batch_size=200,
num_workers=0,
drop_last=True)
G1 = nx.path_graph(6).to_directed()
G_target = nx.path_graph(6).to_directed()
nx.draw(G1)
plt.show()
node_feat_size = 6
edge_feat_size = 3
graph_feat_size = 10
gn = FFGN(graph_feat_size, node_feat_size, edge_feat_size).cuda()
if opt.model != '':
gn.load_state_dict(torch.load(opt.model))
optimizer = optim.Adam(gn.parameters(), lr=1e-4)
schedular = optim.lr_scheduler.StepLR(optimizer, 5e4, gamma=0.975)
savedir = os.path.join('./logs', 'runs',
datetime.now().strftime('%B%d_%H:%M:%S'))
writer = SummaryWriter(savedir)
step = 0
normalizers = torch.load('normalize.pth')
in_normalizer = normalizers['in_normalizer']
out_normalizer = normalizers['out_normalizer']
std = in_normalizer.get_std()
for epoch in range(300):
for i, data in tqdm(enumerate(dl), total=len(dset) / 200 + 1):
optimizer.zero_grad()
action, delta_state, last_state = data
action, delta_state, last_state = action.float(),\
from tqdm import tqdm
from dataset import SwimmerDataset
from utils import *
if __name__ == "__main__":
dset = SwimmerDataset('swimmer.npy')
use_cuda = True
dl = DataLoader(dset, batch_size=200, num_workers=0, drop_last=True)
G1 = nx.path_graph(6).to_directed()
#nx.draw(G1)
#plt.show()
node_feat_size = 6
edge_feat_size = 3
graph_feat_size = 10
gn = FFGN(graph_feat_size, node_feat_size, edge_feat_size).cuda()
optimizer = optim.Adam(gn.parameters(), lr = 1e-3)
savedir = os.path.join('./logs','runs',
datetime.now().strftime('%B%d_%H:%M:%S'))
writer = SummaryWriter(savedir)
step = 0
in_normalizer = Normalizer()
out_normalizer = Normalizer()
for epoch in range(1):
for i,data in tqdm(enumerate(dl)):
action, delta_state, last_state = data
action, delta_state, last_state = action.float(), delta_state.float(), last_state.float()
if use_cuda:
action, delta_state, last_state = action.cuda(), delta_state.cuda(), last_state.cuda()