From 9b0247321b1451a9b42274c7e7442aa49a14ee87 Mon Sep 17 00:00:00 2001 From: Daeyeol kim Date: Fri, 23 Jul 2021 10:29:28 +0900 Subject: [PATCH] add nets dir - nets dir consist of [layers,blocks, modules,+ model] --- nets/AppearanceModel.py | 86 +++++++ nets/Model.py | 0 nets/Models.py | 49 ++++ nets/MotionModel.py | 41 ++++ nets/attentionModel.py | 0 nets/blocks/AttentionBlocks.py | 21 ++ nets/blocks/attenBlocks.py | 0 nets/blocks/attentionBlocks.py | 22 ++ nets/blocks/attentionModel.py | 0 nets/blocks/blocks.py | 78 ++++++ nets/blocks/complexLayers.py | 372 +++++++++++++++++++++++++++++ nets/blocks/motionBlocks.py | 54 +++++ nets/funcs/complexFunctions.py | 163 +++++++++++++ nets/layers/complexLayers.py | 372 +++++++++++++++++++++++++++++ nets/modules/modules | 0 nets/modules/modules.py | 174 ++++++++++++++ unused/Facedetect.py | 233 ++++++++++++++++++ unused/bvpdataset.py | 24 ++ unused/face_detection_front.tflite | Bin 0 -> 423256 bytes unused/sample.py | 27 +++ utils/delta_postprocess.py | 32 +++ 21 files changed, 1748 insertions(+) create mode 100644 nets/AppearanceModel.py create mode 100644 nets/Model.py create mode 100644 nets/Models.py create mode 100644 nets/MotionModel.py create mode 100644 nets/attentionModel.py create mode 100644 nets/blocks/AttentionBlocks.py create mode 100644 nets/blocks/attenBlocks.py create mode 100644 nets/blocks/attentionBlocks.py create mode 100644 nets/blocks/attentionModel.py create mode 100644 nets/blocks/blocks.py create mode 100644 nets/blocks/complexLayers.py create mode 100644 nets/blocks/motionBlocks.py create mode 100644 nets/funcs/complexFunctions.py create mode 100644 nets/layers/complexLayers.py create mode 100644 nets/modules/modules create mode 100644 nets/modules/modules.py create mode 100644 unused/Facedetect.py create mode 100644 unused/bvpdataset.py create mode 100644 unused/face_detection_front.tflite create mode 100644 unused/sample.py create mode 100644 utils/delta_postprocess.py diff --git a/nets/AppearanceModel.py b/nets/AppearanceModel.py new file mode 100644 index 0000000..a3af70b --- /dev/null +++ b/nets/AppearanceModel.py @@ -0,0 +1,86 @@ +import torch +from torch.nn import Module +from modules.modules import DAModule +from blocks.AttentionBlocks import AttentionBlock +from blocks.blocks import EncoderBlock,DecoderBlock + + +class AppearanceModel_2D(Module): + def __init__(self, in_channels, out_channels, kernel_size): + # Appearance model + super().__init__() + self.a_conv1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, + stride=1, padding=1) + self.a_batch_Normalization1 = torch.nn.BatchNorm2d(out_channels) + self.a_conv2 = torch.nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, + padding=1) + self.a_batch_Normalization2 = torch.nn.BatchNorm2d(out_channels) + self.a_dropout1 = torch.nn.Dropout2d(p=0.50) + # Attention mask1 + self.attention_mask1 = AttentionBlock(out_channels) + self.a_avg1 = torch.nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + self.a_conv3 = torch.nn.Conv2d(in_channels=out_channels, out_channels=out_channels * 2, kernel_size=3, stride=1, + padding=1) + self.a_Batch_Normalization3 = torch.nn.BatchNorm2d(out_channels * 2) + self.a_conv4 = torch.nn.Conv2d(in_channels=out_channels * 2, out_channels=out_channels * 2, kernel_size=3, + stride=1, padding=1) + self.a_Batch_Normalization4 = torch.nn.BatchNorm2d(out_channels * 2) + self.a_dropout2 = torch.nn.Dropout2d(p=0.50) + # Attention mask2 + self.attention_mask2 = AttentionBlock(out_channels * 2) + + def forward(self, inputs): + # Convolution layer + A1 = torch.tanh(self.a_batch_Normalization1(self.a_conv1(inputs))) + A2 = torch.tanh(self.a_batch_Normalization2(self.a_conv2(A1))) + A3 = self.a_dropout1(A2) + # Calculate Mask1 + M1 = self.attention_mask1(A3) + # Pooling + A4 = self.a_avg1(A3) + # Convolution layer + A5 = torch.tanh(self.a_Batch_Normalization3(self.a_conv3(A4))) + A6 = torch.tanh(self.a_Batch_Normalization4(self.a_conv4(A5))) + A7 = self.a_dropout2(A6) + # Calculate Mask2 + M2 = self.attention_mask2(A7) + + return M1, M2 + + +class AppearanceModel_DA(torch.nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3): + # in_channels = 3 + # out_channels = 32 + super(AppearanceModel_DA, self).__init__() + self.encoder_1 = EncoderBlock(in_channel=in_channels, out_channel=out_channels) # conv3,conv3,maxpool2 + self.encoder_2 = EncoderBlock(in_channel=out_channels, out_channel=out_channels * 2) + self.encoder_3 = EncoderBlock(in_channel=out_channels * 2, out_channel=out_channels * 4) # conv3,conv3,maxpool2 + + self.decoder_1 = DecoderBlock(in_channel=out_channels * 2, out_channel=out_channels, scale_facotr=2) + self.decoder_2 = DecoderBlock(in_channel=out_channels * 4, out_channel=out_channels * 2, scale_facotr=2.25) + + self.damodul_1 = DAModule(out_channels) + self.damodul_2 = DAModule(out_channels * 2) + + self.conv1x1_1 = torch.nn.Conv2d(out_channels // 4, 1, kernel_size=1, stride=1, padding=0) + self.conv1x1_2 = torch.nn.Conv2d(out_channels // 2, 1, kernel_size=1, stride=1, padding=0) + + def forward(self, x): + out_en_1 = self.encoder_1(x) + out_en_2 = self.encoder_2(out_en_1) + out_en_3 = self.encoder_3(out_en_2) + + out_de_1 = self.decoder_1(out_en_2) + out_de_2 = self.decoder_2(out_en_3) + + out_concat_1 = out_de_1 + out_en_1 + out_concat_2 = out_de_2 + out_en_2 + + out_da_1 = self.damodul_1(out_concat_1) + M1 = self.conv1x1_1(out_da_1) + + out_da_2 = self.damodul_2(out_concat_2) + M2 = self.conv1x1_2(out_da_2) + + return M1, M2 diff --git a/nets/Model.py b/nets/Model.py new file mode 100644 index 0000000..e69de29 diff --git a/nets/Models.py b/nets/Models.py new file mode 100644 index 0000000..3cca1b5 --- /dev/null +++ b/nets/Models.py @@ -0,0 +1,49 @@ +import torch + +from AppearanceModel import AppearanceModel_2D +from MotionModel import motion_model + + +class Deepphys(torch.nn.Module): + def __init__(self): + super().__init__() + self.model = model + self.in_channels = 3 + self.out_channels = 32 + self.kernel_size = 3 + + self.appearance_model = AppearanceModel_2D(in_channels=self.in_channels, out_channels=self.out_channels * 4, + kernel_size=self.kernel_size) + + # mot: c-b-c-b-d-a-c-b-c-b-d-a + # app: c-b-c-b-d-at-a-c-b-c-b-d-at + + # """ + # self.appearance_model = DA_appearance_model(in_channels=self.in_channels, out_channels=self.out_channels, + # kernel_size=self.kernel_size) + self.motion_model = motion_model(in_channels=self.in_channels, out_channels=self.out_channels, + kernel_size=self.kernel_size, model=model) + + self.fully = fc() + # """ + # self.appearance_model = complexAttention(in_channels=self.in_channels, out_channels=self.out_channels, + # kernel_size=self.kernel_size) + # self.fully = Compelxfc() + + # def forward(self, appearance_input, motion_input): + def forward(self, appearance_input, motion_input): + """ + :param appearance_input: + :param motion_input: + :return: + original 2d model + """ + attention_mask1, attention_mask2 = self.appearance_model(appearance_input) + motion_output = self.motion_model(motion_input, attention_mask1, attention_mask2) + out = self.fully(motion_output) + + # X = torch.Tensor(X).cuda() + # attention_mask1, attention_mask2,out = self.appearance_model(X) + # out = self.fully(out) + + return out, attention_mask1, attention_mask2 diff --git a/nets/MotionModel.py b/nets/MotionModel.py new file mode 100644 index 0000000..12e7e07 --- /dev/null +++ b/nets/MotionModel.py @@ -0,0 +1,41 @@ +import torch +import torch.nn as nn +class motion_model(torch.nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, model): + super().__init__() + # Motion model + self.m_conv1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, + stride=1, padding=1) + self.m_batch_Normalization1 = torch.nn.BatchNorm2d(out_channels) + self.m_conv2 = torch.nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=kernel_size, + stride=1, padding=1) + self.m_batch_Normalization2 = torch.nn.BatchNorm2d(out_channels) + self.m_dropout1 = torch.nn.Dropout2d(p=0.50) + + self.m_avg1 = torch.nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + self.m_conv3 = torch.nn.Conv2d(in_channels=out_channels, out_channels=out_channels*2, kernel_size=kernel_size, stride=1, + padding=1) + self.m_batch_Normalization3 = torch.nn.BatchNorm2d(out_channels*2) + self.m_conv4 = torch.nn.Conv2d(in_channels=out_channels*2, out_channels=out_channels*2, kernel_size=kernel_size, stride=1, padding=1) + self.m_batch_Normalization4 = torch.nn.BatchNorm2d(out_channels*2) + self.m_dropout2 = torch.nn.Dropout2d(p=0.50) + self.m_avg2 = torch.nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + + def forward(self, inputs, mask1, mask2): + M1 = torch.tanh(self.m_batch_Normalization1(self.m_conv1(inputs))) + M2 = self.m_batch_Normalization2(self.m_conv2(M1)) + # element wise multiplication Mask1 + g1 = torch.tanh(torch.mul(1 * mask1, M2)) + M3 = self.m_dropout1(g1) + # pooling + M4 = self.m_avg1(M3) + # g1 = torch.tanh(torch.mul(1 * mask1, M4)) + M5 = torch.tanh(self.m_batch_Normalization3(self.m_conv3(M4))) + M6 = self.m_batch_Normalization4(self.m_conv4(M5)) + # element wise multiplication Mask2 + g2 = torch.tanh(torch.mul(1 * mask2, M6)) + M7 = self.m_dropout2(g2) + M8 = self.m_avg2(M7) + out = torch.tanh(M8) + + return out diff --git a/nets/attentionModel.py b/nets/attentionModel.py new file mode 100644 index 0000000..e69de29 diff --git a/nets/blocks/AttentionBlocks.py b/nets/blocks/AttentionBlocks.py new file mode 100644 index 0000000..43a6ef7 --- /dev/null +++ b/nets/blocks/AttentionBlocks.py @@ -0,0 +1,21 @@ +import torch +import torch.nn as nn + +from ..modules.modules import DAModule + + +class AttentionBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.attention = DAModule(in_channels) + self.conv1x1 = torch.nn.Conv2d(self.attention.inter_channels, 1, kernel_size=1, stride=1, padding=0) + + def forward(self, input): + mask = self.attention(input) + mask = torch.sigmoid(mask) + B, _, H, W = input.shape + norm = 2 * torch.norm(mask, p=1, dim=(1, 2, 3)) + norm = norm.reshape(B, 1, 1, 1) + mask = torch.div(mask * H * W, norm) + mask = self.conv1x1(mask) + return mask diff --git a/nets/blocks/attenBlocks.py b/nets/blocks/attenBlocks.py new file mode 100644 index 0000000..e69de29 diff --git a/nets/blocks/attentionBlocks.py b/nets/blocks/attentionBlocks.py new file mode 100644 index 0000000..c3446fa --- /dev/null +++ b/nets/blocks/attentionBlocks.py @@ -0,0 +1,22 @@ +import torch +import torch.nn as nn +from .. + + +class attention_block(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.attention = DAModule(in_channels) + self.conv1x1 = torch.nn.Conv2d(self.attention.inter_channels, 1, kernel_size=1, stride=1, padding=0) + + def forward(self, input): + mask = self.attention(input) + mask = torch.sigmoid(mask) + B, _, H, W = input.shape + norm = 2 * torch.norm(mask, p=1, dim=(1, 2, 3)) + norm = norm.reshape(B, 1, 1, 1) + mask = torch.div(mask * H * W, norm) + mask = self.conv1x1(mask) + return mask + + diff --git a/nets/blocks/attentionModel.py b/nets/blocks/attentionModel.py new file mode 100644 index 0000000..e69de29 diff --git a/nets/blocks/blocks.py b/nets/blocks/blocks.py new file mode 100644 index 0000000..5cd5eef --- /dev/null +++ b/nets/blocks/blocks.py @@ -0,0 +1,78 @@ +import torch + + +class EncoderBlock(torch.nn.Module): + def __init__(self, in_channel, out_channel): + super(EncoderBlock, self).__init__() + self.conv_eb = torch.nn.Sequential( + torch.nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=3, stride=1, padding=1), + torch.nn.BatchNorm2d(out_channel), + torch.nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3, stride=1, padding=1), + torch.nn.BatchNorm2d(out_channel), + torch.nn.MaxPool2d(kernel_size=2) + ) + + def forward(self, x): + out = self.conv_eb(x) + return out + + +class DecoderBlock(torch.nn.Module): + def __init__(self, in_channel, out_channel, scale_facotr): + super(DecoderBlock, self).__init__() + self.conv_db = torch.nn.Sequential( + torch.nn.Upsample(scale_factor=scale_facotr), + torch.nn.ConvTranspose2d(in_channels=in_channel, out_channels=out_channel, kernel_size=3, stride=1, + padding=1), + torch.nn.BatchNorm2d(out_channel), + torch.nn.ConvTranspose2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3, stride=1, + padding=1), + torch.nn.BatchNorm2d(out_channel) + ) + + def forward(self, x): + out = self.conv_db(x) + return out + + +class TSM(torch.nn.Module): + def __init__(self): + super().__init__() + + def __call__(self, input, n_frame=4, fold_div=3): + n_frame = 4 + B, C, H, W = input.shape + input = input.view(-1, n_frame, H, W, C) + fold = C // fold_div + last_fold = C - (fold_div - 1) * fold + out1, out2, out3 = torch.split(input, [fold, fold, last_fold], -1) + + padding1 = torch.zeros_like(out1) + padding1 = padding1[:, -1, :, :, :] + padding1 = torch.unsqueeze(padding1, 1) + _, out1 = torch.split(out1, [1, n_frame - 1], 1) + out1 = torch.cat((out1, padding1), 1) + + padding2 = torch.zeros_like(out2) + padding2 = padding2[:, 0, :, :, :] + padding2 = torch.unsqueeze(padding2, 1) + out2, _ = torch.split(out2, [n_frame - 1, 1], 1) + out2 = torch.cat((padding2, out2), 1) + + out = torch.cat((out1, out2, out3), -1) + out = out.view([-1, C, H, W]) + + return out + + +class TSM_Block(torch.nn.Module): + def __init__(self, in_channels, out_channels, kernel_size): + super().__init__() + self.tsm1 = TSM() + self.t_conv1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, + padding=1) + + def forward(self, input, n_frame=2, fold_div=3): + t = self.tsm1(input, n_frame, fold_div) + t = self.t_conv1(t) + return t diff --git a/nets/blocks/complexLayers.py b/nets/blocks/complexLayers.py new file mode 100644 index 0000000..83f7316 --- /dev/null +++ b/nets/blocks/complexLayers.py @@ -0,0 +1,372 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Mar 19 10:30:02 2019 +@author: Sebastien M. Popoff +Based on https://openreview.net/forum?id=H1T2hmZAb +""" + +import torch +from torch.nn import Module, Parameter, init +from torch.nn import Conv2d, Linear, BatchNorm1d, BatchNorm2d +from torch.nn import ConvTranspose2d +from nets.funcs.complexFunctions import complex_relu, complex_max_pool2d, complex_avg_pool2d, complex_sigmoid +from nets.funcs.complexFunctions import complex_dropout, complex_dropout2d, complex_adaptive_avg_pool2d, \ + complex_adaptive_max_pool2d + + +def apply_complex(fr, fi, input, dtype=torch.complex64): + return (fr(input.real) - fi(input.imag)).type(dtype) \ + + 1j * (fr(input.imag) + fi(input.real)).type(dtype) + + +class ComplexDropout(Module): + def __init__(self, p=0.5): + super(ComplexDropout, self).__init__() + self.p = p + + def forward(self, input): + if self.training: + return complex_dropout(input, self.p) + else: + return input + + +class ComplexDropout2d(Module): + def __init__(self, p=0.5): + super(ComplexDropout2d, self).__init__() + self.p = p + + def forward(self, input): + if self.training: + return complex_dropout2d(input, self.p) + else: + return input + + +class ComplexMaxPool2d(Module): + + def __init__(self, kernel_size, stride=None, padding=0, + dilation=1, return_indices=False, ceil_mode=False): + super(ComplexMaxPool2d, self).__init__() + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.ceil_mode = ceil_mode + self.return_indices = return_indices + + def forward(self, input): + return complex_max_pool2d(input, kernel_size=self.kernel_size, + stride=self.stride, padding=self.padding, + dilation=self.dilation, ceil_mode=self.ceil_mode, + return_indices=self.return_indices) + + +class ComplexAvgPool2d(Module): + + def __init__(self, kernel_size, stride=None, padding=0, + dilation=1, return_indices=False, ceil_mode=False): + super(ComplexAvgPool2d, self).__init__() + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.ceil_mode = ceil_mode + self.return_indices = return_indices + + def forward(self, input): + return complex_avg_pool2d(input, kernel_size=self.kernel_size, + stride=self.stride, padding=self.padding, + ceil_mode=self.ceil_mode + ) + + +class ComplexAdaptiveMaxPool2d(Module): + def __init__(self, output_size): + super(ComplexAdaptiveMaxPool2d, self).__init__() + self.output_size = output_size + + def forward(self, input): + return complex_adaptive_avg_pool2d(input, self.output_size) + + +class ComplexAdaptiveAvgPool2d(Module): + def __init__(self, output_size): + super(ComplexAdaptiveAvgPool2d, self).__init__() + self.output_size = output_size + + def forward(self, input): + return complex_adaptive_max_pool2d(input, self.output_size) + + +class ComplexReLU(Module): + + def forward(self, input): + return complex_relu(input) + + +class ComplexSigmoid(Module): + def forward(self, input): + return complex_sigmoid(input) + + +class ComplexConvTranspose2d(Module): + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, + output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros'): + super(ComplexConvTranspose2d, self).__init__() + + self.conv_tran_r = ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, + output_padding, groups, bias, dilation, padding_mode) + self.conv_tran_i = ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, + output_padding, groups, bias, dilation, padding_mode) + + def forward(self, input): + return apply_complex(self.conv_tran_r, self.conv_tran_i, input) + + +class ComplexConv2d(Module): + + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=0, + dilation=1, groups=1, bias=True): + super(ComplexConv2d, self).__init__() + self.conv_r = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias) + self.conv_i = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias) + + def forward(self, input): + return apply_complex(self.conv_r, self.conv_i, input) + + +class ComplexLinear(Module): + + def __init__(self, in_features, out_features): + super(ComplexLinear, self).__init__() + self.fc_r = Linear(in_features, out_features) + self.fc_i = Linear(in_features, out_features) + + def forward(self, input): + return apply_complex(self.fc_r, self.fc_i, input) + + +class NaiveComplexBatchNorm1d(Module): + ''' + Naive approach to complex batch norm, perform batch norm independently on real and imaginary part. + ''' + + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \ + track_running_stats=True): + super(NaiveComplexBatchNorm1d, self).__init__() + self.bn_r = BatchNorm1d(num_features, eps, momentum, affine, track_running_stats) + self.bn_i = BatchNorm1d(num_features, eps, momentum, affine, track_running_stats) + + def forward(self, input): + return self.bn_r(input.real).type(torch.complex64) + 1j * self.bn_i(input.imag).type(torch.complex64) + + +class NaiveComplexBatchNorm2d(Module): + ''' + Naive approach to complex batch norm, perform batch norm independently on real and imaginary part. + ''' + + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \ + track_running_stats=True): + super(NaiveComplexBatchNorm2d, self).__init__() + self.bn_r = BatchNorm2d(num_features, eps, momentum, affine, track_running_stats) + self.bn_i = BatchNorm2d(num_features, eps, momentum, affine, track_running_stats) + + def forward(self, input): + return self.bn_r(input.real).type(torch.complex64) + 1j * self.bn_i(input.imag).type(torch.complex64) + + +class _ComplexBatchNorm(Module): + + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, + track_running_stats=True): + super(_ComplexBatchNorm, self).__init__() + self.num_features = num_features + self.eps = eps + self.momentum = momentum + self.affine = affine + self.track_running_stats = track_running_stats + if self.affine: + self.weight = Parameter(torch.Tensor(num_features, 3)) + self.bias = Parameter(torch.Tensor(num_features, 2)) + else: + self.register_parameter('weight', None) + self.register_parameter('bias', None) + if self.track_running_stats: + self.register_buffer('running_mean', torch.zeros(num_features, dtype=torch.complex64)) + self.register_buffer('running_covar', torch.zeros(num_features, 3)) + self.running_covar[:, 0] = 1.4142135623730951 + self.running_covar[:, 1] = 1.4142135623730951 + self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long)) + else: + self.register_parameter('running_mean', None) + self.register_parameter('running_covar', None) + self.register_parameter('num_batches_tracked', None) + self.reset_parameters() + + def reset_running_stats(self): + if self.track_running_stats: + self.running_mean.zero_() + self.running_covar.zero_() + self.running_covar[:, 0] = 1.4142135623730951 + self.running_covar[:, 1] = 1.4142135623730951 + self.num_batches_tracked.zero_() + + def reset_parameters(self): + self.reset_running_stats() + if self.affine: + init.constant_(self.weight[:, :2], 1.4142135623730951) + init.zeros_(self.weight[:, 2]) + init.zeros_(self.bias) + + +class ComplexBatchNorm2d(_ComplexBatchNorm): + + def forward(self, input): + exponential_average_factor = 0.0 + + if self.training and self.track_running_stats: + if self.num_batches_tracked is not None: + self.num_batches_tracked += 1 + if self.momentum is None: # use cumulative moving average + exponential_average_factor = 1.0 / float(self.num_batches_tracked) + else: # use exponential moving average + exponential_average_factor = self.momentum + + if self.training or (not self.training and not self.track_running_stats): + # calculate mean of real and imaginary part + # mean does not support automatic differentiation for outputs with complex dtype. + mean_r = input.real.mean([0, 2, 3]).type(torch.complex64) + mean_i = input.imag.mean([0, 2, 3]).type(torch.complex64) + mean = mean_r + 1j * mean_i + else: + mean = self.running_mean + + if self.training and self.track_running_stats: + # update running mean + with torch.no_grad(): + self.running_mean = exponential_average_factor * mean \ + + (1 - exponential_average_factor) * self.running_mean + + input = input - mean[None, :, None, None] + + if self.training or (not self.training and not self.track_running_stats): + # Elements of the covariance matrix (biased for train) + n = input.numel() / input.size(1) + Crr = 1. / n * input.real.pow(2).sum(dim=[0, 2, 3]) + self.eps + Cii = 1. / n * input.imag.pow(2).sum(dim=[0, 2, 3]) + self.eps + Cri = (input.real.mul(input.imag)).mean(dim=[0, 2, 3]) + else: + Crr = self.running_covar[:, 0] + self.eps + Cii = self.running_covar[:, 1] + self.eps + Cri = self.running_covar[:, 2] # +self.eps + + if self.training and self.track_running_stats: + with torch.no_grad(): + self.running_covar[:, 0] = exponential_average_factor * Crr * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 0] + + self.running_covar[:, 1] = exponential_average_factor * Cii * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 1] + + self.running_covar[:, 2] = exponential_average_factor * Cri * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 2] + + # calculate the inverse square root the covariance matrix + det = Crr * Cii - Cri.pow(2) + s = torch.sqrt(det) + t = torch.sqrt(Cii + Crr + 2 * s) + inverse_st = 1.0 / (s * t) + Rrr = (Cii + s) * inverse_st + Rii = (Crr + s) * inverse_st + Rri = -Cri * inverse_st + + input = (Rrr[None, :, None, None] * input.real + Rri[None, :, None, None] * input.imag).type(torch.complex64) \ + + 1j * (Rii[None, :, None, None] * input.imag + Rri[None, :, None, None] * input.real).type( + torch.complex64) + + if self.affine: + input = (self.weight[None, :, 0, None, None] * input.real + self.weight[None, :, 2, None, + None] * input.imag + \ + self.bias[None, :, 0, None, None]).type(torch.complex64) \ + + 1j * (self.weight[None, :, 2, None, None] * input.real + self.weight[None, :, 1, None, + None] * input.imag + \ + self.bias[None, :, 1, None, None]).type(torch.complex64) + + return input + + +class ComplexBatchNorm1d(_ComplexBatchNorm): + + def forward(self, input): + + exponential_average_factor = 0.0 + + if self.training and self.track_running_stats: + if self.num_batches_tracked is not None: + self.num_batches_tracked += 1 + if self.momentum is None: # use cumulative moving average + exponential_average_factor = 1.0 / float(self.num_batches_tracked) + else: # use exponential moving average + exponential_average_factor = self.momentum + + if self.training or (not self.training and not self.track_running_stats): + # calculate mean of real and imaginary part + mean_r = input.real.mean(dim=0).type(torch.complex64) + mean_i = input.imag.mean(dim=0).type(torch.complex64) + mean = mean_r + 1j * mean_i + else: + mean = self.running_mean + + if self.training and self.track_running_stats: + # update running mean + with torch.no_grad(): + self.running_mean = exponential_average_factor * mean \ + + (1 - exponential_average_factor) * self.running_mean + + input = input - mean[None, :, None, None] + + if self.training or (not self.training and not self.track_running_stats): + # Elements of the covariance matrix (biased for train) + n = input.numel() / input.size(1) + Crr = input.real.var(dim=0, unbiased=False) + self.eps + Cii = input.imag.var(dim=0, unbiased=False) + self.eps + Cri = (input.real.mul(input.imag)).mean(dim=0) + else: + Crr = self.running_covar[:, 0] + self.eps + Cii = self.running_covar[:, 1] + self.eps + Cri = self.running_covar[:, 2] + + if self.training and self.track_running_stats: + self.running_covar[:, 0] = exponential_average_factor * Crr * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 0] + + self.running_covar[:, 1] = exponential_average_factor * Cii * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 1] + + self.running_covar[:, 2] = exponential_average_factor * Cri * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 2] + + # calculate the inverse square root the covariance matrix + det = Crr * Cii - Cri.pow(2) + s = torch.sqrt(det) + t = torch.sqrt(Cii + Crr + 2 * s) + inverse_st = 1.0 / (s * t) + Rrr = (Cii + s) * inverse_st + Rii = (Crr + s) * inverse_st + Rri = -Cri * inverse_st + + input = (Rrr[None, :] * input.real + Rri[None, :] * input.imag).type(torch.complex64) \ + + 1j * (Rii[None, :] * input.imag + Rri[None, :] * input.real).type(torch.complex64) + + if self.affine: + input = (self.weight[None, :, 0] * input.real + self.weight[None, :, 2] * input.imag + \ + self.bias[None, :, 0]).type(torch.complex64) \ + + 1j * (self.weight[None, :, 2] * input.real + self.weight[None, :, 1] * input.imag + \ + self.bias[None, :, 1]).type(torch.complex64) + + del Crr, Cri, Cii, Rrr, Rii, Rri, det, s, t + return input diff --git a/nets/blocks/motionBlocks.py b/nets/blocks/motionBlocks.py new file mode 100644 index 0000000..6f048f7 --- /dev/null +++ b/nets/blocks/motionBlocks.py @@ -0,0 +1,54 @@ +import torch +import torch.nn as nn +from blocks import TSM_Block + + +class MotionBlock_2D(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, model): + super().__init__() + self.model = model + + self.m_conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size, padding=1) + self.m_batch1 = torch.nn.BatchNorm2d(out_channels) + self.m_conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size, padding=1) + self.m_batch2 = torch.nn.BatchNorm2d(out_channels) + self.m_drop3 = torch.nn.Dropout2d(0.5) + self.m_avg3 = torch.nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + + def forward(self, inputs, mask): + m = self.m_tsm1(inputs) + m = torch.tanh(m) + m = self.m_tsm2(m) + + m = torch.mul(m, mask) + m = torch.tanh(m) + + m = self.m_drop3(m) + m = self.m_avg3(m) + return m + + +class MotionBlock_TS(torch.nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, model): + super().__init__() + self.model = model + + self.m_tsm1 = TSM_Block(in_channels, out_channels, kernel_size) + self.m_tsm2 = TSM_Block(out_channels, out_channels, kernel_size) + self.m_drop3 = torch.nn.Dropout2d(0.5) + self.m_avg3 = torch.nn.AvgPool2d(kernel_size=2, stride=2, padding=0) + + def forward(self, inputs, mask): + m = self.m_conv1(inputs) + m = self.m_batch1(m) + m = torch.tanh(m) + + m = self.m_conv2(m) + m = self.m_batch2(m) + + m = torch.mul(m, mask) + m = torch.tanh(m) + + m = self.m_drop3(m) + m = self.m_avg3(m) + return m diff --git a/nets/funcs/complexFunctions.py b/nets/funcs/complexFunctions.py new file mode 100644 index 0000000..cb07435 --- /dev/null +++ b/nets/funcs/complexFunctions.py @@ -0,0 +1,163 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +@author: spopoff +""" + +from torch.nn.functional import relu, max_pool2d, avg_pool2d, dropout, dropout2d, interpolate,adaptive_max_pool2d,\ + adaptive_avg_pool2d +from torch import sigmoid,tanh +import torch + + +def complex_matmul(A, B): + ''' + Performs the matrix product between two complex matricess + ''' + + outp_real = torch.matmul(A.real, B.real) - torch.matmul(A.imag, B.imag) + outp_imag = torch.matmul(A.real, B.imag) + torch.matmul(A.imag, B.real) + + return outp_real.type(torch.complex64) + 1j * outp_imag.type(torch.complex64) + + +def complex_avg_pool2d(input, *args, **kwargs): + ''' + Perform complex average pooling. + ''' + absolute_value_real = avg_pool2d(input.real, *args, **kwargs) + absolute_value_imag = avg_pool2d(input.imag, *args, **kwargs) + + return absolute_value_real.type(torch.complex64) + 1j * absolute_value_imag.type(torch.complex64) + + +def complex_normalize(input): + ''' + Perform complex normalization + ''' + real_value, imag_value = input.real, input.imag + real_norm = (real_value - real_value.mean()) / real_value.std() + imag_norm = (imag_value - imag_value.mean()) / imag_value.std() + + return real_norm.type(torch.complex64) + 1j * imag_norm.type(torch.complex64) + +def complex_mean(input): + real_value, imag_value = input.real, input.imag + real_mean = torch.mean(real_value, dim=1, keepdim=True) + imag_mean = torch.mean(imag_value, dim=1, keepdim=True) + return real_mean.type(torch.complex64) + 1j * imag_mean.type(torch.complex64) + +def complex_max(input): + real_value, imag_value = input.real, input.imag + real_mean, _ = torch.max(real_value, dim=1, keepdim=True) + imag_mean, _ = torch.max(imag_value, dim=1, keepdim=True) + return real_mean.type(torch.complex64) + 1j * imag_mean.type(torch.complex64) + +def complex_relu(input): + return relu(input.real).type(torch.complex64) + 1j * relu(input.imag).type(torch.complex64) +def complex_tanh(input): + return tanh(input.real).type(torch.complex64) + 1j * tanh(input.imag).type(torch.complex64) +def complex_sigmoid(input): + return sigmoid(input.real).type(torch.complex64) + 1j * sigmoid(input.imag).type(torch.complex64) + +def _retrieve_elements_from_indices(tensor, indices): + flattened_tensor = tensor.flatten(start_dim=-2) + output = flattened_tensor.gather(dim=-1, index=indices.flatten(start_dim=-2)).view_as(indices) + return output + + +def complex_upsample(input, size=None, scale_factor=None, mode='nearest', + align_corners=None, recompute_scale_factor=None): + ''' + Performs upsampling by separately interpolating the real and imaginary part and recombining + ''' + outp_real = interpolate(input.real, size=size, scale_factor=scale_factor, mode=mode, + align_corners=align_corners, recompute_scale_factor=recompute_scale_factor) + outp_imag = interpolate(input.imag, size=size, scale_factor=scale_factor, mode=mode, + align_corners=align_corners, recompute_scale_factor=recompute_scale_factor) + + return outp_real.type(torch.complex64) + 1j * outp_imag.type(torch.complex64) + + +def complex_upsample2(input, size=None, scale_factor=None, mode='nearest', + align_corners=None, recompute_scale_factor=None): + ''' + Performs upsampling by separately interpolating the amplitude and phase part and recombining + ''' + outp_abs = interpolate(input.abs(), size=size, scale_factor=scale_factor, mode=mode, + align_corners=align_corners, recompute_scale_factor=recompute_scale_factor) + angle = torch.atan2(input.imag, input.real) + outp_angle = interpolate(angle, size=size, scale_factor=scale_factor, mode=mode, + align_corners=align_corners, recompute_scale_factor=recompute_scale_factor) + + return outp_abs \ + * (torch.cos(angle).type(torch.complex64) + 1j * torch.sin(angle).type(torch.complex64)) + + +def complex_max_pool2d(input, kernel_size, stride=None, padding=0, + dilation=1, ceil_mode=False, return_indices=False): + ''' + Perform complex max pooling by selecting on the absolute value on the complex values. + ''' + absolute_value, indices = max_pool2d( + input.abs(), + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + ceil_mode=ceil_mode, + return_indices=True + ) + # performs the selection on the absolute values + absolute_value = absolute_value.type(torch.complex64) + # retrieve the corresonding phase value using the indices + # unfortunately, the derivative for 'angle' is not implemented + angle = torch.atan2(input.imag, input.real) + # get only the phase values selected by max pool + angle = _retrieve_elements_from_indices(angle, indices) + return absolute_value \ + * (torch.cos(angle).type(torch.complex64) + 1j * torch.sin(angle).type(torch.complex64)) +def complex_adaptive_max_pool2d(input, output_size): + ''' + Perform complex max pooling by selecting on the absolute value on the complex values. + ''' + absolute_value, indices = adaptive_max_pool2d( + input.abs(), + output_size, + return_indices=True + ) + # performs the selection on the absolute values + absolute_value = absolute_value.type(torch.complex64) + # retrieve the corresonding phase value using the indices + # unfortunately, the derivative for 'angle' is not implemented + angle = torch.atan2(input.imag, input.real) + # get only the phase values selected by max pool + angle = _retrieve_elements_from_indices(angle, indices) + return absolute_value \ + * (torch.cos(angle).type(torch.complex64) + 1j * torch.sin(angle).type(torch.complex64)) + + + +def complex_dropout(input, p=0.5, training=True): + # need to have the same dropout mask for real and imaginary part, + # this not a clean solution! + # mask = torch.ones_like(input).type(torch.float32) + mask = torch.ones(*input.shape, dtype=torch.float32).to('cuda:0') + mask = dropout(mask, p, training) * 1 / (1 - p) + mask.type(input.dtype) + return mask * input + + +def complex_dropout2d(input, p=0.5, training=True): + # need to have the same dropout mask for real and imaginary part, + # this not a clean solution! + mask = torch.ones(*input.shape, dtype=torch.float32) + mask = dropout2d(mask, p, training) * 1 / (1 - p) + mask.type(input.dtype) + return mask * input + +def complex_adaptive_avg_pool2d(input, *args, **kwargs): + out_value_real = adaptive_avg_pool2d(input.real, *args, **kwargs) + out_value_imag = adaptive_avg_pool2d(input.imag, *args, **kwargs) + return out_value_real.type(torch.complex64)+1j*out_value_imag.type(torch.complex64) diff --git a/nets/layers/complexLayers.py b/nets/layers/complexLayers.py new file mode 100644 index 0000000..83f7316 --- /dev/null +++ b/nets/layers/complexLayers.py @@ -0,0 +1,372 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Mar 19 10:30:02 2019 +@author: Sebastien M. Popoff +Based on https://openreview.net/forum?id=H1T2hmZAb +""" + +import torch +from torch.nn import Module, Parameter, init +from torch.nn import Conv2d, Linear, BatchNorm1d, BatchNorm2d +from torch.nn import ConvTranspose2d +from nets.funcs.complexFunctions import complex_relu, complex_max_pool2d, complex_avg_pool2d, complex_sigmoid +from nets.funcs.complexFunctions import complex_dropout, complex_dropout2d, complex_adaptive_avg_pool2d, \ + complex_adaptive_max_pool2d + + +def apply_complex(fr, fi, input, dtype=torch.complex64): + return (fr(input.real) - fi(input.imag)).type(dtype) \ + + 1j * (fr(input.imag) + fi(input.real)).type(dtype) + + +class ComplexDropout(Module): + def __init__(self, p=0.5): + super(ComplexDropout, self).__init__() + self.p = p + + def forward(self, input): + if self.training: + return complex_dropout(input, self.p) + else: + return input + + +class ComplexDropout2d(Module): + def __init__(self, p=0.5): + super(ComplexDropout2d, self).__init__() + self.p = p + + def forward(self, input): + if self.training: + return complex_dropout2d(input, self.p) + else: + return input + + +class ComplexMaxPool2d(Module): + + def __init__(self, kernel_size, stride=None, padding=0, + dilation=1, return_indices=False, ceil_mode=False): + super(ComplexMaxPool2d, self).__init__() + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.ceil_mode = ceil_mode + self.return_indices = return_indices + + def forward(self, input): + return complex_max_pool2d(input, kernel_size=self.kernel_size, + stride=self.stride, padding=self.padding, + dilation=self.dilation, ceil_mode=self.ceil_mode, + return_indices=self.return_indices) + + +class ComplexAvgPool2d(Module): + + def __init__(self, kernel_size, stride=None, padding=0, + dilation=1, return_indices=False, ceil_mode=False): + super(ComplexAvgPool2d, self).__init__() + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.ceil_mode = ceil_mode + self.return_indices = return_indices + + def forward(self, input): + return complex_avg_pool2d(input, kernel_size=self.kernel_size, + stride=self.stride, padding=self.padding, + ceil_mode=self.ceil_mode + ) + + +class ComplexAdaptiveMaxPool2d(Module): + def __init__(self, output_size): + super(ComplexAdaptiveMaxPool2d, self).__init__() + self.output_size = output_size + + def forward(self, input): + return complex_adaptive_avg_pool2d(input, self.output_size) + + +class ComplexAdaptiveAvgPool2d(Module): + def __init__(self, output_size): + super(ComplexAdaptiveAvgPool2d, self).__init__() + self.output_size = output_size + + def forward(self, input): + return complex_adaptive_max_pool2d(input, self.output_size) + + +class ComplexReLU(Module): + + def forward(self, input): + return complex_relu(input) + + +class ComplexSigmoid(Module): + def forward(self, input): + return complex_sigmoid(input) + + +class ComplexConvTranspose2d(Module): + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, + output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros'): + super(ComplexConvTranspose2d, self).__init__() + + self.conv_tran_r = ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, + output_padding, groups, bias, dilation, padding_mode) + self.conv_tran_i = ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, + output_padding, groups, bias, dilation, padding_mode) + + def forward(self, input): + return apply_complex(self.conv_tran_r, self.conv_tran_i, input) + + +class ComplexConv2d(Module): + + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=0, + dilation=1, groups=1, bias=True): + super(ComplexConv2d, self).__init__() + self.conv_r = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias) + self.conv_i = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias) + + def forward(self, input): + return apply_complex(self.conv_r, self.conv_i, input) + + +class ComplexLinear(Module): + + def __init__(self, in_features, out_features): + super(ComplexLinear, self).__init__() + self.fc_r = Linear(in_features, out_features) + self.fc_i = Linear(in_features, out_features) + + def forward(self, input): + return apply_complex(self.fc_r, self.fc_i, input) + + +class NaiveComplexBatchNorm1d(Module): + ''' + Naive approach to complex batch norm, perform batch norm independently on real and imaginary part. + ''' + + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \ + track_running_stats=True): + super(NaiveComplexBatchNorm1d, self).__init__() + self.bn_r = BatchNorm1d(num_features, eps, momentum, affine, track_running_stats) + self.bn_i = BatchNorm1d(num_features, eps, momentum, affine, track_running_stats) + + def forward(self, input): + return self.bn_r(input.real).type(torch.complex64) + 1j * self.bn_i(input.imag).type(torch.complex64) + + +class NaiveComplexBatchNorm2d(Module): + ''' + Naive approach to complex batch norm, perform batch norm independently on real and imaginary part. + ''' + + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \ + track_running_stats=True): + super(NaiveComplexBatchNorm2d, self).__init__() + self.bn_r = BatchNorm2d(num_features, eps, momentum, affine, track_running_stats) + self.bn_i = BatchNorm2d(num_features, eps, momentum, affine, track_running_stats) + + def forward(self, input): + return self.bn_r(input.real).type(torch.complex64) + 1j * self.bn_i(input.imag).type(torch.complex64) + + +class _ComplexBatchNorm(Module): + + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, + track_running_stats=True): + super(_ComplexBatchNorm, self).__init__() + self.num_features = num_features + self.eps = eps + self.momentum = momentum + self.affine = affine + self.track_running_stats = track_running_stats + if self.affine: + self.weight = Parameter(torch.Tensor(num_features, 3)) + self.bias = Parameter(torch.Tensor(num_features, 2)) + else: + self.register_parameter('weight', None) + self.register_parameter('bias', None) + if self.track_running_stats: + self.register_buffer('running_mean', torch.zeros(num_features, dtype=torch.complex64)) + self.register_buffer('running_covar', torch.zeros(num_features, 3)) + self.running_covar[:, 0] = 1.4142135623730951 + self.running_covar[:, 1] = 1.4142135623730951 + self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long)) + else: + self.register_parameter('running_mean', None) + self.register_parameter('running_covar', None) + self.register_parameter('num_batches_tracked', None) + self.reset_parameters() + + def reset_running_stats(self): + if self.track_running_stats: + self.running_mean.zero_() + self.running_covar.zero_() + self.running_covar[:, 0] = 1.4142135623730951 + self.running_covar[:, 1] = 1.4142135623730951 + self.num_batches_tracked.zero_() + + def reset_parameters(self): + self.reset_running_stats() + if self.affine: + init.constant_(self.weight[:, :2], 1.4142135623730951) + init.zeros_(self.weight[:, 2]) + init.zeros_(self.bias) + + +class ComplexBatchNorm2d(_ComplexBatchNorm): + + def forward(self, input): + exponential_average_factor = 0.0 + + if self.training and self.track_running_stats: + if self.num_batches_tracked is not None: + self.num_batches_tracked += 1 + if self.momentum is None: # use cumulative moving average + exponential_average_factor = 1.0 / float(self.num_batches_tracked) + else: # use exponential moving average + exponential_average_factor = self.momentum + + if self.training or (not self.training and not self.track_running_stats): + # calculate mean of real and imaginary part + # mean does not support automatic differentiation for outputs with complex dtype. + mean_r = input.real.mean([0, 2, 3]).type(torch.complex64) + mean_i = input.imag.mean([0, 2, 3]).type(torch.complex64) + mean = mean_r + 1j * mean_i + else: + mean = self.running_mean + + if self.training and self.track_running_stats: + # update running mean + with torch.no_grad(): + self.running_mean = exponential_average_factor * mean \ + + (1 - exponential_average_factor) * self.running_mean + + input = input - mean[None, :, None, None] + + if self.training or (not self.training and not self.track_running_stats): + # Elements of the covariance matrix (biased for train) + n = input.numel() / input.size(1) + Crr = 1. / n * input.real.pow(2).sum(dim=[0, 2, 3]) + self.eps + Cii = 1. / n * input.imag.pow(2).sum(dim=[0, 2, 3]) + self.eps + Cri = (input.real.mul(input.imag)).mean(dim=[0, 2, 3]) + else: + Crr = self.running_covar[:, 0] + self.eps + Cii = self.running_covar[:, 1] + self.eps + Cri = self.running_covar[:, 2] # +self.eps + + if self.training and self.track_running_stats: + with torch.no_grad(): + self.running_covar[:, 0] = exponential_average_factor * Crr * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 0] + + self.running_covar[:, 1] = exponential_average_factor * Cii * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 1] + + self.running_covar[:, 2] = exponential_average_factor * Cri * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 2] + + # calculate the inverse square root the covariance matrix + det = Crr * Cii - Cri.pow(2) + s = torch.sqrt(det) + t = torch.sqrt(Cii + Crr + 2 * s) + inverse_st = 1.0 / (s * t) + Rrr = (Cii + s) * inverse_st + Rii = (Crr + s) * inverse_st + Rri = -Cri * inverse_st + + input = (Rrr[None, :, None, None] * input.real + Rri[None, :, None, None] * input.imag).type(torch.complex64) \ + + 1j * (Rii[None, :, None, None] * input.imag + Rri[None, :, None, None] * input.real).type( + torch.complex64) + + if self.affine: + input = (self.weight[None, :, 0, None, None] * input.real + self.weight[None, :, 2, None, + None] * input.imag + \ + self.bias[None, :, 0, None, None]).type(torch.complex64) \ + + 1j * (self.weight[None, :, 2, None, None] * input.real + self.weight[None, :, 1, None, + None] * input.imag + \ + self.bias[None, :, 1, None, None]).type(torch.complex64) + + return input + + +class ComplexBatchNorm1d(_ComplexBatchNorm): + + def forward(self, input): + + exponential_average_factor = 0.0 + + if self.training and self.track_running_stats: + if self.num_batches_tracked is not None: + self.num_batches_tracked += 1 + if self.momentum is None: # use cumulative moving average + exponential_average_factor = 1.0 / float(self.num_batches_tracked) + else: # use exponential moving average + exponential_average_factor = self.momentum + + if self.training or (not self.training and not self.track_running_stats): + # calculate mean of real and imaginary part + mean_r = input.real.mean(dim=0).type(torch.complex64) + mean_i = input.imag.mean(dim=0).type(torch.complex64) + mean = mean_r + 1j * mean_i + else: + mean = self.running_mean + + if self.training and self.track_running_stats: + # update running mean + with torch.no_grad(): + self.running_mean = exponential_average_factor * mean \ + + (1 - exponential_average_factor) * self.running_mean + + input = input - mean[None, :, None, None] + + if self.training or (not self.training and not self.track_running_stats): + # Elements of the covariance matrix (biased for train) + n = input.numel() / input.size(1) + Crr = input.real.var(dim=0, unbiased=False) + self.eps + Cii = input.imag.var(dim=0, unbiased=False) + self.eps + Cri = (input.real.mul(input.imag)).mean(dim=0) + else: + Crr = self.running_covar[:, 0] + self.eps + Cii = self.running_covar[:, 1] + self.eps + Cri = self.running_covar[:, 2] + + if self.training and self.track_running_stats: + self.running_covar[:, 0] = exponential_average_factor * Crr * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 0] + + self.running_covar[:, 1] = exponential_average_factor * Cii * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 1] + + self.running_covar[:, 2] = exponential_average_factor * Cri * n / (n - 1) \ + + (1 - exponential_average_factor) * self.running_covar[:, 2] + + # calculate the inverse square root the covariance matrix + det = Crr * Cii - Cri.pow(2) + s = torch.sqrt(det) + t = torch.sqrt(Cii + Crr + 2 * s) + inverse_st = 1.0 / (s * t) + Rrr = (Cii + s) * inverse_st + Rii = (Crr + s) * inverse_st + Rri = -Cri * inverse_st + + input = (Rrr[None, :] * input.real + Rri[None, :] * input.imag).type(torch.complex64) \ + + 1j * (Rii[None, :] * input.imag + Rri[None, :] * input.real).type(torch.complex64) + + if self.affine: + input = (self.weight[None, :, 0] * input.real + self.weight[None, :, 2] * input.imag + \ + self.bias[None, :, 0]).type(torch.complex64) \ + + 1j * (self.weight[None, :, 2] * input.real + self.weight[None, :, 1] * input.imag + \ + self.bias[None, :, 1]).type(torch.complex64) + + del Crr, Cri, Cii, Rrr, Rii, Rri, det, s, t + return input diff --git a/nets/modules/modules b/nets/modules/modules new file mode 100644 index 0000000..e69de29 diff --git a/nets/modules/modules.py b/nets/modules/modules.py new file mode 100644 index 0000000..e3e7ffa --- /dev/null +++ b/nets/modules/modules.py @@ -0,0 +1,174 @@ +import torch +from ..funcs.complexFunctions import complex_matmul + +class DAModule(torch.nn.Module): + def __init__(self, in_channels): + super(DAModule, self).__init__() + self.inter_channels = in_channels // 4 + self.conv_p1 = torch.nn.Sequential( + torch.nn.Conv2d(in_channels, self.inter_channels, 3, padding=1, bias=False), + torch.nn.BatchNorm2d(self.inter_channels), + torch.nn.ReLU(True) + ) + self.conv_c1 = torch.nn.Sequential( + torch.nn.Conv2d(in_channels, self.inter_channels, 3, padding=1, bias=False), + torch.nn.BatchNorm2d(self.inter_channels), + torch.nn.ReLU(True) + ) + self.pam = _PositionAttentionModule(self.inter_channels) + self.cam = _ChannelAttentionModule() + self.conv_p2 = torch.nn.Sequential( + torch.nn.Conv2d(self.inter_channels, self.inter_channels, 3, padding=1, bias=False), + torch.nn.BatchNorm2d(self.inter_channels), + torch.nn.ReLU(True) + ) + self.conv_c2 = torch.nn.Sequential( + torch.nn.Conv2d(self.inter_channels, self.inter_channels, 3, padding=1, bias=False), + torch.nn.BatchNorm2d(self.inter_channels), + torch.nn.ReLU(True) + ) + + def forward(self, x): + feat_p = self.conv_p1(x) + feat_p = self.pam(feat_p) + feat_p = self.conv_p2(feat_p) + + feat_c = self.conv_c1(x) + feat_c = self.cam(feat_c) + feat_c = self.conv_c2(feat_c) + + feat_fusion = feat_p + feat_c + return feat_fusion + + +class _PositionAttentionModule(torch.nn.Module): + """ Position attention module""" + + def __init__(self, in_channels): + super(_PositionAttentionModule, self).__init__() + self.conv_b = torch.nn.Conv2d(in_channels, in_channels // 8, 1) + self.conv_c = torch.nn.Conv2d(in_channels, in_channels // 8, 1) + self.conv_d = torch.nn.Conv2d(in_channels, in_channels, 1) + self.alpha = torch.nn.Parameter(torch.zeros(1)) + self.softmax = torch.nn.Softmax(dim=-1) + + def forward(self, x): + batch_size, _, height, width = x.size() + feat_b = self.conv_b(x).view(batch_size, -1, height * width).permute(0, 2, 1) + feat_c = self.conv_c(x).view(batch_size, -1, height * width) + attention_s = self.softmax(torch.bmm(feat_b, feat_c)) + feat_d = self.conv_d(x).view(batch_size, -1, height * width) + feat_e = torch.bmm(feat_d, attention_s.permute(0, 2, 1)).view(batch_size, -1, height, width) + out = self.alpha * feat_e + x + + return out + + +class _ChannelAttentionModule(torch.nn.Module): + """Channel attention module""" + + def __init__(self): + super(_ChannelAttentionModule, self).__init__() + self.beta = torch.nn.Parameter(torch.zeros(1)) + self.softmax = torch.nn.Softmax(dim=-1) + + def forward(self, x): + batch_size, _, height, width = x.size() + feat_a = x.view(batch_size, -1, height * width) + feat_a_transpose = x.view(batch_size, -1, height * width).permute(0, 2, 1) + attention = torch.bmm(feat_a, feat_a_transpose) + attention_new = torch.max(attention, dim=-1, keepdim=True)[0].expand_as(attention) - attention + attention = self.softmax(attention_new) + + feat_e = torch.bmm(attention, feat_a).view(batch_size, -1, height, width) + out = self.beta * feat_e + x + + return out + + +class ChannelAttentionModule(torch.nn.Module): + def __init__(self, channel, ratio=16): + super(ChannelAttentionModule, self).__init__() + self.avg_pool = torch.nn.AdaptiveAvgPool2d(1) + self.max_pool = torch.nn.AdaptiveMaxPool2d(1) + + self.shared_MLP = torch.nn.Sequential( + torch.nn.Conv2d(channel, channel // ratio, 1, bias=False), + torch.nn.ReLU(), + torch.nn.Conv2d(channel // ratio, channel, 1, bias=False) + ) + self.sigmoid = torch.nn.Sigmoid() + + def forward(self, x): + avgout = self.shared_MLP(self.avg_pool(x)) + # print(avgout.shape) + maxout = self.shared_MLP(self.max_pool(x)) + return self.sigmoid(avgout + maxout) + + +class SpatialAttentionModule(torch.nn.Module): + def __init__(self): + super(SpatialAttentionModule, self).__init__() + self.conv2d = torch.nn.Conv2d(in_channels=2, out_channels=1, kernel_size=7, stride=1, padding=3) + self.sigmoid = torch.nn.Sigmoid() + + def forward(self, x): + avgout = torch.mean(x, dim=1, keepdim=True) + maxout, _ = torch.max(x, dim=1, keepdim=True) + out = torch.cat([avgout, maxout], dim=1) + out = self.sigmoid(self.conv2d(out)) + return out + + +class CBAM(torch.nn.Module): + def __init__(self, channel): + super(CBAM, self).__init__() + self.channel_attention = ChannelAttentionModule(channel) + self.spatial_attention = SpatialAttentionModule() + + def forward(self, x): + out = complex_matmul(self.channel_attention(x), x) + # print('outchannels:{}'.format(out.shape)) + out = complex_matmul(self.spatial_attention(out), out) + return out + + +class ResBlock_CBAM(torch.nn.Module): + def __init__(self, in_places, places, stride=1, downsampling=False, expansion=4): + super(ResBlock_CBAM, self).__init__() + self.expansion = expansion + self.downsampling = downsampling + + self.bottleneck = torch.nn.Sequential( + torch.nn.Conv2d(in_channels=in_places, out_channels=places, kernel_size=1, stride=1, bias=False), + torch.nn.BatchNorm2d(places), + torch.nn.ReLU(inplace=True), + torch.nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, + bias=False), + torch.nn.BatchNorm2d(places), + torch.nn.ReLU(inplace=True), + torch.nn.Conv2d(in_channels=places, out_channels=places * self.expansion, kernel_size=1, stride=1, + bias=False), + torch.nn.BatchNorm2d(places * self.expansion), + ) + self.cbam = CBAM(channel=places * self.expansion) + + if self.downsampling: + self.downsample = torch.nn.Sequential( + torch.nn.Conv2d(in_channels=in_places, out_channels=places * self.expansion, kernel_size=1, + stride=stride, bias=False), + torch.nn.BatchNorm2d(places * self.expansion) + ) + self.relu = torch.nn.ReLU(inplace=True) + + def forward(self, x): + residual = x + out = self.bottleneck(x) + # print(x.shape) + out = self.cbam(out) + if self.downsampling: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + return out diff --git a/unused/Facedetect.py b/unused/Facedetect.py new file mode 100644 index 0000000..fa3f738 --- /dev/null +++ b/unused/Facedetect.py @@ -0,0 +1,233 @@ +import tensorflow as tf +import numpy as np +import math +class Facedetect: + def __init__(self): + #blazenet input + self.INPUT_H = 128 + self.INPUT_W = 128 + #blazenet hyper param + self.NUM_BOXES = 896 + self.NUM_COORDS = 16 + self.BYTE_SIZE_OF_FLOAT = 4 + self.strides = [8,16,16,16] + self.ASPECT_RATIOS_SIZE = 1 + self.MIN_SCALE = 0.3 + self.MAX_SCALE = 0.8 + self.ANCHOR_OFFSET_X = 0.5 + self.ANCHOR_OFFSET_Y = 0.5 + self.X_SCALE = 128 + self.Y_SCALE = 128 + self.W_SCALE = 128 + self.H_SCALE = 128 + self.MIN_SUPPRESSION_THRESHOLD = 0.3 + + # Load TFLite model and allocate tensors. + self.interpreter = tf.lite.Interpreter(model_path="face_detection_front.tflite") + self.interpreter.allocate_tensors() + self.interpreter.allocate_tensors() + + # Get input and output tensors. + self.input_details = self.interpreter.get_input_details() + self.output_details = self.interpreter.get_output_details() + self.anchors = self.generateAnchors() + self.floating_model = False + if self.input_details[0]['dtype'] == np.float32: + self.floating_model = True + + + def calscale(self, min_scale, max_scale, stride_index, num_strides): + return min_scale + (max_scale - min_scale) * stride_index / (num_strides - 1) + + def intersectionArea(self, rect1, rect2): + x1 = max(min(rect1[0], rect1[2]), min(rect2[0], rect2[2])) + y1 = max(min(rect1[1], rect1[3]), min(rect2[1], rect2[3])) + x2 = min(max(rect1[0], rect1[2]), max(rect2[0], rect2[2])) + y2 = min(max(rect1[1], rect1[3]), max(rect2[1], rect2[3])) + + return (x2 - x1) * (y2 - y1) + + def overlapSimilarity(self, rect1, rect2): + area = self.intersectionArea(rect1, rect2) + if area <= 0: + return 0.0 + norm = (rect1[3] - rect1[1]) * (rect1[2] - rect1[0]) + (rect2[3] - rect2[1]) * (rect2[2] - rect2[0]) - area + + if norm > 0.0: + return norm / area + else: + return 0.0 + + def generateAnchors(self): + anchors = [] + layer_id = 0 + + while layer_id < len(self.strides): + anchor_height = [] + anchor_width = [] + aspect_ratios = [] + scales = [] + + last_same_stride_layer = layer_id + while (last_same_stride_layer < len(self.strides) and self.strides[last_same_stride_layer] == self.strides[layer_id]): + scale = self.calscale(self.MIN_SCALE, self.MAX_SCALE, last_same_stride_layer, len(self.strides)) + for aspect_ratio_id in range(self.ASPECT_RATIOS_SIZE): + aspect_ratios.append(1.0) + scales.append(scale) + if len(self.strides) - 1 == last_same_stride_layer: + scale_next = 1.0 + else: + scale_next = self.calscale(self.MIN_SCALE, self.MAX_SCALE, last_same_stride_layer + 1, len(self.strides)) + scales.append(math.sqrt(scale * scale_next)) + aspect_ratios.append(1.0) + last_same_stride_layer = last_same_stride_layer + 1 + + for i in range(len(aspect_ratios)): + ratio_sqrts = math.sqrt(aspect_ratios[i]) + anchor_height.append(scales[i] / ratio_sqrts) + anchor_width.append(scales[i] * ratio_sqrts) + + stride = self.strides[layer_id]; + feature_map_height = math.ceil(1.0 * self.INPUT_H / stride); + feature_map_width = math.ceil(1.0 * self.INPUT_W / stride); + + for y in range(feature_map_height): + for x in range(feature_map_width): + for anchor_id in range(len(anchor_height)): + tmp = [] + x_center = (x + self.ANCHOR_OFFSET_X) * 1.0 / feature_map_width + y_center = (y + self.ANCHOR_OFFSET_Y) * 1.0 / feature_map_height + + tmp.append(x_center) + tmp.append(y_center) + tmp.append(1.0) + tmp.append(1.0) + + anchors.append(tmp) + layer_id = last_same_stride_layer + return anchors + + def weightedNonMaxSuppression(self, indexed_scores, detections): + + remained_indexed_scores = indexed_scores + output_locations = [] + + while len(remained_indexed_scores): + detection = detections[remained_indexed_scores[0][0]] + + if len(detection) < 5: + break + + if detection[4] < -1: + break + remained = [] + candidates = [] + location = detection#detection[0:4] + del location[4] + + + for i in range(len(indexed_scores)): + rest_location = detections[indexed_scores[i][0]][0:4] + sim = self.overlapSimilarity(rest_location, location) + if sim > 0.3: + candidates.append(indexed_scores[i]) + else: + remained.append(indexed_scores[i]) + + weighted_location = location + if len(candidates) == 0: + w_xmin = 0.0 + w_ymin = 0.0 + w_xmax = 0.0 + w_ymax = 0.0 + total_score = 0.0 + for i in range(len(candidates)): + total_score += candidates[i][1] + bbox = detections[candidates[i][0]][0:4] + + w_xmin += bbox[0] * candidates[i][1] + w_ymin += bbox[1] * candidates[i][1] + w_xmax += bbox[2] * candidates[i][1] + w_ymax += bbox[3] * candidates[i][1] + + weighted_location[0] = w_xmin / total_score * self.INPUT_W + weighted_location[1] = w_ymin / total_score * self.INPUT_H + weighted_location[2] = w_xmax / total_score * self.INPUT_W + weighted_location[3] = w_ymax / total_score * self.INPUT_H + + remained_indexed_scores = remained + + output_locations.append(weighted_location) + return output_locations + + def detect(self, img): + input_data = np.expand_dims(img,axis=0) + if self.floating_model: + input_data = np.float32(input_data)/127.5-1 + self.interpreter.set_tensor(self.input_details[0]['index'],input_data) + self.interpreter.invoke() + detected_boxes = self.interpreter.get_tensor(self.output_details[0]['index']) + detected_scores = self.interpreter.get_tensor(self.output_details[1]['index']) + + detections = [] + for i in range(self.NUM_BOXES): + score = detected_scores[0][i][0] + if score < -100: + score = -100 + if score > 100: + score = 100 + score = 1/(1+math.exp(-1*score)) + if score <= 0.4: + continue + + width_test = 1200; + height_test = 800; + x_center = detected_boxes[0][i][0] / self.X_SCALE * self.anchors[i][2] + self.anchors[i][0] + x_center = x_center * width_test + y_center = detected_boxes[0][i][1] / self.Y_SCALE * self.anchors[i][3] + self.anchors[i][1] + y_center = y_center * height_test + w = detected_boxes[0][i][2] / self.X_SCALE * self.anchors[i][2] + w = w * width_test + h = detected_boxes[0][i][3] / self.Y_SCALE * self.anchors[i][3] + h = h * height_test + # 추가 적인것 6개의 좌표임 /keypoint 6개 + box = [] + + ymin = int(round(y_center - h / 2)) + xmin = int(round(x_center - w / 2)) + ymax = int(round(y_center + h / 2)) + xmax = int(round(x_center + w / 2)) + + box.append(xmin) + box.append(ymin) + box.append(xmax) + box.append(ymax) + box.append(score) + + for j in range(4, 16, 2): + pos_x = detected_boxes[0][i][j] / self.X_SCALE * self.anchors[i][2] + self.anchors[i][0] + pos_x = pos_x * width_test + pos_y = detected_boxes[0][i][j+1] / self.Y_SCALE * self.anchors[i][3] + self.anchors[i][1] + pos_y = pos_y * height_test + box.append(pos_x) + box.append(pos_y) + + detections.append(box) + + #print(detections) + + index_list = [] + for i in range(len(detections)): + index = [] + index.append(i) + index.append(detections[i][4]) + index_list.append(index) + + index_list.sort(key=lambda x: x[1]) + index_list.reverse() + + out_result = self.weightedNonMaxSuppression(index_list, detections) + + # print(out_result) + + return out_result \ No newline at end of file diff --git a/unused/bvpdataset.py b/unused/bvpdataset.py new file mode 100644 index 0000000..40229a2 --- /dev/null +++ b/unused/bvpdataset.py @@ -0,0 +1,24 @@ +import torch +from torch.utils.data import Dataset +import torchvision.transforms as transforms +import numpy as np + +class bvpdataset(Dataset): + def __init__(self, A, M, T): + self.transform = transforms.Compose([transforms.ToTensor()]) + self.a = A + self.m = M + self.label = T.reshape(-1, 1) + + def __getitem__(self, index): + if torch.is_tensor(index): + index = index.tolist() + + appearance_img = torch.tensor(np.transpose(self.a[index], (2, 0, 1)), dtype=torch.float32) + mot_img = torch.tensor(np.transpose(self.m[index], (2, 0, 1)), dtype=torch.float32) + label = torch.tensor(self.label[index], dtype=torch.float32) + + return appearance_img, mot_img, label + + def __len__(self): + return len(self.label) diff --git a/unused/face_detection_front.tflite b/unused/face_detection_front.tflite new file mode 100644 index 0000000000000000000000000000000000000000..419e1a8a1009db640c4fb84bb83cc3440b07c04e GIT binary patch literal 423256 zcmYg$2{cvT7ymO438l;LCKVg#d}Kdd_=hGAgZ?)$uEfWvd@e2lyk&+!CB(G zZA#+2SHs15ULs9h{v*b_utkigw^>Zoz8H_^EynwNi^uc1$>YUe=ka!nuJ zaUL%_NhJTLc#)FViO4^FJ6-+$_aB`v!oPKKpXcuQZ>9dNm#nD#-@3?%_+48zZrK{N zg=Z+@{Z_5m4OyKUg7_1KT zi}-)kUWBgy$NPx*uOj{*4Hn`5*VVuBKm6-IQiQc4{MU!Jh?f-Ml;~a&8U2qJspS8T zn?B5)_|0f&qpX^_o3jcS$BIf^&?jPkT|5yL7 zV0Z0*YaT4R*mi+DoXt1H!0cB0Zz+%Mals5>QD3ppbCDfuka1eb4lYH%GiRvpXE!`@ 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matplotlib.pyplot as plt + +from scipy.signal import chirp +from scipy.fftpack import hilbert,ihilbert + +duration = 1.0 +fs = 400.0 +samples = int(fs*duration) +t = np.arange(samples) / fs + +signal = chirp(t, 20.0, t[-1], 100.0) +signal *= (1.0 + 0.5 * np.sin(2.0*np.pi*3.0*t) ) + +analytic_signal = hilbert(signal) +ihil = ihilbert(analytic_signal) +amplitude_envelope = np.abs(analytic_signal) +instantaneous_phase = np.unwrap(np.angle(analytic_signal)) +instantaneous_frequency = (np.diff(instantaneous_phase) / + (2.0*np.pi) * fs) + +print("plot") +plt.rcParams["figure.figsize"] = (14, 5) +plt.plot(range(len(signal[0:300])), signal[0:300], label='inference') +plt.plot(range(len(ihil[0:300])), ihil[0:300], label='target') +plt.legend(fontsize='x-large') +plt.show() \ No newline at end of file diff --git a/utils/delta_postprocess.py b/utils/delta_postprocess.py new file mode 100644 index 0000000..51e6bb5 --- /dev/null +++ b/utils/delta_postprocess.py @@ -0,0 +1,32 @@ +import numpy as np +from scipy.sparse import spdiags + + +def detrend(signal, Lambda): + """detrend(signal, Lambda) -> filtered_signal + This function applies a detrending filter. + This is based on the following article "An advanced detrending method with application + to HRV analysis". Tarvainen et al., IEEE Trans on Biomedical Engineering, 2002. + *Parameters* + ``signal`` (1d numpy array): + The signal where you want to remove the trend. + ``Lambda`` (int): + The smoothing parameter. + *Returns* + ``filtered_signal`` (1d numpy array): + The detrended signal. + """ + signal_length = len(signal) + + # observation matrix + H = np.identity(signal_length) + + # second-order difference matrix + + ones = np.ones(signal_length) + minus_twos = -2 * np.ones(signal_length) + diags_data = np.array([ones, minus_twos, ones]) + diags_index = np.array([0, 1, 2]) + D = spdiags(diags_data, diags_index, (signal_length - 2), signal_length).toarray() + filtered_signal = np.dot((H - np.linalg.inv(H + (Lambda ** 2) * np.dot(D.T, D))), signal) + return filtered_signal

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