I am trying to implement a Faster-RCNN model for object detection written by Yinghan Xu. After I have trained and saved the model with model_all.save('filename.h5'), I am trying to freeze the Keras model as TensorFlow graph (as .pb) for inference using keras_to_tensorflow.py written by Amir Abdi. But when I try to convert it, I get a ValueError: Unknown layer: roipoolingconv due to a custom RoiPoolingConv layer:
class RoiPoolingConv(Layer): '''ROI pooling layer for 2D inputs. See Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, K. He, X. Zhang, S. Ren, J. Sun # Arguments pool_size: int Size of pooling region to use. pool_size = 7 will result in a 7x7 region. num_rois: number of regions of interest to be used # Input shape list of two 4D tensors [X_img,X_roi] with shape: X_img: `(1, rows, cols, channels)` X_roi: `(1,num_rois,4)` list of rois, with ordering (x,y,w,h) # Output shape 3D tensor with shape: `(1, num_rois, channels, pool_size, pool_size)` ''' def __init__(self, pool_size, num_rois, **kwargs): self.dim_ordering = K.image_dim_ordering() self.pool_size = pool_size self.num_rois = num_rois super(RoiPoolingConv, self).__init__(**kwargs) def build(self, input_shape): self.nb_channels = input_shape[0][3] def compute_output_shape(self, input_shape): return None, self.num_rois, self.pool_size, self.pool_size, self.nb_channels def call(self, x, mask=None): assert(len(x) == 2) # x[0] is image with shape (rows, cols, channels) img = x[0] # x[1] is roi with shape (num_rois,4) with ordering (x,y,w,h) rois = x[1] input_shape = K.shape(img) outputs = [] for roi_idx in range(self.num_rois): x = rois[0, roi_idx, 0] y = rois[0, roi_idx, 1] w = rois[0, roi_idx, 2] h = rois[0, roi_idx, 3] x = K.cast(x, 'int32') y = K.cast(y, 'int32') w = K.cast(w, 'int32') h = K.cast(h, 'int32') # Resized roi of the image to pooling size (7x7) rs = tf.image.resize_images(img[:, y:y+h, x:x+w, :], (self.pool_size, self.pool_size)) outputs.append(rs) final_output = K.concatenate(outputs, axis=0) # Reshape to (1, num_rois, pool_size, pool_size, nb_channels) # Might be (1, 4, 7, 7, 3) final_output = K.reshape(final_output, (1, self.num_rois, self.pool_size, self.pool_size, self.nb_channels)) # permute_dimensions is similar to transpose final_output = K.permute_dimensions(final_output, (0, 1, 2, 3, 4)) return final_output def get_config(self): config = {'pool_size': self.pool_size, 'num_rois': self.num_rois} base_config = super(RoiPoolingConv, self).get_config() return dict(list(base_config.items()) + list(config.items())) I have looked at most of the resources out there and almost all of them suggest to comment out this layer. But since this layer is important for object detection, I was wondering if a workaround is possible or not.
The complete traceback of error (note: I've saved filename as freezekeras.py, contents are same as keras_to_tensorflow.py):
Using TensorFlow backend. Traceback (most recent call last): File "freezekeras.py", line 181, in <module> app.run(main) File "/usr/local/lib/python3.5/dist-packages/absl/app.py", line 300, in run _run_main(main, args) File "/usr/local/lib/python3.5/dist-packages/absl/app.py", line 251, in _run_main sys.exit(main(argv)) File "freezekeras.py", line 127, in main model = load_model(FLAGS.input_model, FLAGS.input_model_json, FLAGS.input_model_yaml) File "freezekeras.py", line 105, in load_model raise wrong_file_err File "freezekeras.py", line 62, in load_model model = keras.models.load_model(input_model_path) File "/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py", line 419, in load_model model = _deserialize_model(f, custom_objects, compile) File "/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py", line 225, in _deserialize_model model = model_from_config(model_config, custom_objects=custom_objects) File "/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py", line 458, in model_from_config return deserialize(config, custom_objects=custom_objects) File "/usr/local/lib/python3.5/dist-packages/keras/layers/__init__.py", line 55, in deserialize printable_module_name='layer') File "/usr/local/lib/python3.5/dist-packages/keras/utils/generic_utils.py", line 145, in deserialize_keras_object list(custom_objects.items()))) File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1022, in from_config process_layer(layer_data) File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1008, in process_layer custom_objects=custom_objects) File "/usr/local/lib/python3.5/dist-packages/keras/layers/__init__.py", line 55, in deserialize printable_module_name='layer') File "/usr/local/lib/python3.5/dist-packages/keras/utils/generic_utils.py", line 138, in deserialize_keras_object ': ' + class_name) ValueError: Unknown layer: RoiPoolingConv 
