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/dports/misc/mxnet/incubator-mxnet-1.9.0/example/speech_recognition/
H A D	stt_layer_fc.py	28 no_bias=False, argument 33 net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, no_bias=no_bias, name=name) 36 if no_bias: 37 net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, no_bias=no_bias, name=name) 39 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, bias=bias, no_bias=no_bias, name=name) 42 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, weight=weight, no_bias=no_bias, name=… 45 if no_bias: 46 …= mx.sym.FullyConnected(data=net, num_hidden=num_hidden, weight=weight, no_bias=no_bias, name=name) 48 …llyConnected(data=net, num_hidden=num_hidden, weight=weight, bias=bias, no_bias=no_bias, name=name) 102 no_bias=is_batchnorm,
H A D	stt_layer_conv.py	`28 no_bias=False, argument 34 …Convolution(data=net, num_filter=channels, kernel=filter_dimension, stride=stride, no_bias=no_bias, 38 no_bias=no_bias, name=name) 41 no_bias=no_bias, name=name) 44 bias=bias, no_bias=no_bias, name=name)`
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/speech_recognition/
H A D	stt_layer_fc.py	28 no_bias=False, argument 33 net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, no_bias=no_bias, name=name) 36 if no_bias: 37 net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, no_bias=no_bias, name=name) 39 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, bias=bias, no_bias=no_bias, name=name) 42 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, weight=weight, no_bias=no_bias, name=… 45 if no_bias: 46 …= mx.sym.FullyConnected(data=net, num_hidden=num_hidden, weight=weight, no_bias=no_bias, name=name) 48 …llyConnected(data=net, num_hidden=num_hidden, weight=weight, bias=bias, no_bias=no_bias, name=name) 102 no_bias=is_batchnorm,
H A D	stt_layer_conv.py	`28 no_bias=False, argument 34 …Convolution(data=net, num_filter=channels, kernel=filter_dimension, stride=stride, no_bias=no_bias, 38 no_bias=no_bias, name=name) 41 no_bias=no_bias, name=name) 44 bias=bias, no_bias=no_bias, name=name)`
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/neural-style/
H A D	model_vgg19.py	28 …v1_1', data=data , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 30 …2', data=relu1_1 , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 33 …_1', data=pool1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 35 …', data=relu2_1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 38 …_1', data=pool2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 40 …', data=relu3_1 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 42 …', data=relu3_2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 44 …', data=relu3_3 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 47 …_1', data=pool3 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 49 …', data=relu4_1 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… [all …]
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/neural-style/
H A D	model_vgg19.py	28 …v1_1', data=data , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 30 …2', data=relu1_1 , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 33 …_1', data=pool1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 35 …', data=relu2_1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 38 …_1', data=pool2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 40 …', data=relu3_1 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 42 …', data=relu3_2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 44 …', data=relu3_3 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 47 …_1', data=pool3 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… 49 …', data=relu4_1 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=… [all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/gan/CGAN_mnist_R/
H A D	CGAN_train.R	44 no_bias <- T globalVar 57 4, no_bias = T) nameattr 62 2), pad = c(1, 1), num_filter = gen_features * 2, no_bias = no_bias) nameattr 67 2), pad = c(1, 1), num_filter = gen_features, no_bias = no_bias) nameattr 72 2), pad = c(1, 1), num_filter = image_depth, no_bias = no_bias) nameattr 89 1), pad = c(0, 0), num_filter = 24, no_bias = no_bias) nameattr 96 pad = c(0, 0), num_filter = 32, no_bias = no_bias) nameattr 101 pad = c(0, 0), num_filter = 64, no_bias = no_bias) nameattr 106 pad = c(0, 0), num_filter = 64, no_bias = no_bias) nameattr 115 dfc <- mx.symbol.FullyConnected(data = dflat, name = "dfc", num_hidden = 1, no_bias = F)
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/gan/CGAN_mnist_R/
H A D	CGAN_train.R	44 no_bias <- T globalVar 57 4, no_bias = T) nameattr 62 2), pad = c(1, 1), num_filter = gen_features * 2, no_bias = no_bias) nameattr 67 2), pad = c(1, 1), num_filter = gen_features, no_bias = no_bias) nameattr 72 2), pad = c(1, 1), num_filter = image_depth, no_bias = no_bias) nameattr 89 1), pad = c(0, 0), num_filter = 24, no_bias = no_bias) nameattr 96 pad = c(0, 0), num_filter = 32, no_bias = no_bias) nameattr 101 pad = c(0, 0), num_filter = 64, no_bias = no_bias) nameattr 106 pad = c(0, 0), num_filter = 64, no_bias = no_bias) nameattr 115 dfc <- mx.symbol.FullyConnected(data = dflat, name = "dfc", num_hidden = 1, no_bias = F)
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/neural-style/end_to_end/
H A D	gen_v4.py	26 ….sym.Convolution(data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=False) 33 …sym.Deconvolution(data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=True) 44 conv1_1 = mx.sym.Convolution(data, num_filter=48, kernel=(5, 5), pad=(2, 2), no_bias=False) 48 conv2_1 = mx.sym.Convolution(conv1_1, num_filter=32, kernel=(5, 5), pad=(2, 2), no_bias=False) 52 conv3_1 = mx.sym.Convolution(conv2_1, num_filter=64, kernel=(3, 3), pad=(1, 1), no_bias=False) 56 conv4_1 = mx.sym.Convolution(conv3_1, num_filter=32, kernel=(5, 5), pad=(2, 2), no_bias=False) 60 conv5_1 = mx.sym.Convolution(conv4_1, num_filter=48, kernel=(5, 5), pad=(2, 2), no_bias=False) 64 conv6_1 = mx.sym.Convolution(conv5_1, num_filter=32, kernel=(5, 5), pad=(2, 2), no_bias=True) 68 out = mx.sym.Convolution(conv6_1, num_filter=3, kernel=(3, 3), pad=(1, 1), no_bias=True)
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/neural-style/end_to_end/
H A D	gen_v4.py	26 ….sym.Convolution(data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=False) 33 …sym.Deconvolution(data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=True) 44 conv1_1 = mx.sym.Convolution(data, num_filter=48, kernel=(5, 5), pad=(2, 2), no_bias=False) 48 conv2_1 = mx.sym.Convolution(conv1_1, num_filter=32, kernel=(5, 5), pad=(2, 2), no_bias=False) 52 conv3_1 = mx.sym.Convolution(conv2_1, num_filter=64, kernel=(3, 3), pad=(1, 1), no_bias=False) 56 conv4_1 = mx.sym.Convolution(conv3_1, num_filter=32, kernel=(5, 5), pad=(2, 2), no_bias=False) 60 conv5_1 = mx.sym.Convolution(conv4_1, num_filter=48, kernel=(5, 5), pad=(2, 2), no_bias=False) 64 conv6_1 = mx.sym.Convolution(conv5_1, num_filter=32, kernel=(5, 5), pad=(2, 2), no_bias=True) 68 out = mx.sym.Convolution(conv6_1, num_filter=3, kernel=(3, 3), pad=(1, 1), no_bias=True)
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/tests/python/mkl/
H A D	test_subgraph.py	`330 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 338 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 347 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 363 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 373 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 382 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 394 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 406 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 668 no_bias=no_bias, flatten=flatten) 678 no_bias=no_bias, flatten=flatten) [all …]`
/dports/misc/mxnet/incubator-mxnet-1.9.0/tests/python/mkl/
H A D	test_subgraph.py	`330 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 338 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 347 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 363 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 373 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 382 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 394 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 406 kernel=(3, 3), stride=(1, 1), no_bias=no_bias) 668 no_bias=no_bias, flatten=flatten) 678 no_bias=no_bias, flatten=flatten) [all …]`
/dports/misc/py-gluoncv/gluon-cv-0.9.0/gluoncv/nn/
H A D	feature.py	`143 y, num_filter=num_trans, kernel=(1, 1), no_bias=use_bn, 150 no_bias=use_bn, name='expand_conv{}'.format(i), attr={'__init__': weight_init}) 200 use_elewadd=True, use_p6=False, p6_conv=True, no_bias=True, pretrained=False, argument 220 stride=(1, 1), no_bias=no_bias, 231 stride=(2, 2), no_bias=no_bias, 242 stride=(1, 1), no_bias=no_bias, 264 no_bias=no_bias, name='P{}_conv1'.format(num_stages - i),`
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/src/operator/quantization/
H A D	quantized_conv.cc	39 CHECK_EQ(in_shape->size(), param.no_bias? 6U : 9U); in QuantizedConvShape() 124 const int start = param.no_bias? 2 : 3; in QuantizedConvShape() 125 const int end = param.no_bias? 6 : 9; in QuantizedConvShape() 129 if (!param.no_bias) { in QuantizedConvShape() 140 CHECK_EQ(in_type->size(), param.no_bias? 6U : 9U); in QuantizedConvType() 146 if (!param.no_bias) { in QuantizedConvType() 149 const size_t start = param.no_bias? 2 : 3; in QuantizedConvType() 150 const size_t end = param.no_bias? 6 : 9; in QuantizedConvType() 191 return param.no_bias? 6 : 9; in __anonc4f6c4ae0202() 198 if (param.no_bias) { in __anonc4f6c4ae0302()
/dports/misc/mxnet/incubator-mxnet-1.9.0/src/operator/quantization/
H A D	quantized_conv.cc	39 CHECK_EQ(in_shape->size(), param.no_bias? 6U : 9U); in QuantizedConvShape() 124 const int start = param.no_bias? 2 : 3; in QuantizedConvShape() 125 const int end = param.no_bias? 6 : 9; in QuantizedConvShape() 129 if (!param.no_bias) { in QuantizedConvShape() 140 CHECK_EQ(in_type->size(), param.no_bias? 6U : 9U); in QuantizedConvType() 146 if (!param.no_bias) { in QuantizedConvType() 149 const size_t start = param.no_bias? 2 : 3; in QuantizedConvType() 150 const size_t end = param.no_bias? 6 : 9; in QuantizedConvType() 191 return param.no_bias? 6 : 9; in __anonada5ab580202() 198 if (param.no_bias) { in __anonada5ab580302()
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/vae-gan/
H A D	vaegan_mxnet.py	63 …volution(data, name='enc1', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=nef, no_bias=no_bias) 67 …ution(eact1, name='enc2', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=nef2, no_bias=no_bias) 71 …ution(eact2, name='enc3', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=nef4, no_bias=no_bias) 75 …ution(eact3, name='enc4', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=nef8, no_bias=no_bias) 97 …and, name='gen1', kernel=(5,5), stride=(2,2),target_shape=(2,2), num_filter=ngf8, no_bias=no_bias) 101 …ct1, name='gen2', kernel=(5,5), stride=(2,2),target_shape=(4,4), num_filter=ngf4, no_bias=no_bias) 113 …ct4, name='gen5', kernel=(5,5), stride=(2,2), target_shape=(32,32), num_filter=nc, no_bias=no_bias) 126 …onvolution(data, name='d1', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=ndf, no_bias=no_bias) 129 …olution(dact1, name='d2', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=ndf2, no_bias=no_bias) 133 …olution(dact2, name='d3', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=ndf*4, no_bias=no_bias) [all …]
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/vae-gan/
H A D	vaegan_mxnet.py	63 …volution(data, name='enc1', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=nef, no_bias=no_bias) 67 …ution(eact1, name='enc2', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=nef2, no_bias=no_bias) 71 …ution(eact2, name='enc3', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=nef4, no_bias=no_bias) 75 …ution(eact3, name='enc4', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=nef8, no_bias=no_bias) 97 …and, name='gen1', kernel=(5,5), stride=(2,2),target_shape=(2,2), num_filter=ngf8, no_bias=no_bias) 101 …ct1, name='gen2', kernel=(5,5), stride=(2,2),target_shape=(4,4), num_filter=ngf4, no_bias=no_bias) 113 …ct4, name='gen5', kernel=(5,5), stride=(2,2), target_shape=(32,32), num_filter=nc, no_bias=no_bias) 126 …onvolution(data, name='d1', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=ndf, no_bias=no_bias) 129 …olution(dact1, name='d2', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=ndf2, no_bias=no_bias) 133 …olution(dact2, name='d3', kernel=(5,5), stride=(2,2), pad=(2,2), num_filter=ndf*4, no_bias=no_bias) [all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/image-classification/
H A D	symbol_resnet-v2.R	`33 no_bias=TRUE, workspace=workspace, 40 no_bias=TRUE, workspace=workspace, 46 stride=c(1,1), pad=c(0,0), no_bias=TRUE, nameattr 52 kernel=c(1,1), stride=stride, no_bias=TRUE, nameattr 61 stride=stride, pad=c(1,1), no_bias=TRUE, nameattr 68 stride=c(1,1), pad=c(1,1), no_bias=TRUE, nameattr 74 stride=stride, no_bias=TRUE, 92 no_bias=TRUE, name="conv0", workspace=workspace)`
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/image-classification/
H A D	symbol_resnet-v2.R	`33 no_bias=TRUE, workspace=workspace, 40 no_bias=TRUE, workspace=workspace, 46 stride=c(1,1), pad=c(0,0), no_bias=TRUE, nameattr 52 kernel=c(1,1), stride=stride, no_bias=TRUE, nameattr 61 stride=stride, pad=c(1,1), no_bias=TRUE, nameattr 68 stride=c(1,1), pad=c(1,1), no_bias=TRUE, nameattr 74 stride=stride, no_bias=TRUE, 92 no_bias=TRUE, name="conv0", workspace=workspace)`
/dports/misc/tvm/incubator-tvm-0.6.1/tests/python/frontend/mxnet/model_zoo/
H A D	resnet.py	52 no_bias=True, workspace=workspace, name=name + '_conv1') 56 no_bias=True, workspace=workspace, name=name + '_conv2') 59 ….Convolution(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, 64 …t = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, 73 no_bias=True, workspace=workspace, name=name + '_conv1') 77 no_bias=True, workspace=workspace, name=name + '_conv2') 81 …t = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, 119 no_bias=True, name="conv0", workspace=workspace) 122 no_bias=True, name="conv0", workspace=workspace)
/dports/misc/tvm/incubator-tvm-0.6.1/nnvm/tests/python/frontend/mxnet/model_zoo/
H A D	resnet.py	52 no_bias=True, workspace=workspace, name=name + '_conv1') 56 no_bias=True, workspace=workspace, name=name + '_conv2') 59 ….Convolution(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, 64 …t = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, 73 no_bias=True, workspace=workspace, name=name + '_conv1') 77 no_bias=True, workspace=workspace, name=name + '_conv2') 81 …t = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, 119 no_bias=True, name="conv0", workspace=workspace) 122 no_bias=True, name="conv0", workspace=workspace)
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/frontend/mxnet/model_zoo/
H A D	resnet.py	`70 no_bias=True, 84 no_bias=True, 98 no_bias=True, 110 no_bias=True, 128 no_bias=True, 142 no_bias=True, 154 no_bias=True, 211 no_bias=True, 222 no_bias=True,`
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/ssd/symbol/
H A D	resnet.py	52 no_bias=True, workspace=workspace, name=name + '_conv1') 56 no_bias=True, workspace=workspace, name=name + '_conv2') 59 ….Convolution(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, 64 …t = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, 73 no_bias=True, workspace=workspace, name=name + '_conv1') 77 no_bias=True, workspace=workspace, name=name + '_conv2') 81 …t = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, 112 no_bias=True, name="conv0", workspace=workspace) 115 no_bias=True, name="conv0", workspace=workspace)
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/image-classification/symbols/
H A D	resnet-v1.py	50 no_bias=True, workspace=workspace, name=name + '_conv1') 54 no_bias=True, workspace=workspace, name=name + '_conv2') 57 ….Convolution(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, 64 …c = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, 72 no_bias=True, workspace=workspace, name=name + '_conv1') 76 no_bias=True, workspace=workspace, name=name + '_conv2') 82 …c = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, 119 no_bias=True, name="conv0", workspace=workspace) 124 no_bias=True, name="conv0", workspace=workspace)
H A D	resnet.py	53 no_bias=True, workspace=workspace, name=name + '_conv1') 57 no_bias=True, workspace=workspace, name=name + '_conv2') 60 ….Convolution(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, 65 …t = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, 74 no_bias=True, workspace=workspace, name=name + '_conv1') 78 no_bias=True, workspace=workspace, name=name + '_conv2') 82 …t = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, 119 no_bias=True, name="conv0", workspace=workspace) 122 no_bias=True, name="conv0", workspace=workspace)

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