forked from mindspore-Ecosystem/mindspore
299 lines
11 KiB
Python
299 lines
11 KiB
Python
# Copyright 2019-2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import os
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.dataset as ds
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import mindspore.dataset.transforms as C
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import mindspore.dataset.vision as CV
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import mindspore.nn as nn
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from mindspore import Tensor, ParameterTuple
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from mindspore.common import dtype as mstype
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from mindspore.dataset.vision import Inter
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from mindspore.nn import Dense, TrainOneStepCell, WithLossCell, ForwardValueAndGrad
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from mindspore.nn.metrics import Accuracy
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from mindspore.nn.optim import Momentum
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.train import Model
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from mindspore.train.callback import LossMonitor
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from mindspore.common.initializer import TruncatedNormal
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
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"""weight initial for conv layer"""
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weight = weight_variable()
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=kernel_size, stride=stride, padding=padding,
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weight_init=weight, has_bias=False, pad_mode="valid")
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def fc_with_initialize(input_channels, out_channels):
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"""weight initial for fc layer"""
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weight = weight_variable()
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bias = weight_variable()
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return nn.Dense(input_channels, out_channels, weight, bias)
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def weight_variable():
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"""weight initial"""
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return TruncatedNormal(0.02)
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class LeNet5(nn.Cell):
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def __init__(self, num_class=10, channel=1):
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super(LeNet5, self).__init__()
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self.num_class = num_class
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self.conv1 = conv(channel, 6, 5)
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self.conv2 = conv(6, 16, 5)
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self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
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self.fc2 = fc_with_initialize(120, 84)
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self.fc3 = fc_with_initialize(84, self.num_class)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = nn.Flatten()
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.relu(x)
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x = self.fc3(x)
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return x
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class LeNet(nn.Cell):
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def __init__(self):
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super(LeNet, self).__init__()
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self.relu = P.ReLU()
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self.batch_size = 1
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weight1 = Tensor(np.ones([6, 3, 5, 5]).astype(np.float32) * 0.01)
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weight2 = Tensor(np.ones([16, 6, 5, 5]).astype(np.float32) * 0.01)
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self.conv1 = nn.Conv2d(3, 6, (5, 5), weight_init=weight1, stride=1, padding=0, pad_mode='valid')
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self.conv2 = nn.Conv2d(6, 16, (5, 5), weight_init=weight2, pad_mode='valid', stride=1, padding=0)
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self.pool = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="valid")
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self.reshape = P.Reshape()
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self.reshape1 = P.Reshape()
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self.fc1 = Dense(400, 120)
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self.fc2 = Dense(120, 84)
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self.fc3 = Dense(84, 10)
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def construct(self, input_x):
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output = self.conv1(input_x)
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output = self.relu(output)
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output = self.pool(output)
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output = self.conv2(output)
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output = self.relu(output)
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output = self.pool(output)
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output = self.reshape(output, (self.batch_size, -1))
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output = self.fc1(output)
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output = self.fc2(output)
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output = self.fc3(output)
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return output
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def multisteplr(total_steps, gap, base_lr=0.9, gamma=0.1, dtype=mstype.float32):
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lr = []
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for step in range(total_steps):
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lr_ = base_lr * gamma ** (step // gap)
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lr.append(lr_)
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return Tensor(np.array(lr), dtype)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_train_lenet():
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epoch = 100
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net = LeNet()
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momentum = 0.9
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learning_rate = multisteplr(epoch, 30)
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optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
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criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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net_with_criterion = WithLossCell(net, criterion)
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train_network = TrainOneStepCell(net_with_criterion, optimizer) # optimizer
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train_network.set_train()
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losses = []
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for i in range(epoch):
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data = Tensor(np.ones([net.batch_size, 3, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([net.batch_size]).astype(np.int32))
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loss = train_network(data, label).asnumpy()
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losses.append(loss)
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assert losses[-1] < 0.01
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def create_dataset(data_path, batch_size=32, repeat_size=1,
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num_parallel_workers=1):
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"""
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create dataset for train or test
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"""
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# define dataset
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mnist_ds = ds.MnistDataset(data_path)
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resize_height, resize_width = 32, 32
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rescale = 1.0 / 255.0
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shift = 0.0
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rescale_nml = 1 / 0.3081
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shift_nml = -1 * 0.1307 / 0.3081
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
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rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
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rescale_op = CV.Rescale(rescale, shift)
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hwc2chw_op = CV.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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# apply map operations on images
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mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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buffer_size = 10000
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mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script
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mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
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mnist_ds = mnist_ds.repeat(repeat_size)
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return mnist_ds
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_train_and_eval_lenet():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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network = LeNet5(10)
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net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
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model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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print("============== Starting Training ==============")
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ds_train = create_dataset(os.path.join('/home/workspace/mindspore_dataset/mnist', "train"), 32, 1)
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model.train(1, ds_train, callbacks=[LossMonitor()], dataset_sink_mode=True)
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print("============== Starting Testing ==============")
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ds_eval = create_dataset(os.path.join('/home/workspace/mindspore_dataset/mnist', "test"), 32, 1)
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acc = model.eval(ds_eval, dataset_sink_mode=True)
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print("============== {} ==============".format(acc))
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_train_lenet_with_new_interface(num_classes=10, epoch=20, batch_size=32):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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network = LeNet5(num_classes)
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criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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net_with_criterion = WithLossCell(network, criterion)
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net_with_criterion.set_train()
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weights = ParameterTuple(network.trainable_params())
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optimizer = nn.Momentum(weights, 0.1, 0.9)
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train_network = ForwardValueAndGrad(network=net_with_criterion, weights=weights, get_by_list=True, sens_param=True)
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losses = []
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for i in range(0, epoch):
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data = Tensor(np.ones([batch_size, 1, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([batch_size]).astype(np.int32))
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sens = Tensor(np.ones([1]).astype(np.float32))
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loss, grads = train_network(data, label, sens)
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grads = F.identity(grads)
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optimizer(grads)
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losses.append(loss)
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assert losses[-1].asnumpy() < 0.01
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assert losses[-1].asnumpy() > 0.001
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_train_lenet_with_new_interface_tuple(num_classes=10, epoch=20, batch_size=32):
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"""
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Feature: GradOperation get_by_list pass tuple/list
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Description: Grad with Parameters as input type and fv. list or tuple as fv of grad.
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Expectation: No exception.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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network = LeNet5(num_classes)
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criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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net_with_criterion = WithLossCell(network, criterion)
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net_with_criterion.set_train()
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weights = tuple(network.trainable_params())
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optimizer = nn.Momentum(weights, 0.1, 0.9)
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train_network = ForwardValueAndGrad(network=net_with_criterion, weights=weights, get_by_list=True, sens_param=True)
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losses = []
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for i in range(0, epoch):
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data = Tensor(np.ones([batch_size, 1, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([batch_size]).astype(np.int32))
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sens = Tensor(np.ones([1]).astype(np.float32))
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loss, grads = train_network(data, label, sens)
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grads = F.identity(grads)
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optimizer(grads)
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losses.append(loss)
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assert losses[-1].asnumpy() < 0.01
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assert losses[-1].asnumpy() > 0.001
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_train_lenet_with_new_interface_list(num_classes=10, epoch=20, batch_size=32):
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"""
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Feature: GradOperation get_by_list pass tuple/list
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Description: Grad with Parameters as input type and fv. list or tuple as fv of grad.
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Expectation: No exception.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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network = LeNet5(num_classes)
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criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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net_with_criterion = WithLossCell(network, criterion)
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net_with_criterion.set_train()
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weights = list(network.trainable_params())
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optimizer = nn.Momentum(weights, 0.1, 0.9)
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train_network = ForwardValueAndGrad(network=net_with_criterion, weights=weights, get_by_list=True, sens_param=True)
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losses = []
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for i in range(0, epoch):
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data = Tensor(np.ones([batch_size, 1, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([batch_size]).astype(np.int32))
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sens = Tensor(np.ones([1]).astype(np.float32))
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loss, grads = train_network(data, label, sens)
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grads = F.identity(grads)
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optimizer(grads)
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losses.append(loss)
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assert losses[-1].asnumpy() < 0.01
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assert losses[-1].asnumpy() > 0.001
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