IMAGE_ORDERING="channels_last"# channel lastdefrelu6(x):returnK.relu(x,max_value=6)def_conv_block(inputs,filters,alpha,kernel=(3,3),strides=(1,1)):channel_axis=1ifIMAGE_ORDERING=='channels_first'else-1filters=int(filters*alpha)x=ZeroPadding2D(padding=(1,1),name='conv1_pad',data_...
x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4) x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5) f3 = x x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6) x = _depthwise_...
name='conv_pw_%d_bn'% block_id)(x)returnActivation(relu6, name='conv_pw_%d_relu'% block_id)(x)defget_mobilnet_eocoder(input_shape=(224,224,3),weights_path=""):# 必须是32 的倍数assertinput_shape[0] %32==0assertinput_shape[1] %32==0alpha =1.0depth_multiplier =1img_input =...
x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11) f4 = x x = _depthwise_conv_block(x, 1024, alpha, depth_m...
⼀、什么是语义分割 ⼆、Unet 1.基本原理 2.mini_unet 3. Mobilenet_unet 4.数据加载部分 参考 前⾔ 最近由于在寻找⽅向上迷失⾃我，准备了解更多的计算机视觉任务重的模型。看到语义分割任务重Unet⼀个有意思的模型，我准备来复现⼀下它。⼀、什么是语义分割 语义分割任务，如下图所⽰：简⽽...
u'DEPTH_MULTIPLIER': 1.0, u'ENABLE_DECODER': True, u'ENCODER_WITH_ASPP': True, u'OUTPUT_STRIDE': 16}, u'DEFAULT_EPSILON': 1e-05, u'DEFAULT_GROUP_NUMBER': 32, u'DEFAULT_NORM_TYPE': 'bn', u'FP16': False, 'ICNET': {u'DEPTH_MULTIPLIER': 0.5, u'LAYERS': 50}, u'MODEL_NA...
:param dim_mults: Number of channels multiplier for each layer of the Unet. E.g. a 128 channel, 64x64 image put into a U-Net with :code:`dim_mults=(1, 2, 4)` will be shape - (128, 64, 64) in the first layer of the U-Net - (256, 32, 32) in the sec...
λ represents the Lagrangian multiplier calculated by the quantization parameter (QP). After that, the best three modes with the least JHAD cost are selected for 64×64 and 32×32 CU sizes. Whereas, for CU with sizes 16×16 and 8×8, the best eight intra-modes with the least JHAD ...
class DepthwiseSeparableConv(nn.Module): def __init__(self, in_channels, output_channels, kernel_size, padding=0, kernels_per_layer=1): super(DepthwiseSeparableConv, self).__init__() # In Tensorflow DepthwiseConv2D has depth_multiplier instead of kernels_per_layer self.depthwise = nn.Conv...
, different_sigma_per_channel=True, p_per_sample=0.2, p_per_channel=0.5)) tr_transforms.append(BrightnessMultiplicativeTransform(multiplier_range=(0.75, 1.25), p_per_sample=0.15)) tr_transforms.append(ContrastAugmentationTransform(p_per_sample=0.15)) tr_transforms.append(SimulateLowResolution...