update pytorchtocaffe tool

Summary: update pytorch to caffe toolchain to support model layers and bash scripts

tools/deploy/Caffe/ReadMe.md

@@ -1,4 +1,4 @@
-# The Caffe in PytorchToCaffe Provides some convenient API
+# The Caffe in nn_tools Provides some convenient API
 If there are some problem in parse your prototxt or caffemodel, Please replace
 the caffe.proto with your own version and compile it with command
                    `protoc --python_out ./ caffe.proto`

tools/deploy/Caffe/caffe.proto

@@ -565,7 +565,6 @@ message LayerParameter {
   optional RecurrentParameter recurrent_param = 146;
   optional ReductionParameter reduction_param = 136;
   optional ReLUParameter relu_param = 123;
-  optional ReLU6Parameter relu6_param = 208;
   optional ReshapeParameter reshape_param = 133;
   optional ROIPoolingParameter roi_pooling_param = 8266711; //roi pooling
   optional ScaleParameter scale_param = 142;
@@ -601,7 +600,7 @@ message LayerParameter {
   optional CropParameter crop_param = 167;
   optional DetectionEvaluateParameter detection_evaluate_param = 168;
   optional DetectionOutputParameter detection_output_param = 169;
-  optional NormalizeParameter norm_param = 170;
+  //optional NormalizeParameter normalize_param = 170;
   optional MultiBoxLossParameter multibox_loss_param = 171;
   optional PermuteParameter permute_param = 172;
   optional VideoDataParameter video_data_param = 173;
@@ -633,7 +632,7 @@ message LayerParameter {
   optional UpsampleParameter  upsample_param = 100003;
   optional MatMulParameter matmul_param = 100005;
   optional PassThroughParameter pass_through_param = 100004;
-  //optional NormalizeParameter norm_param = 100001;
+  optional NormalizeParameter norm_param = 100001;
 }
 
 //*********************add by wdd******************
@@ -659,13 +658,13 @@ message PassThroughParameter {
   optional uint32 block_width = 3 [default = 0];
 }
 
-//message NormalizeParameter{
-//optional bool across_spatial = 1 [default = true];
-//optional FillerParameter scale_filler = 2;
-//optional bool channel_shared = 3 [default = true];
-//optional float eps = 4 [default = 1e-10];
-//optional float sqrt_a = 5 [default = 1];
-//}
+message NormalizeParameter{
+optional bool across_spatial = 1 [default = true];
+optional FillerParameter scale_filler = 2;
+optional bool channel_shared = 3 [default = true];
+optional float eps = 4 [default = 1e-10];
+optional float sqrt_a = 5 [default = 1];
+}
 
 
 
@@ -1593,15 +1592,19 @@ message MultiBoxLossParameter {
 }
 
 // Message that stores parameters used by NormalizeLayer
-message NormalizeParameter {
-  optional bool across_spatial = 1 [default = true];
-  // Initial value of scale. Default is 1.0 for all
-  optional FillerParameter scale_filler = 2;
-  // Whether or not scale parameters are shared across channels.
-  optional bool channel_shared = 3 [default = true];
-  // Epsilon for not dividing by zero while normalizing variance
-  optional float eps = 4 [default = 1e-10];
-}
+//message NormalizeParameter {
+//  //optional bool across_spatial = 1 [default = true];
+//  // Initial value of scale. Default is 1.0 for all
+//  //optional FillerParameter scale_filler = 2;
+//  // Whether or not scale parameters are shared across channels.
+//  //optional bool channel_shared = 3 [default = true];
+//  // Epsilon for not dividing by zero while normalizing variance
+//  //optional float eps = 4 [default = 1e-10];
+//  //**************************************************
+//  optional string normalize_type = 1 [default = "L2"];
+//  optional bool fix_gradient = 2 [default = false];
+//  optional bool bp_norm = 3 [default = false];
+//}
 
 message PermuteParameter {
   // The new orders of the axes of data. Notice it should be with
@@ -1789,16 +1792,6 @@ message ReLUParameter {
   optional Engine engine = 2 [default = DEFAULT];
 }
 
-// Message that stores parameters used by ReLU6Layer
-message ReLU6Parameter {
-  enum Engine {
-    DEFAULT = 0;
-    CAFFE = 1;
-    CUDNN = 2;
-  }
-  optional Engine engine = 2 [default = DEFAULT];
-}
-
 message ReshapeParameter {
   // Specify the output dimensions. If some of the dimensions are set to 0,
   // the corresponding dimension from the bottom layer is used (unchanged).

tools/deploy/Caffe/caffe_net.py

@@ -7,7 +7,6 @@ from .layer_param import Layer_param
 class _Net(object):
     def __init__(self):
         self.net=pb.NetParameter()
-        self.needChange = {}
 
     def layer_index(self,layer_name):
         # find a layer's index by name. if the layer was found, return the layer position in the net, else return -1.
@@ -40,20 +39,6 @@ class _Net(object):
                 return
         raise(AttributeError, "cannot found layer %s" % str(layer_name))
 
-
-
-    def remove_layer_by_type(self,type_name):
-        for i,layer in enumerate(self.net.layer):
-            if layer.type == type_name:
-                # self.change_layer_bottom(layer.top,layer.bottom)
-                s1 = "\"" + layer.top[0] + "\""
-                s2 = "\"" + layer.bottom[0] + "\""
-                self.needChange[s1]=s2
-                del self.net.layer[i]
-        return
-
-
-
     def get_layer_by_name(self, layer_name):
         # get the layer by layer_name
         for layer in self.net.layer:
@@ -67,10 +52,7 @@ class _Net(object):
         for layer in prototxt.layer:
             del layer.blobs[:]
         with open(path,'w') as f:
-            string = text_format.MessageToString(prototxt)
-            for origin_name in self.needChange.keys():
-                string = string.replace(origin_name,self.needChange[origin_name])
-            f.write(string)
+            f.write(text_format.MessageToString(prototxt))
 
     def layer(self,layer_name):
         return self.get_layer_by_name(layer_name)
@@ -154,4 +136,4 @@ class Caffemodel(_Net):
 
 class Net():
     def __init__(self,*args,**kwargs):
-        raise(TypeError,'the class Net is no longer used, please use Caffemodel or Prototxt instead')
+        raise(TypeError,'the class Net is no longer used, please use Caffemodel or Prototxt instead')

tools/deploy/Caffe/layer_param.py

@@ -71,46 +71,9 @@ class Layer_param():
             conv_param.dilation.extend(pair_reduce(dilation))
         if groups:
             conv_param.group=groups
-            if groups != 1:
-                conv_param.engine = 1
         self.param.convolution_param.CopyFrom(conv_param)
 
-    def norm_param(self, eps):
-        """
-        add a conv_param layer if you spec the layer type "Convolution"
-        Args:
-            num_output: a int
-            kernel_size: int list
-            stride: a int list
-            weight_filler_type: the weight filer type
-            bias_filler_type: the bias filler type
-        Returns:
-        """
-        l2norm_param = pb.NormalizeParameter()
-        l2norm_param.across_spatial = False
-        l2norm_param.channel_shared = False
-        l2norm_param.eps = eps
-        self.param.norm_param.CopyFrom(l2norm_param)
-
-
-    def permute_param(self, order1, order2, order3, order4):
-        """
-        add a conv_param layer if you spec the layer type "Convolution"
-        Args:
-            num_output: a int
-            kernel_size: int list
-            stride: a int list
-            weight_filler_type: the weight filer type
-            bias_filler_type: the bias filler type
-        Returns:
-        """
-        permute_param = pb.PermuteParameter()
-        permute_param.order.extend([order1, order2, order3, order4])
-
-        self.param.permute_param.CopyFrom(permute_param)
-
-
-    def pool_param(self,type='MAX',kernel_size=2,stride=2,pad=None, ceil_mode = True):
+    def pool_param(self,type='MAX',kernel_size=2,stride=2,pad=None, ceil_mode = False):
         pool_param=pb.PoolingParameter()
         pool_param.pool=pool_param.PoolMethod.Value(type)
         pool_param.kernel_size=pair_process(kernel_size)
@@ -158,10 +121,22 @@ class Layer_param():
             if isinstance(size,int):
                 upsample_param.upsample_h = size
             else:
-                upsample_param.upsample_h = size[0] * scale_factor
-                upsample_param.\
-                    upsample_w = size[1] * scale_factor
+                upsample_param.upsample_h = size[0]
+                upsample_param.upsample_w = size[1]
+                #upsample_param.upsample_h = size[0] * scale_factor
+                #upsample_param.upsample_w = size[1] * scale_factor
         self.param.upsample_param.CopyFrom(upsample_param)
+    def interp_param(self,size=None, scale_factor=None):
+        interp_param=pb.InterpParameter()
+        if scale_factor:
+            if isinstance(scale_factor,int):
+                interp_param.zoom_factor = scale_factor
+
+        if size:
+            print('size:', size)
+            interp_param.height = size[0]
+            interp_param.width = size[1]
+        self.param.interp_param.CopyFrom(interp_param)
 
     def add_data(self,*args):
         """Args are data numpy array
@@ -180,4 +155,4 @@ class Layer_param():
         pass
 
 def set_enum(param,key,value):
-    setattr(param,key,param.Value(value))
+    setattr(param,key,param.Value(value))

tools/deploy/Caffe/net.py

@@ -1 +1 @@
-raise ImportError('the nn_tools.Caffe.net is no longer used, please use nn_tools.Caffe.caffe_net')
+raise ImportError,'the nn_tools.Caffe.net is no longer used, please use nn_tools.Caffe.caffe_net'

tools/deploy/run_inference.sh

@@ -1,8 +1,10 @@
 
 python caffe_inference.py --model-def "logs/caffe_R34/baseline_R34.prototxt" \
 --model-weights "logs/caffe_R34/baseline_R34.caffemodel" \
+--height 256 --width 128 \
 --input \
 '/export/home/DATA/Market-1501-v15.09.15/bounding_box_test/1182_c5s3_015240_04.jpg' \
 '/export/home/DATA/Market-1501-v15.09.15/bounding_box_test/1182_c6s3_038217_01.jpg' \
 '/export/home/DATA/Market-1501-v15.09.15/bounding_box_test/1183_c5s3_006943_05.jpg' \
---output "caffe_R34_output"
+'/export/home/DATA/DukeMTMC-reID/bounding_box_train/0728_c4_f0161265.jpg' \
+--output "caffe_R34_output"