.predict(input_data, input_label) assert out is not None def test_train_feed_mode(test_with_simu): """ test_train_feed_mode """ dataset = get_dataset() model = get_model() if test_with_simu: return model.train(2, dataset) def test_dataset_sink_mode_args_check(): """ test_dataset_sink_mode_args_check """ dataset = get_dataset() model = get_model() with pytest.raises(TypeError): model.train(2, dataset, dataset_sink_mode="True") with pytest.raises(TypeError): model.train(2, dataset, dataset_sink_mode=1) @non_graph_engine def test_eval(): """ test_eval """ dataset_types = (np.float32, np.float32) dataset_shapes = ((32, 3, 224, 224), (32, 3)) dataset = MindData(size=2, batch_size=32, np_types=dataset_types, output_shapes=dataset_shapes, input_indexs=(0, 1)) net = Net() context.set_context(mode=context.GRAPH_MODE) model = Model(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(), metrics={"loss"}) with pytest.raises(ValueError): model.eval(dataset) net2 = LossNet() model2 = Model(net2, eval_network=net2, eval_indexes=[0, 1, 2], metrics={"loss"}) with pytest.raises(ValueError): model2.eval(dataset) net3 = LossNet() model3 = Model(net2, eval_network=net2, metrics={"loss"}) with pytest.raises(ValueError): model3.eval(dataset) class TestGraphMode: """ TestGraphMode definition """ def test_train_minddata_graph_mode(self, test_with_simu): """ test_train_minddata_graph_mode """ # pylint: disable=unused-argument dataset_types = (np.float32, np.float32) dataset_shapes = ((32, 3, 224, 224), (32, 3)) dataset = MindData(size=2, batch_size=32, np_types=dataset_types, output_shapes=dataset_shapes, input_indexs=()) model = get_model() model.train(1, dataset) class CallbackTest: """ CallbackTest definition """ def __init__(self): pass def record(self, step, *args): print(step, args) def test_train_callback(test_with_simu): """ test_train_callback """ dataset = get_dataset() model = get_model() fn = CallbackTest() summary_recode = SummaryStep(fn, 2) if test_with_simu: return model.train(2, dataset, callbacks=summary_recode) log = logging.getLogger("test") log.setLevel(level=logging.ERROR) # Test the invalid args and trigger RuntimeError def test_model_build_abnormal_string(): """ test_model_build_abnormal_string """ net = nn.ReLU() context.set_context(mode=context.GRAPH_MODE) model = Model(net) err = False try: model.predict('aaa') except ValueError as e: log.error("Find value error: %r ", e) err = True finally: assert err def test_model_init(): """ test_model_init_error """ train_dataset = get_dataset() eval_dataset = get_dataset() with pytest.raises(RuntimeError): context.set_context(mode=context.PYNATIVE_MODE) get_model().init(train_dataset) context.set_context(mode=context.GRAPH_MODE) get_model().init(train_dataset) get_model(metrics={'acc'}).init(eval_dataset) with pytest.raises(RuntimeError): get_model().init(train_dataset, eval_dataset) with pytest.raises(ValueError): get_model().init() def test_init_model_error(): """ test_init_model_error """ net = nn.ReLU() loss = nn.SoftmaxCrossEntropyWithLogits() with pytest.raises(KeyError): Model(net, loss, metrics={"top1"}) with pytest.raises(ValueError): Model(net, metrics={"top_1_accuracy"}) with pytest.raises(TypeError): Model(net, metrics={"top5": None}) with pytest.raises(ValueError): Model(net, eval_network=net, eval_indexes=[], metrics={"top_1_accuracy"}) with pytest.raises(ValueError): Model(net, eval_network=net, eval_indexes=(1, 2, 3), metrics={"top_1_accuracy"}) with pytest.raises(TypeError): Model(net, loss, metrics=("top_1_accuracy")) with pytest.raises(TypeError): Model(net, loss, metrics=()) with pytest.raises(TypeError): Model(net, loss, metrics=["top_1_accuracy"]) def test_model_eval_error(): """ test_model_eval_error """ dataset_types = (np.float32, np.float32) dataset_shapes = ((32, 3, 224, 224), (32, 3)) dataset = MindData(size=2, batch_size=32, np_types=dataset_types, output_shapes=dataset_shapes, input_indexs=()) net = nn.ReLU() loss = nn.SoftmaxCrossEntropyWithLogits() context.set_context(mode=context.GRAPH_MODE) model_nometrics = Model(net, loss) with pytest.raises(ValueError): model_nometrics.eval(dataset) model_metrics_empty = Model(net, loss, metrics={}) with pytest.raises(ValueError): model_metrics_empty.eval(dataset)