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train.py
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train.py
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import numpy as np
import torch,pdb
import torch.nn as nn
from ConvLSTM import *
from utils import *
import time
from torch.autograd import Variable
from gan_model import *
ConvLSTM_channel = 128
sequence_length = 8
Boxing_dir = '/home/zhang7/Boxing'
player_list = [[5,6],[7,8],[9,10],[11,12]]
grid_point = 64
batch_size = 10
total_folder = 4
sigma = 0.01
img_h = 64
img_w = 64
max_epoch = 20
lr_rate = 1e-4
weight_decay = 2e-5
eval_loss = 5
save_interval = 500
mymodel = model(ConvLSTM_channel, sequence_length)
mymodel.cuda()
train_pose_dataset = Pose_Dataset(Boxing_dir, sequence_length, player_list, total_folder,sigma,\
max_epoch,img_h,img_w,grid_point = grid_point)
test_pose_dataset = Pose_Dataset(Boxing_dir, sequence_length, [[13,14]], 1,sigma,1,img_h,img_w, \
grid_point = grid_point, reverse_player = False)
train_loader = torch.utils.data.DataLoader(dataset = train_pose_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4)
test_loader = torch.utils.data.DataLoader(dataset = test_pose_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=4)
total_data_num = train_pose_dataset.total_data_num
data_iter = iter(train_loader)
loss_fn = torch.nn.BCELoss()
loss_record = 0
time_stamp = time.time()
optimizer = torch.optim.Adam(mymodel.parameters(),lr = lr_rate, weight_decay = weight_decay)
for it in range(total_data_num):
loss = 0
data,label = data_iter.next()
data = data.permute(1,0,4,2,3)
label = label.permute(1,0,4,2,3)
data = Variable(data.cuda())
label = Variable(label.cuda())
model_output = mymodel(data)
loss = loss_fn(model_output, label)
loss_record += loss.data[0]
mymodel.zero_grad()
loss.backward()
optimizer.step()
if it%eval_loss == 0:
print('iteration: {}, loss = {}, time = {}'.format(it, loss_record/eval_loss, time.time()-time_stamp))
loss_record = 0
time_stamp = time.time()
if it!=0 and (it % save_interval == 0 or it == total_data_num - 1):
counter = 0
test_loss = 0
for data,label in test_loader:
data = data.permute(1,0,4,2,3)
data = Variable(data.cuda())
label = label.permute(1,0,4,2,3)
label = Variable(label.cuda())
model_output = mymodel(data)
loss = loss_fn(model_output, label)
test_loss += loss.data[0]
counter += 1
torch.save(mymodel.state_dict(), 'model_it_{}'.format(it))
print('test_loss:{},model saved'.format(test_loss/counter))