!5447 Support manual convert to quantative network of resnet

Merge pull request !5447 from chenfei_mindspore/r0.6

model_zoo/official/cv/mobilenetv2_quant/src/utils.py

@@ -111,3 +111,34 @@ class CrossEntropyWithLabelSmooth(_Loss):
         out_loss = self.ce(logit, one_hot_label)
         out_loss = self.mean(out_loss, 0)
         return out_loss
+
+def _load_param_into_net(model, params_dict):
+    """
+    load fp32 model parameters to quantization model.
+
+    Args:
+        model: quantization model
+        params_dict: f32 param
+
+    Returns:
+        None
+    """
+    iterable_dict = {
+        'weight': iter([item for item in params_dict.items() if item[0].endswith('weight')]),
+        'bias': iter([item for item in params_dict.items() if item[0].endswith('bias')]),
+        'gamma': iter([item for item in params_dict.items() if item[0].endswith('gamma')]),
+        'beta': iter([item for item in params_dict.items() if item[0].endswith('beta')]),
+        'moving_mean': iter([item for item in params_dict.items() if item[0].endswith('moving_mean')]),
+        'moving_variance': iter(
+            [item for item in params_dict.items() if item[0].endswith('moving_variance')]),
+        'minq': iter([item for item in params_dict.items() if item[0].endswith('minq')]),
+        'maxq': iter([item for item in params_dict.items() if item[0].endswith('maxq')])
+    }
+    for name, param in model.parameters_and_names():
+        key_name = name.split(".")[-1]
+        if key_name not in iterable_dict.keys():
+            continue
+        value_param = next(iterable_dict[key_name], None)
+        if value_param is not None:
+            param.set_parameter_data(value_param[1].data)
+            print(f'init model param {name} with checkpoint param {value_param[0]}')

model_zoo/official/cv/mobilenetv2_quant/train.py

@@ -24,14 +24,14 @@ from mindspore import Tensor
 from mindspore import nn
 from mindspore.train.model import Model, ParallelMode
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.serialization import load_checkpoint
 from mindspore.communication.management import init
 from mindspore.train.quant import quant
 import mindspore.dataset.engine as de
 
 from src.dataset import create_dataset
 from src.lr_generator import get_lr
-from src.utils import Monitor, CrossEntropyWithLabelSmooth
+from src.utils import Monitor, CrossEntropyWithLabelSmooth, _load_param_into_net
 from src.config import config_ascend, config_ascend_quant
 from src.mobilenetV2 import mobilenetV2
 
@@ -92,7 +92,7 @@ if __name__ == '__main__':
     # load pre trained ckpt
     if args_opt.pre_trained:
         param_dict = load_checkpoint(args_opt.pre_trained)
-        load_param_into_net(network, param_dict)
+        _load_param_into_net(network, param_dict)
 
     # convert fusion network to quantization aware network
     if config.quantization_aware:

model_zoo/official/cv/resnet50_quant/eval.py

@@ -21,7 +21,8 @@ from src.config import quant_set, config_quant, config_noquant
 from src.dataset import create_dataset
 from src.crossentropy import CrossEntropy
 from src.utils import _load_param_into_net
-from models.resnet_quant import resnet50_quant
+#from models.resnet_quant import resnet50_quant #auto construct quantative network of resnet50
+from models.resnet_quant_manual import resnet50_quant #manually construct quantative network of resnet50
 
 from mindspore import context
 from mindspore.train.model import Model

model_zoo/official/cv/resnet50_quant/models/resnet_quant_manual.py

@@ -0,0 +1,325 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""ResNet."""
+import numpy as np
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+from mindspore import Tensor
+from mindspore.nn import FakeQuantWithMinMax, Conv2dBnFoldQuant as Conv2dBatchNormQuant
+
+_ema_decay = 0.999
+_symmetric = True
+_fake = True
+_per_channel = True
+
+
+def _weight_variable(shape, factor=0.01):
+    init_value = np.random.randn(*shape).astype(np.float32) * factor
+    return Tensor(init_value)
+
+
+def _conv3x3(in_channel, out_channel, stride=1):
+    weight_shape = (out_channel, in_channel, 3, 3)
+    weight = _weight_variable(weight_shape)
+    return nn.Conv2d(in_channel, out_channel,
+                     kernel_size=3, stride=stride, padding=0, pad_mode='same', weight_init=weight)
+
+
+def _conv1x1(in_channel, out_channel, stride=1):
+    weight_shape = (out_channel, in_channel, 1, 1)
+    weight = _weight_variable(weight_shape)
+    return nn.Conv2d(in_channel, out_channel,
+                     kernel_size=1, stride=stride, padding=0, pad_mode='same', weight_init=weight)
+
+
+def _conv7x7(in_channel, out_channel, stride=1):
+    weight_shape = (out_channel, in_channel, 7, 7)
+    weight = _weight_variable(weight_shape)
+    return nn.Conv2d(in_channel, out_channel,
+                     kernel_size=7, stride=stride, padding=0, pad_mode='same', weight_init=weight)
+
+
+def _bn(channel):
+    return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,
+                          gamma_init=1, beta_init=0, moving_mean_init=0, moving_var_init=1)
+
+
+def _bn_last(channel):
+    return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,
+                          gamma_init=0, beta_init=0, moving_mean_init=0, moving_var_init=1)
+
+
+def _fc(in_channel, out_channel):
+    weight_shape = (out_channel, in_channel)
+    weight = _weight_variable(weight_shape)
+    return nn.Dense(in_channel, out_channel, has_bias=True, weight_init=weight, bias_init=0)
+
+
+class ConvBNReLU(nn.Cell):
+    """
+    Convolution/Depthwise fused with Batchnorm and ReLU block definition.
+
+    Args:
+        in_planes (int): Input channel.
+        out_planes (int): Output channel.
+        kernel_size (int): Input kernel size.
+        stride (int): Stride size for the first convolutional layer. Default: 1.
+        groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.
+
+    Returns:
+        Tensor, output tensor.
+
+    Examples:
+        >>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
+    """
+
+    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
+        super(ConvBNReLU, self).__init__()
+        padding = (kernel_size - 1) // 2
+        conv = Conv2dBatchNormQuant(in_planes, out_planes, kernel_size, stride, pad_mode='pad', padding=padding,
+                                    group=groups, fake=_fake, per_channel=_per_channel, symmetric=_symmetric)
+        layers = [conv, nn.ActQuant(nn.ReLU())] if _fake else [conv, nn.ReLU()]
+        self.features = nn.SequentialCell(layers)
+
+    def construct(self, x):
+        output = self.features(x)
+        return output
+
+
+class ResidualBlock(nn.Cell):
+    """
+    ResNet V1 residual block definition.
+
+    Args:
+        in_channel (int): Input channel.
+        out_channel (int): Output channel.
+        stride (int): Stride size for the first convolutional layer. Default: 1.
+
+    Returns:
+        Tensor, output tensor.
+
+    Examples:
+        >>> ResidualBlock(3, 256, stride=2)
+    """
+    expansion = 4
+
+    def __init__(self,
+                 in_channel,
+                 out_channel,
+                 stride=1):
+        super(ResidualBlock, self).__init__()
+
+        channel = out_channel // self.expansion
+        self.conv1 = ConvBNReLU(in_channel, channel, kernel_size=1, stride=1)
+        self.conv2 = ConvBNReLU(channel, channel, kernel_size=3, stride=stride)
+        self.conv3 = nn.SequentialCell([Conv2dBatchNormQuant(channel, out_channel, fake=_fake, per_channel=_per_channel,
+                                                             symmetric=_symmetric,
+                                                             kernel_size=1, stride=1, pad_mode='same', padding=0),
+                                        FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay, symmetric=False)
+                                        ]) if _fake else Conv2dBatchNormQuant(channel, out_channel, fake=_fake,
+                                                                              per_channel=_per_channel,
+                                                                              symmetric=_symmetric,
+                                                                              kernel_size=1, stride=1,
+                                                                              pad_mode='same', padding=0)
+
+        self.down_sample = False
+
+        if stride != 1 or in_channel != out_channel:
+            self.down_sample = True
+        self.down_sample_layer = None
+
+        if self.down_sample:
+            self.down_sample_layer = nn.SequentialCell([Conv2dBatchNormQuant(in_channel, out_channel,
+                                                                             per_channel=_per_channel,
+                                                                             symmetric=_symmetric,
+                                                                             kernel_size=1, stride=stride,
+                                                                             pad_mode='same', padding=0),
+                                                        FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay,
+                                                                            symmetric=False)
+                                                        ]) if _fake else Conv2dBatchNormQuant(in_channel, out_channel,
+                                                                                              fake=_fake,
+                                                                                              per_channel=_per_channel,
+                                                                                              symmetric=_symmetric,
+                                                                                              kernel_size=1,
+                                                                                              stride=stride,
+                                                                                              pad_mode='same',
+                                                                                              padding=0)
+        self.add = nn.TensorAddQuant()
+        self.relu = P.ReLU()
+
+    def construct(self, x):
+        identity = x
+        out = self.conv1(x)
+        out = self.conv2(out)
+        out = self.conv3(out)
+
+        if self.down_sample:
+            identity = self.down_sample_layer(identity)
+
+        out = self.add(out, identity)
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Cell):
+    """
+    ResNet architecture.
+
+    Args:
+        block (Cell): Block for network.
+        layer_nums (list): Numbers of block in different layers.
+        in_channels (list): Input channel in each layer.
+        out_channels (list): Output channel in each layer.
+        strides (list):  Stride size in each layer.
+        num_classes (int): The number of classes that the training images are belonging to.
+    Returns:
+        Tensor, output tensor.
+
+    Examples:
+        >>> ResNet(ResidualBlock,
+        >>>        [3, 4, 6, 3],
+        >>>        [64, 256, 512, 1024],
+        >>>        [256, 512, 1024, 2048],
+        >>>        [1, 2, 2, 2],
+        >>>        10)
+    """
+
+    def __init__(self,
+                 block,
+                 layer_nums,
+                 in_channels,
+                 out_channels,
+                 strides,
+                 num_classes):
+        super(ResNet, self).__init__()
+
+        if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
+            raise ValueError("the length of layer_num, in_channels, out_channels list must be 4!")
+
+        self.conv1 = ConvBNReLU(3, 64, kernel_size=7, stride=2)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
+
+        self.layer1 = self._make_layer(block,
+                                       layer_nums[0],
+                                       in_channel=in_channels[0],
+                                       out_channel=out_channels[0],
+                                       stride=strides[0])
+        self.layer2 = self._make_layer(block,
+                                       layer_nums[1],
+                                       in_channel=in_channels[1],
+                                       out_channel=out_channels[1],
+                                       stride=strides[1])
+        self.layer3 = self._make_layer(block,
+                                       layer_nums[2],
+                                       in_channel=in_channels[2],
+                                       out_channel=out_channels[2],
+                                       stride=strides[2])
+        self.layer4 = self._make_layer(block,
+                                       layer_nums[3],
+                                       in_channel=in_channels[3],
+                                       out_channel=out_channels[3],
+                                       stride=strides[3])
+
+        self.mean = P.ReduceMean(keep_dims=True)
+        self.flatten = nn.Flatten()
+        self.end_point = nn.DenseQuant(out_channels[3], num_classes, has_bias=True, per_channel=_per_channel,
+                                       symmetric=_symmetric)
+        self.output_fake = nn.FakeQuantWithMinMax(ema=True, ema_decay=_ema_decay)
+
+    def _make_layer(self, block, layer_num, in_channel, out_channel, stride):
+        """
+        Make stage network of ResNet.
+
+        Args:
+            block (Cell): Resnet block.
+            layer_num (int): Layer number.
+            in_channel (int): Input channel.
+            out_channel (int): Output channel.
+            stride (int): Stride size for the first convolutional layer.
+
+        Returns:
+            SequentialCell, the output layer.
+
+        Examples:
+            >>> _make_layer(ResidualBlock, 3, 128, 256, 2)
+        """
+        layers = []
+
+        resnet_block = block(in_channel, out_channel, stride=stride)
+        layers.append(resnet_block)
+
+        for _ in range(1, layer_num):
+            resnet_block = block(out_channel, out_channel, stride=1)
+            layers.append(resnet_block)
+
+        return nn.SequentialCell(layers)
+
+    def construct(self, x):
+        x = self.conv1(x)
+        c1 = self.maxpool(x)
+
+        c2 = self.layer1(c1)
+        c3 = self.layer2(c2)
+        c4 = self.layer3(c3)
+        c5 = self.layer4(c4)
+
+        out = self.mean(c5, (2, 3))
+        out = self.flatten(out)
+        out = self.end_point(out)
+        out = self.output_fake(out)
+        return out
+
+
+def resnet50_quant(class_num=10):
+    """
+    Get ResNet50 neural network.
+
+    Args:
+        class_num (int): Class number.
+
+    Returns:
+        Cell, cell instance of ResNet50 neural network.
+
+    Examples:
+        >>> net = resnet50_quant(10)
+    """
+    return ResNet(ResidualBlock,
+                  [3, 4, 6, 3],
+                  [64, 256, 512, 1024],
+                  [256, 512, 1024, 2048],
+                  [1, 2, 2, 2],
+                  class_num)
+
+
+def resnet101_quant(class_num=1001):
+    """
+    Get ResNet101 neural network.
+
+    Args:
+        class_num (int): Class number.
+
+    Returns:
+        Cell, cell instance of ResNet101 neural network.
+
+    Examples:
+        >>> net = resnet101(1001)
+    """
+    return ResNet(ResidualBlock,
+                  [3, 4, 23, 3],
+                  [64, 256, 512, 1024],
+                  [256, 512, 1024, 2048],
+                  [1, 2, 2, 2],
+                  class_num)

model_zoo/official/cv/resnet50_quant/src/config.py

@@ -31,7 +31,7 @@ config_noquant = ed({
     "buffer_size": 1000,
     "image_height": 224,
     "image_width": 224,
-    "data_load_mode": "mindrecord",
+    "data_load_mode": "mindata",
     "save_checkpoint": True,
     "save_checkpoint_epochs": 1,
     "keep_checkpoint_max": 50,
@@ -54,7 +54,7 @@ config_quant = ed({
     "buffer_size": 1000,
     "image_height": 224,
     "image_width": 224,
-    "data_load_mode": "mindrecord",
+    "data_load_mode": "mindata",
     "save_checkpoint": True,
     "save_checkpoint_epochs": 1,
     "keep_checkpoint_max": 50,

model_zoo/official/cv/resnet50_quant/train.py

@@ -30,7 +30,8 @@ from mindspore.communication.management import init
 import mindspore.nn as nn
 import mindspore.common.initializer as weight_init
 
-from models.resnet_quant import resnet50_quant
+#from models.resnet_quant import resnet50_quant #auto construct quantative network of resnet50
+from models.resnet_quant_manual import resnet50_quant #manually construct quantative network of resnet50
 from src.dataset import create_dataset
 from src.lr_generator import get_lr
 from src.config import quant_set, config_quant, config_noquant