self.data, self.slices = self.collate(data_list)
def _download(self):
return
def _process(self):
return
def __repr__(self):
return '{}()'.format(self.__class__.__name__)
L = 8
P1 = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06]
P2 = [0.01]
H, H_one = error_generate.generate_PCM(2 * L * L - 2, L) #64, 30
H, H_one = torch.from_numpy(H).t(), torch.from_numpy(H_one).t()
h_prep = error_generate.H_Prep(H.t())
H_prep = torch.from_numpy(h_prep.get_H_Prep())
BATCH_SIZE = 128
lr = 3e-4
Nc = 25
run1 = 8192
run2 = 2048
adj = H.to_sparse()
edge_info = torch.cat([adj._indices()[0].unsqueeze(0), \
adj._indices()[1].unsqueeze(0).add(H.size()[0])], dim=0).repeat(1, BATCH_SIZE).cuda()
dataset1 = error_generate.gen_syn(P1, L, H, run1)
dataset2 = error_generate.gen_syn(P2, L, H, run2)
train_dataset = CustomDataset(H, dataset1)
test_dataset = CustomDataset(H, dataset2)
self.data, self.slices = self.collate(data_list)
def _download(self):
return
def _process(self):
return
def __repr__(self):
return '{}()'.format(self.__class__.__name__)
L = 4
P1 = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1]
P2 = [0.1]
H = torch.from_numpy(error_generate.generate_PCM(2 * L * L - 2,
L)).t() #64, 30
h_prep = error_generate.H_Prep(H.t())
H_prep = torch.from_numpy(h_prep.get_H_Prep())
BATCH_SIZE = 512
lr = 3e-4
Nc = 25
run1 = 40960
run2 = 8192
dataset1 = error_generate.gen_syn(P1, L, H, run1)
dataset2 = error_generate.gen_syn(P2, L, H, run2)
train_dataset = CustomDataset(H, dataset1)
test_dataset = CustomDataset(H, dataset2)
rows, cols = H.size(0), H.size(1)
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)
logical, stab = h_prep.get_logical(H_prep)
self.data, self.slices = self.collate(data_list)
def _download(self):
return
def _process(self):
return
def __repr__(self):
return '{}()'.format(self.__class__.__name__)
L = 6
P1 = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06]
P2 = [0.01]
H_init, H_prime = error_generate.generate_PCM(2 * L * L - 2, L)
H = torch.from_numpy(H_init).t() #64, 30
H_prime = torch.from_numpy(H_prime).t()
h_prep = error_generate.H_Prep(H.t())
H_prep = torch.from_numpy(h_prep.get_H_Prep())
BATCH_SIZE = 512
lr = 2e-4
Nc = 25
run1 = 81920
run2 = 2048
adj = H.to_sparse()
edge_info = torch.cat([adj._indices()[0].unsqueeze(0), \
adj._indices()[1].unsqueeze(0).add(H.size()[0])], dim=0).repeat(1, BATCH_SIZE).cuda()
dataset1 = error_generate.gen_syn(P1, L, H, run1)
dataset2 = error_generate.gen_syn(P2, L, H, run2)
train_dataset = CustomDataset(H, dataset1)
