[Darknet] softmax temperature in frontend (#1429)

nnvm/python/nnvm/frontend/darknet.py

@@ -260,6 +260,9 @@ def _darknet_reshape(inputs, attrs):
 
 def _darknet_softmax_output(inputs, attrs):
     """Process the softmax operation."""
+    temperature = attrs.get('temperature', 1)
+    if temperature != 1:
+        inputs[0] = inputs[0] / float(temperature)
     op_name, new_attrs = 'softmax', {}
     if _darknet_parse_bool_str(attrs, 'multi_output'):
         new_attrs['axis'] = 1
@@ -529,6 +532,8 @@ def _get_darknet_attrs(net, layer_num):
     elif LAYERTYPE.SOFTMAX == layer.type:
         attr.update({'axis' : 1})
         attr.update({'use_flatten' : True})
+        if layer.temperature:
+            attr.update({'temperature' : str(layer.temperature)})
 
     elif LAYERTYPE.SHORTCUT == layer.type:
         add_layer = net.layers[layer.index]

nnvm/tests/python/frontend/darknet/test_forward.py

-"""
-Compile Darknet Models
-=====================
-This article is a test script to test darknet models with NNVM.
-All the required models and libraries will be downloaded from the internet
-by the script.
-"""
-import os
-import requests
-import numpy as np
-from nnvm import frontend
-from nnvm.testing.darknet import __darknetffi__
-import nnvm.compiler
-import tvm
-import sys
-import urllib
-if sys.version_info >= (3,):
-    import urllib.request as urllib2
-else:
-    import urllib2
-
-def _download(url, path, overwrite=False, sizecompare=False):
-    ''' Download from internet'''
-    if os.path.isfile(path) and not overwrite:
-        if sizecompare:
-            file_size = os.path.getsize(path)
-            res_head = requests.head(url)
-            res_get = requests.get(url, stream=True)
-            if 'Content-Length' not in res_head.headers:
-                res_get = urllib2.urlopen(url)
-            urlfile_size = int(res_get.headers['Content-Length'])
-            if urlfile_size != file_size:
-                print("exist file got corrupted, downloading", path, " file freshly")
-                _download(url, path, True, False)
-                return
-        print('File {} exists, skip.'.format(path))
-        return
-    print('Downloading from url {} to {}'.format(url, path))
-    try:
-        urllib.request.urlretrieve(url, path)
-        print('')
-    except:
-        urllib.urlretrieve(url, path)
-
-DARKNET_LIB = 'libdarknet.so'
-DARKNETLIB_URL = 'https://github.com/siju-samuel/darknet/blob/master/lib/' \
-                                    + DARKNET_LIB + '?raw=true'
-_download(DARKNETLIB_URL, DARKNET_LIB)
-LIB = __darknetffi__.dlopen('./' + DARKNET_LIB)
-
-def test_forward(net):
-    '''Test network with given input image on both darknet and tvm'''
-    def get_darknet_output(net, img):
-        return LIB.network_predict_image(net, img)
-
-    def get_tvm_output(net, img):
-        '''Compute TVM output'''
-        dtype = 'float32'
-        batch_size = 1
-        sym, params = frontend.darknet.from_darknet(net, dtype)
-        data = np.empty([batch_size, img.c, img.h, img.w], dtype)
-        i = 0
-        for c in range(img.c):
-            for h in range(img.h):
-                for k in range(img.w):
-                    data[0][c][h][k] = img.data[i]
-                    i = i + 1
-
-        target = 'llvm'
-        shape_dict = {'data': data.shape}
-        #with nnvm.compiler.build_config(opt_level=2):
-        graph, library, params = nnvm.compiler.build(sym, target, shape_dict, dtype, params=params)
-        ######################################################################
-        # Execute on TVM
-        # ---------------
-        # The process is no different from other examples.
-        from tvm.contrib import graph_runtime
-        ctx = tvm.cpu(0)
-        m = graph_runtime.create(graph, library, ctx)
-        # set inputs
-        m.set_input('data', tvm.nd.array(data.astype(dtype)))
-        m.set_input(**params)
-        m.run()
-        # get outputs
-        out_shape = (net.outputs,)
-        tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).asnumpy()
-        return tvm_out
-
-    test_image = 'dog.jpg'
-    img_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + test_image   +'?raw=true'
-    _download(img_url, test_image)
-    img = LIB.letterbox_image(LIB.load_image_color(test_image.encode('utf-8'), 0, 0), net.w, net.h)
-    darknet_output = get_darknet_output(net, img)
-    darknet_out = np.zeros(net.outputs, dtype='float32')
-    for i in range(net.outputs):
-        darknet_out[i] = darknet_output[i]
-    tvm_out = get_tvm_output(net, img)
-    np.testing.assert_allclose(darknet_out, tvm_out, rtol=1e-3, atol=1e-3)
-
-def test_forward_extraction():
-    '''test extraction model'''
-    model_name = 'extraction'
-    cfg_name = model_name + '.cfg'
-    weights_name = model_name + '.weights'
-    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
-    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
-    _download(cfg_url, cfg_name)
-    _download(weights_url, weights_name)
-    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_alexnet():
-    '''test alexnet model'''
-    model_name = 'alexnet'
-    cfg_name = model_name + '.cfg'
-    weights_name = model_name + '.weights'
-    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
-    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
-    _download(cfg_url, cfg_name)
-    _download(weights_url, weights_name)
-    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_resnet50():
-    '''test resnet50 model'''
-    model_name = 'resnet50'
-    cfg_name = model_name + '.cfg'
-    weights_name = model_name + '.weights'
-    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
-    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
-    _download(cfg_url, cfg_name)
-    _download(weights_url, weights_name)
-    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_yolo():
-    '''test yolo model'''
-    model_name = 'yolov2'
-    cfg_name = model_name + '.cfg'
-    weights_name = model_name + '.weights'
-    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
-    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
-    _download(cfg_url, cfg_name)
-    _download(weights_url, weights_name)
-    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_convolutional():
-    '''test convolutional layer'''
-    net = LIB.make_network(1)
-    layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
-    net.layers[0] = layer
-    net.w = net.h = 224
-    LIB.resize_network(net, 224, 224)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_dense():
-    '''test fully connected layer'''
-    net = LIB.make_network(1)
-    layer = LIB.make_connected_layer(1, 75, 20, 1, 0, 0)
-    net.layers[0] = layer
-    net.w = net.h = 5
-    LIB.resize_network(net, 5, 5)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_dense_batchnorm():
-    '''test fully connected layer with batchnorm'''
-    net = LIB.make_network(1)
-    layer = LIB.make_connected_layer(1, 12, 2, 1, 1, 0)
-    for i in range(5):
-        layer.rolling_mean[i] = np.random.rand(1)
-        layer.rolling_variance[i] = np.random.rand(1)
-        layer.scales[i] = np.random.rand(1)
-    net.layers[0] = layer
-    net.w = net.h = 2
-    LIB.resize_network(net, 2, 2)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_maxpooling():
-    '''test maxpooling layer'''
-    net = LIB.make_network(1)
-    layer = LIB.make_maxpool_layer(1, 224, 224, 3, 2, 2, 0)
-    net.layers[0] = layer
-    net.w = net.h = 224
-    LIB.resize_network(net, 224, 224)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_avgpooling():
-    '''test avgerage pooling layer'''
-    net = LIB.make_network(1)
-    layer = LIB.make_avgpool_layer(1, 224, 224, 3)
-    net.layers[0] = layer
-    net.w = net.h = 224
-    LIB.resize_network(net, 224, 224)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_batch_norm():
-    '''test batch normalization layer'''
-    net = LIB.make_network(1)
-    layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 1, 0, 0, 0)
-    for i in range(32):
-        layer.rolling_mean[i] = np.random.rand(1)
-        layer.rolling_variance[i] = np.random.rand(1)
-    net.layers[0] = layer
-    net.w = net.h = 224
-    LIB.resize_network(net, 224, 224)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_shortcut():
-    '''test shortcut layer'''
-    net = LIB.make_network(3)
-    layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
-    layer_2 = LIB.make_convolutional_layer(1, 111, 111, 32, 32, 1, 1, 1, 0, 1, 0, 0, 0, 0)
-    layer_3 = LIB.make_shortcut_layer(1, 0, 111, 111, 32, 111, 111, 32)
-    layer_3.activation = 1
-    net.layers[0] = layer_1
-    net.layers[1] = layer_2
-    net.layers[2] = layer_3
-    net.w = net.h = 224
-    LIB.resize_network(net, 224, 224)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_reorg():
-    '''test reorg layer'''
-    net = LIB.make_network(2)
-    layer_1 = LIB.make_convolutional_layer(1, 222, 222, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
-    layer_2 = LIB.make_reorg_layer(1, 110, 110, 32, 2, 0, 0, 0)
-    net.layers[0] = layer_1
-    net.layers[1] = layer_2
-    net.w = net.h = 222
-    LIB.resize_network(net, 222, 222)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_region():
-    '''test region layer'''
-    net = LIB.make_network(2)
-    layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 8, 1, 3, 2, 0, 1, 0, 0, 0, 0)
-    layer_2 = LIB.make_region_layer(1, 111, 111, 2, 2, 1)
-    layer_2.softmax = 1
-    net.layers[0] = layer_1
-    net.layers[1] = layer_2
-    net.w = net.h = 224
-    LIB.resize_network(net, 224, 224)
-    test_forward(net)
-    LIB.free_network(net)
-
-def test_forward_elu():
-    '''test elu activation layer'''
-    net = LIB.make_network(1)
-    layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
-    layer_1.activation = 8
-    net.layers[0] = layer_1
-    net.w = net.h = 224
-    LIB.resize_network(net, 224, 224)
-    test_forward(net)
-    LIB.free_network(net)
-
-if __name__ == '__main__':
-    test_forward_resnet50()
-    test_forward_alexnet()
-    test_forward_extraction()
-    test_forward_yolo()
-    test_forward_convolutional()
-    test_forward_maxpooling()
-    test_forward_avgpooling()
-    test_forward_batch_norm()
-    test_forward_shortcut()
-    test_forward_dense()
-    test_forward_dense_batchnorm()
-    test_forward_reorg()
-    test_forward_region()
-    test_forward_elu()
+"""
+Compile Darknet Models
+=====================
+This article is a test script to test darknet models with NNVM.
+All the required models and libraries will be downloaded from the internet
+by the script.
+"""
+import os
+import requests
+import numpy as np
+from nnvm import frontend
+from nnvm.testing.darknet import __darknetffi__
+import nnvm.compiler
+import tvm
+import sys
+import urllib
+if sys.version_info >= (3,):
+    import urllib.request as urllib2
+else:
+    import urllib2
+
+def _download(url, path, overwrite=False, sizecompare=False):
+    ''' Download from internet'''
+    if os.path.isfile(path) and not overwrite:
+        if sizecompare:
+            file_size = os.path.getsize(path)
+            res_head = requests.head(url)
+            res_get = requests.get(url, stream=True)
+            if 'Content-Length' not in res_head.headers:
+                res_get = urllib2.urlopen(url)
+            urlfile_size = int(res_get.headers['Content-Length'])
+            if urlfile_size != file_size:
+                print("exist file got corrupted, downloading", path, " file freshly")
+                _download(url, path, True, False)
+                return
+        print('File {} exists, skip.'.format(path))
+        return
+    print('Downloading from url {} to {}'.format(url, path))
+    try:
+        urllib.request.urlretrieve(url, path)
+        print('')
+    except:
+        urllib.urlretrieve(url, path)
+
+DARKNET_LIB = 'libdarknet.so'
+DARKNETLIB_URL = 'https://github.com/siju-samuel/darknet/blob/master/lib/' \
+                                    + DARKNET_LIB + '?raw=true'
+_download(DARKNETLIB_URL, DARKNET_LIB)
+LIB = __darknetffi__.dlopen('./' + DARKNET_LIB)
+
+def test_forward(net):
+    '''Test network with given input image on both darknet and tvm'''
+    def get_darknet_output(net, img):
+        return LIB.network_predict_image(net, img)
+
+    def get_tvm_output(net, img):
+        '''Compute TVM output'''
+        dtype = 'float32'
+        batch_size = 1
+        sym, params = frontend.darknet.from_darknet(net, dtype)
+        data = np.empty([batch_size, img.c, img.h, img.w], dtype)
+        i = 0
+        for c in range(img.c):
+            for h in range(img.h):
+                for k in range(img.w):
+                    data[0][c][h][k] = img.data[i]
+                    i = i + 1
+
+        target = 'llvm'
+        shape_dict = {'data': data.shape}
+        #with nnvm.compiler.build_config(opt_level=2):
+        graph, library, params = nnvm.compiler.build(sym, target, shape_dict, dtype, params=params)
+        ######################################################################
+        # Execute on TVM
+        # ---------------
+        # The process is no different from other examples.
+        from tvm.contrib import graph_runtime
+        ctx = tvm.cpu(0)
+        m = graph_runtime.create(graph, library, ctx)
+        # set inputs
+        m.set_input('data', tvm.nd.array(data.astype(dtype)))
+        m.set_input(**params)
+        m.run()
+        # get outputs
+        out_shape = (net.outputs,)
+        tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).asnumpy()
+        return tvm_out
+
+    test_image = 'dog.jpg'
+    img_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + test_image   +'?raw=true'
+    _download(img_url, test_image)
+    img = LIB.letterbox_image(LIB.load_image_color(test_image.encode('utf-8'), 0, 0), net.w, net.h)
+    darknet_output = get_darknet_output(net, img)
+    darknet_out = np.zeros(net.outputs, dtype='float32')
+    for i in range(net.outputs):
+        darknet_out[i] = darknet_output[i]
+    tvm_out = get_tvm_output(net, img)
+    np.testing.assert_allclose(darknet_out, tvm_out, rtol=1e-3, atol=1e-3)
+
+def test_forward_extraction():
+    '''test extraction model'''
+    model_name = 'extraction'
+    cfg_name = model_name + '.cfg'
+    weights_name = model_name + '.weights'
+    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
+    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
+    _download(cfg_url, cfg_name)
+    _download(weights_url, weights_name)
+    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_alexnet():
+    '''test alexnet model'''
+    model_name = 'alexnet'
+    cfg_name = model_name + '.cfg'
+    weights_name = model_name + '.weights'
+    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
+    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
+    _download(cfg_url, cfg_name)
+    _download(weights_url, weights_name)
+    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_resnet50():
+    '''test resnet50 model'''
+    model_name = 'resnet50'
+    cfg_name = model_name + '.cfg'
+    weights_name = model_name + '.weights'
+    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
+    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
+    _download(cfg_url, cfg_name)
+    _download(weights_url, weights_name)
+    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_yolo():
+    '''test yolo model'''
+    model_name = 'yolov2'
+    cfg_name = model_name + '.cfg'
+    weights_name = model_name + '.weights'
+    cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
+    weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
+    _download(cfg_url, cfg_name)
+    _download(weights_url, weights_name)
+    net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_convolutional():
+    '''test convolutional layer'''
+    net = LIB.make_network(1)
+    layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
+    net.layers[0] = layer
+    net.w = net.h = 224
+    LIB.resize_network(net, 224, 224)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_dense():
+    '''test fully connected layer'''
+    net = LIB.make_network(1)
+    layer = LIB.make_connected_layer(1, 75, 20, 1, 0, 0)
+    net.layers[0] = layer
+    net.w = net.h = 5
+    LIB.resize_network(net, 5, 5)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_dense_batchnorm():
+    '''test fully connected layer with batchnorm'''
+    net = LIB.make_network(1)
+    layer = LIB.make_connected_layer(1, 12, 2, 1, 1, 0)
+    for i in range(5):
+        layer.rolling_mean[i] = np.random.rand(1)
+        layer.rolling_variance[i] = np.random.rand(1)
+        layer.scales[i] = np.random.rand(1)
+    net.layers[0] = layer
+    net.w = net.h = 2
+    LIB.resize_network(net, 2, 2)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_maxpooling():
+    '''test maxpooling layer'''
+    net = LIB.make_network(1)
+    layer = LIB.make_maxpool_layer(1, 224, 224, 3, 2, 2, 0)
+    net.layers[0] = layer
+    net.w = net.h = 224
+    LIB.resize_network(net, 224, 224)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_avgpooling():
+    '''test avgerage pooling layer'''
+    net = LIB.make_network(1)
+    layer = LIB.make_avgpool_layer(1, 224, 224, 3)
+    net.layers[0] = layer
+    net.w = net.h = 224
+    LIB.resize_network(net, 224, 224)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_batch_norm():
+    '''test batch normalization layer'''
+    net = LIB.make_network(1)
+    layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 1, 0, 0, 0)
+    for i in range(32):
+        layer.rolling_mean[i] = np.random.rand(1)
+        layer.rolling_variance[i] = np.random.rand(1)
+    net.layers[0] = layer
+    net.w = net.h = 224
+    LIB.resize_network(net, 224, 224)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_shortcut():
+    '''test shortcut layer'''
+    net = LIB.make_network(3)
+    layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
+    layer_2 = LIB.make_convolutional_layer(1, 111, 111, 32, 32, 1, 1, 1, 0, 1, 0, 0, 0, 0)
+    layer_3 = LIB.make_shortcut_layer(1, 0, 111, 111, 32, 111, 111, 32)
+    layer_3.activation = 1
+    net.layers[0] = layer_1
+    net.layers[1] = layer_2
+    net.layers[2] = layer_3
+    net.w = net.h = 224
+    LIB.resize_network(net, 224, 224)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_reorg():
+    '''test reorg layer'''
+    net = LIB.make_network(2)
+    layer_1 = LIB.make_convolutional_layer(1, 222, 222, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
+    layer_2 = LIB.make_reorg_layer(1, 110, 110, 32, 2, 0, 0, 0)
+    net.layers[0] = layer_1
+    net.layers[1] = layer_2
+    net.w = net.h = 222
+    LIB.resize_network(net, 222, 222)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_region():
+    '''test region layer'''
+    net = LIB.make_network(2)
+    layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 8, 1, 3, 2, 0, 1, 0, 0, 0, 0)
+    layer_2 = LIB.make_region_layer(1, 111, 111, 2, 2, 1)
+    layer_2.softmax = 1
+    net.layers[0] = layer_1
+    net.layers[1] = layer_2
+    net.w = net.h = 224
+    LIB.resize_network(net, 224, 224)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_elu():
+    '''test elu activation layer'''
+    net = LIB.make_network(1)
+    layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
+    layer_1.activation = 8
+    net.layers[0] = layer_1
+    net.w = net.h = 224
+    LIB.resize_network(net, 224, 224)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_softmax():
+    '''test softmax layer'''
+    net = LIB.make_network(1)
+    layer_1 = LIB.make_softmax_layer(1, 75, 1)
+    layer_1.temperature=1
+    net.layers[0] = layer_1
+    net.w = net.h = 5
+    LIB.resize_network(net, net.w, net.h)
+    test_forward(net)
+    LIB.free_network(net)
+
+def test_forward_softmax_temperature():
+    '''test softmax layer'''
+    net = LIB.make_network(1)
+    layer_1 = LIB.make_softmax_layer(1, 75, 1)
+    layer_1.temperature=0.8
+    net.layers[0] = layer_1
+    net.w = net.h = 5
+    LIB.resize_network(net, net.w, net.h)
+    test_forward(net)
+    LIB.free_network(net)
+
+if __name__ == '__main__':
+    test_forward_resnet50()
+    test_forward_alexnet()
+    test_forward_extraction()
+    test_forward_yolo()
+    test_forward_convolutional()
+    test_forward_maxpooling()
+    test_forward_avgpooling()
+    test_forward_batch_norm()
+    test_forward_shortcut()
+    test_forward_dense()
+    test_forward_dense_batchnorm()
+    test_forward_softmax()
+    test_forward_softmax_temperature()
+    test_forward_reorg()
+    test_forward_region()
+    test_forward_elu()