更换文档检测模型

paddle_detection/ppdet/utils/fuse_utils.py

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+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import paddle
+import paddle.nn as nn
+
+__all__ = ['fuse_conv_bn']
+
+
+def fuse_conv_bn(model):
+    is_train = False
+    if model.training:
+        model.eval()
+        is_train = True
+    fuse_list = []
+    tmp_pair = [None, None]
+    for name, layer in model.named_sublayers():
+        if isinstance(layer, nn.Conv2D):
+            tmp_pair[0] = name
+        if isinstance(layer, nn.BatchNorm2D):
+            tmp_pair[1] = name
+
+        if tmp_pair[0] and tmp_pair[1] and len(tmp_pair) == 2:
+            fuse_list.append(tmp_pair)
+            tmp_pair = [None, None]
+    model = fuse_layers(model, fuse_list)
+    if is_train:
+        model.train()
+    return model
+
+
+def find_parent_layer_and_sub_name(model, name):
+    """
+    Given the model and the name of a layer, find the parent layer and
+    the sub_name of the layer.
+    For example, if name is 'block_1/convbn_1/conv_1', the parent layer is
+    'block_1/convbn_1' and the sub_name is `conv_1`.
+    Args:
+        model(paddle.nn.Layer): the model to be quantized.
+        name(string): the name of a layer
+
+    Returns:
+        parent_layer, subname
+    """
+    assert isinstance(model, nn.Layer), \
+            "The model must be the instance of paddle.nn.Layer."
+    assert len(name) > 0, "The input (name) should not be empty."
+
+    last_idx = 0
+    idx = 0
+    parent_layer = model
+    while idx < len(name):
+        if name[idx] == '.':
+            sub_name = name[last_idx:idx]
+            if hasattr(parent_layer, sub_name):
+                parent_layer = getattr(parent_layer, sub_name)
+                last_idx = idx + 1
+        idx += 1
+    sub_name = name[last_idx:idx]
+    return parent_layer, sub_name
+
+
+class Identity(nn.Layer):
+    '''a layer to replace bn or relu layers'''
+
+    def __init__(self, *args, **kwargs):
+        super(Identity, self).__init__()
+
+    def forward(self, input):
+        return input
+
+
+def fuse_layers(model, layers_to_fuse, inplace=False):
+    '''
+       fuse layers in layers_to_fuse
+
+       Args:
+           model(nn.Layer): The model to be fused.
+           layers_to_fuse(list): The layers' names to be fused. For
+               example,"fuse_list = [["conv1", "bn1"], ["conv2", "bn2"]]".
+               A TypeError would be raised if "fuse" was set as
+               True but "fuse_list" was None.
+                                 Default: None.
+           inplace(bool): Whether apply fusing to the input model.
+                          Default: False.
+
+       Return
+           fused_model(paddle.nn.Layer): The fused model.
+    '''
+    if not inplace:
+        model = copy.deepcopy(model)
+    for layers_list in layers_to_fuse:
+        layer_list = []
+        for layer_name in layers_list:
+            parent_layer, sub_name = find_parent_layer_and_sub_name(model,
+                                                                    layer_name)
+            layer_list.append(getattr(parent_layer, sub_name))
+        new_layers = _fuse_func(layer_list)
+        for i, item in enumerate(layers_list):
+            parent_layer, sub_name = find_parent_layer_and_sub_name(model, item)
+            setattr(parent_layer, sub_name, new_layers[i])
+    return model
+
+
+def _fuse_func(layer_list):
+    '''choose the fuser method and fuse layers'''
+    types = tuple(type(m) for m in layer_list)
+    fusion_method = types_to_fusion_method.get(types, None)
+    new_layers = [None] * len(layer_list)
+    fused_layer = fusion_method(*layer_list)
+    for handle_id, pre_hook_fn in layer_list[0]._forward_pre_hooks.items():
+        fused_layer.register_forward_pre_hook(pre_hook_fn)
+        del layer_list[0]._forward_pre_hooks[handle_id]
+    for handle_id, hook_fn in layer_list[-1]._forward_post_hooks.items():
+        fused_layer.register_forward_post_hook(hook_fn)
+        del layer_list[-1]._forward_post_hooks[handle_id]
+    new_layers[0] = fused_layer
+    for i in range(1, len(layer_list)):
+        identity = Identity()
+        identity.training = layer_list[0].training
+        new_layers[i] = identity
+    return new_layers
+
+
+def _fuse_conv_bn(conv, bn):
+    '''fuse conv and bn for train or eval'''
+    assert(conv.training == bn.training),\
+        "Conv and BN both must be in the same mode (train or eval)."
+    if conv.training:
+        assert bn._num_features == conv._out_channels, 'Output channel of Conv2d must match num_features of BatchNorm2d'
+        raise NotImplementedError
+    else:
+        return _fuse_conv_bn_eval(conv, bn)
+
+
+def _fuse_conv_bn_eval(conv, bn):
+    '''fuse conv and bn for eval'''
+    assert (not (conv.training or bn.training)), "Fusion only for eval!"
+    fused_conv = copy.deepcopy(conv)
+
+    fused_weight, fused_bias = _fuse_conv_bn_weights(
+        fused_conv.weight, fused_conv.bias, bn._mean, bn._variance, bn._epsilon,
+        bn.weight, bn.bias)
+    fused_conv.weight.set_value(fused_weight)
+    if fused_conv.bias is None:
+        fused_conv.bias = paddle.create_parameter(
+            shape=[fused_conv._out_channels], is_bias=True, dtype=bn.bias.dtype)
+    fused_conv.bias.set_value(fused_bias)
+    return fused_conv
+
+
+def _fuse_conv_bn_weights(conv_w, conv_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b):
+    '''fuse weights and bias of conv and bn'''
+    if conv_b is None:
+        conv_b = paddle.zeros_like(bn_rm)
+    if bn_w is None:
+        bn_w = paddle.ones_like(bn_rm)
+    if bn_b is None:
+        bn_b = paddle.zeros_like(bn_rm)
+    bn_var_rsqrt = paddle.rsqrt(bn_rv + bn_eps)
+    conv_w = conv_w * \
+        (bn_w * bn_var_rsqrt).reshape([-1] + [1] * (len(conv_w.shape) - 1))
+    conv_b = (conv_b - bn_rm) * bn_var_rsqrt * bn_w + bn_b
+    return conv_w, conv_b
+
+
+types_to_fusion_method = {(nn.Conv2D, nn.BatchNorm2D): _fuse_conv_bn, }