[Darknet] softmax temperature in frontend (#1429)
This commit is contained in:
Siju
Tianqi Chen
Родитель
3b476ffe31
Коммит
b5ff88b0cd
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@ -260,6 +260,9 @@ def _darknet_reshape(inputs, attrs):
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def _darknet_softmax_output(inputs, attrs):
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"""Process the softmax operation."""
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temperature = attrs.get('temperature', 1)
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if temperature != 1:
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inputs[0] = inputs[0] / float(temperature)
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op_name, new_attrs = 'softmax', {}
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if _darknet_parse_bool_str(attrs, 'multi_output'):
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new_attrs['axis'] = 1
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@ -529,6 +532,8 @@ def _get_darknet_attrs(net, layer_num):
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elif LAYERTYPE.SOFTMAX == layer.type:
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attr.update({'axis' : 1})
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attr.update({'use_flatten' : True})
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if layer.temperature:
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attr.update({'temperature' : str(layer.temperature)})
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elif LAYERTYPE.SHORTCUT == layer.type:
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add_layer = net.layers[layer.index]
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@ -1,284 +1,308 @@
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"""
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Compile Darknet Models
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=====================
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This article is a test script to test darknet models with NNVM.
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All the required models and libraries will be downloaded from the internet
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by the script.
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"""
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import os
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import requests
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import numpy as np
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from nnvm import frontend
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from nnvm.testing.darknet import __darknetffi__
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import nnvm.compiler
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import tvm
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import sys
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import urllib
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if sys.version_info >= (3,):
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import urllib.request as urllib2
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else:
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import urllib2
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def _download(url, path, overwrite=False, sizecompare=False):
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''' Download from internet'''
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if os.path.isfile(path) and not overwrite:
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if sizecompare:
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file_size = os.path.getsize(path)
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res_head = requests.head(url)
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res_get = requests.get(url, stream=True)
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if 'Content-Length' not in res_head.headers:
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res_get = urllib2.urlopen(url)
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urlfile_size = int(res_get.headers['Content-Length'])
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if urlfile_size != file_size:
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print("exist file got corrupted, downloading", path, " file freshly")
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_download(url, path, True, False)
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return
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print('File {} exists, skip.'.format(path))
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return
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print('Downloading from url {} to {}'.format(url, path))
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try:
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urllib.request.urlretrieve(url, path)
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print('')
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except:
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urllib.urlretrieve(url, path)
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DARKNET_LIB = 'libdarknet.so'
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DARKNETLIB_URL = 'https://github.com/siju-samuel/darknet/blob/master/lib/' \
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+ DARKNET_LIB + '?raw=true'
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_download(DARKNETLIB_URL, DARKNET_LIB)
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LIB = __darknetffi__.dlopen('./' + DARKNET_LIB)
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def test_forward(net):
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'''Test network with given input image on both darknet and tvm'''
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def get_darknet_output(net, img):
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return LIB.network_predict_image(net, img)
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def get_tvm_output(net, img):
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'''Compute TVM output'''
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dtype = 'float32'
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batch_size = 1
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sym, params = frontend.darknet.from_darknet(net, dtype)
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data = np.empty([batch_size, img.c, img.h, img.w], dtype)
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i = 0
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for c in range(img.c):
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for h in range(img.h):
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for k in range(img.w):
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data[0][c][h][k] = img.data[i]
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i = i + 1
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target = 'llvm'
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shape_dict = {'data': data.shape}
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#with nnvm.compiler.build_config(opt_level=2):
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graph, library, params = nnvm.compiler.build(sym, target, shape_dict, dtype, params=params)
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######################################################################
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# Execute on TVM
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# ---------------
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# The process is no different from other examples.
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from tvm.contrib import graph_runtime
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ctx = tvm.cpu(0)
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m = graph_runtime.create(graph, library, ctx)
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# set inputs
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m.set_input('data', tvm.nd.array(data.astype(dtype)))
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m.set_input(**params)
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m.run()
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# get outputs
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out_shape = (net.outputs,)
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tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).asnumpy()
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return tvm_out
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test_image = 'dog.jpg'
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img_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + test_image +'?raw=true'
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_download(img_url, test_image)
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img = LIB.letterbox_image(LIB.load_image_color(test_image.encode('utf-8'), 0, 0), net.w, net.h)
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darknet_output = get_darknet_output(net, img)
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darknet_out = np.zeros(net.outputs, dtype='float32')
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for i in range(net.outputs):
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darknet_out[i] = darknet_output[i]
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tvm_out = get_tvm_output(net, img)
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np.testing.assert_allclose(darknet_out, tvm_out, rtol=1e-3, atol=1e-3)
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def test_forward_extraction():
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'''test extraction model'''
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model_name = 'extraction'
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cfg_name = model_name + '.cfg'
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weights_name = model_name + '.weights'
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cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
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weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
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_download(cfg_url, cfg_name)
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_download(weights_url, weights_name)
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net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_alexnet():
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'''test alexnet model'''
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model_name = 'alexnet'
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cfg_name = model_name + '.cfg'
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weights_name = model_name + '.weights'
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cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
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weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
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_download(cfg_url, cfg_name)
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_download(weights_url, weights_name)
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net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_resnet50():
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'''test resnet50 model'''
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model_name = 'resnet50'
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cfg_name = model_name + '.cfg'
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weights_name = model_name + '.weights'
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cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
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weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
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_download(cfg_url, cfg_name)
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_download(weights_url, weights_name)
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net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_yolo():
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'''test yolo model'''
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model_name = 'yolov2'
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cfg_name = model_name + '.cfg'
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weights_name = model_name + '.weights'
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cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true'
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weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true'
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_download(cfg_url, cfg_name)
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_download(weights_url, weights_name)
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net = LIB.load_network(cfg_name.encode('utf-8'), weights_name.encode('utf-8'), 0)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_convolutional():
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'''test convolutional layer'''
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net = LIB.make_network(1)
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layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
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net.layers[0] = layer
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net.w = net.h = 224
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LIB.resize_network(net, 224, 224)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_dense():
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'''test fully connected layer'''
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net = LIB.make_network(1)
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layer = LIB.make_connected_layer(1, 75, 20, 1, 0, 0)
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net.layers[0] = layer
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net.w = net.h = 5
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LIB.resize_network(net, 5, 5)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_dense_batchnorm():
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'''test fully connected layer with batchnorm'''
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net = LIB.make_network(1)
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layer = LIB.make_connected_layer(1, 12, 2, 1, 1, 0)
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for i in range(5):
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layer.rolling_mean[i] = np.random.rand(1)
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layer.rolling_variance[i] = np.random.rand(1)
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layer.scales[i] = np.random.rand(1)
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net.layers[0] = layer
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net.w = net.h = 2
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LIB.resize_network(net, 2, 2)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_maxpooling():
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'''test maxpooling layer'''
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net = LIB.make_network(1)
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layer = LIB.make_maxpool_layer(1, 224, 224, 3, 2, 2, 0)
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net.layers[0] = layer
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net.w = net.h = 224
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LIB.resize_network(net, 224, 224)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_avgpooling():
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'''test avgerage pooling layer'''
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net = LIB.make_network(1)
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layer = LIB.make_avgpool_layer(1, 224, 224, 3)
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net.layers[0] = layer
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net.w = net.h = 224
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LIB.resize_network(net, 224, 224)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_batch_norm():
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'''test batch normalization layer'''
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net = LIB.make_network(1)
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layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 1, 0, 0, 0)
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for i in range(32):
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layer.rolling_mean[i] = np.random.rand(1)
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layer.rolling_variance[i] = np.random.rand(1)
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net.layers[0] = layer
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net.w = net.h = 224
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LIB.resize_network(net, 224, 224)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_shortcut():
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'''test shortcut layer'''
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net = LIB.make_network(3)
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layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
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layer_2 = LIB.make_convolutional_layer(1, 111, 111, 32, 32, 1, 1, 1, 0, 1, 0, 0, 0, 0)
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layer_3 = LIB.make_shortcut_layer(1, 0, 111, 111, 32, 111, 111, 32)
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layer_3.activation = 1
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net.layers[0] = layer_1
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net.layers[1] = layer_2
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net.layers[2] = layer_3
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net.w = net.h = 224
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LIB.resize_network(net, 224, 224)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_reorg():
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'''test reorg layer'''
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net = LIB.make_network(2)
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layer_1 = LIB.make_convolutional_layer(1, 222, 222, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
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layer_2 = LIB.make_reorg_layer(1, 110, 110, 32, 2, 0, 0, 0)
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net.layers[0] = layer_1
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net.layers[1] = layer_2
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net.w = net.h = 222
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LIB.resize_network(net, 222, 222)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_region():
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'''test region layer'''
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net = LIB.make_network(2)
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layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 8, 1, 3, 2, 0, 1, 0, 0, 0, 0)
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layer_2 = LIB.make_region_layer(1, 111, 111, 2, 2, 1)
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layer_2.softmax = 1
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net.layers[0] = layer_1
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net.layers[1] = layer_2
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net.w = net.h = 224
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LIB.resize_network(net, 224, 224)
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test_forward(net)
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LIB.free_network(net)
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def test_forward_elu():
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'''test elu activation layer'''
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net = LIB.make_network(1)
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layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0)
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layer_1.activation = 8
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net.layers[0] = layer_1
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net.w = net.h = 224
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LIB.resize_network(net, 224, 224)
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test_forward(net)
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LIB.free_network(net)
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if __name__ == '__main__':
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test_forward_resnet50()
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test_forward_alexnet()
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test_forward_extraction()
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test_forward_yolo()
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test_forward_convolutional()
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test_forward_maxpooling()
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test_forward_avgpooling()
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test_forward_batch_norm()
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test_forward_shortcut()
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test_forward_dense()
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test_forward_dense_batchnorm()
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test_forward_reorg()
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test_forward_region()
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test_forward_elu()
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"""
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Compile Darknet Models
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=====================
|
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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.
|
||||
"""
|
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import os
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import requests
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import numpy as np
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from nnvm import frontend
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from nnvm.testing.darknet import __darknetffi__
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import nnvm.compiler
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import tvm
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import sys
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import urllib
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if sys.version_info >= (3,):
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import urllib.request as urllib2
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else:
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import urllib2
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|
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def _download(url, path, overwrite=False, sizecompare=False):
|
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''' Download from internet'''
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if os.path.isfile(path) and not overwrite:
|
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if sizecompare:
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file_size = os.path.getsize(path)
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res_head = requests.head(url)
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res_get = requests.get(url, stream=True)
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if 'Content-Length' not in res_head.headers:
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res_get = urllib2.urlopen(url)
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urlfile_size = int(res_get.headers['Content-Length'])
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if urlfile_size != file_size:
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print("exist file got corrupted, downloading", path, " file freshly")
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_download(url, path, True, False)
|
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return
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print('File {} exists, skip.'.format(path))
|
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return
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print('Downloading from url {} to {}'.format(url, path))
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try:
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urllib.request.urlretrieve(url, path)
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print('')
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except:
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urllib.urlretrieve(url, path)
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DARKNET_LIB = 'libdarknet.so'
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DARKNETLIB_URL = 'https://github.com/siju-samuel/darknet/blob/master/lib/' \
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+ DARKNET_LIB + '?raw=true'
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_download(DARKNETLIB_URL, DARKNET_LIB)
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LIB = __darknetffi__.dlopen('./' + DARKNET_LIB)
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def test_forward(net):
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'''Test network with given input image on both darknet and tvm'''
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def get_darknet_output(net, img):
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return LIB.network_predict_image(net, img)
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def get_tvm_output(net, img):
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'''Compute TVM output'''
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dtype = 'float32'
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batch_size = 1
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sym, params = frontend.darknet.from_darknet(net, dtype)
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data = np.empty([batch_size, img.c, img.h, img.w], dtype)
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i = 0
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for c in range(img.c):
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for h in range(img.h):
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for k in range(img.w):
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data[0][c][h][k] = img.data[i]
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i = i + 1
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target = 'llvm'
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shape_dict = {'data': data.shape}
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#with nnvm.compiler.build_config(opt_level=2):
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graph, library, params = nnvm.compiler.build(sym, target, shape_dict, dtype, params=params)
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######################################################################
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# Execute on TVM
|
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# ---------------
|
||||
# The process is no different from other examples.
|
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from tvm.contrib import graph_runtime
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ctx = tvm.cpu(0)
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m = graph_runtime.create(graph, library, ctx)
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# set inputs
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m.set_input('data', tvm.nd.array(data.astype(dtype)))
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m.set_input(**params)
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m.run()
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# get outputs
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out_shape = (net.outputs,)
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tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).asnumpy()
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return tvm_out
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test_image = 'dog.jpg'
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img_url = 'https://github.com/siju-samuel/darknet/blob/master/data/' + test_image +'?raw=true'
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_download(img_url, test_image)
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img = LIB.letterbox_image(LIB.load_image_color(test_image.encode('utf-8'), 0, 0), net.w, net.h)
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darknet_output = get_darknet_output(net, img)
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darknet_out = np.zeros(net.outputs, dtype='float32')
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for i in range(net.outputs):
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darknet_out[i] = darknet_output[i]
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tvm_out = get_tvm_output(net, img)
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np.testing.assert_allclose(darknet_out, tvm_out, rtol=1e-3, atol=1e-3)
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|
||||
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()
|
||||
|
|
|
|||
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