Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
res2a_branch2c = convolution2d.Batch(fptype=np.float32)
res2a_branch2c.parameter.nKernels = 256
res2a_branch2c.parameter.kernelSizes = convolution2d.KernelSizes(1, 1)
res2a_branch2c.parameter.strides = convolution2d.Strides(1, 1)
res2a_branch2c.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res2a_branch2c.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res2a_branch2c_id = topology.add(res2a_branch2c)
bn2a_branch2c = batch_normalization.Batch(fptype=np.float32)
bn2a_branch2c_id = topology.add(bn2a_branch2c)
res2a = eltwise_sum.Batch(fptype=np.float32)
res2a_id = topology.add(res2a)
res2a_relu = relu.Batch(fptype=np.float32)
res2a_relu_id = topology.add(res2a_relu)
res2a_relu_split2 = split.Batch(2, 2, fptype=np.float32)
res2a_relu_split2_id = topology.add(res2a_relu_split2)
res2b_branch2a = convolution2d.Batch(fptype=np.float32)
res2b_branch2a.parameter.nKernels = 64
res2b_branch2a.parameter.kernelSizes = convolution2d.KernelSizes(1, 1)
res2b_branch2a.parameter.strides = convolution2d.Strides(1, 1)
res2b_branch2a.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res2b_branch2a.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res2b_branch2a_id = topology.add(res2b_branch2a)
bn2b_branch2a = batch_normalization.Batch(fptype=np.float32)
bn2b_branch2a_id = topology.add(bn2b_branch2a)
res3a_branch2a_relu = relu.Batch(fptype=np.float32)
res3a_branch2a_relu_id = topology.add(res3a_branch2a_relu)
res3a_branch2b = convolution2d.Batch(fptype=np.float32)
res3a_branch2b.parameter.nKernels = 128
res3a_branch2b.parameter.kernelSizes = convolution2d.KernelSizes(3, 3)
res3a_branch2b.parameter.strides = convolution2d.Strides(1, 1)
res3a_branch2b.parameter.paddings = convolution2d.Paddings(1, 1)
res3a_branch2b.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res3a_branch2b.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res3a_branch2b_id = topology.add(res3a_branch2b)
bn3a_branch2b = batch_normalization.Batch(fptype=np.float32)
bn3a_branch2b_id = topology.add(bn3a_branch2b)
res3a_branch2b_relu = relu.Batch(fptype=np.float32)
res3a_branch2b_relu_id = topology.add(res3a_branch2b_relu)
res3a_branch2c = convolution2d.Batch(fptype=np.float32)
res3a_branch2c.parameter.nKernels = 512
res3a_branch2c.parameter.kernelSizes = convolution2d.KernelSizes(1, 1)
res3a_branch2c.parameter.strides = convolution2d.Strides(1, 1)
res3a_branch2c.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res3a_branch2c.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res3a_branch2c_id = topology.add(res3a_branch2c)
bn3a_branch2c = batch_normalization.Batch(fptype=np.float32)
bn3a_branch2c_id = topology.add(bn3a_branch2c)
res3a = eltwise_sum.Batch(fptype=np.float32)
res3a_id = topology.add(res3a)
res4a_branch2c = convolution2d.Batch(fptype=np.float32)
res4a_branch2c.parameter.nKernels = 1024
res4a_branch2c.parameter.kernelSizes = convolution2d.KernelSizes(1, 1)
res4a_branch2c.parameter.strides = convolution2d.Strides(1, 1)
res4a_branch2c.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res4a_branch2c.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res4a_branch2c_id = topology.add(res4a_branch2c)
bn4a_branch2c = batch_normalization.Batch(fptype=np.float32)
bn4a_branch2c_id = topology.add(bn4a_branch2c)
res4a = eltwise_sum.Batch(fptype=np.float32)
res4a_id = topology.add(res4a)
res4a_relu = relu.Batch(fptype=np.float32)
res4a_relu_id = topology.add(res4a_relu)
res4a_relu_split9 = split.Batch(2, 2, fptype=np.float32)
res4a_relu_split9_id = topology.add(res4a_relu_split9)
res4b_branch2a = convolution2d.Batch(fptype=np.float32)
res4b_branch2a.parameter.nKernels = 256
res4b_branch2a.parameter.kernelSizes = convolution2d.KernelSizes(1, 1)
res4b_branch2a.parameter.strides = convolution2d.Strides(1, 1)
res4b_branch2a.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res4b_branch2a.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res4b_branch2a_id = topology.add(res4b_branch2a)
bn4b_branch2a = batch_normalization.Batch(fptype=np.float32)
bn4b_branch2a_id = topology.add(bn4b_branch2a)
bn3a_branch1 = batch_normalization.Batch(fptype=np.float32)
bn3a_branch1_id = topology.add(bn3a_branch1)
res3a_branch2a = convolution2d.Batch(fptype=np.float32)
res3a_branch2a.parameter.nKernels = 128
res3a_branch2a.parameter.kernelSizes = convolution2d.KernelSizes(1, 1)
res3a_branch2a.parameter.strides = convolution2d.Strides(2, 2)
res3a_branch2a.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res3a_branch2a.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res3a_branch2a_id = topology.add(res3a_branch2a)
bn3a_branch2a = batch_normalization.Batch(fptype=np.float32)
bn3a_branch2a_id = topology.add(bn3a_branch2a)
res3a_branch2a_relu = relu.Batch(fptype=np.float32)
res3a_branch2a_relu_id = topology.add(res3a_branch2a_relu)
res3a_branch2b = convolution2d.Batch(fptype=np.float32)
res3a_branch2b.parameter.nKernels = 128
res3a_branch2b.parameter.kernelSizes = convolution2d.KernelSizes(3, 3)
res3a_branch2b.parameter.strides = convolution2d.Strides(1, 1)
res3a_branch2b.parameter.paddings = convolution2d.Paddings(1, 1)
res3a_branch2b.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res3a_branch2b.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res3a_branch2b_id = topology.add(res3a_branch2b)
bn3a_branch2b = batch_normalization.Batch(fptype=np.float32)
bn3a_branch2b_id = topology.add(bn3a_branch2b)
res3a_branch2b_relu = relu.Batch(fptype=np.float32)
res3a_branch2b_relu_id = topology.add(res3a_branch2b_relu)
res2b_branch2a_relu = relu.Batch(fptype=np.float32)
res2b_branch2a_relu_id = topology.add(res2b_branch2a_relu)
res2b_branch2b = convolution2d.Batch(fptype=np.float32)
res2b_branch2b.parameter.nKernels = 64
res2b_branch2b.parameter.kernelSizes = convolution2d.KernelSizes(3, 3)
res2b_branch2b.parameter.strides = convolution2d.Strides(1, 1)
res2b_branch2b.parameter.paddings = convolution2d.Paddings(1, 1)
res2b_branch2b.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res2b_branch2b.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res2b_branch2b_id = topology.add(res2b_branch2b)
bn2b_branch2b = batch_normalization.Batch(fptype=np.float32)
bn2b_branch2b_id = topology.add(bn2b_branch2b)
res2b_branch2b_relu = relu.Batch(fptype=np.float32)
res2b_branch2b_relu_id = topology.add(res2b_branch2b_relu)
res2b_branch2c = convolution2d.Batch(fptype=np.float32)
res2b_branch2c.parameter.nKernels = 256
res2b_branch2c.parameter.kernelSizes = convolution2d.KernelSizes(1, 1)
res2b_branch2c.parameter.strides = convolution2d.Strides(1, 1)
res2b_branch2c.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res2b_branch2c.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res2b_branch2c_id = topology.add(res2b_branch2c)
bn2b_branch2c = batch_normalization.Batch(fptype=np.float32)
bn2b_branch2c_id = topology.add(bn2b_branch2c)
res2b = eltwise_sum.Batch(fptype=np.float32)
res2b_id = topology.add(res2b)
res3c_relu_split7 = split.Batch(2, 2, fptype=np.float32)
res3c_relu_split7_id = topology.add(res3c_relu_split7)
res3d_branch2a = convolution2d.Batch(fptype=np.float32)
res3d_branch2a.parameter.nKernels = 128
res3d_branch2a.parameter.kernelSizes = convolution2d.KernelSizes(1, 1)
res3d_branch2a.parameter.strides = convolution2d.Strides(1, 1)
res3d_branch2a.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res3d_branch2a.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res3d_branch2a_id = topology.add(res3d_branch2a)
bn3d_branch2a = batch_normalization.Batch(fptype=np.float32)
bn3d_branch2a_id = topology.add(bn3d_branch2a)
res3d_branch2a_relu = relu.Batch(fptype=np.float32)
res3d_branch2a_relu_id = topology.add(res3d_branch2a_relu)
res3d_branch2b = convolution2d.Batch(fptype=np.float32)
res3d_branch2b.parameter.nKernels = 128
res3d_branch2b.parameter.kernelSizes = convolution2d.KernelSizes(3, 3)
res3d_branch2b.parameter.strides = convolution2d.Strides(1, 1)
res3d_branch2b.parameter.paddings = convolution2d.Paddings(1, 1)
res3d_branch2b.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res3d_branch2b.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res3d_branch2b_id = topology.add(res3d_branch2b)
bn3d_branch2b = batch_normalization.Batch(fptype=np.float32)
bn3d_branch2b_id = topology.add(bn3d_branch2b)
res3d_branch2b_relu = relu.Batch(fptype=np.float32)
res3d_branch2b_relu_id = topology.add(res3d_branch2b_relu)
maxpooling1.parameter.kernelSizes = pooling2d.KernelSizes(3, 3)
maxpooling1.parameter.paddings = pooling2d.Paddings(0, 0)
maxpooling1.parameter.strides = pooling2d.Strides(2, 2)
# convolution: 5x5@256 + 1x1s
convolution2 = convolution2d.Batch()
convolution2.parameter.kernelSizes = convolution2d.KernelSizes(5, 5)
convolution2.parameter.strides = convolution2d.Strides(1, 1)
convolution2.parameter.paddings = convolution2d.Paddings(2, 2)
convolution2.parameter.nKernels = 256
convolution2.parameter.nGroups = 2
convolution2.parameter.weightsInitializer = gaussian.Batch(0, 0.01)
convolution2.parameter.biasesInitializer = uniform.Batch(0, 0)
# relu
relu2 = relu.Batch()
# lrn: alpha=0.0001, beta=0.75, local_size=5
lrn2 = lrn.Batch()
lrn2.parameter.kappa = 1
lrn2.parameter.nAdjust = 5
lrn2.parameter.alpha = 0.0001 / lrn2.parameter.nAdjust
lrn2.parameter.beta = 0.75
# pooling: 3x3 + 2x2s
maxpooling2 = maximum_pooling2d.Batch(4)
maxpooling2.parameter.kernelSizes = pooling2d.KernelSizes(3, 3)
maxpooling2.parameter.paddings = pooling2d.Paddings(0, 0)
maxpooling2.parameter.strides = pooling2d.Strides(2, 2)
# convolution: 3x3@384 + 2x2s
convolution3 = convolution2d.Batch()
res3c_branch2a_relu = relu.Batch(fptype=np.float32)
res3c_branch2a_relu_id = topology.add(res3c_branch2a_relu)
res3c_branch2b = convolution2d.Batch(fptype=np.float32)
res3c_branch2b.parameter.nKernels = 128
res3c_branch2b.parameter.kernelSizes = convolution2d.KernelSizes(3, 3)
res3c_branch2b.parameter.strides = convolution2d.Strides(1, 1)
res3c_branch2b.parameter.paddings = convolution2d.Paddings(1, 1)
res3c_branch2b.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res3c_branch2b.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res3c_branch2b_id = topology.add(res3c_branch2b)
bn3c_branch2b = batch_normalization.Batch(fptype=np.float32)
bn3c_branch2b_id = topology.add(bn3c_branch2b)
res3c_branch2b_relu = relu.Batch(fptype=np.float32)
res3c_branch2b_relu_id = topology.add(res3c_branch2b_relu)
res3c_branch2c = convolution2d.Batch(fptype=np.float32)
res3c_branch2c.parameter.nKernels = 512
res3c_branch2c.parameter.kernelSizes = convolution2d.KernelSizes(1, 1)
res3c_branch2c.parameter.strides = convolution2d.Strides(1, 1)
res3c_branch2c.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res3c_branch2c.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res3c_branch2c_id = topology.add(res3c_branch2c)
bn3c_branch2c = batch_normalization.Batch(fptype=np.float32)
bn3c_branch2c_id = topology.add(bn3c_branch2c)
res3c = eltwise_sum.Batch(fptype=np.float32)
res3c_id = topology.add(res3c)
res5b_branch2a_relu = relu.Batch(fptype=np.float32)
res5b_branch2a_relu_id = topology.add(res5b_branch2a_relu)
res5b_branch2b = convolution2d.Batch(fptype=np.float32)
res5b_branch2b.parameter.nKernels = 512
res5b_branch2b.parameter.kernelSizes = convolution2d.KernelSizes(3, 3)
res5b_branch2b.parameter.strides = convolution2d.Strides(1, 1)
res5b_branch2b.parameter.paddings = convolution2d.Paddings(1, 1)
res5b_branch2b.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res5b_branch2b.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res5b_branch2b_id = topology.add(res5b_branch2b)
bn5b_branch2b = batch_normalization.Batch(fptype=np.float32)
bn5b_branch2b_id = topology.add(bn5b_branch2b)
res5b_branch2b_relu = relu.Batch(fptype=np.float32)
res5b_branch2b_relu_id = topology.add(res5b_branch2b_relu)
res5b_branch2c = convolution2d.Batch(fptype=np.float32)
res5b_branch2c.parameter.nKernels = 2048
res5b_branch2c.parameter.kernelSizes = convolution2d.KernelSizes(1, 1)
res5b_branch2c.parameter.strides = convolution2d.Strides(1, 1)
res5b_branch2c.parameter.weightsInitializer = xavier.Batch(fptype=np.float32)
res5b_branch2c.parameter.biasesInitializer = uniform.Batch(0, 0, fptype=np.float32)
res5b_branch2c_id = topology.add(res5b_branch2c)
bn5b_branch2c = batch_normalization.Batch(fptype=np.float32)
bn5b_branch2c_id = topology.add(bn5b_branch2c)
res5b = eltwise_sum.Batch(fptype=np.float32)
res5b_id = topology.add(res5b)
def configureNet():
topology = training.Topology()
# convolution(conv1/7x7_s2): 7x7@64 + 2x2s
conv1_7x7_s2 = convolution2d.Batch()
conv1_7x7_s2.parameter.nKernels = 64
conv1_7x7_s2.parameter.kernelSizes = convolution2d.KernelSizes(7, 7)
conv1_7x7_s2.parameter.strides = convolution2d.Strides(2, 2)
conv1_7x7_s2.parameter.paddings = convolution2d.Paddings(3, 3)
conv1_7x7_s2.parameter.weightsInitializer = xavier.Batch()
conv1_7x7_s2.parameter.biasesInitializer = uniform.Batch(0.2, 0.2)
conv1_7x7_s2_id = topology.add(conv1_7x7_s2)
# relu(conv1/relu_7x7)
conv1_relu_7x7 = relu.Batch()
conv1_relu_7x7_id = topology.add(conv1_relu_7x7)
# pooling(pool1/3x3_s2): 3x3 + 2x2s
pool1_3x3_s2 = maximum_pooling2d.Batch(4)
pool1_3x3_s2.parameter.kernelSizes = pooling2d.KernelSizes(3, 3)
pool1_3x3_s2.parameter.strides = pooling2d.Strides(2, 2)
pool1_3x3_s2_id = topology.add(pool1_3x3_s2)
# lrn(pool1/norm1): alpha=0.0001, beta=0.75, local_size=5
pool1_norm1 = lrn.Batch()
pool1_norm1.parameter.kappa = 1
pool1_norm1.parameter.nAdjust = 5
pool1_norm1.parameter.beta = 0.75
pool1_norm1.parameter.alpha = 0.0001 / pool1_norm1.parameter.nAdjust
pool1_norm1_id = topology.add(pool1_norm1)