Add resnet50-v1 to benchmark_score (#12595)

* add resnet50-v1 to benchmark_score

* rename back and duplicated

* rename v2 back to resnet.py

example/image-classification/benchmark_score.py

@@ -32,7 +32,7 @@ def get_symbol(network, batch_size, dtype):
     num_layers = 0
     if 'resnet' in network:
         num_layers = int(network.split('-')[1])
-        network = 'resnet'
+        network = network.split('-')[0]
     if 'vgg' in network:
         num_layers = int(network.split('-')[1])
         network = 'vgg'
@@ -69,7 +69,7 @@ def score(network, dev, batch_size, num_batches, dtype):
     return num_batches*batch_size/(time.time() - tic)
 
 if __name__ == '__main__':
-    networks = ['alexnet', 'vgg-16', 'inception-bn', 'inception-v3', 'resnet-50', 'resnet-152']
+    networks = ['alexnet', 'vgg-16', 'inception-bn', 'inception-v3', 'resnetv1-50', 'resnet-50', 'resnet-152']
     devs = [mx.gpu(0)] if len(get_gpus()) > 0 else []
     # Enable USE_MKLDNN for better CPU performance
     devs.append(mx.cpu())

example/image-classification/symbols/resnetv1.py

@@ -0,0 +1,200 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+'''
+Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
+(Original author Wei Wu) by Antti-Pekka Hynninen
+
+Implementing the original resnet ILSVRC 2015 winning network from:
+
+Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition"
+'''
+import mxnet as mx
+import numpy as np
+
+def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, bn_mom=0.9, workspace=256, memonger=False):
+    """Return ResNet Unit symbol for building ResNet
+    Parameters
+    ----------
+    data : str
+        Input data
+    num_filter : int
+        Number of output channels
+    bnf : int
+        Bottle neck channels factor with regard to num_filter
+    stride : tuple
+        Stride used in convolution
+    dim_match : Boolean
+        True means channel number between input and output is the same, otherwise means differ
+    name : str
+        Base name of the operators
+    workspace : int
+        Workspace used in convolution operator
+    """
+    if bottle_neck:
+        conv1 = mx.sym.Convolution(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=stride, pad=(0,0),
+                                   no_bias=True, workspace=workspace, name=name + '_conv1')
+        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
+        act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
+        conv2 = mx.sym.Convolution(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1),
+                                   no_bias=True, workspace=workspace, name=name + '_conv2')
+        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
+        act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
+        conv3 = mx.sym.Convolution(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
+                                   workspace=workspace, name=name + '_conv3')
+        bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
+
+        if dim_match:
+            shortcut = data
+        else:
+            conv1sc = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
+                                            workspace=workspace, name=name+'_conv1sc')
+            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')
+        if memonger:
+            shortcut._set_attr(mirror_stage='True')
+        return mx.sym.Activation(data=bn3 + shortcut, act_type='relu', name=name + '_relu3')
+    else:
+        conv1 = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
+                                      no_bias=True, workspace=workspace, name=name + '_conv1')
+        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
+        act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
+        conv2 = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
+                                      no_bias=True, workspace=workspace, name=name + '_conv2')
+        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')
+
+        if dim_match:
+            shortcut = data
+        else:
+            conv1sc = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
+                                            workspace=workspace, name=name+'_conv1sc')
+            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')
+        if memonger:
+            shortcut._set_attr(mirror_stage='True')
+        return mx.sym.Activation(data=bn2 + shortcut, act_type='relu', name=name + '_relu3')
+
+def resnet(units, num_stages, filter_list, num_classes, image_shape, bottle_neck=True, bn_mom=0.9, workspace=256, dtype='float32', memonger=False):
+    """Return ResNet symbol of
+    Parameters
+    ----------
+    units : list
+        Number of units in each stage
+    num_stages : int
+        Number of stage
+    filter_list : list
+        Channel size of each stage
+    num_classes : int
+        Ouput size of symbol
+    dataset : str
+        Dataset type, only cifar10 and imagenet supports
+    workspace : int
+        Workspace used in convolution operator
+    dtype : str
+        Precision (float32 or float16)
+    """
+    num_unit = len(units)
+    assert(num_unit == num_stages)
+    data = mx.sym.Variable(name='data')
+    if dtype == 'float32':
+        data = mx.sym.identity(data=data, name='id')
+    else:
+        if dtype == 'float16':
+            data = mx.sym.Cast(data=data, dtype=np.float16)
+    (nchannel, height, width) = image_shape
+    if height <= 32:            # such as cifar10
+        body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1,1), pad=(1, 1),
+                                  no_bias=True, name="conv0", workspace=workspace)
+        # Is this BatchNorm supposed to be here?
+        body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
+    else:                       # often expected to be 224 such as imagenet
+        body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),
+                                  no_bias=True, name="conv0", workspace=workspace)
+        body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
+        body = mx.sym.Activation(data=body, act_type='relu', name='relu0')
+        body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')
+
+    for i in range(num_stages):
+        body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False,
+                             name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, workspace=workspace,
+                             memonger=memonger)
+        for j in range(units[i]-1):
+            body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i + 1, j + 2),
+                                 bottle_neck=bottle_neck, workspace=workspace, memonger=memonger)
+    # bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
+    # relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1')
+    # Although kernel is not used here when global_pool=True, we should put one
+    pool1 = mx.sym.Pooling(data=body, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
+    flat = mx.sym.Flatten(data=pool1)
+    fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='fc1')
+    if dtype == 'float16':
+        fc1 = mx.sym.Cast(data=fc1, dtype=np.float32)
+    return mx.sym.SoftmaxOutput(data=fc1, name='softmax')
+
+def get_symbol(num_classes, num_layers, image_shape, conv_workspace=256, dtype='float32', **kwargs):
+    """
+    Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
+    (Original author Wei Wu) by Antti-Pekka Hynninen
+    Implementing the original resnet ILSVRC 2015 winning network from:
+    Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition"
+    """
+    image_shape = [int(l) for l in image_shape.split(',')]
+    (nchannel, height, width) = image_shape
+    if height <= 28:
+        num_stages = 3
+        if (num_layers-2) % 9 == 0 and num_layers >= 164:
+            per_unit = [(num_layers-2)//9]
+            filter_list = [16, 64, 128, 256]
+            bottle_neck = True
+        elif (num_layers-2) % 6 == 0 and num_layers < 164:
+            per_unit = [(num_layers-2)//6]
+            filter_list = [16, 16, 32, 64]
+            bottle_neck = False
+        else:
+            raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))
+        units = per_unit * num_stages
+    else:
+        if num_layers >= 50:
+            filter_list = [64, 256, 512, 1024, 2048]
+            bottle_neck = True
+        else:
+            filter_list = [64, 64, 128, 256, 512]
+            bottle_neck = False
+        num_stages = 4
+        if num_layers == 18:
+            units = [2, 2, 2, 2]
+        elif num_layers == 34:
+            units = [3, 4, 6, 3]
+        elif num_layers == 50:
+            units = [3, 4, 6, 3]
+        elif num_layers == 101:
+            units = [3, 4, 23, 3]
+        elif num_layers == 152:
+            units = [3, 8, 36, 3]
+        elif num_layers == 200:
+            units = [3, 24, 36, 3]
+        elif num_layers == 269:
+            units = [3, 30, 48, 8]
+        else:
+            raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))
+
+    return resnet(units       = units,
+                  num_stages  = num_stages,
+                  filter_list = filter_list,
+                  num_classes = num_classes,
+                  image_shape = image_shape,
+                  bottle_neck = bottle_neck,
+                  workspace   = conv_workspace,
+                  dtype       = dtype)