Examples: turn examples folder into brevitas_examples package

.github/workflows/base.yml.template

@@ -57,6 +57,11 @@ jobs:
         mkdir $CONDA_PKGS_DIRS
         mkdir $PYTORCH_PKG_CACHE_PATH
 
+    - name: Install libsndfile on Ubuntu
+      shell: bash
+      run: sudo apt-get install -y libsndfile-dev
+      if: startsWith(runner.os, 'Linux') == true
+
     - name: Setup Conda
       uses: s-weigand/setup-conda@v1
 

.github/workflows/develop_install.yml

@@ -17,21 +17,28 @@ jobs:
         pytorch_version: ['1.1.0', '1.2.0', '1.3.0', '1.3.1', '1.4.0']
         platform: ['windows-latest', 'ubuntu-latest', 'macos-latest']
 
+
         exclude:
           - platform: 'windows-latest'
             conda_python_version: '3.6'
+
           - platform: 'macos-latest'
             pytorch_version: '1.1.0'
+
           - pytorch_version: '1.1.0'
             conda_python_version: '3.8'
+
           - pytorch_version: '1.2.0'
             conda_python_version: '3.8'
+
           - pytorch_version: '1.3.0'
             conda_python_version: '3.8'
+
           - pytorch_version: '1.3.1'
             conda_python_version: '3.8'
 
 
+
     steps:
 
     - name: Checkout repo
@@ -72,6 +79,11 @@ jobs:
         mkdir $CONDA_PKGS_DIRS
         mkdir $PYTORCH_PKG_CACHE_PATH
 
+    - name: Install libsndfile on Ubuntu
+      shell: bash
+      run: sudo apt-get install -y libsndfile-dev
+      if: startsWith(runner.os, 'Linux') == true
+
     - name: Setup Conda
       uses: s-weigand/setup-conda@v1
 
@@ -115,9 +127,14 @@ jobs:
         rm -R ${{ steps.cache-dirs.outputs.pytorch_pkg_cache_path }}/pytorch*
       if: steps.pytorch-cache.outputs.cache-hit == 'true'
 
-    - name: Run Nox session for testing develop install and imports
+    - name: Run Nox session for testing brevitas develop install and imports
       shell: bash
-      run: nox --verbose --session tests_install_develop-${{ matrix.conda_python_version }}\(\pytorch_${{ matrix.pytorch_version }}\)
+      run: nox -v -s tests_brevitas_install_dev-${{ matrix.conda_python_version }}\(\pytorch_${{ matrix.pytorch_version }}\)
+
+    - name: Run Nox session for testing brevitas_examples develop install and imports
+      shell: bash
+      run: nox -v -s tests_brevitas_examples_install_dev-${{ matrix.conda_python_version }}\(\pytorch_${{ matrix.pytorch_version }}\)
+
 
 
     - name: Remove tarballs before caching Pytorch deps pkgs

.github/workflows/gen_github_actions.py

@@ -42,15 +42,23 @@
 
 PYTEST_MATRIX_EXTRA = od([('jit_status', list(JIT_STATUSES))])
 
-PYTEST_STEP_LIST = [od([
-    ('name', 'Run Nox session for pytest'),
-    ('shell', 'bash'),
-    ('run', 'nox --verbose --session tests_cpu-${{ matrix.conda_python_version }}\(${{ matrix.jit_status }}\,\ pytorch_${{ matrix.pytorch_version }}\)')])]
-
-TEST_INSTALL_DEVELOP_STEP_LIST = [od([
-    ('name', 'Run Nox session for testing develop install and imports'),
-    ('shell', 'bash'),
-    ('run', 'nox --verbose --session tests_install_develop-${{ matrix.conda_python_version }}\(\pytorch_${{ matrix.pytorch_version }}\)')])]
+PYTEST_STEP_LIST = [
+    od([
+        ('name', 'Run Nox session for pytest'),
+        ('shell', 'bash'),
+        ('run', 'nox -v -s tests_brevitas_cpu-${{ matrix.conda_python_version }}\(${{ matrix.jit_status }}\,\ pytorch_${{ matrix.pytorch_version }}\)')
+    ])]
+
+TEST_INSTALL_DEV_STEP_LIST = [
+    od([
+        ('name', 'Run Nox session for testing brevitas develop install and imports'),
+        ('shell', 'bash'),
+        ('run', 'nox -v -s tests_brevitas_install_dev-${{ matrix.conda_python_version }}\(\pytorch_${{ matrix.pytorch_version }}\)')]),
+    od([
+        ('name', 'Run Nox session for testing brevitas_examples develop install and imports'),
+        ('shell', 'bash'),
+        ('run', 'nox -v -s tests_brevitas_examples_install_dev-${{ matrix.conda_python_version }}\(\pytorch_${{ matrix.pytorch_version }}\)')
+    ])]
 
 
 # whitespaces to indent generated portions of output yaml
@@ -90,6 +98,7 @@ def dict_str(d, quote_val, indent_first):
                 repr += f"{name}: {val}\n"
         if indent_first:
             repr = indent(repr, RELATIVE_INDENT*' ', predicate=lambda line: not first_line_prefix in line)
+        repr += '\n'
         return repr
 
     def gen_yaml(self, output_path):
@@ -122,7 +131,7 @@ def gen_test_develop_install_yml():
         'Test develop install',
         EXCLUDE_LIST,
         MATRIX,
-        TEST_INSTALL_DEVELOP_STEP_LIST)
+        TEST_INSTALL_DEV_STEP_LIST)
     test_develop_install.gen_yaml(DEVELOP_INSTALL_YML)
 
 

.github/workflows/pytest.yml

@@ -18,23 +18,31 @@ jobs:
         platform: ['windows-latest', 'ubuntu-latest', 'macos-latest']
         jit_status: ['jit_enabled', 'jit_disabled']
 
+
         exclude:
           - platform: 'windows-latest'
             conda_python_version: '3.6'
+
           - platform: 'macos-latest'
             pytorch_version: '1.1.0'
+
           - pytorch_version: '1.1.0'
             conda_python_version: '3.8'
+
           - pytorch_version: '1.2.0'
             conda_python_version: '3.8'
+
           - pytorch_version: '1.3.0'
             conda_python_version: '3.8'
+
           - pytorch_version: '1.3.1'
             conda_python_version: '3.8'
+
           - pytorch_version: '1.1.0'
             jit_status: 'jit_disabled'
 
 
+
     steps:
 
     - name: Checkout repo
@@ -75,6 +83,11 @@ jobs:
         mkdir $CONDA_PKGS_DIRS
         mkdir $PYTORCH_PKG_CACHE_PATH
 
+    - name: Install libsndfile on Ubuntu
+      shell: bash
+      run: sudo apt-get install -y libsndfile-dev
+      if: startsWith(runner.os, 'Linux') == true
+
     - name: Setup Conda
       uses: s-weigand/setup-conda@v1
 
@@ -120,7 +133,8 @@ jobs:
 
     - name: Run Nox session for pytest
       shell: bash
-      run: nox --verbose --session tests_cpu-${{ matrix.conda_python_version }}\(${{ matrix.jit_status }}\,\ pytorch_${{ matrix.pytorch_version }}\)
+      run: nox -v -s tests_brevitas_cpu-${{ matrix.conda_python_version }}\(${{ matrix.jit_status }}\,\ pytorch_${{ matrix.pytorch_version }}\)
+
 
 
     - name: Remove tarballs before caching Pytorch deps pkgs

examples/imagenet_classification/README.md → brevitas_examples/imagenet_classification/README.md

@@ -16,12 +16,12 @@ Below in the table is a list of example pretrained models made available for ref
 To evaluate a pretrained quantized model on ImageNet:
 
  - Make sure you have Brevitas installed and the ImageNet dataset in a Pytorch friendly format (following this [script](https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh)).
- - Pass the corresponding cfg .ini file as an input to the evaluation script. The required checkpoint will be downloaded automatically. 
+ - Pass the name of the model as an input to the evaluation script. The required checkpoint will be downloaded automatically. 
 
  For example, for *quant_mobilenet_v1_4b* evaluated on GPU 0:
 
 ```
-python imagenet_val.py --imagenet-dir /path/to/imagenet --model-cfg ./cfg/quant_mobilenet_v1_4b.ini --gpu 0
+brevitas_imagenet_val --imagenet-dir /path/to/imagenet --model quant_mobilenet_v1_4b --gpu 0 --pretrained
 ```
 
 ## MobileNet V1

brevitas_examples/imagenet_classification/__init__.py

@@ -0,0 +1 @@
+from .models import *

examples/imagenet_classification/imagenet_val.py → brevitas_examples/imagenet_classification/imagenet_val.py

@@ -12,17 +12,15 @@
 import torchvision.transforms as transforms
 import torchvision.datasets as datasets
 
-from models import *
+from .models import model_with_cfg
 
 SEED = 123456
 
-models = {'quant_mobilenet_v1': quant_mobilenet_v1,
-          'quant_proxylessnas_mobile14': quant_proxylessnas_mobile14}
-
 
 parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
 parser.add_argument('--imagenet-dir', help='path to folder containing Imagenet val folder')
-parser.add_argument('--model-cfg', type=str, help='Path to pretrained model .ini configuration file')
+parser.add_argument('--model', type=str, default='quant_mobilenet_v1_4b', help='Name of the model')
+parser.add_argument('--pretrained', action='store_true', help='Load pretrained checkpoint')
 parser.add_argument('--workers', default=4, type=int, help='number of data loading workers')
 parser.add_argument('--batch-size', default=256, type=int, help='Minibatch size')
 parser.add_argument('--gpu', default=None, type=int, help='GPU id to use.')
@@ -33,28 +31,13 @@ def main():
     random.seed(SEED)
     torch.manual_seed(SEED)
 
-    assert os.path.exists(args.model_cfg)
-    cfg = configparser.ConfigParser()
-    cfg.read(args.model_cfg)
-    arch = cfg.get('MODEL', 'ARCH')
-
-    model = models[arch](cfg)
+    model, cfg = model_with_cfg(args.model, args.pretrained)
 
     if args.gpu is not None:
         torch.cuda.set_device(args.gpu)
         model = model.cuda(args.gpu)
         cudnn.benchmark = True
 
-    pretrained_url = cfg.get('MODEL', 'PRETRAINED_URL')
-    print("=> Loading checkpoint from:'{}'".format(pretrained_url))
-    if args.gpu is None:
-        checkpoint = torch.hub.load_state_dict_from_url(pretrained_url)
-    else:
-        # Map model to be loaded to specified single gpu.
-        loc = 'cuda:{}'.format(args.gpu)
-        checkpoint = torch.hub.load_state_dict_from_url(pretrained_url, map_location=loc)
-    model.load_state_dict(checkpoint, strict=True)
-
     valdir = os.path.join(args.imagenet_dir, 'val')
     mean = [float(cfg.get('PREPROCESS', 'MEAN_0')), float(cfg.get('PREPROCESS', 'MEAN_1')),
             float(cfg.get('PREPROCESS', 'MEAN_2'))]

brevitas_examples/imagenet_classification/models/__init__.py

@@ -0,0 +1,48 @@
+import os
+from configparser import ConfigParser
+
+from torch import hub
+
+from .mobilenetv1 import *
+from .vgg import *
+from .proxylessnas import *
+
+model_impl = {
+    'quant_mobilenet_v1': quant_mobilenet_v1,
+    'quant_proxylessnas_mobile14': quant_proxylessnas_mobile14
+}
+
+
+def model_with_cfg(name, pretrained):
+    cfg = ConfigParser()
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    config_path = os.path.join(current_dir, '..', 'cfg', name + '.ini')
+    assert os.path.exists(config_path)
+    cfg.read(config_path)
+    arch = cfg.get('MODEL', 'ARCH')
+    model = model_impl[arch](cfg)
+    if pretrained:
+        checkpoint = cfg.get('MODEL', 'PRETRAINED_URL')
+        state_dict = hub.load_state_dict_from_url(checkpoint, progress=True, map_location='cpu')
+        model.load_state_dict(state_dict, strict=True)
+    return model, cfg
+
+
+def quant_mobilenet_v1_4b(pretrained=True):
+    model, _ = model_with_cfg('quant_mobilenet_v1_4b', pretrained)
+    return model
+
+
+def quant_proxylessnas_mobile14_4b(pretrained=True):
+    model, _ = model_with_cfg('quant_proxylessnas_mobile14_4b', pretrained)
+    return model
+
+
+def quant_proxylessnas_mobile14_4b5b(pretrained=True):
+    model, _ = model_with_cfg('quant_proxylessnas_mobile14_4b5b', pretrained)
+    return model
+
+
+def quant_proxylessnas_mobile14_hadamard_4b(pretrained=True):
+    model, _ = model_with_cfg('quant_proxylessnas_mobile14_hadamard_4b', pretrained)
+    return model

examples/imagenet_classification/models/common.py → brevitas_examples/imagenet_classification/models/common.py

@@ -4,6 +4,7 @@
 from brevitas.core.scaling import ScalingImplType
 from brevitas.core.stats import StatsOp
 
+
 QUANT_TYPE = QuantType.INT
 SCALING_MIN_VAL = 2e-16
 
@@ -151,3 +152,6 @@ def make_hadamard_classifier(in_channels,
     return qnn.HadamardClassifier(in_channels=in_channels,
                                   out_channels=out_channels,
                                   fixed_scale=fixed_scale)
+
+
+

examples/imagenet_classification/models/mobilenetv1.py → brevitas_examples/imagenet_classification/models/mobilenetv1.py

@@ -33,7 +33,7 @@
 
 from brevitas.quant_tensor import pack_quant_tensor
 
-from .common import make_quant_conv2d, make_quant_linear, make_quant_relu, make_quant_avg_pool
+from .common import *
 
 
 FIRST_LAYER_BIT_WIDTH = 8
@@ -170,3 +170,6 @@ def quant_mobilenet_v1(cfg):
                     first_stage_stride=first_stage_stride,
                     bit_width=bit_width)
     return net
+
+
+

examples/speech_to_text/README.md → brevitas_examples/speech_to_text/README.md

@@ -15,19 +15,20 @@ It is highly recommended to setup a virtual environment.
 
 Download and pre-process the LibriSpeech dataset with the following command:
 ```
-python utilities/get_librispeech_data.py --data_root=/path/to/validation/folder --data_set=DEV_OTHER
+brevitas_quartznet_preprocess --data_root=/path/to/validation/folder --data_set=DEV_OTHER
 ```
 
 To evaluate a pretrained quantized model on LibriSpeech:
 
  - Install pytorch from the [Pytorch Website](https://pytorch.org/), and Cython with the following command:
  `python install --upgrade cython`
- - Install  the Quartznet requirements with `pip install requirements.txt`
- - Make sure you have Brevitas installed
- - Pass the corresponding cfg .ini file as an input to the evaluation script. The required checkpoint will be downloaded automatically. 
+ - Install SoX (this [guide](https://at.projects.genivi.org/wiki/display/PROJ/Installation+of+SoX+on+different+Platforms)
+ may be helpful)
+ - After cloning the repository, install Brevitas and QuartzNet requirements with `pip install .[stt]`
+ - Pass the name of the model as an input to the evaluation script. The required checkpoint will be downloaded automatically. 
 
  For example, for the evaluation on GPU 0:
 
 ```
-python quartznet_val.py --input-folder /path/to/validation/folder --model-cfg cfg/quant_quartznet_pertensorscaling_8b.ini --gpu 0
+brevitas_quartznet_val --input-folder /path/to/validation/folder --model quant_quartznet_pertensorscaling_8b --gpu 0 --pretrained
 ```

brevitas_examples/speech_to_text/__init__.py

@@ -0,0 +1 @@
+from .quartznet import *

examples/speech_to_text/cfg/quant_quartznet_perchannelscaling_4b.ini → brevitas_examples/speech_to_text/cfg/quant_quartznet_perchannelscaling_4b.ini

@@ -1,6 +1,6 @@
 [MODEL]
 ARCH: quartznet
-TOPOLOGY_FILE: cfg/topology/quartznet15x5.yaml
+TOPOLOGY_FILE: quartznet15x5.yaml
 PRETRAINED_ENCODER_URL: https://github.com/Xilinx/brevitas/releases/download/quant_quartznet_4b-r0/quant_quartznet_encoder_4b-0a46a232.pth
 PRETRAINED_DECODER_URL: https://github.com/Xilinx/brevitas/releases/download/quant_quartznet_4b-r0/quant_quartznet_decoder_4b-bcbf8c7b.pth
 

examples/speech_to_text/cfg/quant_quartznet_perchannelscaling_8b.ini → brevitas_examples/speech_to_text/cfg/quant_quartznet_perchannelscaling_8b.ini

@@ -1,6 +1,6 @@
 [MODEL]
 ARCH: quartznet
-TOPOLOGY_FILE: cfg/topology/quartznet15x5.yaml
+TOPOLOGY_FILE: quartznet15x5.yaml
 PRETRAINED_ENCODER_URL: https://github.com/Xilinx/brevitas/releases/download/quant_quartznet_8b-r0/quant_quartznet_encoder_8b-50f12b4b.pth
 PRETRAINED_DECODER_URL: https://github.com/Xilinx/brevitas/releases/download/quant_quartznet_8b-r0/quant_quartznet_decoder_8b-af09651c.pth
 

examples/speech_to_text/cfg/quant_quartznet_pertensorscaling_8b.ini → brevitas_examples/speech_to_text/cfg/quant_quartznet_pertensorscaling_8b.ini

@@ -1,6 +1,6 @@
 [MODEL]
 ARCH: quartznet
-TOPOLOGY_FILE: cfg/topology/quartznet15x5.yaml
+TOPOLOGY_FILE: quartznet15x5.yaml
 PRETRAINED_ENCODER_URL: https://github.com/Xilinx/brevitas/releases/download/quant_quartznet_8b-r0/quant_quartznet_encoder_8b-50f12b4b.pth
 PRETRAINED_DECODER_URL: https://github.com/Xilinx/brevitas/releases/download/quant_quartznet_8b-r0/quant_quartznet_decoder_8b-af09651c.pth