Merge pull request #1 from gauravgupta22/keras_support

Keras support

README.md

@@ -2,13 +2,14 @@
 
 This is a React+Django webapp with a simple drag and drop interface to build and configure deep neural networks with support for export of model configuration files to caffe and tensorflow. It also supports import from these frameworks to visualize different model architectures. Our motivation is to build an online IDE where researchers can share models and collaborate without having to deal with deep learning code.
 
-![alt text](https://github.com/Cloud-CV/IDE/blob/master/example/snapshot.png "IDE Snapshot")
+![alt text](https://github.com/gauravgupta22/IDE/blob/master/example/snapshot.png "IDE Snapshot")
 
 This app is presently under active development and we welcome contributions. Please check out our [issues thread](https://github.com/Cloud-CV/IDE/issues) to find things to work on, or ping us on [Gitter](https://gitter.im/batra-mlp-lab/CloudCV). 
 
 ### Requirements
 1. install [caffe](http://caffe.berkeleyvision.org/installation.html)
 2. install [tensorflow](https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html#pip-installation)
+3. install [keras](https://keras.io/#installation)
 
 ### Build instructions
 1. `git clone https://github.com/Cloud-CV/IDE.git`
@@ -23,6 +24,7 @@ This app is presently under active development and we welcome contributions. Ple
 ### Example
 * Use `example/GoogleNet.pbtxt` for tensorflow import
 * Use `example/GoogleNet.prototxt` for caffe import
+* Use `example/GoogleNet.json` for keras import
 
 ### License
 

caffe_app/views/import_prototxt.py

@@ -43,13 +43,13 @@ def importPrototxt(request):
                 params['scale'] = layer.transform_param.scale
 
             elif(layer.type == 'Convolution'):
-                if len(layer.convolution_param.kernel_size):
+                if len(layer.convolution_param.kernel_size) or (layer.convolution_param.kernel_h and layer.convolution_param.kernel_w):
                     params['kernel_h'] = layer.convolution_param.kernel_h or layer.convolution_param.kernel_size[0]
                     params['kernel_w'] = layer.convolution_param.kernel_w or layer.convolution_param.kernel_size[0]
-                if len(layer.convolution_param.pad):
+                if len(layer.convolution_param.pad) or (layer.convolution_param.pad_h and layer.convolution_param.pad_w):
                     params['pad_h'] = layer.convolution_param.pad_h or layer.convolution_param.pad[0]
                     params['pad_w'] = layer.convolution_param.pad_w or layer.convolution_param.pad[0]
-                if len(layer.convolution_param.stride):
+                if len(layer.convolution_param.stride) or (layer.convolution_param.stride_h and layer.convolution_param.stride_w):
                     params['stride_h'] = layer.convolution_param.stride_h or layer.convolution_param.stride[0]
                     params['stride_w'] = layer.convolution_param.stride_w or layer.convolution_param.stride[0]
                 params['weight_filler'] = layer.convolution_param.weight_filler.type
@@ -67,7 +67,11 @@ def importPrototxt(request):
                 params['stride_w'] = layer.pooling_param.stride_w or layer.pooling_param.stride
                 params['kernel_h'] = layer.pooling_param.kernel_h or layer.pooling_param.kernel_size
                 params['kernel_w'] = layer.pooling_param.kernel_w or layer.pooling_param.kernel_size
-                params['pool'] = layer.pooling_param.pool
+                pool = layer.pooling_param.pool
+                if(pool == 0):
+                    params['pool'] = 'MAX'
+                elif(pool == 1):
+                    params['pool'] = 'AVE'
 
             elif(layer.type == 'InnerProduct'):
                 params['num_output'] = layer.inner_product_param.num_output
@@ -83,6 +87,7 @@ def importPrototxt(request):
                 # string '64,1,28,28'
 
             jsonLayer = {
+                'name': layer.name,
                 'info': {
                     'type': layer.type,
                     'phase': phase