model = nn.Sequential(
layer, nn.Flatten(),
nn.Linear(in_features=args.channels * args.numf**DIM, out_features=10))
train_dataset = give(DIM, args.numf, args.channels)
if args.nodes > 1:
model, train_loader = lib_torch.distribute(model, train_dataset,
args.nodes, args.batch)
else:
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=args.batch,
shuffle=True)
time = lib_torch.profile(['batch_norm', 'NativeBatchNormBackward'], model,
train_loader, args.epochs)
import numpy as np
data = np.array([[
args.epochs,
tor_data.ds_size, # dataset size
args.numf,
args.channels,
args.batch,
args.nodes,
time
]])
with open('norm{}d.ptorch'.format(DIM), 'a') as file:
np.savetxt(file, data, delimiter=",", fmt="%s")
layer = nn.Linear(in_features=args.numf, out_features=args.units)
model = nn.Sequential(
nn.Flatten(), nn.Linear(in_features=args.numf, out_features=args.units))
train_dataset = give(1, args.numf, 1, out_size=args.units)
if args.nodes > 1:
model, train_loader = lib_torch.distribute(model, train_dataset,
args.nodes, args.batch)
else:
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=args.batch,
shuffle=True)
time = lib_torch.profile(['addmm', 'AddmmBackward'], model, train_loader,
args.epochs)
import numpy as np
data = np.array([[
args.epochs,
tor_data.ds_size, # dataset size
args.numf,
args.batch,
args.nodes,
args.units,
time
]])
with open('final_dense.ptorch', 'a') as file:
np.savetxt(file, data, delimiter=",", fmt="%s")
model = nn.Sequential(
nn.Flatten(),
nn.Linear(in_features=args.channels * args.numf**DIM, out_features=10))
train_dataset = give(DIM, args.numf, args.channels)
if args.nodes > 1:
model, train_loader = lib_torch.distribute(model, train_dataset,
args.nodes, args.batch)
else:
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=args.batch,
shuffle=True)
time = lib_torch.profile(['flatten'], model, train_loader, args.epochs)
import numpy as np
data = np.array([[
args.epochs,
tor_data.ds_size, # dataset size
args.numf,
args.channels,
args.batch,
args.nodes,
time
]])
with open('flatten{}d.ptorch'.format(DIM), 'a') as file:
np.savetxt(file, data, delimiter=",", fmt="%s")
out_features = 10
)
)
train_dataset = give(DIM, args.numf, args.channels)
if args.nodes > 1:
model, train_loader = lib_torch.distribute(model, train_dataset, args.nodes, args.batch)
else:
train_loader = torch.utils.data.DataLoader(
dataset = train_dataset,
batch_size = args.batch,
shuffle = True
)
time = lib_torch.profile(['max_pool{}d'.format(DIM)],
model, train_loader, args.epochs)
import numpy as np
data = np.array([[
args.epochs, tor_data.ds_size, # dataset size
args.numf,
args.channels,
args.batch,
args.nodes,
args.pool,
args.stride,
time
]])
with open('max{}d.ptorch'.format(DIM),'a') as file:
np.savetxt(file, data, delimiter=",", fmt="%s")
model = nn.Sequential(
layer, nn.Flatten(),
nn.Linear(in_features=args.channels * args.numf**DIM, out_features=10))
train_dataset = give(DIM, args.numf, args.channels)
if args.nodes > 1:
model, train_loader = lib_torch.distribute(model, train_dataset,
args.nodes, args.batch)
else:
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=args.batch,
shuffle=True)
time = lib_torch.profile(['relu'], model, train_loader, args.epochs)
import numpy as np
data = np.array([[
args.epochs,
tor_data.ds_size, # dataset size
args.numf,
args.channels,
args.batch,
args.nodes,
time
]])
with open('relu{}d.ptorch'.format(DIM), 'a') as file:
np.savetxt(file, data, delimiter=",", fmt="%s")
nn.Linear(
in_features=args.filters *
lib_torch.conv_size_out(args.numf, args.kernel, args.stride)**DIM,
out_features=10))
train_dataset = give(DIM, args.numf, args.channels)
if args.nodes > 1:
model, train_loader = lib_torch.distribute(model, train_dataset,
args.nodes, args.batch)
else:
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=args.batch,
shuffle=True)
time = lib_torch.profile(['conv{}d'.format(DIM), 'MkldnnConvolutionBackward'],
model, train_loader, args.epochs)
import numpy as np
data = np.array([[
args.epochs,
tor_data.ds_size, # dataset size
args.numf,
args.channels,
args.batch,
args.nodes,
args.kernel,
args.stride,
args.filters,
time
]])
model = nn.Sequential(
layer, nn.Flatten(),
nn.Linear(in_features=args.channels * args.numf**DIM, out_features=10))
train_dataset = give(DIM, args.numf, args.channels)
if args.nodes > 1:
model, train_loader = lib_torch.distribute(model, train_dataset,
args.nodes, args.batch)
else:
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=args.batch,
shuffle=True)
time = lib_torch.profile(['dropout', 'feature_dropout'], model, train_loader,
args.epochs)
import numpy as np
data = np.array([[
args.epochs,
tor_data.ds_size, # dataset size
args.numf,
args.channels,
args.batch,
args.nodes,
args.drop,
time
]])
with open('drop{}d.ptorch'.format(DIM), 'a') as file:
np.savetxt(file, data, delimiter=",", fmt="%s")