Upload VGG19 symbol (#2502)

* add vgg19 symbol

example/neural-style/end_to_end/model_vgg19.py

@@ -0,0 +1,96 @@
+import mxnet as mx
+import os, sys
+from collections import namedtuple
+
+ConvExecutor = namedtuple('ConvExecutor', ['executor', 'data', 'data_grad', 'style', 'content', 'arg_dict'])
+
+def get_vgg_symbol(prefix, content_only=False):
+    # declare symbol
+    data = mx.sym.Variable("%s_data" % prefix)
+    conv1_1 = mx.symbol.Convolution(name='%s_conv1_1' % prefix, data=data , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu1_1 = mx.symbol.Activation(data=conv1_1 , act_type='relu')
+    conv1_2 = mx.symbol.Convolution(name='%s_conv1_2' % prefix, data=relu1_1 , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu1_2 = mx.symbol.Activation(data=conv1_2 , act_type='relu')
+    pool1 = mx.symbol.Pooling(data=relu1_2 , pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='avg')
+    conv2_1 = mx.symbol.Convolution(name='%s_conv2_1' % prefix, data=pool1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu2_1 = mx.symbol.Activation(data=conv2_1 , act_type='relu')
+    conv2_2 = mx.symbol.Convolution(name='%s_conv2_2' % prefix, data=relu2_1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu2_2 = mx.symbol.Activation(data=conv2_2 , act_type='relu')
+    pool2 = mx.symbol.Pooling(data=relu2_2 , pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='avg')
+    conv3_1 = mx.symbol.Convolution(name='%s_conv3_1' % prefix, data=pool2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu3_1 = mx.symbol.Activation(data=conv3_1 , act_type='relu')
+    conv3_2 = mx.symbol.Convolution(name='%s_conv3_2' % prefix, data=relu3_1 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu3_2 = mx.symbol.Activation(data=conv3_2 , act_type='relu')
+    conv3_3 = mx.symbol.Convolution(name='%s_conv3_3' % prefix, data=relu3_2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu3_3 = mx.symbol.Activation(data=conv3_3 , act_type='relu')
+    conv3_4 = mx.symbol.Convolution(name='%s_conv3_4' % prefix, data=relu3_3 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu3_4 = mx.symbol.Activation(data=conv3_4 , act_type='relu')
+    pool3 = mx.symbol.Pooling(data=relu3_4 , pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='avg')
+    conv4_1 = mx.symbol.Convolution(name='%s_conv4_1' % prefix, data=pool3 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu4_1 = mx.symbol.Activation(data=conv4_1 , act_type='relu')
+    conv4_2 = mx.symbol.Convolution(name='%s_conv4_2' % prefix, data=relu4_1 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu4_2 = mx.symbol.Activation(data=conv4_2 , act_type='relu')
+    conv4_3 = mx.symbol.Convolution(name='%s_conv4_3' % prefix, data=relu4_2 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu4_3 = mx.symbol.Activation(data=conv4_3 , act_type='relu')
+    conv4_4 = mx.symbol.Convolution(name='%s_conv4_4' % prefix, data=relu4_3 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu4_4 = mx.symbol.Activation(data=conv4_4 , act_type='relu')
+    pool4 = mx.symbol.Pooling(data=relu4_4 , pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='avg')
+    conv5_1 = mx.symbol.Convolution(name='%s_conv5_1' % prefix, data=pool4 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), workspace=1024)
+    relu5_1 = mx.symbol.Activation(data=conv5_1 , act_type='relu')
+
+
+    if content_only:
+        return relu4_2
+    # style and content layers
+    style = mx.sym.Group([relu1_1, relu2_1, relu3_1, relu4_1, relu5_1])
+    content = mx.sym.Group([relu4_2])
+    return style, content
+
+
+def get_executor_with_style(style, content, input_size, ctx):
+    out = mx.sym.Group([style, content])
+    # make executor
+    arg_shapes, output_shapes, aux_shapes = out.infer_shape(data=(1, 3, input_size[0], input_size[1]))
+    arg_names = out.list_arguments()
+    arg_dict = dict(zip(arg_names, [mx.nd.zeros(shape, ctx=ctx) for shape in arg_shapes]))
+    grad_dict = {"data": arg_dict["data"].copyto(ctx)}
+    # init with pretrained weight
+    pretrained = mx.nd.load("./model/vgg19.params")
+    for name in arg_names:
+        if name == "data":
+            continue
+        key = "arg:" + name
+        if key in pretrained:
+            pretrained[key].copyto(arg_dict[name])
+        else:
+            print("Skip argument %s" % name)
+    executor = out.bind(ctx=ctx, args=arg_dict, args_grad=grad_dict, grad_req="write")
+    return ConvExecutor(executor=executor,
+                        data=arg_dict["data"],
+                        data_grad=grad_dict["data"],
+                        style=executor.outputs[:-1],
+                        content=executor.outputs[-1],
+                        arg_dict=arg_dict)
+
+def get_executor_content(content, input_size, ctx):
+    arg_shapes, output_shapes, aux_shapes = content.infer_shape(data=(1, 3, input_size[0], input_size[1]))
+    arg_names = out.list_arguments()
+    arg_dict = dict(zip(arg_names, [mx.nd.zeros(shape, ctx=ctx) for shape in arg_shapes]))
+    pretrained = mx.nd.load("./model/vgg19.params")
+    for name in arg_names:
+        if name == "data":
+            continue
+        key = "arg:" + name
+        if key in pretrained:
+            pretrained[key].copyto(arg_dict[name])
+        else:
+            print("Skip argument %s" % name)
+    executor = out.bind(ctx=ctx, args=arg_dict, args_grad=[], grad_req="null")
+    return ConvExecutor(executor=executor,
+                        data=arg_dict["data"],
+                        data_grad=None,
+                        style=None,
+                        content=executor.outputs[0],
+                        arg_dict=arg_dict)
+
+