SWA script added

CHANGELOG.md

@@ -14,6 +14,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - SoftMax, CosFace, ArcFace layers to contrib ([#939](https://github.com/catalyst-team/catalyst/pull/939))
 - ArcMargin layer to contrib ([#957](https://github.com/catalyst-team/catalyst/pull/957))
 - AdaCos to contrib ([#958](https://github.com/catalyst-team/catalyst/pull/958))
+- Manual SWA to utils (https://github.com/catalyst-team/catalyst/pull/945)
 
 ### Changed
 
@@ -305,4 +306,4 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
-- 
+- 

bin/tests/check_dl_cv.sh

@@ -43,6 +43,11 @@ PYTHONPATH=./examples:.:${PYTHONPATH} \
   python catalyst/dl/scripts/trace.py \
   ${LOGDIR}
 
+echo 'pipeline 01 - swa'
+PYTHONPATH=./examples:.:${PYTHONPATH} \
+  python catalyst/dl/scripts/swa.py \
+  --logdir=${LOGDIR} --output-path=./swa.pth
+
 rm -rf ${LOGDIR}
 
 

catalyst/dl/scripts/swa.py

@@ -0,0 +1,50 @@
+import argparse
+from argparse import ArgumentParser
+from pathlib import Path
+
+import torch
+
+from catalyst.dl.utils.swa import generate_averaged_weights
+
+
+def build_args(parser: ArgumentParser):
+    """Builds the command line parameters."""
+    parser.add_argument("--logdir", type=Path, help="Path to models logdir")
+    parser.add_argument(
+        "--models-mask",
+        "-m",
+        type=str,
+        default="*.pth",
+        help="Pattern for models to average",
+    )
+    parser.add_argument(
+        "--output-path",
+        type=Path,
+        default="./swa.pth",
+        help="Path to save averaged model",
+    )
+
+    return parser
+
+
+def parse_args():
+    """Parses the command line arguments for the main method."""
+    parser = argparse.ArgumentParser()
+    build_args(parser)
+    args = parser.parse_args()
+    return args
+
+
+def main(args, _):
+    """Main method for ``catalyst-dl swa``."""
+    logdir: Path = args.logdir
+    models_mask: str = args.models_mask
+    output_path: Path = args.output_path
+
+    averaged_weights = generate_averaged_weights(logdir, models_mask)
+
+    torch.save(averaged_weights, str(output_path))
+
+
+if __name__ == "__main__":
+    main(parse_args(), None)

catalyst/dl/tests/test_swa.py

@@ -0,0 +1,53 @@
+import os
+from pathlib import Path
+import shutil
+import unittest
+
+import torch
+import torch.nn as nn
+
+from catalyst.dl.utils.swa import generate_averaged_weights
+
+
+class Net(nn.Module):
+    """Dummy network class."""
+
+    def __init__(self, init_weight=4):
+        """Initialization of network and filling it with given numbers."""
+        super(Net, self).__init__()
+        self.fc = nn.Linear(2, 1)
+        self.fc.weight.data.fill_(init_weight)
+        self.fc.bias.data.fill_(init_weight)
+
+
+class TestSwa(unittest.TestCase):
+    """Test SWA class."""
+
+    def setUp(self):
+        """Test set up."""
+        net1 = Net(init_weight=2.0)
+        net2 = Net(init_weight=5.0)
+        os.mkdir("./checkpoints")
+        torch.save(net1.state_dict(), "./checkpoints/net1.pth")
+        torch.save(net2.state_dict(), "./checkpoints/net2.pth")
+
+    def tearDown(self):
+        """Test tear down."""
+        shutil.rmtree("./checkpoints")
+
+    def test_averaging(self):
+        """Test SWA method."""
+        weights = generate_averaged_weights(
+            logdir=Path("./"), models_mask="net*"
+        )
+        torch.save(weights, str("./checkpoints/swa_weights.pth"))
+        model = Net()
+        model.load_state_dict(torch.load("./checkpoints/swa_weights.pth"))
+
+        self.assertEqual(float(model.fc.weight.data[0][0]), 3.5)
+        self.assertEqual(float(model.fc.weight.data[0][1]), 3.5)
+        self.assertEqual(float(model.fc.bias.data[0]), 3.5)
+
+
+if __name__ == "__main__":
+    unittest.main()

catalyst/dl/utils/swa.py

@@ -0,0 +1,75 @@
+from typing import List
+from collections import OrderedDict
+import glob
+import os
+from pathlib import Path
+
+import torch
+
+
+def average_weights(state_dicts: List[dict]) -> OrderedDict:
+    """
+    Averaging of input weights.
+
+    Args:
+        state_dicts (List[dict]): Weights to average
+
+    Raises:
+        KeyError: If states do not match
+
+    Returns:
+        Averaged weights
+    """
+    # source https://gist.github.com/qubvel/70c3d5e4cddcde731408f478e12ef87b
+    params_keys = None
+    for i, state_dict in enumerate(state_dicts):
+        model_params_keys = list(state_dict.keys())
+        if params_keys is None:
+            params_keys = model_params_keys
+        elif params_keys != model_params_keys:
+            raise KeyError(
+                "For checkpoint {}, expected list of params: {}, "
+                "but found: {}".format(i, params_keys, model_params_keys)
+            )
+
+    average_dict = OrderedDict()
+    for k in state_dicts[0].keys():
+        average_dict[k] = torch.div(
+            sum(state_dict[k] for state_dict in state_dicts), len(state_dicts),
+        )
+    return average_dict
+
+
+def load_weight(path: str) -> dict:
+    """
+    Load weights of a model.
+
+    Args:
+        path (str): Path to model weights
+
+    Returns:
+        Weights
+    """
+    weights = torch.load(path)
+    if "model_state_dict" in weights:
+        weights = weights["model_state_dict"]
+    return weights
+
+
+def generate_averaged_weights(logdir: Path, models_mask: str) -> OrderedDict:
+    """
+    Averaging of input weights and saving them.
+
+    Args:
+        logdir (Path): Path to logs directory
+        models_mask (str): globe-like pattern for models to average
+
+    Returns:
+        Averaged weights
+    """
+    models_pathes = glob.glob(os.path.join(logdir, "checkpoints", models_mask))
+
+    all_weights = [load_weight(path) for path in models_pathes]
+    averaged_dict = average_weights(all_weights)
+
+    return averaged_dict