diff --git a/nnvm/python/nnvm/frontend/darknet.py b/nnvm/python/nnvm/frontend/darknet.py
index c18585a27dccb66af2c8afaa9d648bb245f913e2..878b31846406d0b9e68511087388503f69da90ad 100644
--- a/nnvm/python/nnvm/frontend/darknet.py
+++ b/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]
diff --git a/nnvm/tests/python/frontend/darknet/test_forward.py b/nnvm/tests/python/frontend/darknet/test_forward.py
index 6077d6b9d127d4467df039a5d8c17d0b52aa91e3..0abc595c426f550ed9d5d5aea21a563037fde0c5 100644
--- a/nnvm/tests/python/frontend/darknet/test_forward.py
+++ b/nnvm/tests/python/frontend/darknet/test_forward.py
@@ -1,284 +1,308 @@
-"""
-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()