Prepare for v1.1 release

.github/workflows/integration-test.yml

@@ -6,7 +6,7 @@ name: Integrated Test
 on: [push]
 
 jobs:
-  build-linux:
+  model-test:
     runs-on: ubuntu-latest
     strategy:
       max-parallel: 5

.github/workflows/integration-test_nni_based_torch.yml

@@ -6,7 +6,7 @@ name: Integrated Test for Torch Model Based on NNI
 on: [push]
 
 jobs:
-  build-linux:
+  torch-model-test:
     runs-on: ubuntu-latest
     strategy:
       max-parallel: 5
@@ -42,7 +42,7 @@ jobs:
 
     - name: Install nni
       run: pip install nni==2.4
-        
+
     - name: Install nn-Meter
       run: pip install -U .
 

.github/workflows/integration-test_onnx_based_torch.yml

@@ -6,7 +6,7 @@ name: Integrated Test for Torch Model Based on ONNX
 on: [push]
 
 jobs:
-  build-linux:
+  torch-model-test:
     runs-on: ubuntu-latest
     strategy:
       max-parallel: 5

README.md

@@ -48,10 +48,10 @@ nn-Meter is a latency predictor of models with type of Tensorflow, PyTorch, Onnx
 | Testing Model Type |                                                       Requirements                                                       |
 | :----------------: | :-----------------------------------------------------------------------------------------------------------------------: |
 |     Tensorflow     |                                                  `tensorflow==1.15.0`                                                  |
-|       Torch       | `torch==1.7.1`, `torchvision==0.8.2`, (alternative)[`onnx==1.9.0`, `onnx-simplifier==0.3.6`] or [`nni==2.4`][1] |
+|       Torch       | `torch==1.9.0`, `torchvision==0.10.0`, (alternative)[`onnx==1.9.0`, `onnx-simplifier==0.3.6`] or [`nni>=2.4`][1] |
 |        Onnx        |                                                      `onnx==1.9.0`                                                      |
 | nn-Meter IR graph |                                                            ---                                                            |
-|    NNI IR graph    |                                                       `nni==2.4`                                                       |
+|    NNI IR graph    |                                                       `nni>=2.4`                                                       |
 
 [1] Please refer to [nn-Meter Usage](#torch-model-converters) for more information.
 
@@ -73,7 +73,7 @@ Here is a summary of supported inputs of the two methods.
 |     Tensorflow     |             Checkpoint file dumped by `tf.saved_model()` and end with `.pb`             |                 Checkpoint file dumped by `tf.saved_model` and end with `.pb`                 |
 |       Torch       |                              Models in `torchvision.models`                              |                                   Object of `torch.nn.Module`                                   |
 |        Onnx        | Checkpoint file dumped by `torch.onnx.export()` or `onnx.save()` and end with `.onnx` |           Checkpoint file dumped by `onnx.save()` or model loaded by `onnx.load()`           |
-| nn-Meter IR graph |     Json file in the format of[nn-Meter IR Graph](./docs/input_models.md#nnmeter-ir-graph)     | `dict` object following the format of [nn-Meter IR Graph](./docs/input_models.md#nnmeter-ir-graph) |
+| nn-Meter IR graph |     Json file in the format of [nn-Meter IR Graph](./docs/input_models.md#nnmeter-ir-graph)     | `dict` object following the format of [nn-Meter IR Graph](./docs/input_models.md#nnmeter-ir-graph) |
 |    NNI IR graph    |                                              -                                              |                                        NNI IR graph object                                        |
 
 In both methods, users could appoint predictor name and version to target a specific hardware platform (device). Currently, nn-Meter supports prediction on the following four configs:

docs/quick_start.md

@@ -23,10 +23,10 @@ nn-Meter is a latency predictor of models with type of Tensorflow, PyTorch, Onnx
 |  Testing Model Type   |                       Requirements                      |
 | :-------------------: | :------------------------------------------------:     |
 |       Tensorflow      |  `tensorflow==1.15.0`                                  |
-|         Torch         |  `torch==1.7.1`, `torchvision==0.8.2`, (alternative)[`onnx==1.9.0`, `onnx-simplifier==0.3.6`] or [`nni==2.4`][1] |
+|         Torch         |  `torch==1.9.0`, `torchvision==0.10.0`, (alternative)[`onnx==1.9.0`, `onnx-simplifier==0.3.6`] or [`nni>=2.4`][1] |
 |          Onnx         |  `onnx==1.9.0`                                         |
 |    nn-Meter IR graph  |   ---                                                  |
-|      NNI IR graph     |  `nni==2.4`                                            |
+|      NNI IR graph     |  `nni>=2.4`                                            |
 
 [1] Please refer to [nn-Meter Usage](usage.md#torch-model-converters) for more information.
 

nn_meter/ir_converters/torch_converter/converter.py

@@ -1,6 +1,6 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
-from nn_meter.utils.utils import try_import_onnx, try_import_torch, try_import_onnxsim
+from nn_meter.utils.utils import try_import_onnx, try_import_torch, try_import_onnxsim, try_import_nni
 import tempfile
 from nn_meter.ir_converters.onnx_converter import OnnxConverter
 
@@ -19,6 +19,7 @@ def _nchw_to_nhwc(shapes):
 
 class NNIIRConverter:
     def __init__(self, ir_model):
+        try_import_nni()
         try:
             from nni.retiarii.converter.utils import flatten_model_graph
             self.ir_model = flatten_model_graph(ir_model)
@@ -100,6 +101,7 @@ def _remove_unshaped_nodes(self, graph):
 class NNIBasedTorchConverter(NNIIRConverter):
     def __init__(self, model, example_inputs):
         torch = try_import_torch()
+        try_import_nni()
         from nni.retiarii.converter import convert_to_graph
         from nni.retiarii.converter.graph_gen import GraphConverterWithShape
 

nn_meter/utils/utils.py

@@ -36,34 +36,52 @@ def download_from_url(urladdr, ppath):
     progress_bar.close()
     os.remove(file_name)
 
-def try_import_onnx(require_version = "1.9.0"):
+def try_import_onnx(require_version = ["1.9.0"]):
+    if isinstance(require_version, str):
+        require_version = [require_version]
     try:
         import onnx
-        if version.parse(onnx.__version__) != version.parse(require_version):
-            logging.warning(f'onnx=={onnx.__version__} is not well tested now, well tested version: onnx=={require_version}' )
+        if version.parse(onnx.__version__).release not in [version.parse(v).release for v in require_version]:
+            logging.warning(f'onnx=={onnx.__version__} is not well tested now, well tested version: onnx=={", ".join(require_version)}' )
         return onnx
     except ImportError:
-        logging.error(f'You have not install the onnx package, please install onnx=={require_version} and try again.')
+        logging.error(f'You have not install the onnx package, please install onnx=={require_version[0]} and try again.')
         exit()
 
-def try_import_torch(require_version = "1.7.1"):
+def try_import_torch(require_version = ["1.9.0", "1.7.1"]):
+    if isinstance(require_version, str):
+        require_version = [require_version]
     try:
         import torch
-        if version.parse(torch.__version__) != version.parse(require_version):
-            logging.warning(f'torch=={torch.__version__} is not well tested now, well tested version: torch=={require_version}' )
+        if version.parse(torch.__version__).release not in [version.parse(v).release for v in require_version]:
+            logging.warning(f'torch=={torch.__version__} is not well tested now, well tested version: torch=={", ".join(require_version)}' )
         return torch
     except ImportError:
-        logging.error(f'You have not install the torch package, please install torch=={require_version} and try again.')
+        logging.error(f'You have not install the torch package, please install torch=={require_version[0]} and try again.')
         exit()
 
-def try_import_tensorflow(require_version = "1.15.0"):
+def try_import_tensorflow(require_version = ["1.15.0"]):
+    if isinstance(require_version, str):
+        require_version = [require_version]
     try:
         import tensorflow
-        if version.parse(tensorflow.__version__) != version.parse(require_version):
-            logging.warning(f'tensorflow=={tensorflow.__version__} is not well tested now, well tested version: tensorflow=={require_version}' )
+        if version.parse(tensorflow.__version__).release not in [version.parse(v).release for v in require_version]:
+            logging.warning(f'tensorflow=={tensorflow.__version__} is not well tested now, well tested version: tensorflow=={", ".join(require_version)}' )
         return tensorflow
     except ImportError:
-        logging.error(f'You have not install the tensorflow package, please install tensorflow=={require_version} and try again.')
+        logging.error(f'You have not install the tensorflow package, please install tensorflow=={require_version[0]} and try again.')
+        exit()
+
+def try_import_nni(require_version = ["2.4", "2.5"]):
+    if isinstance(require_version, str):
+        require_version = [require_version]
+    try:
+        import nni
+        if version.parse(nni.__version__).release not in [version.parse(v).release for v in require_version]:
+            logging.warning(f'nni=={nni.__version__} is not well tested now, well tested version: nni=={", ".join(require_version)}' )
+        return nni
+    except ImportError:
+        logging.error(f'You have not install the tensorflow package, please install tensorflow=={require_version[0]} and try again.')
         exit()
 
 def try_import_torchvision_models():

tests/integration_test_torch.py

@@ -46,7 +46,7 @@ def integration_test_onnx_based_torch(model_type, model_list, output_name = "tes
             result = subprocess.check_output(['nn-meter', 'lat_pred', 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_string} --predictor {pred_name} --predictor-version {pred_version}")
+            logging.error("Meets ERROR when checking --torchvision {model_list} --predictor {pred_name} --predictor-version {pred_version}")
 
         latency_list = parse_latency_info(result.decode('utf-8'))
         for model, latency in latency_list: