caffe2fluid:support ssd model conversion (#1039)

fluid/image_classification/caffe2fluid/kaffe/custom_layers/__init__.py

@@ -8,6 +8,11 @@ import axpy
 import flatten
 import argmax
 import reshape
+import roipooling
+import priorbox
+import permute
+import detection_out
+import normalize
 
 #custom layer import ends
 

fluid/image_classification/caffe2fluid/kaffe/custom_layers/flatten.py

@@ -4,11 +4,6 @@
 from .register import register
 
 
-def import_fluid():
-    import paddle.fluid as fluid
-    return fluid
-
-
 def flatten_shape(input_shape, axis=1, end_axis=-1):
     """ calculate the output shape of this layer using input shape
 
@@ -28,7 +23,7 @@ def flatten_shape(input_shape, axis=1, end_axis=-1):
         start_axis += len(input_shape)
 
     if end_axis < 0:
-        end_axis += len(input_shape)
+        end_axis += len(input_shape) + 1
 
     assert start_axis <= end_axis, 'invalid axis[%d] or end_axis[%d] params'\
             % (start_axis, end_axis)
@@ -52,18 +47,16 @@ def flatten_layer(input, name, axis=1, end_axis=-1):
     Returns:
         output (variable): output variable for this layer
     """
-    fluid = import_fluid()
+    import paddle.fluid as fluid
 
     input_shape = list(input.shape)
-    dims = len(input_shape)
-    start_axis = axis if axis >= 0 else axis + dims
-    end_axis = end_axis if end_axis >= 0 else end_axis + dims
 
-    assert start_axis <= end_axis, 'invalid axis or end_axis params'
-    output_shape = input_shape[0:start_axis]
-    flat_sz = reduce(lambda a, b: a * b, input_shape[start_axis:end_axis])
-    output_shape += [flat_sz]
-    output_shape += input_shape[end_axis:-1]
+    if input_shape[0] == -1:
+        input_shape[0] = 1
+        output_shape = flatten_shape(input_shape, axis=axis, end_axis=end_axis)
+        output_shape[0] = -1
+    else:
+        output_shape = flatten_shape(input_shape, axis=axis, end_axis=end_axis)
 
     output = fluid.layers.reshape(input, shape=output_shape, name=name)
 

fluid/image_classification/caffe2fluid/kaffe/custom_layers/reshape.py

@@ -68,15 +68,23 @@ def reshape_shape(input_sp, shape, axis=0, num_axes=-1):
         top_dim = shape['dim'][i]
         if top_dim == 0:
             copy_axes.append(i)
+            copy_axis_index = start_axis + i
+            output_shape[copy_axis_index] = input_shape[copy_axis_index]
         elif top_dim == -1:
             assert inferred_axis == -1, "[Reshape]new shape contains multiple -1 dims"
+            inferred_axis = i
         else:
             constant_count *= top_dim
 
     if inferred_axis >= 0:
         explicit_count = constant_count
-        explicit_count *= count(input_shape[0:start_axis])
-        explicit_count *= count(input_shape[end_axis:])
+        l = input_shape[0:start_axis]
+        if len(l) > 0:
+            explicit_count *= count(l)
+
+        l = input_shape[end_axis:]
+        if len(l) > 0:
+            explicit_count *= count(l)
 
         for i in range(len(copy_axes)):
             explicit_count *= output_shape[start_axis + copy_axes[i]]
@@ -84,6 +92,7 @@ def reshape_shape(input_sp, shape, axis=0, num_axes=-1):
         assert input_count % explicit_count == 0, "[Reshape]botom count[%d] "\
                 "must be divisible by product of the specified dimensions[%d] "\
                 % (input_count, explicit_count)
+        output_shape[start_axis + inferred_axis] = input_count / explicit_count
 
     output_count = count(output_shape)
     assert output_count == input_count, "[Reshape]output count[%d] must match input count[%d]" % (
@@ -117,6 +126,7 @@ def reshape_layer(input, name, shape, axis=0, num_axes=-1):
         output_shape = reshape_shape(input_shape, shape, axis, num_axes)
 
     output = fluid.layers.reshape(input, shape=output_shape, name=name)
+
     return output
 
 

fluid/image_classification/caffe2fluid/kaffe/layers.py

@@ -179,6 +179,9 @@ class LayerAdapter(object):
     @property
     def parameters(self):
         name = NodeDispatch.get_handler_name(self.kind)
+        if self.kind.lower() == "normalize":
+            name = "norm"
+
         name = '_'.join((name, 'param'))
         try:
             return getattr(self.layer, name)
@@ -217,9 +220,25 @@ class LayerAdapter(object):
             params.stride_w, params.stride, 1, default=1)
         p_h = self.get_kernel_value(params.pad_h, params.pad, 0, default=0)
         p_w = self.get_kernel_value(params.pad_w, params.pad, 1, default=0)
-        return KernelParameters(k_h, k_w, s_h, s_w, p_h, p_w)
-
 
-KernelParameters = namedtuple('KernelParameters', [
-    'kernel_h', 'kernel_w', 'stride_h', 'stride_w', 'pad_h', 'pad_w'
-])
+        dila_h = dila_w = 1
+        if self.kind in (NodeKind.Convolution, ):
+            dila_len = len(params.dilation)
+            if dila_len == 2:
+                dila_h = params.dilation[0]
+                dila_w = params.dilation[1]
+            elif dila_len == 1:
+                dila_h = dila_w = params.dilation[0]
+            else:
+                assert dila_len == 0, "invalid length[%s] of dilation in convolution" % (
+                    dila_len)
+
+        return KernelParameters(k_h, k_w, s_h, s_w, p_h, p_w, dila_h, dila_w)
+
+
+KernelParameters = namedtuple(
+    'KernelParameters',
+    [
+        'kernel_h', 'kernel_w', 'stride_h', 'stride_w', 'pad_h', 'pad_w',
+        'dila_h', 'dila_w'
+    ], )

fluid/image_classification/caffe2fluid/kaffe/paddle/network.py

@@ -91,7 +91,7 @@ class Network(object):
                     name = '%s_%s' % (op_name, param_name)
                     v = fluid.global_scope().find_var(name)
                     w = v.get_tensor()
-                    w.set(data, place)
+                    w.set(data.reshape(w.shape()), place)
                 except ValueError:
                     if not ignore_missing:
                         raise
@@ -144,6 +144,7 @@ class Network(object):
              relu=True,
              relu_negative_slope=0.0,
              padding=None,
+             dilation=1,
              group=1,
              biased=True):
         if padding is None:
@@ -173,6 +174,7 @@ class Network(object):
             num_filters=c_o,
             stride=[s_h, s_w],
             padding=padding,
+            dilation=dilation,
             groups=group,
             param_attr=fluid.ParamAttr(name=prefix + "weights"),
             bias_attr=fluid.ParamAttr(name=prefix + "biases"),

fluid/image_classification/caffe2fluid/kaffe/paddle/transformer.py

@@ -9,21 +9,6 @@ from ..transformers import (DataInjector, DataReshaper, NodeRenamer,
 from . import network
 
 
-def get_padding_type(kernel_params, input_shape, output_shape):
-    '''Translates Caffe's numeric padding to one of ('SAME', 'VALID').
-    Caffe supports arbitrary padding values, while Paddle only
-    supports 'SAME' and 'VALID' modes. So, not all Caffe paddings
-    can be translated to Paddle. There are some subtleties to
-    how the padding edge-cases are handled. These are described here:
-    https://github.com/Yangqing/caffe2/blob/master/caffe2/proto/caffe2_legacy.proto
-    '''
-    k_h, k_w, s_h, s_w, p_h, p_w = kernel_params
-    if p_h > 0 or p_w > 0:
-        return [p_h, p_w]
-    else:
-        return None
-
-
 class PaddleNode(object):
     '''An intermediate representation for Paddle operations.'''
 
@@ -78,10 +63,11 @@ class PaddleMapper(NodeMapper):
     def get_kernel_params(self, node):
         kernel_params = node.layer.kernel_parameters
         input_shape = node.get_only_parent().output_shape
-        padding = get_padding_type(kernel_params, input_shape,
-                                   node.output_shape)
-        # Only emit the padding if it's not the default value.
-        padding = {'padding': padding} if padding is not None else {}
+        padding = [kernel_params.pad_h, kernel_params.pad_w]
+        if padding[0] == 0 and padding[1] == 0:
+            padding = {}
+        else:
+            padding = {'padding': padding}
         return (kernel_params, padding)
 
     def map_convolution(self, node):
@@ -95,6 +81,10 @@ class PaddleMapper(NodeMapper):
             kwargs['group'] = group
         if not node.parameters.bias_term:
             kwargs['biased'] = False
+
+        if kernel_params.dila_h != 1 or kernel_params.dila_w != 1:
+            kwargs['dilation'] = (kernel_params.dila_h, kernel_params.dila_w)
+
         assert kernel_params.kernel_h == h
         assert kernel_params.kernel_w == w
         return MaybeActivated(node)(

fluid/image_classification/caffe2fluid/kaffe/shapes.py

@@ -6,6 +6,8 @@ from .errors import KaffeError
 Tensor4DShape = namedtuple('Tensor4DShape',
                            ['batch_size', 'channels', 'height', 'width'])
 
+Tensor3DShape = namedtuple('Tensor3DShape', ['batch_size', 'data1', 'data2'])
+
 Tensor2DShape = namedtuple('Tensor2DShape', ['batch_size', 'data'])
 
 ScalarShape = namedtuple('ScalarShape', ['batch_size'])
@@ -14,6 +16,8 @@ ScalarShape = namedtuple('ScalarShape', ['batch_size'])
 def make_tensor(batch_size, d1=None, d2=None, d3=None):
     if d3 is not None:
         return Tensor4DShape(batch_size, d1, d2, d3)
+    elif d1 is not None and d2 is not None:
+        return Tensor3DShape(batch_size, d1, d2)
     elif d1 is not None and d2 is None:
         return Tensor2DShape(batch_size, d1)
     elif d1 is None and d2 is None and d3 is None:
@@ -24,10 +28,14 @@ def make_tensor(batch_size, d1=None, d2=None, d3=None):
 
 
 def get_filter_output_shape(i_h, i_w, params, round_func):
-    o_h = (i_h + 2 * params.pad_h - params.kernel_h
-           ) / float(params.stride_h) + 1
-    o_w = (i_w + 2 * params.pad_w - params.kernel_w
-           ) / float(params.stride_w) + 1
+    dila_h = getattr(params, 'dila_h', 1)
+    dila_w = getattr(params, 'dila_w', 1)
+
+    o_h = (i_h + 2 * params.pad_h -
+           (dila_h * (params.kernel_h - 1) + 1)) / float(params.stride_h) + 1
+    o_w = (i_w + 2 * params.pad_w -
+           (dila_w * (params.kernel_w - 1) + 1)) / float(params.stride_w) + 1
+
     return (int(round_func(o_h)), int(round_func(o_w)))
 
 

fluid/image_classification/caffe2fluid/kaffe/transformers.py

@@ -337,6 +337,8 @@ class ParameterNamer(object):
                 names = ('scale', )
                 if getattr(node.parameters, 'bias_term', False):
                     names = ('scale', 'offset')
+            elif node.kind == "Normalize":
+                names = ('scale', )
             else:
                 warn('Unhandled parameters when naming this it[%s]' %
                      (node.kind))