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cnn_dmp.py
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cnn_dmp.py
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import torch
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
from datetime import datetime as dt
import argparse
from tensorboardX import SummaryWriter
from config import Config
from utils import *
from model import *
parser = argparse.ArgumentParser(description='cnn_dmp')
parser.add_argument('--model-path', type=str, nargs='?', default='', help='load model')
args = parser.parse_args()
g_net_param = torch.load(args.model_path) if args.model_path else None
if g_net_param:
cfg = g_net_param['config']
else:
cfg = Config()
logger = SummaryWriter(os.path.join(cfg.log_path, cfg.experiment_name))
torch.cuda.set_device(cfg.gpu)
#loader
generator_train = build_loader(cfg, True) # function pointer
generator_test = build_loader(cfg, False) # function pointer
class CNN_DMP(object):
def __init__(self, config):
self.cfg = config
self.condition_net = NN_img_c(sz_image=self.cfg.image_size,
ch_image=self.cfg.image_channels,
tasks=self.cfg.number_of_tasks,
task_img_sz=(self.cfg.object_size[0] if self.cfg.img_as_task else -1))
self.cnn_dmp = NN_cnn_dmp(dim_w=self.cfg.trajectory_dimension,
n_k=self.cfg.number_of_MP_kernels)
if g_net_param:
self.cnn_dmp.load_state_dict(g_net_param['cnn_dmp'])
self.condition_net.load_state_dict(g_net_param['condition_net'])
self.use_gpu = (self.cfg.use_gpu and torch.cuda.is_available())
if self.use_gpu:
print("Use GPU for training, all parameters will move to GPU {}".format(self.cfg.gpu))
self.cnn_dmp.cuda()
self.condition_net.cuda()
# TODO: loading from check points
# generator: (traj, task_id, img) x n_batch
def train(self):
def batchToVariable(traj_batch):
batch_im = torch.zeros(self.cfg.batch_size_train, self.cfg.image_channels,
self.cfg.image_size[0], self.cfg.image_size[1])
batch_w = torch.zeros(
self.cfg.batch_size_train, self.cfg.number_of_MP_kernels, self.cfg.trajectory_dimension)
if self.cfg.img_as_task:
batch_c = torch.zeros(self.cfg.batch_size_train, self.cfg.image_channels,
self.cfg.object_size[0], self.cfg.object_size[1])
else:
batch_c = torch.zeros(self.cfg.batch_size_train, self.cfg.number_of_tasks)
for i, b in enumerate(traj_batch):
batch_w[i] = torch.from_numpy(b[0])
if self.cfg.img_as_task:
batch_c[i] = torch.from_numpy(b[2].transpose(2, 0, 1))
batch_im[i] = torch.from_numpy(b[3].transpose(2, 0, 1))
else:
batch_c[i,b[1]] = 1.
batch_im[i] = torch.from_numpy(b[2].transpose(2, 0, 1))
if self.use_gpu:
return torch.autograd.Variable(batch_w.cuda()),\
torch.autograd.Variable(batch_c.cuda()),\
torch.autograd.Variable(batch_im.cuda())
else:
return torch.autograd.Variable(batch_w),\
torch.autograd.Variable(batch_c),\
torch.autograd.Variable(batch_im)
optim = torch.optim.Adam(
list(self.cnn_dmp.parameters()) + list(self.condition_net.parameters()))
loss = []
if g_net_param:
base = g_net_param['epoch']
else:
base = 0
for epoch in range(base, self.cfg.epochs+base):
avg_loss = []
for i, batch in enumerate(generator_train):
w, c, im = batchToVariable(batch)
optim.zero_grad()
im_c = self.condition_net(im, c)
w_pred = self.cnn_dmp(im_c)
l = F.mse_loss(w, w_pred).mean()
l.backward()
optim.step()
avg_loss.append(l.item())
bar(i + 1, self.cfg.batches_train, "Epoch %d/%d: " % (epoch + 1, self.cfg.epochs),
" | Err=%f" % (l.item()), end_string='')
# update training counter and make check points
if i + 1 >= self.cfg.batches_train:
loss.append(sum(avg_loss) / len(avg_loss))
print("Epoch=%d, Average Loss=%f" % (epoch + 1, loss[-1]))
logger.add_scalar('loss', sum(avg_loss)/len(avg_loss), epoch)
break
if (epoch % self.cfg.save_interval == 0 and epoch != 0) or\
(self.cfg.save_interval <= 0 and loss[-1] == min(loss)):
net_param = {
"epoch": epoch,
"config": self.cfg,
"loss": loss,
"cnn_dmp": self.cnn_dmp.state_dict(),
"condition_net": self.condition_net.state_dict()
}
os.makedirs(self.cfg.check_point_path, exist_ok=True)
check_point_file = os.path.join(self.cfg.check_point_path,
"%s:%s.check" % (self.cfg.experiment_name, str(dt.now())))
torch.save(net_param, check_point_file)
print("Check point saved @ %s" % check_point_file)
if epoch != 0 and epoch % self.cfg.display_interval == 0:
if self.cfg.img_as_task:
img, img_gt, feature, c = self.test()
else:
img, img_gt, feature = self.test()
feature = feature.transpose([0,2,3,1]).sum(axis=-1, keepdims=True)
h = feature.shape[1]*4 # CNN factor
heatmap = np.zeros((h*2 + 20*3, h*3 + 20*4, 3), # output 2*3
dtype=np.uint8)
for ind in range(feature.shape[0]):
heatmap[(ind//3)*(h+20)+20:(ind//3)*(h+20)+20+h,
(ind%3)*(h+20)+20:(ind%3)*(h+20)+20+h, :] = colorize(feature[ind, ...], 4)
if self.cfg.img_as_task:
# output 2*3
h, w = self.cfg.object_size
task_map = np.zeros((h*2+20*3, w*3+20*4, 3)).astype(np.uint8)
for ind, task_img in enumerate(c.cpu().data.numpy()):
task_map[(ind//3)*(h+20)+20:(ind//3)*(h+20)+20+h,
(ind%3)*(w+20)+20:(ind%3)*(w+20)+20+w, :] = task_img.transpose([1,2,0])*255
logger.add_image('test_task_img', task_map, epoch)
logger.add_image('test_img', img, epoch)
logger.add_image('heatmap', heatmap, epoch)
logger.add_image('test_img_gt', img_gt, epoch)
# generator: (task_id, img) x n_batch
def test(self):
def batchToVariable(traj_batch):
batch_im = torch.zeros(self.cfg.batch_size_test, self.cfg.image_channels,
self.cfg.image_size[0], self.cfg.image_size[1])
batch_z = torch.normal(torch.zeros(self.cfg.batch_size_test, self.cfg.number_of_hidden),
torch.ones(self.cfg.batch_size_test, self.cfg.number_of_hidden))
batch_w = torch.zeros(
self.cfg.batch_size_test, self.cfg.number_of_MP_kernels, self.cfg.trajectory_dimension)
batch_target = torch.zeros(
self.cfg.batch_size_test, 2)
if self.cfg.img_as_task:
batch_c = torch.zeros(self.cfg.batch_size_test, self.cfg.image_channels,
self.cfg.object_size[0], self.cfg.object_size[1])
else:
batch_c = torch.zeros(self.cfg.batch_size_test, self.cfg.number_of_tasks)
for i, b in enumerate(traj_batch):
batch_w[i] = torch.from_numpy(b[0])
batch_target[i] = torch.from_numpy(b[-1])
if self.cfg.img_as_task:
batch_c[i] = torch.from_numpy(b[2].transpose(2, 0, 1))
batch_im[i] = torch.from_numpy(b[3].transpose(2, 0, 1))
else:
batch_c[i,b[1]] = 1.
batch_im[i] = torch.from_numpy(b[2].transpose(2, 0, 1))
if self.use_gpu:
return torch.autograd.Variable(batch_z.cuda(), volatile=True),\
torch.autograd.Variable(batch_c.cuda(), volatile=True),\
torch.autograd.Variable(batch_im.cuda(), volatile=True),\
batch_target,\
batch_w
else:
return torch.autograd.Variable(batch_z, volatile=True),\
torch.autograd.Variable(batch_c, volatile=True),\
torch.autograd.Variable(batch_im, volatile=True),\
batch_target,\
batch_w
for batch in generator_test:
break
z, c, im, target, wgt = batchToVariable(batch)
if self.cfg.use_DMP:
p0 = np.tile(np.asarray((0., self.cfg.image_y_range[0]), dtype=np.float32), (self.cfg.batch_size_test, 1))
w = self.cnn_dmp(self.condition_net(im, c)).cpu().data.numpy()
tauo = tuple(dmp.generate(w, target.cpu().numpy(), self.cfg.number_time_samples, p0=p0, init=True))
tau = tuple(dmp.generate(wgt.cpu().numpy(), target.cpu().numpy(), self.cfg.number_time_samples, p0=p0, init=True))
else:
tauo = tuple(RBF.generate(wo, self.cfg.number_time_samples)
for wo in self.cnn_dmp(self.condition_net(im, c)).cpu().data.numpy())
tau = tuple(RBF.generate(wo, self.cfg.number_of_MP_kernels)
for wo in wgt)
if self.cfg.img_as_task:
_, cls, _, imo, _ = tuple(zip(*batch))
else:
_, cls, imo, _ = tuple(zip(*batch))
env = self.cfg.env(self.cfg)
img = display(self.cfg, tauo, imo, cls, interactive=True)
img_gt = display(self.cfg, tau, imo, cls, interactive=True)
feature = self.condition_net.feature_map(im).data.cpu().numpy()
if self.cfg.img_as_task:
return img, img_gt, feature, c
else:
return img, img_gt, feature
def main():
alg = CNN_DMP(config=cfg)
alg.train()
alg.test()
if __name__ == "__main__":
main()
# from env import ToyEnv, display
# cfg = Config()
# env. = Env(cfg)
# for i in range(10):
# batch = (env.sample(task_id=0, im_id=list(range(10))) for j in range(6))
# batch = tuple(zip(*batch))
# display(cfg, batch[0], batch[2], batch[1], interactive=True)
# plt.pause(3)