From fd99fe5ac55122fe9f0d833f9a3e1a55983af08f Mon Sep 17 00:00:00 2001 From: GreenGarnets Date: Fri, 17 Jan 2020 18:39:33 +0900 Subject: [PATCH] import torchsummary --- Asset/modelLog.png | Bin 0 -> 28638 bytes __pycache__/dataset.cpython-36.pyc | Bin 0 -> 4221 bytes __pycache__/music_processer.cpython-36.pyc | Bin 0 -> 2938 bytes net/__pycache__/model.cpython-36.pyc | Bin 1626 -> 1842 bytes net/model.py | 42 +++++---------------- train.py | 6 +-- 6 files changed, 13 insertions(+), 35 deletions(-) create mode 100644 Asset/modelLog.png create mode 100644 __pycache__/dataset.cpython-36.pyc create mode 100644 __pycache__/music_processer.cpython-36.pyc diff --git a/Asset/modelLog.png b/Asset/modelLog.png new file mode 100644 index 0000000000000000000000000000000000000000..79f7ecd39a0720eb832f85da9dcfa08ddb5b3c4d GIT binary patch literal 28638 zcmb?@2{=^y|G(;%%2ufe(PBwuSp~X?YpFOi=~bgy(WHFm$iwSjWI%+BiIwWI(b6GX4^Cs|v`~)(l1IC0u8>(h-gRL6`UTgXhbM(zUOsSqILY($E@#XZ(`vk;kYIh1 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import torch.nn as nn import torch.nn.functional as F - -# note model = DDC Step Selection + Step Placement -# nove model = Step Placement Score => FCLayer이 곧 Nove의 위치를 나타냄 -# DDC Step Selection - -#torch.Size([1024, 1, 1764]) -#torch.Size([1024, 128, 1764]) -#torch.Size([1024, 128, 441]) -#torch.Size([1024, 128, 110]) -#torch.Size([1024, 256, 27]) -#torch.Size([1024, 256, 6]) -#torch.Size([1024, 512, 1]) -#torch.Size([1024, 512, 1]) -#torch.Size([1024, 512]) -#torch.Size([1024, 256]) -#torch.Size([1024, 4]) +from torchsummary import summary class voltexNet(nn.Module): @@ -71,43 +56,36 @@ def __init__(self): #self.output = F.softmax(linear(output),1) - def forward(self, x, batch): + def forward(self, x): + batch = x.size(0) - #print(x.shape) out = self.conv1(x) - #print(out.shape) out = self.conv2(out) - #print(out.shape) out = self.conv3(out) - #print(out.shape) out = self.conv4(out) - #print(out.shape) out = self.conv5(out) - #print(out.shape) out = self.conv6(out) - #print(out.shape) out = self.conv7(out) - #print(out.shape) out = self.conv8(out) - #print(out.shape) out = out.reshape(batch,out.size(1)* out.size(2)) #print(out.shape) out = self.fc(out) - #print(out.shape) #out = self.fc2(out) - #print(out.shape) # out = out.reshape(1,batch,out.size(1)) - # print(out.shape) # out, hidden = self.LSTM(out) - # print(out.shape) # out = out.squeeze() - # print(out.shape) return out - #return F.softmax(out,1) + +if __name__ == "__main__": + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # PyTorch v0.4.0 + model = voltexNet().to(device) + + summary(model, [(1, 1764)]) + ''' class VoltexLSTM diff --git a/train.py b/train.py index 2aaffd7..7efe274 100644 --- a/train.py +++ b/train.py @@ -71,7 +71,7 @@ def infer(model, device, batch, filename, savename) : def main(): model = voltexNet() - model.load_state_dict(torch.load("./model_99_.pth")) + #model.load_state_dict(torch.load("./model_99_.pth")) #print ("load model") device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") @@ -102,7 +102,7 @@ def main(): best_Acc = 0 epoch_loss = 0.0 - ''' + for epoch in range(0,800) : epoch_loss = 0.0 train_loss = 0.0 @@ -213,7 +213,7 @@ def main(): break torch.save(model.state_dict(), "./model/model_"+ str(epoch)+ "_.pth") - ''' + infer(model, device, batch, "./data_test/songs/smooooch_kn/","infer.wav") infer(model, device, batch, "./data_test/songs/dynasty_yooh/","infer2.wav")