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提交 12a426be 编写于 作者: D Dun 提交者： qingqing01
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add mem opt settings of deeplabv3+ (#1648) 

* add mem opt settings
* inplace normalize
* code polish
* chagne url
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fluid/PaddleCV/deeplabv3+/README.md
浏览文件 @ 12a426be
...@@ -40,13 +40,13 @@ data/cityscape/ ...@@ -40,13 +40,13 @@ data/cityscape/
如果需要从头开始训练模型，用户需要下载我们的初始化模型 如果需要从头开始训练模型，用户需要下载我们的初始化模型
``` ```
wget http://paddlemodels.cdn.bcebos.com/deeplab/deeplabv3plus_xception65_initialize.tar.gz wget https://paddle-deeplab.bj.bcebos.com/deeplabv3plus_xception65_initialize.tgz
tar -xf deeplabv3plus_xception65_initialize.tar.gz && rm deeplabv3plus_xception65_initialize.tar.gz tar -xf deeplabv3plus_xception65_initialize.tgz && rm deeplabv3plus_xception65_initialize.tgz
``` ```
如果需要最终训练模型进行fine tune或者直接用于预测，请下载我们的最终模型 如果需要最终训练模型进行fine tune或者直接用于预测，请下载我们的最终模型
``` ```
wget http://paddlemodels.cdn.bcebos.com/deeplab/deeplabv3plus.tar.gz wget https://paddle-deeplab.bj.bcebos.com/deeplabv3plus.tgz
tar -xf deeplabv3plus.tar.gz && rm deeplabv3plus.tar.gz tar -xf deeplabv3plus.tgz && rm deeplabv3plus.tgz
``` ```
...@@ -99,9 +99,10 @@ step: 500, mIoU: 0.7873 ...@@ -99,9 +99,10 @@ step: 500, mIoU: 0.7873
``` ```
## 其他信息 ## 其他信息
|数据集 | pretrained model | trained model | mean IoU |数据集 | pretrained model | trained model | mean IoU
|---|---|---|---| |---|---|---|---|
|CityScape | [deeplabv3plus_xception65_initialize.tar.gz](http://paddlemodels.cdn.bcebos.com/deeplab/deeplabv3plus_xception65_initialize.tar.gz) | [deeplabv3plus.tar.gz](http://paddlemodels.cdn.bcebos.com/deeplab/deeplabv3plus.tar.gz) | 0.7873 | |CityScape | [deeplabv3plus_xception65_initialize.tgz](https://paddle-deeplab.bj.bcebos.com/deeplabv3plus_xception65_initialize.tgz) | [deeplabv3plus.tgz](https://paddle-deeplab.bj.bcebos.com/deeplabv3plus.tgz) | 0.7873 |
## 参考 ## 参考
......
fluid/PaddleCV/deeplabv3+/eval.py
浏览文件 @ 12a426be
...@@ -2,7 +2,9 @@ from __future__ import absolute_import ...@@ -2,7 +2,9 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import os import os
os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = '0.98' if 'FLAGS_fraction_of_gpu_memory_to_use' not in os.environ:
os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = '0.98'
os.environ['FLAGS_enable_parallel_graph'] = '1'
import paddle import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
...@@ -12,21 +14,20 @@ from reader import CityscapeDataset ...@@ -12,21 +14,20 @@ from reader import CityscapeDataset
import reader import reader
import models import models
import sys import sys
import utility
parser = argparse.ArgumentParser()
add_arg = lambda *args: utility.add_arguments(*args, argparser=parser)
def add_argument(name, type, default, help): # yapf: disable
parser.add_argument('--' + name, default=default, type=type, help=help) add_arg('total_step', int, -1, "Number of the step to be evaluated, -1 for full evaluation.")
add_arg('init_weights_path', str, None, "Path of the weights to evaluate.")
add_arg('dataset_path', str, None, "Cityscape dataset path.")
def add_arguments(): add_arg('verbose', bool, False, "Print mIoU for each step if verbose.")
add_argument('total_step', int, -1, add_arg('use_gpu', bool, True, "Whether use GPU or CPU.")
"Number of the step to be evaluated, -1 for full evaluation.") add_arg('num_classes', int, 19, "Number of classes.")
add_argument('init_weights_path', str, None, add_arg('use_py_reader', bool, True, "Use py_reader.")
"Path of the weights to evaluate.") #yapf: enable
add_argument('dataset_path', str, None, "Cityscape dataset path.")
add_argument('verbose', bool, False, "Print mIoU for each step if verbose.")
add_argument('use_gpu', bool, True, "Whether use GPU or CPU.")
add_argument('num_classes', int, 19, "Number of classes.")
def mean_iou(pred, label): def mean_iou(pred, label):
...@@ -43,7 +44,7 @@ def mean_iou(pred, label): ...@@ -43,7 +44,7 @@ def mean_iou(pred, label):
def load_model(): def load_model():
if args.init_weights_path.endswith('/'): if os.path.isdir(args.init_weights_path):
fluid.io.load_params( fluid.io.load_params(
exe, dirname=args.init_weights_path, main_program=tp) exe, dirname=args.init_weights_path, main_program=tp)
else: else:
...@@ -53,9 +54,6 @@ def load_model(): ...@@ -53,9 +54,6 @@ def load_model():
CityscapeDataset = reader.CityscapeDataset CityscapeDataset = reader.CityscapeDataset
parser = argparse.ArgumentParser()
add_arguments()
args = parser.parse_args() args = parser.parse_args()
models.clean() models.clean()
...@@ -73,8 +71,15 @@ reader.default_config['shuffle'] = False ...@@ -73,8 +71,15 @@ reader.default_config['shuffle'] = False
num_classes = args.num_classes num_classes = args.num_classes
with fluid.program_guard(tp, sp): with fluid.program_guard(tp, sp):
if args.use_py_reader:
py_reader = fluid.layers.py_reader(capacity=64,
shapes=[[1, 3, 0, 0], [1] + eval_shape],
dtypes=['float32', 'int32'])
img, label = fluid.layers.read_file(py_reader)
else:
img = fluid.layers.data(name='img', shape=[3, 0, 0], dtype='float32') img = fluid.layers.data(name='img', shape=[3, 0, 0], dtype='float32')
label = fluid.layers.data(name='label', shape=eval_shape, dtype='int32') label = fluid.layers.data(name='label', shape=eval_shape, dtype='int32')
img = fluid.layers.resize_bilinear(img, image_shape) img = fluid.layers.resize_bilinear(img, image_shape)
logit = deeplabv3p(img) logit = deeplabv3p(img)
logit = fluid.layers.resize_bilinear(logit, eval_shape) logit = fluid.layers.resize_bilinear(logit, eval_shape)
...@@ -105,16 +110,25 @@ else: ...@@ -105,16 +110,25 @@ else:
total_step = args.total_step total_step = args.total_step
batches = dataset.get_batch_generator(batch_size, total_step) batches = dataset.get_batch_generator(batch_size, total_step)
if args.use_py_reader:
py_reader.decorate_tensor_provider(lambda :[ (yield b[1],b[2]) for b in batches])
py_reader.start()
sum_iou = 0 sum_iou = 0
all_correct = np.array([0], dtype=np.int64) all_correct = np.array([0], dtype=np.int64)
all_wrong = np.array([0], dtype=np.int64) all_wrong = np.array([0], dtype=np.int64)
for i, imgs, labels, names in batches: for i in range(total_step):
if not args.use_py_reader:
_, imgs, labels, names = next(batches)
result = exe.run(tp, result = exe.run(tp,
feed={'img': imgs, feed={'img': imgs,
'label': labels}, 'label': labels},
fetch_list=[pred, miou, out_wrong, out_correct]) fetch_list=[pred, miou, out_wrong, out_correct])
else:
result = exe.run(tp,
fetch_list=[pred, miou, out_wrong, out_correct])
wrong = result[2][:-1] + all_wrong wrong = result[2][:-1] + all_wrong
right = result[3][:-1] + all_correct right = result[3][:-1] + all_correct
all_wrong = wrong.copy() all_wrong = wrong.copy()
...@@ -122,7 +136,6 @@ for i, imgs, labels, names in batches: ...@@ -122,7 +136,6 @@ for i, imgs, labels, names in batches:
mp = (wrong + right) != 0 mp = (wrong + right) != 0
miou2 = np.mean((right[mp] * 1.0 / (right[mp] + wrong[mp]))) miou2 = np.mean((right[mp] * 1.0 / (right[mp] + wrong[mp])))
if args.verbose: if args.verbose:
print('step: %s, mIoU: %s' % (i + 1, miou2)) print('step: %s, mIoU: %s' % (i + 1, miou2), flush=True)
else: else:
print('\rstep: %s, mIoU: %s' % (i + 1, miou2)) print('\rstep: %s, mIoU: %s' % (i + 1, miou2), end='\r', flush=True)
sys.stdout.flush()
fluid/PaddleCV/deeplabv3+/models.py
浏览文件 @ 12a426be
...@@ -5,6 +5,7 @@ import paddle ...@@ -5,6 +5,7 @@ import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
import contextlib import contextlib
import os
name_scope = "" name_scope = ""
decode_channel = 48 decode_channel = 48
...@@ -146,10 +147,12 @@ def bn_relu(data): ...@@ -146,10 +147,12 @@ def bn_relu(data):
def relu(data): def relu(data):
return append_op_result(fluid.layers.relu(data), 'relu') return append_op_result(
fluid.layers.relu(
data, name=name_scope + 'relu'), 'relu')
def seq_conv(input, channel, stride, filter, dilation=1, act=None): def seperate_conv(input, channel, stride, filter, dilation=1, act=None):
with scope('depthwise'): with scope('depthwise'):
input = conv( input = conv(
input, input,
...@@ -187,14 +190,14 @@ def xception_block(input, ...@@ -187,14 +190,14 @@ def xception_block(input,
with scope('separable_conv' + str(i + 1)): with scope('separable_conv' + str(i + 1)):
if not activation_fn_in_separable_conv: if not activation_fn_in_separable_conv:
data = relu(data) data = relu(data)
data = seq_conv( data = seperate_conv(
data, data,
channels[i], channels[i],
strides[i], strides[i],
filters[i], filters[i],
dilation=dilation) dilation=dilation)
else: else:
data = seq_conv( data = seperate_conv(
data, data,
channels[i], channels[i],
strides[i], strides[i],
...@@ -273,11 +276,11 @@ def encoder(input): ...@@ -273,11 +276,11 @@ def encoder(input):
with scope("aspp0"): with scope("aspp0"):
aspp0 = bn_relu(conv(input, channel, 1, 1, groups=1, padding=0)) aspp0 = bn_relu(conv(input, channel, 1, 1, groups=1, padding=0))
with scope("aspp1"): with scope("aspp1"):
aspp1 = seq_conv(input, channel, 1, 3, dilation=6, act=relu) aspp1 = seperate_conv(input, channel, 1, 3, dilation=6, act=relu)
with scope("aspp2"): with scope("aspp2"):
aspp2 = seq_conv(input, channel, 1, 3, dilation=12, act=relu) aspp2 = seperate_conv(input, channel, 1, 3, dilation=12, act=relu)
with scope("aspp3"): with scope("aspp3"):
aspp3 = seq_conv(input, channel, 1, 3, dilation=18, act=relu) aspp3 = seperate_conv(input, channel, 1, 3, dilation=18, act=relu)
with scope("concat"): with scope("concat"):
data = append_op_result( data = append_op_result(
fluid.layers.concat( fluid.layers.concat(
...@@ -300,10 +303,10 @@ def decoder(encode_data, decode_shortcut): ...@@ -300,10 +303,10 @@ def decoder(encode_data, decode_shortcut):
[encode_data, decode_shortcut], axis=1) [encode_data, decode_shortcut], axis=1)
append_op_result(encode_data, 'concat') append_op_result(encode_data, 'concat')
with scope("separable_conv1"): with scope("separable_conv1"):
encode_data = seq_conv( encode_data = seperate_conv(
encode_data, encode_channel, 1, 3, dilation=1, act=relu) encode_data, encode_channel, 1, 3, dilation=1, act=relu)
with scope("separable_conv2"): with scope("separable_conv2"):
encode_data = seq_conv( encode_data = seperate_conv(
encode_data, encode_channel, 1, 3, dilation=1, act=relu) encode_data, encode_channel, 1, 3, dilation=1, act=relu)
return encode_data return encode_data
......
fluid/PaddleCV/deeplabv3+/train.py
浏览文件 @ 12a426be
...@@ -2,7 +2,9 @@ from __future__ import absolute_import ...@@ -2,7 +2,9 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import os import os
os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = '0.98' if 'FLAGS_fraction_of_gpu_memory_to_use' not in os.environ:
os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = '0.98'
os.environ['FLAGS_enable_parallel_graph'] = '1'
import paddle import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
...@@ -12,105 +14,94 @@ from reader import CityscapeDataset ...@@ -12,105 +14,94 @@ from reader import CityscapeDataset
import reader import reader
import models import models
import time import time
import contextlib
import paddle.fluid.profiler as profiler
import utility
parser = argparse.ArgumentParser()
def add_argument(name, type, default, help): add_arg = lambda *args: utility.add_arguments(*args, argparser=parser)
parser.add_argument('--' + name, default=default, type=type, help=help)
# yapf: disable
add_arg('batch_size', int, 2, "The number of images in each batch during training.")
def add_arguments(): add_arg('train_crop_size', int, 769, "Image crop size during training.")
add_argument('batch_size', int, 2, add_arg('base_lr', float, 0.0001, "The base learning rate for model training.")
"The number of images in each batch during training.") add_arg('total_step', int, 90000, "Number of the training step.")
add_argument('train_crop_size', int, 769, add_arg('init_weights_path', str, None, "Path of the initial weights in paddlepaddle format.")
"'Image crop size during training.") add_arg('save_weights_path', str, None, "Path of the saved weights during training.")
add_argument('base_lr', float, 0.0001, add_arg('dataset_path', str, None, "Cityscape dataset path.")
"The base learning rate for model training.") add_arg('parallel', bool, True, "using ParallelExecutor.")
add_argument('total_step', int, 90000, "Number of the training step.") add_arg('use_gpu', bool, True, "Whether use GPU or CPU.")
add_argument('init_weights_path', str, None, add_arg('num_classes', int, 19, "Number of classes.")
"Path of the initial weights in paddlepaddle format.") add_arg('load_logit_layer', bool, True, "Load last logit fc layer or not. If you are training with different number of classes, you should set to False.")
add_argument('save_weights_path', str, None, add_arg('memory_optimize', bool, True, "Using memory optimizer.")
"Path of the saved weights during training.") add_arg('norm_type', str, 'bn', "Normalization type, should be bn or gn.")
add_argument('dataset_path', str, None, "Cityscape dataset path.") add_arg('profile', bool, False, "Enable profiler.")
add_argument('parallel', bool, False, "using ParallelExecutor.") add_arg('use_py_reader', bool, True, "Use py reader.")
add_argument('use_gpu', bool, True, "Whether use GPU or CPU.") parser.add_argument(
add_argument('num_classes', int, 19, "Number of classes.")
parser.add_argument(
'--enable_ce', '--enable_ce',
action='store_true', action='store_true',
help='If set, run the task with continuous evaluation logs.') help='If set, run the task with continuous evaluation logs.')
#yapf: enable
@contextlib.contextmanager
def profile_context(profile=True):
if profile:
with profiler.profiler('All', 'total', '/tmp/profile_file2'):
yield
else:
yield
def load_model(): def load_model():
myvars = [ if os.path.isdir(args.init_weights_path):
load_vars = [
x for x in tp.list_vars() x for x in tp.list_vars()
if isinstance(x, fluid.framework.Parameter) and x.name.find('logit') == if isinstance(x, fluid.framework.Parameter) and x.name.find('logit') ==
-1 -1
] ]
if args.init_weights_path.endswith('/'): if args.load_logit_layer:
if args.num_classes == 19:
fluid.io.load_params( fluid.io.load_params(
exe, dirname=args.init_weights_path, main_program=tp) exe, dirname=args.init_weights_path, main_program=tp)
else: else:
fluid.io.load_vars(exe, dirname=args.init_weights_path, vars=myvars) fluid.io.load_vars(exe, dirname=args.init_weights_path, vars=load_vars)
else: else:
if args.num_classes == 19:
fluid.io.load_params( fluid.io.load_params(
exe, exe,
dirname="", dirname="",
filename=args.init_weights_path, filename=args.init_weights_path,
main_program=tp) main_program=tp)
else:
fluid.io.load_vars(
exe, dirname="", filename=args.init_weights_path, vars=myvars)
def save_model(): def save_model():
if args.save_weights_path.endswith('/'): assert not os.path.isfile(args.save_weights_path)
fluid.io.save_params( fluid.io.save_params(
exe, dirname=args.save_weights_path, main_program=tp) exe, dirname=args.save_weights_path, main_program=tp)
else:
fluid.io.save_params(
exe, dirname="", filename=args.save_weights_path, main_program=tp)
def loss(logit, label): def loss(logit, label):
label_nignore = (label < num_classes).astype('float32') label_nignore = fluid.layers.less_than(
label = fluid.layers.elementwise_min( label.astype('float32'),
label, fluid.layers.assign(np.array([num_classes], 'float32')),
fluid.layers.assign(np.array( force_cpu=False).astype('float32')
[num_classes - 1], dtype=np.int32)))
logit = fluid.layers.transpose(logit, [0, 2, 3, 1]) logit = fluid.layers.transpose(logit, [0, 2, 3, 1])
logit = fluid.layers.reshape(logit, [-1, num_classes]) logit = fluid.layers.reshape(logit, [-1, num_classes])
label = fluid.layers.reshape(label, [-1, 1]) label = fluid.layers.reshape(label, [-1, 1])
label = fluid.layers.cast(label, 'int64') label = fluid.layers.cast(label, 'int64')
label_nignore = fluid.layers.reshape(label_nignore, [-1, 1]) label_nignore = fluid.layers.reshape(label_nignore, [-1, 1])
loss = fluid.layers.softmax_with_cross_entropy(logit, label) loss = fluid.layers.softmax_with_cross_entropy(logit, label, ignore_index=255, numeric_stable_mode=True)
loss = loss * label_nignore label_nignore.stop_gradient = True
no_grad_set.add(label_nignore.name) label.stop_gradient = True
no_grad_set.add(label.name)
return loss, label_nignore return loss, label_nignore
def get_cards(args):
if args.enable_ce:
cards = os.environ.get('CUDA_VISIBLE_DEVICES')
num = len(cards.split(","))
return num
else:
return args.num_devices
CityscapeDataset = reader.CityscapeDataset
parser = argparse.ArgumentParser()
add_arguments()
args = parser.parse_args() args = parser.parse_args()
utility.print_arguments(args)
models.clean() models.clean()
models.bn_momentum = 0.9997 models.bn_momentum = 0.9997
models.dropout_keep_prop = 0.9 models.dropout_keep_prop = 0.9
models.label_number = args.num_classes models.label_number = args.num_classes
models.default_norm_type = args.norm_type
deeplabv3p = models.deeplabv3p deeplabv3p = models.deeplabv3p
sp = fluid.Program() sp = fluid.Program()
...@@ -133,9 +124,14 @@ weight_decay = 0.00004 ...@@ -133,9 +124,14 @@ weight_decay = 0.00004
base_lr = args.base_lr base_lr = args.base_lr
total_step = args.total_step total_step = args.total_step
no_grad_set = set()
with fluid.program_guard(tp, sp): with fluid.program_guard(tp, sp):
if args.use_py_reader:
batch_size_each = batch_size // fluid.core.get_cuda_device_count()
py_reader = fluid.layers.py_reader(capacity=64,
shapes=[[batch_size_each, 3] + image_shape, [batch_size_each] + image_shape],
dtypes=['float32', 'int32'])
img, label = fluid.layers.read_file(py_reader)
else:
img = fluid.layers.data( img = fluid.layers.data(
name='img', shape=[3] + image_shape, dtype='float32') name='img', shape=[3] + image_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=image_shape, dtype='int32') label = fluid.layers.data(name='label', shape=image_shape, dtype='int32')
...@@ -154,11 +150,21 @@ with fluid.program_guard(tp, sp): ...@@ -154,11 +150,21 @@ with fluid.program_guard(tp, sp):
lr, lr,
momentum=0.9, momentum=0.9,
regularization=fluid.regularizer.L2DecayRegularizer( regularization=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=weight_decay), ) regularization_coeff=weight_decay))
retv = opt.minimize(loss_mean, startup_program=sp, no_grad_set=no_grad_set) optimize_ops, params_grads = opt.minimize(loss_mean, startup_program=sp)
# ir memory optimizer has some issues, we need to seed grad persistable to
fluid.memory_optimize( # avoid this issue
tp, print_log=False, skip_opt_set=set([pred.name, loss_mean.name]), level=1) for p,g in params_grads: g.persistable = True
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = fluid.core.get_cuda_device_count()
exec_strategy.num_iteration_per_drop_scope = 100
build_strategy = fluid.BuildStrategy()
if args.memory_optimize:
build_strategy.fuse_relu_depthwise_conv = True
build_strategy.enable_inplace = True
build_strategy.memory_optimize = True
place = fluid.CPUPlace() place = fluid.CPUPlace()
if args.use_gpu: if args.use_gpu:
...@@ -170,47 +176,59 @@ if args.init_weights_path: ...@@ -170,47 +176,59 @@ if args.init_weights_path:
print("load from:", args.init_weights_path) print("load from:", args.init_weights_path)
load_model() load_model()
dataset = CityscapeDataset(args.dataset_path, 'train') dataset = reader.CityscapeDataset(args.dataset_path, 'train')
if args.parallel: if args.parallel:
exe_p = fluid.ParallelExecutor( binary = fluid.compiler.CompiledProgram(tp).with_data_parallel(
use_cuda=True, loss_name=loss_mean.name, main_program=tp) loss_name=loss_mean.name,
build_strategy=build_strategy,
batches = dataset.get_batch_generator(batch_size, total_step) exec_strategy=exec_strategy)
else:
binary = fluid.compiler.CompiledProgram(main)
if args.use_py_reader:
assert(batch_size % fluid.core.get_cuda_device_count() == 0)
def data_gen():
batches = dataset.get_batch_generator(
batch_size // fluid.core.get_cuda_device_count(),
total_step * fluid.core.get_cuda_device_count())
for b in batches:
yield b[1], b[2]
py_reader.decorate_tensor_provider(data_gen)
py_reader.start()
else:
batches = dataset.get_batch_generator(batch_size, total_step)
total_time = 0.0 total_time = 0.0
epoch_idx = 0 epoch_idx = 0
train_loss = 0 train_loss = 0
for i, imgs, labels, names in batches: with profile_context(args.profile):
for i in range(total_step):
epoch_idx += 1 epoch_idx += 1
begin_time = time.time() begin_time = time.time()
prev_start_time = time.time() prev_start_time = time.time()
if args.parallel: if not args.use_py_reader:
retv = exe_p.run(fetch_list=[pred.name, loss_mean.name], _, imgs, labels, names = next(batches)
train_loss, = exe.run(binary,
feed={'img': imgs, feed={'img': imgs,
'label': labels}) 'label': labels}, fetch_list=[loss_mean])
else: else:
retv = exe.run(tp, train_loss, = exe.run(binary, fetch_list=[loss_mean])
feed={'img': imgs, train_loss = np.mean(train_loss)
'label': labels},
fetch_list=[pred, loss_mean])
end_time = time.time() end_time = time.time()
total_time += end_time - begin_time total_time += end_time - begin_time
if i % 100 == 0: if i % 100 == 0:
print("Model is saved to", args.save_weights_path) print("Model is saved to", args.save_weights_path)
save_model() save_model()
print("step {:d}, loss: {:.6f}, step_time_cost: {:.3f}".format( print("step {:d}, loss: {:.6f}, step_time_cost: {:.3f}".format(
i, np.mean(retv[1]), end_time - prev_start_time)) i, train_loss, end_time - prev_start_time))
# only for ce print("Training done. Model is saved to", args.save_weights_path)
train_loss = np.mean(retv[1]) save_model()
py_reader.stop()
if args.enable_ce: if args.enable_ce:
gpu_num = get_cards(args) gpu_num = fluid.core.get_cuda_device_count()
print("kpis\teach_pass_duration_card%s\t%s" % print("kpis\teach_pass_duration_card%s\t%s" %
(gpu_num, total_time / epoch_idx)) (gpu_num, total_time / epoch_idx))
print("kpis\ttrain_loss_card%s\t%s" % (gpu_num, train_loss)) print("kpis\ttrain_loss_card%s\t%s" % (gpu_num, train_loss))
print("Training done. Model is saved to", args.save_weights_path)
save_model()
fluid/PaddleCV/deeplabv3+/utility.py 0 → 100644
浏览文件 @ 12a426be
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import distutils.util
import six
def print_arguments(args):
"""Print argparse's arguments.
Usage:
.. code-block:: python
parser = argparse.ArgumentParser()
parser.add_argument("name", default="Jonh", type=str, help="User name.")
args = parser.parse_args()
print_arguments(args)
:param args: Input argparse.Namespace for printing.
:type args: argparse.Namespace
"""
print("----------- Configuration Arguments -----------")
for arg, value in sorted(six.iteritems(vars(args))):
print("%s: %s" % (arg, value))
print("------------------------------------------------")
def add_arguments(argname, type, default, help, argparser, **kwargs):
"""Add argparse's argument.
Usage:
.. code-block:: python
parser = argparse.ArgumentParser()
add_argument("name", str, "Jonh", "User name.", parser)
args = parser.parse_args()
"""
type = distutils.util.strtobool if type == bool else type
argparser.add_argument(
"--" + argname,
default=default,
type=type,
help=help + ' Default: %(default)s.',
**kwargs)
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