multiple fix with path/prefix

demo.py

@@ -111,7 +111,10 @@ def parse_class_names(class_names):
 
     network = None if args.deploy_net else args.network
     class_names = parse_class_names(args.class_names)
-    prefix = args.prefix + args.network + '_' + str(args.data_shape)
+    if args.prefix.endswith('_'):
+        prefix = args.prefix + args.network + '_' + str(args.data_shape)
+    else:
+        prefix = args.prefix
     detector = get_detector(network, prefix, args.epoch,
                             args.data_shape,
                             (args.mean_r, args.mean_g, args.mean_b),

deploy.py

@@ -30,13 +30,16 @@ def parse_args():
 
 if __name__ == '__main__':
     args = parse_args()
-    net = get_symbol(args.network).get_symbol(args.network, args.data_shape,
+    net = get_symbol(args.network, args.data_shape,
         num_classes=args.num_classes, nms_thresh=args.nms_thresh,
         force_suppress=args.force_nms, nms_topk=args.nms_topk)
-    prefix = args.prefix + args.network + '_' + str(args.data_shape)
+    if args.prefix.endswith('_'):
+        prefix = args.prefix + args.network + '_' + str(args.data_shape)
+    else:
+        prefix = args.prefix
     _, arg_params, aux_params = mx.model.load_checkpoint(prefix, args.epoch)
     # new name
-    tmp = args.prefix.rsplit('/', 1)
+    tmp = prefix.rsplit('/', 1)
     save_prefix = '/deploy_'.join(tmp)
     mx.model.save_checkpoint(save_prefix, args.epoch, net, arg_params, aux_params)
     print("Saved model: {}-{:04d}.param".format(save_prefix, args.epoch))

evaluate.py

@@ -76,9 +76,13 @@ def parse_args():
         class_names = None
 
     network = None if args.deploy_net else args.network
+    if args.prefix.endswith('_'):
+        prefix = args.prefix + args.network
+    else:
+        prefix = args.prefix
     evaluate_net(network, args.rec_path, num_class,
                  (args.mean_r, args.mean_g, args.mean_b), args.data_shape,
-                 args.prefix + args.network, args.epoch, ctx, batch_size=args.batch_size,
+                 prefix, args.epoch, ctx, batch_size=args.batch_size,
                  path_imglist=args.list_path, nms_thresh=args.nms_thresh,
                  force_nms=args.force_nms, ovp_thresh=args.overlap_thresh,
                  use_difficult=args.use_difficult, class_names=class_names,

symbol/common.py

@@ -37,6 +37,45 @@ def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
         name="{}_{}".format(name, act_type))
     return relu
 
+def legacy_conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \
+    stride=(1,1), act_type="relu", use_batchnorm=False):
+    """
+    wrapper for a small Convolution group
+
+    Parameters:
+    ----------
+    from_layer : mx.symbol
+        continue on which layer
+    name : str
+        base name of the new layers
+    num_filter : int
+        how many filters to use in Convolution layer
+    kernel : tuple (int, int)
+        kernel size (h, w)
+    pad : tuple (int, int)
+        padding size (h, w)
+    stride : tuple (int, int)
+        stride size (h, w)
+    act_type : str
+        activation type, can be relu...
+    use_batchnorm : bool
+        whether to use batch normalization
+
+    Returns:
+    ----------
+    (conv, relu) mx.Symbols
+    """
+    assert not use_batchnorm, "batchnorm not yet supported"
+    bias = mx.symbol.Variable(name="conv{}_bias".format(name),
+        init=mx.init.Constant(0.0), attr={'__lr_mult__': '2.0'})
+    conv = mx.symbol.Convolution(data=from_layer, bias=bias, kernel=kernel, pad=pad, \
+        stride=stride, num_filter=num_filter, name="conv{}".format(name))
+    relu = mx.symbol.Activation(data=conv, act_type=act_type, \
+        name="{}{}".format(act_type, name))
+    if use_batchnorm:
+        relu = mx.symbol.BatchNorm(data=relu, name="bn{}".format(name))
+    return conv, relu
+
 def multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=128):
     """Wrapper function to extract features from base network, attaching extra
     layers and SSD specific layers

symbol/legacy_vgg16_ssd_300.py

@@ -1,5 +1,5 @@
 import mxnet as mx
-from common import conv_act_layer
+from common import legacy_conv_act_layer
 from common import multibox_layer
 
 def get_symbol_train(num_classes=20, nms_thresh=0.5, force_suppress=False,
@@ -97,21 +97,21 @@ def get_symbol_train(num_classes=20, nms_thresh=0.5, force_suppress=False,
     # drop7 = mx.symbol.Dropout(data=relu7, p=0.5, name="drop7")
 
     ### ssd extra layers ###
-    conv8_1, relu8_1 = conv_act_layer(relu7, "8_1", 256, kernel=(1,1), pad=(0,0), \
+    conv8_1, relu8_1 = legacy_conv_act_layer(relu7, "8_1", 256, kernel=(1,1), pad=(0,0), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
-    conv8_2, relu8_2 = conv_act_layer(relu8_1, "8_2", 512, kernel=(3,3), pad=(1,1), \
+    conv8_2, relu8_2 = legacy_conv_act_layer(relu8_1, "8_2", 512, kernel=(3,3), pad=(1,1), \
         stride=(2,2), act_type="relu", use_batchnorm=False)
-    conv9_1, relu9_1 = conv_act_layer(relu8_2, "9_1", 128, kernel=(1,1), pad=(0,0), \
+    conv9_1, relu9_1 = legacy_conv_act_layer(relu8_2, "9_1", 128, kernel=(1,1), pad=(0,0), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
-    conv9_2, relu9_2 = conv_act_layer(relu9_1, "9_2", 256, kernel=(3,3), pad=(1,1), \
+    conv9_2, relu9_2 = legacy_conv_act_layer(relu9_1, "9_2", 256, kernel=(3,3), pad=(1,1), \
         stride=(2,2), act_type="relu", use_batchnorm=False)
-    conv10_1, relu10_1 = conv_act_layer(relu9_2, "10_1", 128, kernel=(1,1), pad=(0,0), \
+    conv10_1, relu10_1 = legacy_conv_act_layer(relu9_2, "10_1", 128, kernel=(1,1), pad=(0,0), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
-    conv10_2, relu10_2 = conv_act_layer(relu10_1, "10_2", 256, kernel=(3,3), pad=(0,0), \
+    conv10_2, relu10_2 = legacy_conv_act_layer(relu10_1, "10_2", 256, kernel=(3,3), pad=(0,0), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
-    conv11_1, relu11_1 = conv_act_layer(relu10_2, "11_1", 128, kernel=(1,1), pad=(0,0), \
+    conv11_1, relu11_1 = legacy_conv_act_layer(relu10_2, "11_1", 128, kernel=(1,1), pad=(0,0), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
-    conv11_2, relu11_2 = conv_act_layer(relu11_1, "11_2", 256, kernel=(3,3), pad=(0,0), \
+    conv11_2, relu11_2 = legacy_conv_act_layer(relu11_1, "11_2", 256, kernel=(3,3), pad=(0,0), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
 
     # specific parameters for VGG16 network

symbol/legacy_vgg16_ssd_512.py

@@ -1,5 +1,5 @@
 import mxnet as mx
-from common import conv_act_layer
+from common import legacy_conv_act_layer
 from common import multibox_layer
 
 def get_symbol_train(num_classes=20, nms_thresh=0.5, force_suppress=False, nms_topk=400):
@@ -96,25 +96,25 @@ def get_symbol_train(num_classes=20, nms_thresh=0.5, force_suppress=False, nms_t
     # drop7 = mx.symbol.Dropout(data=relu7, p=0.5, name="drop7")
 
     ### ssd extra layers ###
-    conv8_1, relu8_1 = conv_act_layer(relu7, "8_1", 256, kernel=(1,1), pad=(0,0), \
+    conv8_1, relu8_1 = legacy_conv_act_layer(relu7, "8_1", 256, kernel=(1,1), pad=(0,0), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
-    conv8_2, relu8_2 = conv_act_layer(relu8_1, "8_2", 512, kernel=(3,3), pad=(1,1), \
+    conv8_2, relu8_2 = legacy_conv_act_layer(relu8_1, "8_2", 512, kernel=(3,3), pad=(1,1), \
         stride=(2,2), act_type="relu", use_batchnorm=False)
-    conv9_1, relu9_1 = conv_act_layer(relu8_2, "9_1", 128, kernel=(1,1), pad=(0,0), \
+    conv9_1, relu9_1 = legacy_conv_act_layer(relu8_2, "9_1", 128, kernel=(1,1), pad=(0,0), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
-    conv9_2, relu9_2 = conv_act_layer(relu9_1, "9_2", 256, kernel=(3,3), pad=(1,1), \
+    conv9_2, relu9_2 = legacy_conv_act_layer(relu9_1, "9_2", 256, kernel=(3,3), pad=(1,1), \
         stride=(2,2), act_type="relu", use_batchnorm=False)
-    conv10_1, relu10_1 = conv_act_layer(relu9_2, "10_1", 128, kernel=(1,1), pad=(0,0), \
+    conv10_1, relu10_1 = legacy_conv_act_layer(relu9_2, "10_1", 128, kernel=(1,1), pad=(0,0), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
-    conv10_2, relu10_2 = conv_act_layer(relu10_1, "10_2", 256, kernel=(3,3), pad=(1,1), \
+    conv10_2, relu10_2 = legacy_conv_act_layer(relu10_1, "10_2", 256, kernel=(3,3), pad=(1,1), \
         stride=(2,2), act_type="relu", use_batchnorm=False)
-    conv11_1, relu11_1 = conv_act_layer(relu10_2, "11_1", 128, kernel=(1,1), pad=(0,0), \
+    conv11_1, relu11_1 = legacy_conv_act_layer(relu10_2, "11_1", 128, kernel=(1,1), pad=(0,0), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
-    conv11_2, relu11_2 = conv_act_layer(relu11_1, "11_2", 256, kernel=(3,3), pad=(1,1), \
+    conv11_2, relu11_2 = legacy_conv_act_layer(relu11_1, "11_2", 256, kernel=(3,3), pad=(1,1), \
         stride=(2,2), act_type="relu", use_batchnorm=False)
-    conv12_1, relu12_1 = conv_act_layer(relu11_2, "12_1", 128, kernel=(1,1), pad=(0,0), \
+    conv12_1, relu12_1 = legacy_conv_act_layer(relu11_2, "12_1", 128, kernel=(1,1), pad=(0,0), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
-    conv12_2, relu12_2 = conv_act_layer(relu12_1, "12_2", 256, kernel=(4,4), pad=(1,1), \
+    conv12_2, relu12_2 = legacy_conv_act_layer(relu12_1, "12_2", 256, kernel=(4,4), pad=(1,1), \
         stride=(1,1), act_type="relu", use_batchnorm=False)
 
     # specific parameters for VGG16 network

symbol/symbol_factory.py

@@ -96,6 +96,7 @@ def get_symbol_train(network, data_shape, **kwargs):
         see symbol_builder.get_symbol_train for more details
     """
     if network.startswith('legacy'):
+        logging.warn('Using legacy model.')
         return symbol_builder.import_module(network).get_symbol_train(**kwargs)
     config = get_config(network, data_shape, **kwargs).copy()
     config.update(kwargs)
@@ -114,6 +115,7 @@ def get_symbol(network, data_shape, **kwargs):
         see symbol_builder.get_symbol for more details
     """
     if network.startswith('legacy'):
+        logging.warn('Using legacy model.')
         return symbol_builder.import_module(network).get_symbol(**kwargs)
     config = get_config(network, data_shape, **kwargs).copy()
     config.update(kwargs)