estimator inference files
This commit is contained in:
Shua Dissen
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from __future__ import print_function, division
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import torch
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import torch.nn as nn
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from torch.autograd import Variable
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import torch.nn.functional as F
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from torch import optim
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import numpy as np
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test_data = np.load("timitTest.npy")
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Xtest = test_data[:,5:].astype(np.float32)
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Ytest = test_data[:,1:5].astype(np.float32)
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use_cuda = torch.cuda.is_available()
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device = torch.device("cuda" if use_cuda else "cpu")
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_, D = Xtest.shape
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print(D)
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.Dense1 = nn.Linear(D, 1024)
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self.Dense2 = nn.Linear(1024, 512)
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self.Dense3 = nn.Linear(512, 256)
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self.out = nn.Linear(256, 4)
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def forward(self, x):
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x = torch.sigmoid(self.Dense1(x))
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x = torch.sigmoid(self.Dense2(x))
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x = torch.sigmoid(self.Dense3(x))
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return self.out(x)
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def scaledLoss(output, target):
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scale = torch.tensor([2.0, 1.0, .5, .1]).to(device)
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loss = torch.abs(output - target)
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scaled = loss*scale
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return torch.mean(scaled)
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#loss = nn.L1Loss()
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def train(model, optimizer, inputs, labels):
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inputs = Variable(inputs.to(device))
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labels = Variable(labels.to(device))
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optimizer.zero_grad()
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logits = model.forward(inputs)
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output = scaledLoss(logits, labels)
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output.backward()
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optimizer.step()
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return output.item()
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def predict(model, inputs):
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inputs = Variable(inputs)
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logits = model.forward(inputs.to(device))
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return logits.data.cpu().numpy()
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torch.manual_seed(0)
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Xtest = torch.from_numpy(Xtest).float().to(device)
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Ytest = torch.from_numpy(Ytest).float().to(device)
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model = Net().to(device)
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optimizer = optim.Adagrad(model.parameters(), lr=0.01)
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model.load_state_dict(torch.load("LPC_NN_scaledLoss.pt"))
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model.eval()
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loss1 = 0.0
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loss2 = 0.0
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loss3 = 0.0
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loss4 = 0.0
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max_1 = 0.0
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max_2 = 0.0
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max_3 = 0.0
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max_4 = 0.0
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list_1 = []
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list_2 = []
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list_3 = []
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list_4 = []
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print('predicting...')
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Ypred = predict(model, Xtest)
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for k in range(0, len(Ytest)):
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# print(y_hat[i])
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l1 = np.abs(float(Ytest[k, 0]) - Ypred[k, 0])
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l2 = np.abs(float(Ytest[k, 1]) - Ypred[k, 1])
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l3 = np.abs(float(Ytest[k, 2]) - Ypred[k, 2])
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l4 = np.abs(float(Ytest[k, 3]) - Ypred[k, 3])
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list_1.append(l1)
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list_2.append(l2)
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list_3.append(l3)
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list_4.append(l4)
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max_1 = max(max_1, l1)
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max_2 = max(max_2, l2)
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max_3 = max(max_3, l3)
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max_4 = max(max_4, l4)
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loss1 += l1
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loss2 += l2
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loss3 += l3
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loss4 += l4
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loss1 /= len(Ytest)
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loss2 /= len(Ytest)
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loss3 /= len(Ytest)
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loss4 /= len(Ytest)
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total_loss = loss1 + loss2 + loss3 + loss4
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total_loss /= 4.0
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print('median: %.3f %.3f %.3f %.3f' % (np.median(list_1), np.median(list_2), np.median(list_3), np.median(list_4)))
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print('max loss: %.3f %.3f %.3f %.3f' % (max_1, max_2, max_3, max_4))
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print('Real test score: %.3f %.3f %.3f %.3f' % (loss1, loss2, loss3, loss4))
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print("acc: %.3f" % (total_loss))
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@@ -0,0 +1,121 @@
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from __future__ import print_function, division
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import torch
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import torch.nn as nn
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from torch.autograd import Variable
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import torch.nn.functional as F
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from torch import optim
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import numpy as np
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torch.manual_seed(1)
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testY = np.load("norm_cnn_timit_test_Y.npy")
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Xtest = np.load("norm_cnn_timit_test_X.npy").astype(np.float32)
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Ytest = testY[:,1:5].astype(np.float32)
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use_cuda = torch.cuda.is_available()
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device = torch.device("cuda" if use_cuda else "cpu")
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D = Xtest.shape
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print(D)
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print(Xtest.shape[1], len(Ytest))
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.Conv1 = nn.Conv2d(in_channels=1, out_channels=96, kernel_size=(3, 3), stride=1, padding=0)
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self.Conv2 = nn.Conv2d(in_channels=96, out_channels=32, kernel_size=(3, 3), stride=1, padding=0)
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self.Conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3, 3), stride=1, padding=0)
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self.Conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(5, 5), stride=1, padding=0)
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self.Dense5 = nn.Linear(43*38*64, 512)
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self.out = nn.Linear(512, 4)
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def forward(self, x):
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in_size = x.size(0)
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x = F.relu(self.Conv1(x))
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x = F.relu(self.Conv2(x))
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x = F.max_pool2d(x, kernel_size=2, stride=1)
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x = F.relu(self.Conv3(x))
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x = F.relu(self.Conv4(x))
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x = F.max_pool2d(x, kernel_size=2, stride=1)
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#print(in_size)
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x = x.view(x.size(0), -1)
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x = F.relu(self.Dense5(x))
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return self.out(x)
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def train(model, loss, optimizer, inputs, labels):
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inputs = Variable(inputs.to(device))
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labels = Variable(labels.to(device))
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optimizer.zero_grad()
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logits = model.forward(inputs)
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output = loss.forward(logits, labels)
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output.backward()
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optimizer.step()
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return output.item()
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def predict(model, inputs):
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inputs = Variable(inputs)
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with torch.no_grad():
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logits = model.forward(inputs.to(device))
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return logits.data.cpu().numpy()
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Xtest = torch.from_numpy(Xtest).float().to(device)
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Ytest = torch.from_numpy(Ytest).float().to(device)
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model = Net().to(device)
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loss = nn.L1Loss()
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optimizer = optim.Adagrad(model.parameters())
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model.load_state_dict(torch.load("CNN_estimate.pt"))
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model.eval()
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loss1 = 0.0
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loss2 = 0.0
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loss3 = 0.0
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loss4 = 0.0
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max_1 = 0.0
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max_2 = 0.0
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max_3 = 0.0
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max_4 = 0.0
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list_1 = []
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list_2 = []
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list_3 = []
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list_4 = []
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print('predicting...')
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Ypred1 = predict(model, Xtest[:1000])
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Ypred2 = predict(model, Xtest[1000:2000])
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Ypred3 = predict(model, Xtest[2000:])
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Ypred = np.concatenate((Ypred1, Ypred2, Ypred3))
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for k in range(0, len(Ytest)):
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# print(y_hat[i])
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l1 = np.abs(float(Ytest[k, 0]) - Ypred[k, 0])
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l2 = np.abs(float(Ytest[k, 1]) - Ypred[k, 1])
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l3 = np.abs(float(Ytest[k, 2]) - Ypred[k, 2])
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l4 = np.abs(float(Ytest[k, 3]) - Ypred[k, 3])
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list_1.append(l1)
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list_2.append(l2)
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list_3.append(l3)
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list_4.append(l4)
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max_1 = max(max_1, l1)
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max_2 = max(max_2, l2)
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max_3 = max(max_3, l3)
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max_4 = max(max_4, l4)
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loss1 += l1
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loss2 += l2
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loss3 += l3
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loss4 += l4
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loss1 /= len(Ytest)
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loss2 /= len(Ytest)
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loss3 /= len(Ytest)
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loss4 /= len(Ytest)
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total_loss = loss1 + loss2 + loss3 + loss4
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total_loss /= 4.0
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print('median: %.3f %.3f %.3f %.3f' % (np.median(list_1), np.median(list_2), np.median(list_3), np.median(list_4)))
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print('max loss: %.3f %.3f %.3f %.3f' % (max_1, max_2, max_3, max_4))
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print('Real test score: %.3f %.3f %.3f %.3f' % (loss1, loss2, loss3, loss4))
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print("acc: %.3f" % (total_loss))
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