add eltwise and sigmoid

.gitignore

@@ -5,4 +5,5 @@
 *.caffemodel
 *.prototxt
 *.dot
-hw/
+hw_seg
+pva

README.md

@@ -6,6 +6,8 @@ Add support for
 + Dilated Convolution Layer
 + Concat Layer
 + Upsampling (converted to Deconvolution with bilinear initialization)
++ Eltwise Product
++ Sigmoid Layer
 
 ```python
 import torch

pytorch2caffe.py

@@ -23,7 +23,10 @@
               'ConcatBackward': 'Concat',
               'UpsamplingNearest2d': 'Deconvolution',
               'UpsamplingBilinear2d': 'Deconvolution',
-              'SigmoidBackward': 'Sigmoid'}
+              'SigmoidBackward': 'Sigmoid',
+              'LeakyReLUBackward': 'ReLU',
+              'NegateBackward': 'Power',
+              'MulBackward': 'Eltwise'}
 
 layer_id = 0
 
@@ -85,9 +88,10 @@ def convert_layer(func):
             elif parent_type == 'UpsamplingNearest2d':
                 print('UpsamplingNearest2d')
 
-    convert_layer(output_var.grad_fn)
-    print('save caffemodel to %s' % caffemodel)
-    net.save(caffemodel)
+    if caffemodel is not None:
+        convert_layer(output_var.grad_fn)
+        print('save caffemodel to %s' % caffemodel)
+        net.save(caffemodel)
 
 
 def save_conv2caffe(weights, biases, conv_param):
@@ -162,7 +166,23 @@ def add_layer(func):
             layer['bottom'] = ['data']
         layer['top'] = parent_top
 
-        if parent_type == 'UpsamplingNearest2d':
+        if parent_type == 'MulBackward':
+            eltwise_param = {
+                'operation': 'PROD',
+            }
+            layer['eltwise_param'] = eltwise_param
+        elif parent_type == 'NegateBackward':
+            power_param = {
+                'power': 1,
+                'scale': -1.,
+                'shift': 0
+            }
+            layer['power_param'] = power_param
+        elif parent_type == 'LeakyReLUBackward':
+            negative_slope = func.additional_args[0]
+            layer['relu_param'] = {'negative_slope': negative_slope}
+
+        elif parent_type == 'UpsamplingNearest2d':
             conv_param = OrderedDict()
             factor = func.scale_factor
             conv_param['num_output'] = func.saved_tensors[0].size(1)