AlexNet-Hub

tests/st/alexnet.py

@@ -21,7 +21,7 @@
 from tinyms import context
 from tinyms.data import Cifar10Dataset, download_dataset
 from tinyms.vision import cifar10_transform
-from tinyms.model import Model, AlexNet
+from tinyms.model import Model, alexnet
 from tinyms.callbacks import ModelCheckpoint, CheckpointConfig, LossMonitor
 from tinyms.metrics import Accuracy
 from tinyms.optimizers import Momentum
@@ -31,17 +31,21 @@
 
 
 def parse_args():
-    parser = argparse.ArgumentParser(description='Image classification')
+    parser = argparse.ArgumentParser(description='TinyMS AlexNet Example')
     parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU'],
                         help='device where the code will be implemented (default: CPU)')
     parser.add_argument('--dataset_path', type=str, default=None, help='Cifar10 dataset path.')
-    parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
     parser.add_argument('--epoch_size', type=int, default=90, help='Epoch size.')
     parser.add_argument('--batch_size', type=int, default=32, help='Batch size.')
+    parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
     parser.add_argument('--num_classes', type=int, default=10, help='Num classes.')
+    parser.add_argument('--load_pretrained', type=str, choices=['hub', 'local'], default='local',
+                        help='Specify where to load pretrained model, only valid in do_eval mode. (default: local)')
     parser.add_argument('--save_checkpoint_epochs', type=int, default=5,
                         help='Specify epochs interval to save each checkpoints.')
     parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path.')
+    parser.add_argument('--hub_uid', type=str, default=None,
+                        help='Model asset uid. Only valid when load_pretrained is `hub`.')
     args_opt = parser.parse_args()
 
     return args_opt
@@ -76,8 +80,12 @@ def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers
     if not args_opt.dataset_path:
         args_opt.dataset_path = download_dataset('cifar10')
     # build the network
-    net = AlexNet(args_opt.num_classes)
-    net.update_parameters_name(prefix='huawei')
+    if args_opt.do_eval and args_opt.load_pretrained == 'hub':
+        from tinyms import hub
+        net = hub.load(args_opt.hub_uid)
+    else:
+        net = alexnet(class_num=args_opt.num_classes)
+        net.update_parameters_name(prefix='huawei')
     model = Model(net)
     # define the loss function
     net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
@@ -90,10 +98,13 @@ def create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers
     cifar10_path = args_opt.dataset_path
     save_checkpoint_epochs = args_opt.save_checkpoint_epochs
     dataset_sink_mode = not args_opt.device_target == "CPU"
+
     if args_opt.do_eval:  # as for evaluation, users could use model.eval
         ds_eval = create_dataset(cifar10_path, batch_size=batch_size, is_training=False)
-        if args_opt.checkpoint_path:
-            model.load_checkpoint(args_opt.checkpoint_path)
+        # load the saved model for evaluation
+        if args_opt.load_pretrained == 'local':
+            if args_opt.checkpoint_path:
+                model.load_checkpoint(args_opt.checkpoint_path)
         acc = model.eval(ds_eval, dataset_sink_mode=dataset_sink_mode)
         print("============== Accuracy:{} ==============".format(acc))
     else:  # as for train, users could use model.train

tests/st/cycle_gan.py

@@ -32,7 +32,7 @@
 
 
 def parse_args():
-    parser = argparse.ArgumentParser(description='MindSpore Cycle GAN Example')
+    parser = argparse.ArgumentParser(description='TinyMS Cycle GAN Example')
     parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU'],
                         help='device where the code will be implemented (default: CPU)')
     parser.add_argument('--dataset_path', type=str, default=None, help='cityscape dataset path.')

tests/st/lenet5.py

@@ -29,7 +29,7 @@
 
 
 def parse_args():
-    parser = argparse.ArgumentParser(description='MindSpore LeNet Example')
+    parser = argparse.ArgumentParser(description='TinyMS LeNet Example')
     parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU'],
                         help='device where the code will be implemented. (default: CPU)')
     parser.add_argument('--dataset_path', type=str, default=None, help='Mnist dataset path.')

tinyms/hub/assets/tinyms/0.2/alexnet_v1_cifar10.yaml

@@ -0,0 +1,27 @@
+##################################################################################################
+# AlexNet with Cifar10
+##################################################################################################
+---
+model-name: alexnet
+backbone-name: alexnet
+module-type: cv-classification
+fine-tunable: False
+input-shape: [3, 224, 224]
+model-version: v1
+train-dataset: cifar10
+train-backend: GPU
+accuracy: 0.887
+author: TinyMS team
+update-time: 2021-06-04
+user-id: TinyMS
+used-for: inference
+infer-backend:
+  - CPU
+  - GPU
+tinyms-version: 0.2
+asset:
+  file-format: ckpt
+  asset-link: https://tinyms-hub.obs.cn-north-4.myhuaweicloud.com/tinyms/0.2/alexnet_v1_cifar10/alexnet.ckpt
+  asset-sha256: 941cbd3dcf9af40decf992a67c3635f77e1a797ca0c69e9cc4dae23bb3300bb4
+license: Apache-2.0
+summary: AlexNet model used to classify the 10 classes of Cifar10 dataset.

tinyms/hub/hubconf.py

@@ -18,6 +18,7 @@
 from tinyms import model
 
 MODEL_HUB = ed({
+    "alexnet_v1": model.alexnet,
     "lenet5_v1": model.lenet5,
     "resnet50_v1": model.resnet50,
     "mobilenet_v2": model.mobilenetv2,