Upload utils/sync_batchnorm/replicate.py

utils/sync_batchnorm/replicate.py

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+# -*- coding: utf-8 -*-
+# File   : replicate.py
+# Author : Jiayuan Mao
+# Email  : [email protected]
+# Date   : 27/01/2018
+# 
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import functools
+
+from torch.nn.parallel.data_parallel import DataParallel
+
+__all__ = [
+    'CallbackContext',
+    'execute_replication_callbacks',
+    'DataParallelWithCallback',
+    'patch_replication_callback'
+]
+
+
+class CallbackContext(object):
+    pass
+
+
+def execute_replication_callbacks(modules):
+    """
+    Execute an replication callback `__data_parallel_replicate__` on each module created by original replication.
+
+    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
+
+    Note that, as all modules are isomorphism, we assign each sub-module with a context
+    (shared among multiple copies of this module on different devices).
+    Through this context, different copies can share some information.
+
+    We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback
+    of any slave copies.
+    """
+    master_copy = modules[0]
+    nr_modules = len(list(master_copy.modules()))
+    ctxs = [CallbackContext() for _ in range(nr_modules)]
+
+    for i, module in enumerate(modules):
+        for j, m in enumerate(module.modules()):
+            if hasattr(m, '__data_parallel_replicate__'):
+                m.__data_parallel_replicate__(ctxs[j], i)
+
+
+class DataParallelWithCallback(DataParallel):
+    """
+    Data Parallel with a replication callback.
+
+    An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by
+    original `replicate` function.
+    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
+
+    Examples:
+        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+        # sync_bn.__data_parallel_replicate__ will be invoked.
+    """
+
+    def replicate(self, module, device_ids):
+        modules = super(DataParallelWithCallback, self).replicate(module, device_ids)
+        execute_replication_callbacks(modules)
+        return modules
+
+
+def patch_replication_callback(data_parallel):
+    """
+    Monkey-patch an existing `DataParallel` object. Add the replication callback.
+    Useful when you have customized `DataParallel` implementation.
+
+    Examples:
+        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+        > sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
+        > patch_replication_callback(sync_bn)
+        # this is equivalent to
+        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+    """
+
+    assert isinstance(data_parallel, DataParallel)
+
+    old_replicate = data_parallel.replicate
+
+    @functools.wraps(old_replicate)
+    def new_replicate(module, device_ids):
+        modules = old_replicate(module, device_ids)
+        execute_replication_callbacks(modules)
+        return modules
+
+    data_parallel.replicate = new_replicate