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Unverified Commit 75028bd7 authored by SparkSnail's avatar SparkSnail Committed by GitHub
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Merge pull request #235 from microsoft/master 

merge master
parents 1d74ae5e 2e42d1d8
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examples/model_compress/L1_torch_cifar10.py
View file @ 75028bd7
......@@ -88,14 +88,14 @@ def main():
# Prune model and test accuracy without fine tuning.
print('=' * 10 + 'Test on the pruned model before fine tune' + '=' * 10)
pruner = L1FilterPruner(model, configure_list)
optimizer_finetune = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
pruner = L1FilterPruner(model, configure_list, optimizer_finetune)
model = pruner.compress()
test(model, device, test_loader)
# top1 = 88.19%
# Fine tune the pruned model for 40 epochs and test accuracy
print('=' * 10 + 'Fine tuning' + '=' * 10)
optimizer_finetune = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
best_top1 = 0
for epoch in range(40):
pruner.update_epoch(epoch)
......
examples/model_compress/MeanActivation_torch_cifar10.py deleted 100644 → 0
View file @ 1d74ae5e
import math
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
from nni.compression.torch import ActivationMeanRankFilterPruner
from models.cifar10.vgg import VGG
def train(model, device, train_loader, optimizer):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.cross_entropy(output, target)
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('{:2.0f}% Loss {}'.format(100 * batch_idx / len(train_loader), loss.item()))
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
acc = 100 * correct / len(test_loader.dataset)
print('Loss: {} Accuracy: {}%)\n'.format(
test_loss, acc))
return acc
def main():
parser = argparse.ArgumentParser("multiple gpu with pruning")
parser.add_argument("--epochs", type=int, default=160)
parser.add_argument("--retrain", default=False, action="store_true")
parser.add_argument("--parallel", default=False, action="store_true")
args = parser.parse_args()
torch.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('./data.cifar10', train=True, download=True,
transform=transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('./data.cifar10', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=200, shuffle=False)
model = VGG(depth=16)
model.to(device)
# Train the base VGG-16 model
if args.retrain:
print('=' * 10 + 'Train the unpruned base model' + '=' * 10)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=1e-4)
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 160, 0)
for epoch in range(args.epochs):
train(model, device, train_loader, optimizer)
test(model, device, test_loader)
lr_scheduler.step(epoch)
torch.save(model.state_dict(), 'vgg16_cifar10.pth')
else:
assert os.path.isfile('vgg16_cifar10.pth'), "can not find checkpoint 'vgg16_cifar10.pth'"
model.load_state_dict(torch.load('vgg16_cifar10.pth'))
# Test base model accuracy
print('=' * 10 + 'Test on the original model' + '=' * 10)
test(model, device, test_loader)
# top1 = 93.51%
# Pruning Configuration, in paper 'PRUNING FILTERS FOR EFFICIENT CONVNETS',
# Conv_1, Conv_8, Conv_9, Conv_10, Conv_11, Conv_12 are pruned with 50% sparsity, as 'VGG-16-pruned-A'
configure_list = [{
'sparsity': 0.5,
'op_types': ['default'],
'op_names': ['feature.0', 'feature.24', 'feature.27', 'feature.30', 'feature.34', 'feature.37']
}]
# Prune model and test accuracy without fine tuning.
print('=' * 10 + 'Test on the pruned model before fine tune' + '=' * 10)
pruner = ActivationMeanRankFilterPruner(model, configure_list)
model = pruner.compress()
if args.parallel:
if torch.cuda.device_count() > 1:
print("use {} gpus for pruning".format(torch.cuda.device_count()))
model = nn.DataParallel(model)
else:
print("only detect 1 gpu, fall back")
model.to(device)
test(model, device, test_loader)
# top1 = 88.19%
# Fine tune the pruned model for 40 epochs and test accuracy
print('=' * 10 + 'Fine tuning' + '=' * 10)
optimizer_finetune = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
best_top1 = 0
for epoch in range(40):
pruner.update_epoch(epoch)
print('# Epoch {} #'.format(epoch))
train(model, device, train_loader, optimizer_finetune)
top1 = test(model, device, test_loader)
if top1 > best_top1:
best_top1 = top1
# Export the best model, 'model_path' stores state_dict of the pruned model,
# mask_path stores mask_dict of the pruned model
pruner.export_model(model_path='pruned_vgg16_cifar10.pth', mask_path='mask_vgg16_cifar10.pth')
# Test the exported model
print('=' * 10 + 'Test on the pruned model after fine tune' + '=' * 10)
new_model = VGG(depth=16)
new_model.to(device)
new_model.load_state_dict(torch.load('pruned_vgg16_cifar10.pth'))
test(new_model, device, test_loader)
# top1 = 93.53%
if __name__ == '__main__':
main()
examples/model_compress/QAT_torch_quantizer.py
View file @ 75028bd7
......@@ -79,15 +79,15 @@ def main():
}, {
'quant_types': ['output'],
'quant_bits': 8,
'quant_start_step': 7000,
'quant_start_step': 1000,
'op_types':['ReLU6']
}]
quantizer = QAT_Quantizer(model, configure_list)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
quantizer = QAT_Quantizer(model, configure_list, optimizer)
quantizer.compress()
model.to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
for epoch in range(10):
for epoch in range(40):
print('# Epoch {} #'.format(epoch))
train(model, quantizer, device, train_loader, optimizer)
test(model, device, test_loader)
......
examples/model_compress/fpgm_torch_mnist.py deleted 100644 → 0
View file @ 1d74ae5e
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
from nni.compression.torch import FPGMPruner
class Mnist(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5, 1)
self.conv2 = nn.Conv2d(20, 50, 5, 1)
self.fc1 = nn.Linear(4 * 4 * 50, 500)
self.fc2 = nn.Linear(500, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2, 2)
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
def _get_conv_weight_sparsity(self, conv_layer):
num_zero_filters = (conv_layer.weight.data.sum((1, 2, 3)) == 0).sum()
num_filters = conv_layer.weight.data.size(0)
return num_zero_filters, num_filters, float(num_zero_filters)/num_filters
def print_conv_filter_sparsity(self):
if isinstance(self.conv1, nn.Conv2d):
conv1_data = self._get_conv_weight_sparsity(self.conv1)
conv2_data = self._get_conv_weight_sparsity(self.conv2)
else:
# self.conv1 is wrapped as PrunerModuleWrapper
conv1_data = self._get_conv_weight_sparsity(self.conv1.module)
conv2_data = self._get_conv_weight_sparsity(self.conv2.module)
print('conv1: num zero filters: {}, num filters: {}, sparsity: {:.4f}'.format(conv1_data[0], conv1_data[1], conv1_data[2]))
print('conv2: num zero filters: {}, num filters: {}, sparsity: {:.4f}'.format(conv2_data[0], conv2_data[1], conv2_data[2]))
def train(model, device, train_loader, optimizer):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
if batch_idx % 100 == 0:
print('{:.2f}% Loss {:.4f}'.format(100 * batch_idx / len(train_loader), loss.item()))
if batch_idx == 0:
model.print_conv_filter_sparsity()
loss.backward()
optimizer.step()
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('Loss: {:.4f} Accuracy: {}%)\n'.format(
test_loss, 100 * correct / len(test_loader.dataset)))
def main():
torch.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True, transform=trans),
batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=False, transform=trans),
batch_size=1000, shuffle=True)
model = Mnist()
model.to(device)
model.print_conv_filter_sparsity()
configure_list = [{
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
pruner = FPGMPruner(model, configure_list)
pruner.compress()
model.to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
for epoch in range(10):
pruner.update_epoch(epoch)
print('# Epoch {} #'.format(epoch))
train(model, device, train_loader, optimizer)
test(model, device, test_loader)
pruner.export_model('model.pth', 'mask.pth')
if __name__ == '__main__':
main()
examples/model_compress/lottery_torch_mnist_fc.py
View file @ 75028bd7
......@@ -71,7 +71,6 @@ if __name__ == '__main__':
pruner = LotteryTicketPruner(model, configure_list, optimizer)
pruner.compress()
#model = nn.DataParallel(model)
for i in pruner.get_prune_iterations():
pruner.prune_iteration_start()
......
examples/model_compress/main_torch_pruner.py deleted 100644 → 0
View file @ 1d74ae5e
from nni.compression.torch import AGP_Pruner
import torch
import torch.nn.functional as F
from torchvision import datasets, transforms
class Mnist(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(1, 20, 5, 1)
self.conv2 = torch.nn.Conv2d(20, 50, 5, 1)
self.fc1 = torch.nn.Linear(4 * 4 * 50, 500)
self.fc2 = torch.nn.Linear(500, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2, 2)
x = x.view(-1, 4 * 4 * 50)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
def train(model, device, train_loader, optimizer):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('{:2.0f}% Loss {}'.format(100 * batch_idx / len(train_loader), loss.item()))
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('Loss: {} Accuracy: {}%)\n'.format(
test_loss, 100 * correct / len(test_loader.dataset)))
def main():
torch.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True, transform=trans),
batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=False, transform=trans),
batch_size=1000, shuffle=True)
model = Mnist()
model = model.to(device)
'''you can change this to LevelPruner to implement it
pruner = LevelPruner(configure_list)
'''
configure_list = [{
'initial_sparsity': 0,
'final_sparsity': 0.8,
'start_epoch': 0,
'end_epoch': 10,
'frequency': 1,
'op_types': ['default']
}]
pruner = AGP_Pruner(model, configure_list)
model = pruner.compress()
model = model.to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
for epoch in range(10):
pruner.update_epoch(epoch)
print('# Epoch {} #'.format(epoch))
train(model, device, train_loader, optimizer)
test(model, device, test_loader)
pruner.export_model('model.pth', 'mask.pth', 'model.onnx', [1, 1, 28, 28], device)
if __name__ == '__main__':
main()
examples/model_compress/model_prune_torch.py 0 → 100644
View file @ 75028bd7
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from models.cifar10.vgg import VGG
import nni
from nni.compression.torch import LevelPruner, SlimPruner, FPGMPruner, L1FilterPruner, \
L2FilterPruner, AGP_Pruner, ActivationMeanRankFilterPruner, ActivationAPoZRankFilterPruner
prune_config = {
'level': {
'dataset_name': 'mnist',
'model_name': 'naive',
'pruner_class': LevelPruner,
'config_list': [{
'sparsity': 0.5,
'op_types': ['default'],
}]
},
'agp': {
'dataset_name': 'mnist',
'model_name': 'naive',
'pruner_class': AGP_Pruner,
'config_list': [{
'initial_sparsity': 0,
'final_sparsity': 0.8,
'start_epoch': 0,
'end_epoch': 10,
'frequency': 1,
'op_types': ['default']
}]
},
'slim': {
'dataset_name': 'cifar10',
'model_name': 'vgg19',
'pruner_class': SlimPruner,
'config_list': [{
'sparsity': 0.7,
'op_types': ['BatchNorm2d']
}]
},
'fpgm': {
'dataset_name': 'mnist',
'model_name': 'naive',
'pruner_class': FPGMPruner,
'config_list':[{
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
},
'l1': {
'dataset_name': 'cifar10',
'model_name': 'vgg16',
'pruner_class': L1FilterPruner,
'config_list': [{
'sparsity': 0.5,
'op_types': ['default'],
'op_names': ['feature.0', 'feature.24', 'feature.27', 'feature.30', 'feature.34', 'feature.37']
}]
},
'mean_activation': {
'dataset_name': 'cifar10',
'model_name': 'vgg16',
'pruner_class': ActivationMeanRankFilterPruner,
'configure_list': [{
'sparsity': 0.5,
'op_types': ['default'],
'op_names': ['feature.0', 'feature.24', 'feature.27', 'feature.30', 'feature.34', 'feature.37']
}]
},
'apoz': {
'dataset_name': 'cifar10',
'model_name': 'vgg16',
'pruner_class': ActivationAPoZRankFilterPruner,
'config_list': [{
'sparsity': 0.5,
'op_types': ['default'],
'op_names': ['feature.0', 'feature.24', 'feature.27', 'feature.30', 'feature.34', 'feature.37']
}]
}
}
def get_data_loaders(dataset_name='mnist', batch_size=128):
assert dataset_name in ['cifar10', 'mnist']
if dataset_name == 'cifar10':
ds_class = datasets.CIFAR10 if dataset_name == 'cifar10' else datasets.MNIST
MEAN, STD = (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
else:
ds_class = datasets.MNIST
MEAN, STD = (0.1307,), (0.3081,)
train_loader = DataLoader(
ds_class(
'./data', train=True, download=True,
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(MEAN, STD)])
),
batch_size=batch_size, shuffle=True
)
test_loader = DataLoader(
ds_class(
'./data', train=False, download=True,
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(MEAN, STD)])
),
batch_size=batch_size, shuffle=False
)
return train_loader, test_loader
class NaiveModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5, 1)
self.conv2 = nn.Conv2d(20, 50, 5, 1)
self.bn1 = nn.BatchNorm2d(self.conv1.out_channels)
self.bn2 = nn.BatchNorm2d(self.conv2.out_channels)
self.fc1 = nn.Linear(4 * 4 * 50, 500)
self.fc2 = nn.Linear(500, 10)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.max_pool2d(x, 2, 2)
x = F.relu(self.bn2(self.conv2(x)))
x = F.max_pool2d(x, 2, 2)
x = x.view(-1, 4 * 4 * 50)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
def create_model(model_name='naive'):
assert model_name in ['naive', 'vgg16', 'vgg19']
if model_name == 'naive':
return NaiveModel()
elif model_name == 'vgg16':
return VGG(16)
else:
return VGG(19)
def create_pruner(model, pruner_name, optimizer=None):
pruner_class = prune_config[pruner_name]['pruner_class']
config_list = prune_config[pruner_name]['config_list']
return pruner_class(model, config_list, optimizer)
def train(model, device, train_loader, optimizer):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.cross_entropy(output, target)
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('{:2.0f}% Loss {}'.format(100 * batch_idx / len(train_loader), loss.item()))
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.cross_entropy(output, target, reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
acc = 100 * correct / len(test_loader.dataset)
print('Loss: {} Accuracy: {}%)\n'.format(
test_loss, acc))
return acc
def main(args):
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
os.makedirs(args.checkpoints_dir, exist_ok=True)
model_name = prune_config[args.pruner_name]['model_name']
dataset_name = prune_config[args.pruner_name]['dataset_name']
train_loader, test_loader = get_data_loaders(dataset_name, args.batch_size)
model = create_model(model_name).cuda()
if args.resume_from is not None and os.path.exists(args.resume_from):
print('loading checkpoint {} ...'.format(args.resume_from))
model.load_state_dict(torch.load(args.resume_from))
test(model, device, test_loader)
else:
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=1e-4)
if args.multi_gpu and torch.cuda.device_count():
model = nn.DataParallel(model)
print('start training')
pretrain_model_path = os.path.join(
args.checkpoints_dir, 'pretrain_{}_{}_{}.pth'.format(model_name, dataset_name, args.pruner_name))
for epoch in range(args.pretrain_epochs):
train(model, device, train_loader, optimizer)
test(model, device, test_loader)
torch.save(model.state_dict(), pretrain_model_path)
print('start model pruning...')
model_path = os.path.join(args.checkpoints_dir, 'pruned_{}_{}_{}.pth'.format(model_name, dataset_name, args.pruner_name))
mask_path = os.path.join(args.checkpoints_dir, 'mask_{}_{}_{}.pth'.format(model_name, dataset_name, args.pruner_name))
# pruner needs to be initialized from a model not wrapped by DataParallel
if isinstance(model, nn.DataParallel):
model = model.module
optimizer_finetune = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
best_top1 = 0
pruner = create_pruner(model, args.pruner_name, optimizer_finetune)
model = pruner.compress()
if args.multi_gpu and torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
for epoch in range(args.prune_epochs):
pruner.update_epoch(epoch)
print('# Epoch {} #'.format(epoch))
train(model, device, train_loader, optimizer_finetune)
top1 = test(model, device, test_loader)
if top1 > best_top1:
best_top1 = top1
# Export the best model, 'model_path' stores state_dict of the pruned model,
# mask_path stores mask_dict of the pruned model
pruner.export_model(model_path=model_path, mask_path=mask_path)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--pruner_name", type=str, default="level", help="pruner name")
parser.add_argument("--batch_size", type=int, default=256)
parser.add_argument("--pretrain_epochs", type=int, default=10, help="training epochs before model pruning")
parser.add_argument("--prune_epochs", type=int, default=10, help="training epochs for model pruning")
parser.add_argument("--checkpoints_dir", type=str, default="./checkpoints", help="checkpoints directory")
parser.add_argument("--resume_from", type=str, default=None, help="pretrained model weights")
parser.add_argument("--multi_gpu", action="store_true", help="Use multiple GPUs for training")
args = parser.parse_args()
main(args)
examples/model_compress/multi_gpu.py deleted 100644 → 0
View file @ 1d74ae5e
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from nni.compression.torch import SlimPruner
class fc1(nn.Module):
def __init__(self, num_classes=10):
super(fc1, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(32)
self.relu1 = nn.ReLU(inplace=True)
self.linear1 = nn.Linear(32*28*28, 300)
self.relu2 = nn.ReLU(inplace=True)
self.linear2 = nn.Linear(300, 100)
self.relu3 = nn.ReLU(inplace=True)
self.linear3 = nn.Linear(100, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = torch.flatten(x,1)
x = self.linear1(x)
x = self.relu2(x)
x = self.linear2(x)
x = self.relu3(x)
x = self.linear3(x)
return x
def train(model, train_loader, optimizer, criterion, device):
model.train()
for imgs, targets in train_loader:
optimizer.zero_grad()
imgs, targets = imgs.to(device), targets.to(device)
output = model(imgs)
train_loss = criterion(output, targets)
train_loss.backward()
optimizer.step()
return train_loss.item()
def test(model, test_loader, criterion, device):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.data.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset)
return accuracy
if __name__ == '__main__':
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
traindataset = datasets.MNIST('./data', train=True, download=True, transform=transform)
testdataset = datasets.MNIST('./data', train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(traindataset, batch_size=60, shuffle=True, num_workers=10, drop_last=False)
test_loader = torch.utils.data.DataLoader(testdataset, batch_size=60, shuffle=False, num_workers=10, drop_last=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = fc1()
criterion = nn.CrossEntropyLoss()
configure_list = [{
'prune_iterations': 5,
'sparsity': 0.86,
'op_types': ['BatchNorm2d']
}]
pruner = SlimPruner(model, configure_list)
pruner.compress()
if torch.cuda.device_count()>1:
model = nn.DataParallel(model)
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1.2e-3)
for name, par in model.named_parameters():
print(name)
# for i in pruner.get_prune_iterations():
# pruner.prune_iteration_start()
loss = 0
accuracy = 0
for epoch in range(10):
loss = train(model, train_loader, optimizer, criterion, device)
accuracy = test(model, test_loader, criterion, device)
print('current epoch: {0}, loss: {1}, accuracy: {2}'.format(epoch, loss, accuracy))
# print('prune iteration: {0}, loss: {1}, accuracy: {2}'.format(i, loss, accuracy))
pruner.export_model('model.pth', 'mask.pth')
\ No newline at end of file
examples/model_compress/slim_torch_cifar10.py
View file @ 75028bd7
......@@ -107,7 +107,8 @@ def main():
# Prune model and test accuracy without fine tuning.
print('=' * 10 + 'Test the pruned model before fine tune' + '=' * 10)
pruner = SlimPruner(model, configure_list)
optimizer_finetune = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
pruner = SlimPruner(model, configure_list, optimizer_finetune)
model = pruner.compress()
if args.parallel:
if torch.cuda.device_count() > 1:
......@@ -119,13 +120,12 @@ def main():
model.to(device)
# Fine tune the pruned model for 40 epochs and test accuracy
print('=' * 10 + 'Fine tuning' + '=' * 10)
optimizer_finetune = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
best_top1 = 0
for epoch in range(40):
pruner.update_epoch(epoch)
print('# Epoch {} #'.format(epoch))
train(model, device, train_loader, optimizer_finetune)
top1 = test(model, device, test_loader)
if top1 > best_top1:
best_top1 = top1
# Export the best model, 'model_path' stores state_dict of the pruned model,
......
examples/trials/mnist-pytorch/mnist.py
View file @ 75028bd7
......@@ -15,6 +15,13 @@ import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
# Temporary patch this example until the MNIST dataset download issue get resolved
# https://github.com/pytorch/vision/issues/1938
import urllib
opener = urllib.request.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0')]
urllib.request.install_opener(opener)
logger = logging.getLogger('mnist_AutoML')
......@@ -128,7 +135,7 @@ def get_params():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument("--data_dir", type=str,
default='/tmp/tensorflow/mnist/input_data', help="data directory")
default='/tmp/pytorch/mnist/input_data', help="data directory")
parser.add_argument('--batch_size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument("--hidden_size", type=int, default=512, metavar='N',
......
examples/trials/mnist-tfv1/config_dlts.yml 0 → 100644
View file @ 75028bd7
debug: true
authorName: default
experimentName: example_mnist
trialConcurrency: 1
maxExecDuration: 1h
maxTrialNum: 10
#choice: local, remote, pai
trainingServicePlatform: dlts
searchSpacePath: search_space.json
#choice: true, false
useAnnotation: false
tuner:
#choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
#SMAC (SMAC should be installed through nnictl)
builtinTunerName: TPE
classArgs:
#choice: maximize, minimize
optimize_mode: maximize
trial:
command: python3 mnist.py
codeDir: .
gpuNum: 1
#The docker image to run nni job on dlts
image: msranni/nni:latest
dltsConfig:
dashboard: http://azure-eastus-p40-dev1-infra01.eastus.cloudapp.azure.com/
# The following fields are all optional and could be retrieved from environment
# variables if running in DLTS job container.
# cluster: .default
# team: platform
# email: example@microsoft.com
# password: # Paste from DLTS dashboard
examples/trials/network_morphism/FashionMNIST/FashionMNIST_keras.py
View file @ 75028bd7
......@@ -152,7 +152,7 @@ class SendMetrics(keras.callbacks.Callback):
if logs is None:
logs = dict()
logger.debug(logs)
nni.report_intermediate_result(logs["val_acc"])
nni.report_intermediate_result(logs["val_accuracy"])
# Training
......
examples/trials/network_morphism/cifar10/cifar10_keras.py
View file @ 75028bd7
......@@ -152,7 +152,9 @@ class SendMetrics(keras.callbacks.Callback):
if logs is None:
logs = dict()
logger.debug(logs)
nni.report_intermediate_result(logs["val_acc"])
# accuracy key for keras 2.2.2: val_acc
# for keras 2.3.1: val_accuracy
nni.report_intermediate_result(logs["val_accuracy"])
# Training
......
examples/trials/network_morphism/requirements.txt
View file @ 75028bd7
numpy==1.14.2
tensorflow==1.13.1
tensorflow==1.15.2
torchvision==0.2.1
Keras==2.2.2
Keras==2.3.1
torch==0.4.1
src/nasui/.gitignore 0 → 100644
View file @ 75028bd7
# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
# dependencies
/node_modules
/.pnp
.pnp.js
# testing
/coverage
# production
/build
# misc
.DS_Store
.env.local
.env.development.local
.env.test.local
.env.production.local
npm-debug.log*
yarn-debug.log*
yarn-error.log*
src/nasui/package.json 0 → 100644
View file @ 75028bd7
{
"name": "nasnni-vis-ts",
"version": "0.1.0",
"private": true,
"dependencies": {
"@material-ui/core": "^4.9.3",
"@material-ui/icons": "^4.9.1",
"cytoscape": "^3.14.0",
"cytoscape-dagre": "^2.2.2",
"cytoscape-panzoom": "^2.5.3",
"express": "^4.17.1",
"lodash": "^4.17.15",
"react": "^16.12.0",
"react-dom": "^16.12.0",
"react-scripts": "3.4.0",
"typeface-roboto": "^0.0.75",
"typescript": "~3.7.2"
},
"scripts": {
"start": "react-scripts start",
"build": "react-scripts build",
"eject": "react-scripts eject",
"backend": "node server.js"
},
"eslintConfig": {
"extends": "react-app"
},
"browserslist": {
"production": [
">0.2%",
"not dead",
"not op_mini all"
],
"development": [
"last 1 chrome version",
"last 1 firefox version",
"last 1 safari version"
]
},
"devDependencies": {
"@testing-library/jest-dom": "^4.2.4",
"@testing-library/react": "^9.3.2",
"@testing-library/user-event": "^7.1.2",
"@types/cytoscape": "^3.14.0",
"@types/jest": "^24.0.0",
"@types/lodash": "^4.14.149",
"@types/node": "^12.0.0",
"@types/react": "^16.9.0",
"@types/react-dom": "^16.9.0"
},
"proxy": "http://localhost:8080"
}
src/nasui/public/index.html 0 → 100644
View file @ 75028bd7
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<link rel="icon" href="%PUBLIC_URL%/icon.png" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="theme-color" content="#000000" />
<title>NNI NAS Board</title>
</head>
<body>
<noscript>You need to enable JavaScript to run this app.</noscript>
<div id="root"></div>
<!--
This HTML file is a template.
If you open it directly in the browser, you will see an empty page.
You can add webfonts, meta tags, or analytics to this file.
The build step will place the bundled scripts into the <body> tag.
To begin the development, run `npm start` or `yarn start`.
To create a production bundle, use `npm run build` or `yarn build`.
-->
</body>
</html>
src/nasui/server.js 0 → 100644
View file @ 75028bd7
const express = require('express');
const path = require('path');
const fs = require('fs');
const app = express();
const argv = require('minimist')(process.argv.slice(2));
const port = argv.port || 8080;
const logdir = argv.logdir || './mockdata';
app.use(express.static(path.join(__dirname, 'build')));
app.get('/', (req, res) => {
res.sendFile(path.join(__dirname, 'build', 'index.html'));
});
app.get('/refresh', (req, res) => {
const graph = fs.readFileSync(path.join(logdir, 'graph.json'), 'utf8');
const log = fs.readFileSync(path.join(logdir, 'log'), 'utf-8')
.split('\n')
.filter(Boolean)
.map(JSON.parse);
res.send({
'graph': JSON.parse(graph),
'log': log,
});
});
app.listen(port, '0.0.0.0', () => {
console.log(`NNI NAS board is running on port ${port}, logdir is ${logdir}.`);
});
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