add accuracy for resnet cifar

tests/st/tbe_networks/test_resnet_cifar_1p.py

+# 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.
+# ============================================================================
+
+import pytest
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.model import Model
+from mindspore import context
+import mindspore.common.dtype as mstype
+import os
+import numpy as np
+import mindspore.ops.functional as F
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms.vision.c_transforms as vision
+from resnet import resnet50
+import random
+import time
+
+random.seed(1)
+np.random.seed(1)
+ds.config.set_seed(1)
+
+data_home = "/home/workspace/mindspore_dataset"
+
+
+def create_dataset(repeat_num=1, training=True, batch_size=32):
+    data_dir = data_home + "/cifar-10-batches-bin"
+    if not training:
+        data_dir = data_home + "/cifar-10-verify-bin"
+    data_set = ds.Cifar10Dataset(data_dir)
+
+    resize_height = 224
+    resize_width = 224
+    rescale = 1.0 / 255.0
+    shift = 0.0
+
+    # define map operations
+    random_crop_op = vision.RandomCrop(
+        (32, 32), (4, 4, 4, 4))  # padding_mode default CONSTANT
+    random_horizontal_op = vision.RandomHorizontalFlip()
+    # interpolation default BILINEAR
+    resize_op = vision.Resize((resize_height, resize_width))
+    rescale_op = vision.Rescale(rescale, shift)
+    normalize_op = vision.Normalize(
+        (0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
+    changeswap_op = vision.HWC2CHW()
+    type_cast_op = C.TypeCast(mstype.int32)
+
+    c_trans = []
+    if training:
+        c_trans = [random_crop_op, random_horizontal_op]
+    c_trans += [resize_op, rescale_op, normalize_op,
+                changeswap_op]
+
+    # apply map operations on images
+    data_set = data_set.map(input_columns="label", operations=type_cast_op)
+    data_set = data_set.map(input_columns="image", operations=c_trans)
+
+    # apply shuffle operations
+    data_set = data_set.shuffle(buffer_size=1000)
+
+    # apply batch operations
+    data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
+
+    # apply repeat operations
+    data_set = data_set.repeat(repeat_num)
+
+    return data_set
+
+
+class CrossEntropyLoss(nn.Cell):
+    def __init__(self):
+        super(CrossEntropyLoss, self).__init__()
+        self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
+        self.mean = P.ReduceMean()
+        self.one_hot = P.OneHot()
+        self.one = Tensor(1.0, mstype.float32)
+        self.zero = Tensor(0.0, mstype.float32)
+
+    def construct(self, logits, label):
+        label = self.one_hot(label, F.shape(logits)[1], self.one, self.zero)
+        loss = self.cross_entropy(logits, label)[0]
+        loss = self.mean(loss, (-1,))
+        return loss
+
+
+class LossGet(Callback):
+    def __init__(self, per_print_times=1):
+        super(LossGet, self).__init__()
+        if not isinstance(per_print_times, int) or per_print_times < 0:
+            raise ValueError("print_step must be int and >= 0.")
+        self._per_print_times = per_print_times
+        self._loss = 0.0
+
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+        loss = cb_params.net_outputs
+
+        if isinstance(loss, (tuple, list)):
+            if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
+                loss = loss[0]
+
+        if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
+            loss = np.mean(loss.asnumpy())
+
+        cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
+
+        if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)):
+            raise ValueError("epoch: {} step: {}. Invalid loss, terminating training."
+                             .format(cb_params.cur_epoch_num, cur_step_in_epoch))
+        if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
+            self._loss = loss
+            print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num, cur_step_in_epoch, loss))
+
+    def get_loss(self):
+        return self._loss
+
+
+def train_process(device_id, epoch_size, num_classes, device_num, batch_size):
+    os.system("mkdir " + str(device_id))
+    os.chdir(str(device_id))
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    context.set_context(enable_task_sink=True, device_id=device_id)
+    context.set_context(enable_loop_sink=True)
+    context.set_context(enable_mem_reuse=True)
+    context.set_context(mode=context.GRAPH_MODE)
+    net = resnet50(batch_size, num_classes)
+    loss = CrossEntropyLoss()
+    opt = Momentum(filter(lambda x: x.requires_grad,
+                          net.get_parameters()), 0.01, 0.9)
+
+    model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
+
+    dataset = create_dataset(epoch_size, training=True, batch_size=batch_size)
+    batch_num = dataset.get_dataset_size()
+    config_ck = CheckpointConfig(save_checkpoint_steps=batch_num, keep_checkpoint_max=1)
+    ckpoint_cb = ModelCheckpoint(prefix="train_resnet_cifar10_device_id_" + str(device_id), directory="./",
+                                 config=config_ck)
+    loss_cb = LossGet()
+    model.train(epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb])
+
+
+def eval(batch_size, num_classes):
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    context.set_context(enable_task_sink=True, device_id=0)
+    context.set_context(enable_loop_sink=True)
+    context.set_context(enable_mem_reuse=True)
+
+    net = resnet50(batch_size, num_classes)
+    loss = CrossEntropyLoss()
+    opt = Momentum(filter(lambda x: x.requires_grad,
+                          net.get_parameters()), 0.01, 0.9)
+
+    model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
+    checkpoint_path = "./train_resnet_cifar10_device_id_0-1_1562.ckpt"
+    param_dict = load_checkpoint(checkpoint_path)
+    load_param_into_net(net, param_dict)
+    net.set_train(False)
+    eval_dataset = create_dataset(1, training=False)
+    res = model.eval(eval_dataset)
+    print("result: ", res)
+    return res
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_resnet_cifar_1p():
+    device_num = 1
+    epoch_size = 1
+    num_classes = 10
+    batch_size = 32
+    device_id = 0
+    train_process(device_id, epoch_size, num_classes, device_num, batch_size)
+    time.sleep(3)
+    acc = eval(batch_size, num_classes)
+    os.chdir("../")
+    os.system("rm -rf " + str(device_id))
+    print("End training...")
+    assert (acc['acc'] > 0.35)