add get_head

src/losses/center_loss.py

@@ -1,7 +1,7 @@
 import os
 
 # MXNET_CPU_WORKER_NTHREADS must be greater than 1 for custom op to work on CPU
-os.environ['MXNET_CPU_WORKER_NTHREADS'] = '2'
+#os.environ['MXNET_CPU_WORKER_NTHREADS'] = '2'
 import mxnet as mx
 
 

src/symbols/fdpn.py

@@ -177,16 +177,15 @@ def get_symbol(num_classes = 1000, num_layers=92, **kwargs):
 
     ## define Dual Path Network
     data = mx.symbol.Variable(name="data")
-    data = data-127.5
-    data = data*0.0078125
-
-    # conv1
-    if version_input==0:
-      conv1_x_1  = Conv(data=data,  num_filter=128,  kernel=(7, 7), name='conv1_x_1', pad=(3,3), stride=(2,2))
-    else:
-      conv1_x_1  = Conv(data=data,  num_filter=128,  kernel=(3, 3), name='conv1_x_1', pad=(3,3), stride=(1,1))
-    conv1_x_1  = BN_AC(conv1_x_1, name='conv1_x_1__relu-sp')
-    conv1_x_x  = mx.symbol.Pooling(data=conv1_x_1, pool_type="max", kernel=(3, 3),  pad=(1,1), stride=(2,2), name="pool1")
+    #data = data-127.5
+    #data = data*0.0078125
+    #if version_input==0:
+    #  conv1_x_1  = Conv(data=data,  num_filter=128,  kernel=(7, 7), name='conv1_x_1', pad=(3,3), stride=(2,2))
+    #else:
+    #  conv1_x_1  = Conv(data=data,  num_filter=128,  kernel=(3, 3), name='conv1_x_1', pad=(3,3), stride=(1,1))
+    #conv1_x_1  = BN_AC(conv1_x_1, name='conv1_x_1__relu-sp')
+    #conv1_x_x  = mx.symbol.Pooling(data=conv1_x_1, pool_type="max", kernel=(3, 3),  pad=(1,1), stride=(2,2), name="pool1")
+    conv1_x_x = symbol_utils.get_head(data, version_input, 128)
 
     # conv2
     bw = 256

src/symbols/symbol_utils.py

@@ -44,3 +44,68 @@ def get_fc1(last_conv, num_classes, fc_type):
         fc1 = Act(data=fc1, act_type='relu', name='fc1_relu')
   return fc1
 
+def residual_unit_v3(data, num_filter, stride, dim_match, name, **kwargs):
+    
+    """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
+    """
+    bn_mom = kwargs.get('bn_mom', 0.9)
+    workspace = kwargs.get('workspace', 256)
+    memonger = kwargs.get('memonger', False)
+    #print('in unit3')
+    bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
+    conv1 = Conv(data=bn1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
+                                  no_bias=True, workspace=workspace, name=name + '_conv1')
+    bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
+    act1 = Act(data=bn2, act_type='relu', name=name + '_relu1')
+    conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
+                                  no_bias=True, workspace=workspace, name=name + '_conv2')
+    bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
+
+    if dim_match:
+        shortcut = data
+    else:
+        conv1sc = Conv(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 bn3 + shortcut
+
+
+def get_head(data, version_input, num_filter):
+    data = data-127.5
+    data = data*0.0078125
+    #data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
+    if version_input==0:
+      body = Conv(data=data, num_filter=num_filter, 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 = Act(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')
+    else:
+      body = data
+      body = Conv(data=body, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1, 1),
+                                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 = Act(data=body, act_type='relu', name='relu0')
+    body = residual_unit_v3(body, num_filter, (2, 2), False,
+      name='head', **kwargs)
+    return body
+
+