change lat_pred to predict

README.md

@@ -98,16 +98,16 @@ After installation, a command named `nn-meter` is enabled. To predict the latenc
 
 ```bash
 # for Tensorflow (*.pb) file
-nn-meter lat_pred --predictor <hardware> [--predictor-version <version>] --tensorflow <pb-file_or_folder> 
+nn-meter predict --predictor <hardware> [--predictor-version <version>] --tensorflow <pb-file_or_folder> 
 
 # for ONNX (*.onnx) file
-nn-meter lat_pred --predictor <hardware> [--predictor-version <version>] --onnx <onnx-file_or_folder>
+nn-meter predict --predictor <hardware> [--predictor-version <version>] --onnx <onnx-file_or_folder>
 
 # for torch model from torchvision model zoo (str)
-nn-meter lat_pred --predictor <hardware> [--predictor-version <version>] --torchvision <model-name> <model-name>... 
+nn-meter predict --predictor <hardware> [--predictor-version <version>] --torchvision <model-name> <model-name>... 
 
 # for nn-Meter IR (*.json) file
-nn-meter lat_pred --predictor <hardware> [--predictor-version <version>] --nn-meter-ir <json-file_or_folder> 
+nn-meter predict --predictor <hardware> [--predictor-version <version>] --nn-meter-ir <json-file_or_folder> 
 ```
 
 `--predictor-version <version>` arguments is optional. When the predictor version is not specified by users, nn-meter will use the latest version of the predictor.

docs/input_models.md

@@ -10,17 +10,17 @@ You can save tensorflow models into frozen pb formats, and use the following nn-
 
 ```bash
 # for Tensorflow (*.pb) file
-nn-meter --predictor <hardware> --tensorflow <pb-file> 
+nn-meter predict --predictor <hardware> [--predictor-version <version>] --tensorflow <pb-file_or_folder> 
 ```
 
 For the other frameworks (e.g., PyTorch), you can convert the models into onnx models, and use the following nn-meter command to predict the latency:
 
 ```bash
 # for ONNX (*.onnx) file
-nn-meter --predictor <hardware> --onnx <onnx-file>
+nn-meter predict --predictor <hardware> [--predictor-version <version>] --onnx <onnx-file_or_folder>
 ```
 
-You can download the test [tensorflow models]("https://github.com/Lynazhang/nnmeter/releases/download/0.1/pb_models.zip") and [onnx models](https://github.com/Lynazhang/nnmeter/releases/download/0.1/onnx_models.zip). 
+You can download the test [tensorflow models]("https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/pb_models.zip") and [onnx models](https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/onnx_models.zip). 
 
 ### Input model as a code object
 
@@ -29,7 +29,7 @@ You can also directly apply nn-Meter in your python code. In this case, please d
 ```python
 from nn_meter import load_latency_predictor
 
-predictor = load_lat_predictor(hardware_name) # case insensitive in backend
+predictor = load_latency_predictor(hardware_name) # case insensitive in backend
 
 # build your model here
 model = ... # model is instance of torch.nn.Module
@@ -57,14 +57,14 @@ For a *node*, we use the identical node name ("conv1.conv/Conv2D") as the node k
 * outbounds: a list of outgoing node names. The inbounds and outbounds describe the node connections.
 * attr: a set of attributes for the node. The attributes can be different for different types of NN node.
 
-You can download the example nn-Meter IR graphs through [here](https://github.com/Lynazhang/nnmeter/releases/download/0.1/ir_graphs.zip).
+You can download the example nn-Meter IR graphs through [here](https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/ir_graphs.zip).
 
 When you have a large amount of models to predict, you can also convert them into nn-Meter IR graphs to save the pre-processing time:
 
 ```
 # for Tensorflow (*.pb) file
-nn-meter getir --tensorflow <pb-file> --output <output-name>
+nn-meter get_ir --tensorflow <pb-file> [--output <output-name>]
 
 # for ONNX (*.onnx) file
-nn-meter getir --onnx <onnx-file> --output <output-name>
+nn-meter get_ir --onnx <onnx-file> [--output <output-name>]
 ```

docs/predictor/usage.md

@@ -36,16 +36,16 @@ After installation, a command named `nn-meter` is enabled. To predict the latenc
 
 ```bash
 # for Tensorflow (*.pb) file
-nn-meter lat_pred --predictor <hardware> [--predictor-version <version>] --tensorflow <pb-file_or_folder> 
+nn-meter predict --predictor <hardware> [--predictor-version <version>] --tensorflow <pb-file_or_folder> 
 
 # for ONNX (*.onnx) file
-nn-meter lat_pred --predictor <hardware> [--predictor-version <version>] --onnx <onnx-file_or_folder>
+nn-meter predict --predictor <hardware> [--predictor-version <version>] --onnx <onnx-file_or_folder>
 
 # for torch model from torchvision model zoo (str)
-nn-meter lat_pred --predictor <hardware> [--predictor-version <version>] --torchvision <model-name> <model-name>... 
+nn-meter predict --predictor <hardware> [--predictor-version <version>] --torchvision <model-name> <model-name>... 
 
 # for nn-Meter IR (*.json) file
-nn-meter lat_pred --predictor <hardware> [--predictor-version <version>] --nn-meter-ir <json-file_or_folder> 
+nn-meter predict --predictor <hardware> [--predictor-version <version>] --nn-meter-ir <json-file_or_folder> 
 ```
 
 `--predictor-version <version>` arguments is optional. When the predictor version is not specified by users, nn-meter will use the latest version of the predictor.

nn_meter/nn_meter_cli.py

@@ -113,7 +113,7 @@ def nn_meter_cli():
     subparsers = parser.add_subparsers()
 
     # Usage 1: latency predictors
-    lat_pred = subparsers.add_parser('lat_pred', help='apply latency predictor for testing model')
+    lat_pred = subparsers.add_parser('predict', help='apply latency predictor for testing model')
     lat_pred.add_argument(
         "--predictor",
         type=str,

tests/integration_test.py

@@ -82,7 +82,7 @@ def integration_test(model_type, url, ppath, output_name = "tests/test_result.tx
         try:
             since = time.time()
             # print(f'nn-meter --{model_type} {ppath} --predictor {pred_name} --predictor-version {pred_version}')
-            result = subprocess.check_output(['nn-meter', 'lat_pred', f'--{model_type}', f'{ppath}', '--predictor', f'{pred_name}', '--predictor-version', f'{pred_version}'])
+            result = subprocess.check_output(['nn-meter', 'predict', f'--{model_type}', f'{ppath}', '--predictor', f'{pred_name}', '--predictor-version', f'{pred_version}'])
             runtime = time.time() - since
         except NotImplementedError:
             logging.error(f"Meets ERROR when checking --{model_type} {ppath} --predictor {pred_name} --predictor-version {pred_version}")

tests/integration_test_torch.py

@@ -43,7 +43,7 @@ def integration_test_onnx_based_torch(model_type, model_list, output_name = "tes
         try:
             since = time.time()
             # print(f'nn-meter --torchvision ' + " ".join(model_list) + f' --predictor {pred_name} --predictor-version {pred_version}')
-            result = subprocess.check_output(['nn-meter', 'lat_pred', f'--torchvision'] + model_list + ['--predictor', f'{pred_name}', '--predictor-version', f'{pred_version}'])
+            result = subprocess.check_output(['nn-meter', 'predict', f'--torchvision'] + model_list + ['--predictor', f'{pred_name}', '--predictor-version', f'{pred_version}'])
             runtime = time.time() - since
         except NotImplementedError:
             logging.error("Meets ERROR when checking --torchvision {model_list} --predictor {pred_name} --predictor-version {pred_version}")