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gcn_L6_N100.py
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gcn_L6_N100.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from torch.autograd import Variable
from reader import *
import sys
path = './checkpoints/gcn/L6_N100/'
Train = np.load('iccv_dataset_train.npy')
Val = np.load('iccv_dataset_val.npy')
Test = np.load('iccv_dataset_test.npy')
def create_graph(points):
graph = np.zeros((len(points),len(points)))
for i, a in enumerate(points):
for j, b in enumerate(points):
if i == j:
continue
a = np.array(a)
b = np.array(b)
graph[i,j] = np.sqrt(np.sum((a-b)**2))
d = []
for i in range(len(points)):
d.append(np.sum(graph[i,:]))
D = np.diag(d)
D = np.linalg.cholesky(D)
D = np.linalg.inv(D)
I = np.eye(len(points))
graph_hat = graph + I
return graph_hat, D
class Scinfaxi(nn.Module):
def __init__(self):
super(Scinfaxi,self).__init__()
self.w1 = Variable(torch.randn(8,100).cuda(),requires_grad=True)
self.w2 = Variable(torch.randn(100,100).cuda(),requires_grad=True)
self.w3 = Variable(torch.randn(100,100).cuda(),requires_grad=True)
self.w4 = Variable(torch.randn(100,100).cuda(),requires_grad=True)
self.w5 = Variable(torch.randn(100,100).cuda(),requires_grad=True)
self.w6 = Variable(torch.randn(100,1).cuda(),requires_grad=True)
def forward(self,A,D,l):
hidden_layer_1 = F.relu(D.mm(A).mm(l).mm(self.w1))
hidden_layer_2 = F.relu(D.mm(A).mm(hidden_layer_1).mm(self.w2))
hidden_layer_3 = F.relu(D.mm(A).mm(hidden_layer_2).mm(self.w3))
hidden_layer_4 = F.relu(D.mm(A).mm(hidden_layer_3).mm(self.w4))
hidden_layer_5 = F.relu(D.mm(A).mm(hidden_layer_4).mm(self.w5))
y_pred = F.relu(D.mm(A).mm(hidden_layer_5).mm(self.w6))
return torch.clamp(y_pred,max=200)
model = Scinfaxi()
learning_rate = 1e-3
optimizer = optim.Adam([model.w1,model.w2,model.w3,model.w4,model.w5,model.w6],lr=learning_rate)
max_epochs = 15
loss_fn = nn.MSELoss()
import time
candidate_models = []
validation_losses = []
# Training and Validation
for epoch in range(max_epochs):
epoch_loss = []
start = time.time()
for train in Train:
x,l,y = [],[],[]
dirs = glob.glob(train)
data = get_data_bbox_graph(dirs)[0]
for i in data:
x.append(i[0])
l.append(i[1])
y.append(i[2])
A, D = create_graph(x)
A = Variable(torch.Tensor(A),requires_grad=False).view(len(data),len(data)).cuda()
D = Variable(torch.Tensor(D),requires_grad=False).view(len(data),len(data)).cuda()
l = Variable(torch.Tensor(l),requires_grad=False).view(len(data),8).cuda()
pred = model(A,D,l)
y = Variable(torch.Tensor(y).cuda(),requires_grad=False).view(len(data),1)
optimizer.zero_grad()
loss = loss_fn(pred,y)
epoch_loss.append(loss.item())
loss.backward()
optimizer.step()
# Validation
validation_loss = []
for val in Val:
val_x,val_l,val_y = [],[],[]
dirs = glob.glob(val)
data = get_data_bbox_graph(dirs)[0]
for t in data:
val_x.append(t[0])
val_l.append(t[1])
val_y.append(t[2])
A, D = create_graph(val_x)
A = Variable(torch.Tensor(A),requires_grad=False).view(len(data),len(data)).cuda()
D = Variable(torch.Tensor(D),requires_grad=False).view(len(data),len(data)).cuda()
l = Variable(torch.Tensor(val_l),requires_grad=False).view(len(data),8).cuda()
val_pred = model.forward(A,D,l)
val_y = Variable(torch.Tensor(val_y).cuda(),requires_grad=False).view(len(data),1)
val_loss = loss_fn(val_pred,val_y)
validation_loss.append(val_loss.item())
validation_losses.append(np.array(validation_loss).mean())
end = time.time()
model_path = path + 'gcn-N_100-6_layer-epoch_'+str(epoch)+'.model'
torch.save(model.state_dict(), model_path)
candidate_models.append(model_path)
print('epoch loss: ' + str(np.array(epoch_loss).mean()) + ', Val loss: ' + str(np.array(validation_loss).mean()) + ', Time: ' + str((end-start)))
if len(validation_losses) > 1:
check = (((validation_losses[-2] - validation_losses[-1])/(validation_losses[-2])) * 100)
if check < 1.0 and check > 0:
break
if check < 0:
candidate_models.pop()
break
# Test
test_model = Scinfaxi().cuda()
test_model.load_state_dict(torch.load(candidate_models[-1]))
test_loss = []
for test in Test:
test_x,test_l,test_y = [],[],[]
dirs = glob.glob(test)
data = get_data_bbox_graph(dirs)[0]
for t in data:
test_x.append(t[0])
test_l.append(t[1])
test_y.append(t[2])
A, D = create_graph(test_x)
A = Variable(torch.Tensor(A),requires_grad=False).view(len(data),len(data)).cuda()
D = Variable(torch.Tensor(D),requires_grad=False).view(len(data),len(data)).cuda()
l = Variable(torch.Tensor(test_l),requires_grad=False).view(len(data),8).cuda()
test_pred = test_model.forward(A,D,l).detach().cpu().numpy().squeeze().tolist()
for i,j in zip(test_pred,test_y):
test_loss.append(abs(i-j))
np.save(path+'test_loss.npy',test_loss)
print('Test Loss:',np.mean(test_loss))