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CCDN: [Cocktail Causal Container]

This is a PyTorch implementation of Cocktail Causal Container proposed by our paper "End to End Video based Cocktail Causal Container for Blood Pressure Estimation and Glucose Prediction".

Blood Pressure Estimation and Blood Glucose Prediction

Main results on PPG-BP

Model acc_bp@1 acc_bg@1 Model
Cocktail Causal Container 75.0 91.7 soon

Main results on Clinical

Model overall rmse_bg acc_bp@1 acc_bg@1 Model
Cocktail Causal Container 0.766 89.0 89.0 soon

The code and model will be released soon.

Model summary is shown below (using pytorch-summary package):

    Layer (type)               Output Shape         Param #

================================================================
Conv2d-1 [-1, 32, 50, 50] 5,184
SiLU-2 [-1, 32, 50, 50] 0
BatchNormAct2d-3 [-1, 32, 50, 50] 64
ConvBnAct-4 [-1, 32, 50, 50] 0
Conv2d-5 [-1, 32, 50, 50] 288
Identity-6 [-1, 32, 50, 50] 0
BatchNormAct2d-7 [-1, 32, 50, 50] 64
ConvBnAct-8 [-1, 32, 50, 50] 0
ReLU6-9 [-1, 32, 50, 50] 0
Conv2d-10 [-1, 16, 50, 50] 512
Identity-11 [-1, 16, 50, 50] 0
BatchNormAct2d-12 [-1, 16, 50, 50] 32
ConvBnAct-13 [-1, 16, 50, 50] 0
LinearBottleneck-14 [-1, 16, 50, 50] 0
Conv2d-15 [-1, 96, 50, 50] 1,536
SiLU-16 [-1, 96, 50, 50] 0
BatchNormAct2d-17 [-1, 96, 50, 50] 192
ConvBnAct-18 [-1, 96, 50, 50] 0
Conv2d-19 [-1, 96, 25, 25] 864
Identity-20 [-1, 96, 25, 25] 0
BatchNormAct2d-21 [-1, 96, 25, 25] 192
ConvBnAct-22 [-1, 96, 25, 25] 0
ReLU6-23 [-1, 96, 25, 25] 0
Conv2d-24 [-1, 27, 25, 25] 2,592
Identity-25 [-1, 27, 25, 25] 0
BatchNormAct2d-26 [-1, 27, 25, 25] 54
ConvBnAct-27 [-1, 27, 25, 25] 0
LinearBottleneck-28 [-1, 27, 25, 25] 0
Conv2d-29 [-1, 162, 25, 25] 4,374
SiLU-30 [-1, 162, 25, 25] 0
BatchNormAct2d-31 [-1, 162, 25, 25] 324
ConvBnAct-32 [-1, 162, 25, 25] 0
Conv2d-33 [-1, 162, 25, 25] 1,458
Identity-34 [-1, 162, 25, 25] 0
BatchNormAct2d-35 [-1, 162, 25, 25] 324
ConvBnAct-36 [-1, 162, 25, 25] 0
ReLU6-37 [-1, 162, 25, 25] 0
Conv2d-38 [-1, 38, 25, 25] 6,156
Identity-39 [-1, 38, 25, 25] 0
BatchNormAct2d-40 [-1, 38, 25, 25] 76
ConvBnAct-41 [-1, 38, 25, 25] 0
LinearBottleneck-42 [-1, 38, 25, 25] 0
Conv2d-43 [-1, 228, 25, 25] 8,664
SiLU-44 [-1, 228, 25, 25] 0
BatchNormAct2d-45 [-1, 228, 25, 25] 456
ConvBnAct-46 [-1, 228, 25, 25] 0
Conv2d-47 [-1, 228, 13, 13] 2,052
Identity-48 [-1, 228, 13, 13] 0
BatchNormAct2d-49 [-1, 228, 13, 13] 456
ConvBnAct-50 [-1, 228, 13, 13] 0
Conv2d-51 [-1, 19, 1, 1] 4,351
BatchNorm2d-52 [-1, 19, 1, 1] 38
ReLU-53 [-1, 19, 1, 1] 0
Conv2d-54 [-1, 228, 1, 1] 4,560
Sigmoid-55 [-1, 228, 1, 1] 0
SEWithNorm-56 [-1, 228, 13, 13] 0
ReLU6-57 [-1, 228, 13, 13] 0
Conv2d-58 [-1, 50, 13, 13] 11,400
Identity-59 [-1, 50, 13, 13] 0
BatchNormAct2d-60 [-1, 50, 13, 13] 100
ConvBnAct-61 [-1, 50, 13, 13] 0
LinearBottleneck-62 [-1, 50, 13, 13] 0
Conv2d-63 [-1, 300, 13, 13] 15,000
SiLU-64 [-1, 300, 13, 13] 0
BatchNormAct2d-65 [-1, 300, 13, 13] 600
ConvBnAct-66 [-1, 300, 13, 13] 0
Conv2d-67 [-1, 300, 13, 13] 2,700
Identity-68 [-1, 300, 13, 13] 0
BatchNormAct2d-69 [-1, 300, 13, 13] 600
ConvBnAct-70 [-1, 300, 13, 13] 0
Conv2d-71 [-1, 25, 1, 1] 7,525
BatchNorm2d-72 [-1, 25, 1, 1] 50
ReLU-73 [-1, 25, 1, 1] 0
Conv2d-74 [-1, 300, 1, 1] 7,800
Sigmoid-75 [-1, 300, 1, 1] 0
SEWithNorm-76 [-1, 300, 13, 13] 0
ReLU6-77 [-1, 300, 13, 13] 0
Conv2d-78 [-1, 61, 13, 13] 18,300
Identity-79 [-1, 61, 13, 13] 0
BatchNormAct2d-80 [-1, 61, 13, 13] 122
ConvBnAct-81 [-1, 61, 13, 13] 0
LinearBottleneck-82 [-1, 61, 13, 13] 0
Conv2d-83 [-1, 366, 13, 13] 22,326
SiLU-84 [-1, 366, 13, 13] 0
BatchNormAct2d-85 [-1, 366, 13, 13] 732
ConvBnAct-86 [-1, 366, 13, 13] 0
Conv2d-87 [-1, 366, 7, 7] 3,294
Identity-88 [-1, 366, 7, 7] 0
BatchNormAct2d-89 [-1, 366, 7, 7] 732
ConvBnAct-90 [-1, 366, 7, 7] 0
Conv2d-91 [-1, 30, 1, 1] 11,010
BatchNorm2d-92 [-1, 30, 1, 1] 60
ReLU-93 [-1, 30, 1, 1] 0
Conv2d-94 [-1, 366, 1, 1] 11,346
Sigmoid-95 [-1, 366, 1, 1] 0
SEWithNorm-96 [-1, 366, 7, 7] 0
ReLU6-97 [-1, 366, 7, 7] 0
Conv2d-98 [-1, 72, 7, 7] 26,352
Identity-99 [-1, 72, 7, 7] 0
BatchNormAct2d-100 [-1, 72, 7, 7] 144
ConvBnAct-101 [-1, 72, 7, 7] 0
LinearBottleneck-102 [-1, 72, 7, 7] 0
Conv2d-103 [-1, 432, 7, 7] 31,104
SiLU-104 [-1, 432, 7, 7] 0
BatchNormAct2d-105 [-1, 432, 7, 7] 864
ConvBnAct-106 [-1, 432, 7, 7] 0
Conv2d-107 [-1, 432, 7, 7] 3,888
Identity-108 [-1, 432, 7, 7] 0
BatchNormAct2d-109 [-1, 432, 7, 7] 864
ConvBnAct-110 [-1, 432, 7, 7] 0
Conv2d-111 [-1, 36, 1, 1] 15,588
BatchNorm2d-112 [-1, 36, 1, 1] 72
ReLU-113 [-1, 36, 1, 1] 0
Conv2d-114 [-1, 432, 1, 1] 15,984
Sigmoid-115 [-1, 432, 1, 1] 0
SEWithNorm-116 [-1, 432, 7, 7] 0
ReLU6-117 [-1, 432, 7, 7] 0
Conv2d-118 [-1, 84, 7, 7] 36,288
Identity-119 [-1, 84, 7, 7] 0
BatchNormAct2d-120 [-1, 84, 7, 7] 168
ConvBnAct-121 [-1, 84, 7, 7] 0
LinearBottleneck-122 [-1, 84, 7, 7] 0
Conv2d-123 [-1, 504, 7, 7] 42,336
SiLU-124 [-1, 504, 7, 7] 0
BatchNormAct2d-125 [-1, 504, 7, 7] 1,008
ConvBnAct-126 [-1, 504, 7, 7] 0
Conv2d-127 [-1, 504, 7, 7] 4,536
Identity-128 [-1, 504, 7, 7] 0
BatchNormAct2d-129 [-1, 504, 7, 7] 1,008
ConvBnAct-130 [-1, 504, 7, 7] 0
Conv2d-131 [-1, 42, 1, 1] 21,210
BatchNorm2d-132 [-1, 42, 1, 1] 84
ReLU-133 [-1, 42, 1, 1] 0 Conv2d-134 [-1, 504, 1, 1] 21,672
Sigmoid-135 [-1, 504, 1, 1] 0
SEWithNorm-136 [-1, 504, 7, 7] 0
ReLU6-137 [-1, 504, 7, 7] 0
Conv2d-138 [-1, 95, 7, 7] 47,880
Identity-139 [-1, 95, 7, 7] 0
BatchNormAct2d-140 [-1, 95, 7, 7] 190
ConvBnAct-141 [-1, 95, 7, 7] 0
LinearBottleneck-142 [-1, 95, 7, 7] 0
Conv2d-143 [-1, 570, 7, 7] 54,150
SiLU-144 [-1, 570, 7, 7] 0
BatchNormAct2d-145 [-1, 570, 7, 7] 1,140
ConvBnAct-146 [-1, 570, 7, 7] 0
Conv2d-147 [-1, 570, 7, 7] 5,130
Identity-148 [-1, 570, 7, 7] 0
BatchNormAct2d-149 [-1, 570, 7, 7] 1,140
ConvBnAct-150 [-1, 570, 7, 7] 0
Conv2d-151 [-1, 47, 1, 1] 26,837
BatchNorm2d-152 [-1, 47, 1, 1] 94
ReLU-153 [-1, 47, 1, 1] 0
Conv2d-154 [-1, 570, 1, 1] 27,360
Sigmoid-155 [-1, 570, 1, 1] 0
SEWithNorm-156 [-1, 570, 7, 7] 0
ReLU6-157 [-1, 570, 7, 7] 0
Conv2d-158 [-1, 106, 7, 7] 60,420
Identity-159 [-1, 106, 7, 7] 0
BatchNormAct2d-160 [-1, 106, 7, 7] 212
ConvBnAct-161 [-1, 106, 7, 7] 0
LinearBottleneck-162 [-1, 106, 7, 7] 0
Conv2d-163 [-1, 636, 7, 7] 67,416
SiLU-164 [-1, 636, 7, 7] 0
BatchNormAct2d-165 [-1, 636, 7, 7] 1,272
ConvBnAct-166 [-1, 636, 7, 7] 0
Conv2d-167 [-1, 636, 7, 7] 5,724
Identity-168 [-1, 636, 7, 7] 0
BatchNormAct2d-169 [-1, 636, 7, 7] 1,272
ConvBnAct-170 [-1, 636, 7, 7] 0
Conv2d-171 [-1, 53, 1, 1] 33,761
BatchNorm2d-172 [-1, 53, 1, 1] 106
ReLU-173 [-1, 53, 1, 1] 0
Conv2d-174 [-1, 636, 1, 1] 34,344
Sigmoid-175 [-1, 636, 1, 1] 0
SEWithNorm-176 [-1, 636, 7, 7] 0
ReLU6-177 [-1, 636, 7, 7] 0
Conv2d-178 [-1, 117, 7, 7] 74,412
Identity-179 [-1, 117, 7, 7] 0
BatchNormAct2d-180 [-1, 117, 7, 7] 234
ConvBnAct-181 [-1, 117, 7, 7] 0
LinearBottleneck-182 [-1, 117, 7, 7] 0
Conv2d-183 [-1, 702, 7, 7] 82,134
SiLU-184 [-1, 702, 7, 7] 0
BatchNormAct2d-185 [-1, 702, 7, 7] 1,404
ConvBnAct-186 [-1, 702, 7, 7] 0
Conv2d-187 [-1, 702, 7, 7] 6,318
Identity-188 [-1, 702, 7, 7] 0
BatchNormAct2d-189 [-1, 702, 7, 7] 1,404
ConvBnAct-190 [-1, 702, 7, 7] 0
Conv2d-191 [-1, 58, 1, 1] 40,774
BatchNorm2d-192 [-1, 58, 1, 1] 116
ReLU-193 [-1, 58, 1, 1] 0
Conv2d-194 [-1, 702, 1, 1] 41,418
Sigmoid-195 [-1, 702, 1, 1] 0
SEWithNorm-196 [-1, 702, 7, 7] 0
ReLU6-197 [-1, 702, 7, 7] 0
Conv2d-198 [-1, 128, 7, 7] 89,856
Identity-199 [-1, 128, 7, 7] 0
BatchNormAct2d-200 [-1, 128, 7, 7] 256
...

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