test

MindSpore/src/step1/step1_test.py

@@ -0,0 +1,86 @@
+random_crop_op = C.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
+    random_horizontal_op = C.RandomHorizontalFlip()
+    resize_op = C.Resize((resize_height, resize_width)) # interpolation default BILINEAR
+    rescale_op = C.Rescale(rescale, shift)
+    normalize_op = C.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
+    changeswap_op = C.HWC2CHW()
+    type_cast_op = C2.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
+    cifar_ds = cifar_ds.map(input_columns="label", operations=type_cast_op)
+    cifar_ds = cifar_ds.map(input_columns="image", operations=c_trans)
+
+    # apply shuffle operations
+    cifar_ds = cifar_ds.shuffle(buffer_size=10)
+
+    # apply batch operations
+    cifar_ds = cifar_ds.batch(batch_size=args_opt.batch_size, drop_remainder=True)
+
+    # apply repeat operations
+    cifar_ds = cifar_ds.repeat(repeat_num)
+
+ls = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction="mean")
+opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
+
+config_ck = CheckpointConfig(save_checkpoint_steps=batch_num, keep_checkpoint_max=35)
+        ckpoint_cb = ModelCheckpoint(prefix="train_resnet_cifar10", directory="./", config=config_ck)
+        loss_cb = LossMonitor()
+        model.train(epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb])
+
+if args_opt.checkpoint_path:
+    param_dict = load_checkpoint(args_opt.checkpoint_path)
+    load_param_into_net(net, param_dict)
+eval_dataset = create_dataset(1, training=False)
+res = model.eval(eval_dataset)
+print("result: ", res)
+
+
+import cifar_resnet50
+import object
+
+if __name__ == '__main__':
+
+    # add objects for searching
+    objs = [
+
+
+        "random_crop_op=C.RandomCrop((32,32),(4,4,4,4))",
+        "random_horizontal_op=C.RandomHorizontalFlip()",
+        "resize_op=C.Resize((resize_height,resize_width))",
+        "rescale_op=C.Rescale(rescale,shift)",
+        "normalize_op=C.Normalize((0.4914,0.4822,0.4465),(0.2023,0.1994,0.2010))",
+        "changeswap_op=C.HWC2CHW()",
+        "type_cast_op=C2.TypeCast(mstype.int32)",
+        "c_trans=[random_crop_op,random_horizontal_op]",
+        "cifar_ds=cifar_ds.map(input_columns=",
+        "cifar_ds=cifar_ds.shuffle(buffer_size=10)",
+        "cifar_ds=cifar_ds.batch(batch_size=args_opt.batch_size,drop_remainder=True)",
+        "cifar_ds=cifar_ds.repeat(repeat_num)",
+        "ls=SoftmaxCrossEntropyWithLogits(sparse=True,is_grad=False,reduction=",
+        "opt=Momentum(filter(lambda x:x.requires_grad,net.get_parameters()),0.01,0.9)",
+        "config_ck=CheckpointConfig(save_checkpoint_steps=batch_num,keep_checkpoint_max=35)",
+        "ckpoint_cb=ModelCheckpoint(prefix=",
+        "loss_cb=LossMonitor()",
+        "model.train(epoch_size,dataset,callbacks=[ckpoint_cb,loss_cb])",
+        "param_dict=load_checkpoint(args_opt.checkpoint_path)",
+        "load_param_into_net(net,param_dict)",
+        "eval_dataset=create_dataset(1,training=False)",
+        "res=model.eval(eval_dataset)",
+
+    ]
+
+    filepath = "./MindSpore/src/step1/cifar_resnet50.py"
+
+    if (object.objectFind(objs, filepath)):
+        print("----------------")
+        print("ok!")
+    else:
+        print("object error!")
+
+