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train_MG.py
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train_MG.py
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from dataset_metallic_glass import PartDataset
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim
from kdnet import KDNet_Batch_mp as KDNet_Batch
def split_ps(point_set):
#print point_set.size()
num_points = point_set.size()[0]/2
diff = point_set.max(dim=0)[0] - point_set.min(dim=0)[0]
dim = torch.max(diff, dim = 1)[1][0,0]
cut = torch.median(point_set[:,dim])[0][0]
left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut))
right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut))
middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut))
if torch.numel(left_idx) < num_points:
left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0)
if torch.numel(right_idx) < num_points:
right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0)
left_ps = torch.index_select(point_set, dim = 0, index = left_idx)
right_ps = torch.index_select(point_set, dim = 0, index = right_idx)
return left_ps, right_ps, dim
def split_ps_reuse(point_set, level, pos, tree, cutdim):
sz = point_set.size()
num_points = np.array(sz)[0]/2
max_value = point_set.max(dim=0)[0]
min_value = -(-point_set).max(dim=0)[0]
diff = max_value - min_value
dim = torch.max(diff, dim = 1)[1][0,0]
cut = torch.median(point_set[:,dim])[0][0]
left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut))
right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut))
middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut))
if torch.numel(left_idx) < num_points:
left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0)
if torch.numel(right_idx) < num_points:
right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0)
left_ps = torch.index_select(point_set, dim = 0, index = left_idx)
right_ps = torch.index_select(point_set, dim = 0, index = right_idx)
tree[level+1][pos * 2] = left_ps
tree[level+1][pos * 2 + 1] = right_ps
cutdim[level][pos * 2] = dim
cutdim[level][pos * 2 + 1] = dim
return
test = False
import sys
if len(sys.argv) > 1 and sys.argv[1] == 'test':
test = True
d = PartDataset(root = 'mg', classification = True, train = False)
else:
d = PartDataset(root = 'mg', classification = True)
l = len(d)
print(len(d.classes), l)
levels = (np.log(2048)/np.log(2)).astype(int)
net = KDNet_Batch().cuda()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
if test:
net.load_state_dict(torch.load(sys.argv[2]))
net.eval()
sum_correct = 0
sum_sample = 0
for it in range(10000):
optimizer.zero_grad()
losses = []
corrects = []
points_batch = []
cutdim_batch = []
targets = []
bt = 20
for batch in range(bt):
j = np.random.randint(l)
point_set, class_label = d[j]
#print(point_set, class_label)
targets.append(class_label)
if batch == 0 and it ==0:
tree = [[] for i in range(levels + 1)]
cutdim = [[] for i in range(levels)]
tree[0].append(point_set)
for level in range(levels):
for item in tree[level]:
left_ps, right_ps, dim = split_ps(item)
tree[level+1].append(left_ps)
tree[level+1].append(right_ps)
cutdim[level].append(dim)
cutdim[level].append(dim)
else:
tree[0] = [point_set]
for level in range(levels):
for pos, item in enumerate(tree[level]):
split_ps_reuse(item, level, pos, tree, cutdim)
#print level, pos
#cutdim_v = [(torch.from_numpy(np.array(item).astype(np.int64))) for item in cutdim]
cutdim_v = [(torch.from_numpy(np.array(item).astype(np.int64))) for item in cutdim]
points = torch.stack(tree[-1])
points_batch.append(torch.unsqueeze(torch.squeeze(points), 0).transpose(2,1))
cutdim_batch.append(cutdim_v)
points_v = Variable(torch.cat(points_batch, 0)).cuda()
target_v = Variable(torch.cat(targets, 0)).cuda()
cutdim_processed = []
for i in range(len(cutdim_batch[0])):
cutdim_processed.append(torch.stack([item[i] for item in cutdim_batch], 0))
pred = net(points_v, cutdim_processed)
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(target_v.data).cpu().sum()
loss = F.nll_loss(pred, target_v)
if not test:
loss.backward()
losses.append(loss.data[0])
if not test:
optimizer.step()
else:
sum_correct += correct
sum_sample += bt
if sum_sample > 0:
print("accuracy: %d/%d = %f" % (sum_correct, sum_sample, sum_correct / float(sum_sample)))
print('batch: %d, loss: %f, correct %d/%d' %( it, np.mean(losses), correct, bt))
if it % 1000 == 0:
torch.save(net.state_dict(), 'mg_model_cuda_%d.pth' % (it))