modify lenet&alexnet dir

mindspore/model_zoo/alexnet.py → model_zoo/alexnet/alexnet.py

@@ -36,10 +36,9 @@ class AlexNet(nn.Cell):
     """
     Alexnet
     """
-    def __init__(self, num_classes=10):
+    def __init__(self, num_classes=10, channel=3):
         super(AlexNet, self).__init__()
-        self.batch_size = 32
-        self.conv1 = conv(3, 96, 11, stride=4)
+        self.conv1 = conv(channel, 96, 11, stride=4)
         self.conv2 = conv(96, 256, 5, pad_mode="same")
         self.conv3 = conv(256, 384, 3, pad_mode="same")
         self.conv4 = conv(384, 384, 3, pad_mode="same")

example/alexnet_cifar10/dataset.py → model_zoo/alexnet/dataset.py

@@ -23,7 +23,7 @@ import mindspore.dataset.transforms.vision.c_transforms as CV
 from mindspore.common import dtype as mstype
 
 
-def create_dataset(data_path, batch_size=32, repeat_size=1, status="train"):
+def create_dataset_mnist(data_path, batch_size=32, repeat_size=1, status="train"):
     """
     create dataset for train or test
     """

example/alexnet_cifar10/eval.py → model_zoo/alexnet/eval.py

@@ -20,10 +20,10 @@ python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
 
 import argparse
 from config import alexnet_cfg as cfg
-from dataset import create_dataset
+from dataset import create_dataset_mnist
+from alexnet import AlexNet
 import mindspore.nn as nn
 from mindspore import context
-from mindspore.model_zoo.alexnet import AlexNet
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
@@ -50,9 +50,8 @@ if __name__ == "__main__":
     print("============== Starting Testing ==============")
     param_dict = load_checkpoint(args.ckpt_path)
     load_param_into_net(network, param_dict)
-    ds_eval = create_dataset(args.data_path,
-                             cfg.batch_size,
-                             1,
-                             "test")
+    ds_eval = create_dataset_mnist(args.data_path,
+                                   cfg.batch_size,
+                                   status="test")
     acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
     print("============== Accuracy:{} ==============".format(acc))

example/alexnet_cifar10/train.py → model_zoo/alexnet/train.py

@@ -20,14 +20,14 @@ python train.py --data_path /YourDataPath
 
 import argparse
 from config import alexnet_cfg as cfg
-from dataset import create_dataset
+from dataset import create_dataset_mnist
 from generator_lr import get_lr
+from alexnet import AlexNet
 import mindspore.nn as nn
 from mindspore import context
 from mindspore import Tensor
 from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
-from mindspore.model_zoo.alexnet import AlexNet
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 
 
@@ -50,9 +50,9 @@ if __name__ == "__main__":
     model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})  # test
 
     print("============== Starting Training ==============")
-    ds_train = create_dataset(args.data_path,
-                              cfg.batch_size,
-                              cfg.epoch_size)
+    ds_train = create_dataset_mnist(args.data_path,
+                                    cfg.batch_size,
+                                    cfg.epoch_size)
     time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
     config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
                                  keep_checkpoint_max=cfg.keep_checkpoint_max)

example/lenet_mnist/eval.py → model_zoo/lenet/eval.py

@@ -22,8 +22,8 @@ import os
 import argparse
 from dataset import create_dataset
 from config import mnist_cfg as cfg
+from lenet import LeNet5
 import mindspore.nn as nn
-from mindspore.model_zoo.lenet import LeNet5
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig

mindspore/model_zoo/lenet.py → model_zoo/lenet/lenet.py

@@ -50,11 +50,10 @@ class LeNet5(nn.Cell):
         >>> LeNet(num_class=10)
 
     """
-    def __init__(self, num_class=10):
+    def __init__(self, num_class=10, channel=1):
         super(LeNet5, self).__init__()
         self.num_class = num_class
-        self.batch_size = 32
-        self.conv1 = conv(1, 6, 5)
+        self.conv1 = conv(channel, 6, 5)
         self.conv2 = conv(6, 16, 5)
         self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
         self.fc2 = fc_with_initialize(120, 84)

example/lenet_mnist/train.py → model_zoo/lenet/train.py

@@ -22,8 +22,8 @@ import os
 import argparse
 from config import mnist_cfg as cfg
 from dataset import create_dataset
+from lenet import LeNet5
 import mindspore.nn as nn
-from mindspore.model_zoo.lenet import LeNet5
 from mindspore import context
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train import Model
@@ -36,7 +36,7 @@ if __name__ == "__main__":
                         help='device where the code will be implemented (default: Ascend)')
     parser.add_argument('--data_path', type=str, default="./MNIST_Data",
                         help='path where the dataset is saved')
-    parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
+    parser.add_argument('--dataset_sink_mode', type=bool, default=True, help='dataset_sink_mode is False or True')
 
     args = parser.parse_args()
 

tests/perf_test/lenet.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.
+# ============================================================================
+"""LeNet."""
+import mindspore.nn as nn
+from mindspore.common.initializer import TruncatedNormal
+
+
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    """weight initial for conv layer"""
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode="valid")
+
+
+def fc_with_initialize(input_channels, out_channels):
+    """weight initial for fc layer"""
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+
+def weight_variable():
+    """weight initial"""
+    return TruncatedNormal(0.02)
+
+
+class LeNet5(nn.Cell):
+    """
+    Lenet network
+
+    Args:
+        num_class (int): Num classes. Default: 10.
+
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> LeNet(num_class=10)
+
+    """
+    def __init__(self, num_class=10, channel=1):
+        super(LeNet5, self).__init__()
+        self.num_class = num_class
+        self.conv1 = conv(channel, 6, 5)
+        self.conv2 = conv(6, 16, 5)
+        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
+        self.fc2 = fc_with_initialize(120, 84)
+        self.fc3 = fc_with_initialize(84, self.num_class)
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.relu(x)
+        x = self.fc3(x)
+        return x

tests/perf_test/test_lenet.py

@@ -17,12 +17,12 @@
 
 import numpy as np
 
+from lenet import LeNet5
 import mindspore.nn as nn
 import mindspore.ops.composite as C
 from mindspore import Tensor
 from mindspore import context
 from mindspore.common.api import _executor
-from mindspore.model_zoo.lenet import LeNet
 
 context.set_context(mode=context.GRAPH_MODE)
 
@@ -61,7 +61,7 @@ def test_compile():
 
 def test_compile_grad():
     """Compile forward and backward graph"""
-    net = LeNet(num_class=num_class)
+    net = LeNet5(num_class=num_class)
     inp = Tensor(np.array(np.random.randn(batch_size,
                                           channel,
                                           height,

tests/st/networks/models/lenet.py

-# Copyright 2019 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 mindspore.nn as nn
-from mindspore.ops import operations as P
-
-
-class LeNet(nn.Cell):
-    def __init__(self):
-        super(LeNet, self).__init__()
-        self.relu = P.ReLU()
-        self.batch_size = 32
-
-        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
-        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
-        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.reshape = P.Reshape()
-        self.fc1 = nn.Dense(400, 120)
-        self.fc2 = nn.Dense(120, 84)
-        self.fc3 = nn.Dense(84, 10)
-
-    def construct(self, input_x):
-        output = self.conv1(input_x)
-        output = self.relu(output)
-        output = self.pool(output)
-        output = self.conv2(output)
-        output = self.relu(output)
-        output = self.pool(output)
-        output = self.reshape(output, (self.batch_size, -1))
-        output = self.fc1(output)
-        output = self.relu(output)
-        output = self.fc2(output)
-        output = self.relu(output)
-        output = self.fc3(output)
-        return output

tests/st/networks/test_gpu_lenet.py

@@ -26,17 +26,66 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
 from mindspore.dataset.transforms.vision import Inter
-from mindspore.model_zoo.lenet import LeNet5
 from mindspore.nn import Dense, TrainOneStepCell, WithLossCell
 from mindspore.nn.metrics import Accuracy
 from mindspore.nn.optim import Momentum
 from mindspore.ops import operations as P
 from mindspore.train import Model
 from mindspore.train.callback import LossMonitor
+from mindspore.common.initializer import TruncatedNormal
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
 
+def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
+    """weight initial for conv layer"""
+    weight = weight_variable()
+    return nn.Conv2d(in_channels, out_channels,
+                     kernel_size=kernel_size, stride=stride, padding=padding,
+                     weight_init=weight, has_bias=False, pad_mode="valid")
+
+
+def fc_with_initialize(input_channels, out_channels):
+    """weight initial for fc layer"""
+    weight = weight_variable()
+    bias = weight_variable()
+    return nn.Dense(input_channels, out_channels, weight, bias)
+
+
+def weight_variable():
+    """weight initial"""
+    return TruncatedNormal(0.02)
+
+
+class LeNet5(nn.Cell):
+    def __init__(self, num_class=10, channel=1):
+        super(LeNet5, self).__init__()
+        self.num_class = num_class
+        self.conv1 = conv(channel, 6, 5)
+        self.conv2 = conv(6, 16, 5)
+        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
+        self.fc2 = fc_with_initialize(120, 84)
+        self.fc3 = fc_with_initialize(84, self.num_class)
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.flatten(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.relu(x)
+        x = self.fc3(x)
+        return x
+
+
 class LeNet(nn.Cell):
     def __init__(self):
         super(LeNet, self).__init__()