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未验证 提交 e64bd9c4 编写于 作者: Y Yibing Liu 提交者： GitHub
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Fix core.xxx usage in cv (#2181)

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PaddleCV/image_classification/dist_train/dist_train.py
浏览文件 @ e64bd9c4
......@@ -23,7 +23,6 @@ import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import six
import sys
sys.path.append("..")
......@@ -35,6 +34,7 @@ from batch_merge import copyback_repeat_bn_params, append_bn_repeat_init_op
from dist_utils import pserver_prepare, nccl2_prepare
from env import dist_env
def parse_args():
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
......@@ -74,6 +74,7 @@ def parse_args():
args = parser.parse_args()
return args
def get_device_num():
if os.getenv("CPU_NUM"):
return int(os.getenv("CPU_NUM"))
......@@ -81,24 +82,24 @@ def get_device_num():
if visible_device:
device_num = len(visible_device.split(','))
else:
device_num = subprocess.check_output(['nvidia-smi', '-L']).decode().count('\n')
device_num = subprocess.check_output(
['nvidia-smi', '-L']).decode().count('\n')
return device_num
def prepare_reader(is_train, pyreader, args, pass_id=1):
# NOTE: always use infinite reader for dist training
if is_train:
reader = train(data_dir=args.data_dir, pass_id_as_seed=pass_id,
infinite=True)
reader = train(
data_dir=args.data_dir, pass_id_as_seed=pass_id, infinite=True)
else:
reader = val(data_dir=args.data_dir)
if is_train:
bs = args.batch_size / get_device_num()
else:
bs = 16
pyreader.decorate_paddle_reader(
paddle.batch(
reader,
batch_size=bs))
pyreader.decorate_paddle_reader(paddle.batch(reader, batch_size=bs))
def build_program(is_train, main_prog, startup_prog, args):
pyreader = None
......@@ -118,9 +119,11 @@ def build_program(is_train, main_prog, startup_prog, args):
image, label = fluid.layers.read_file(pyreader)
if args.fp16:
image = fluid.layers.cast(image, "float16")
model_def = models.__dict__[args.model](layers=50, is_train=is_train)
model_def = models.__dict__[args.model](layers=50,
is_train=is_train)
predict = model_def.net(image, class_dim=class_dim)
cost, pred = fluid.layers.softmax_with_cross_entropy(predict, label, return_softmax=True)
cost, pred = fluid.layers.softmax_with_cross_entropy(
predict, label, return_softmax=True)
if args.scale_loss > 1:
avg_cost = fluid.layers.mean(x=cost) * float(args.scale_loss)
else:
......@@ -140,20 +143,20 @@ def build_program(is_train, main_prog, startup_prog, args):
total_images = args.total_images / trainer_count
if os.getenv("FLAGS_selected_gpus"):
step = int(total_images / (args.batch_size / device_num_per_worker * args.multi_batch_repeat) + 1)
step = int(total_images /
(args.batch_size / device_num_per_worker *
args.multi_batch_repeat) + 1)
else:
step = int(total_images / (args.batch_size * args.multi_batch_repeat) + 1)
step = int(total_images / (args.batch_size *
args.multi_batch_repeat) + 1)
warmup_steps = step * 5 # warmup 5 passes
epochs = [30, 60, 80]
bd = [step * e for e in epochs]
base_lr = end_lr
lr = []
lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
print("start lr: %s, end lr: %s, decay boundaries: %s" % (
start_lr,
end_lr,
bd
))
print("start lr: %s, end lr: %s, decay boundaries: %s" %
(start_lr, end_lr, bd))
# NOTE: we put weight decay in layers config, and remove
# weight decay on bn layers, so don't add weight decay in
......@@ -162,7 +165,9 @@ def build_program(is_train, main_prog, startup_prog, args):
learning_rate=utils.learning_rate.lr_warmup(
fluid.layers.piecewise_decay(
boundaries=bd, values=lr),
warmup_steps, start_lr, end_lr),
warmup_steps,
start_lr,
end_lr),
momentum=0.9)
if args.enable_dgc:
......@@ -170,7 +175,9 @@ def build_program(is_train, main_prog, startup_prog, args):
learning_rate=utils.learning_rate.lr_warmup(
fluid.layers.piecewise_decay(
boundaries=bd, values=lr),
warmup_steps, start_lr, end_lr),
warmup_steps,
start_lr,
end_lr),
momentum=0.9,
sparsity=[0.999, 0.999],
rampup_begin_step=args.rampup_begin_step)
......@@ -178,10 +185,14 @@ def build_program(is_train, main_prog, startup_prog, args):
if args.fp16:
params_grads = optimizer.backward(avg_cost)
master_params_grads = utils.create_master_params_grads(
params_grads, main_prog, startup_prog, args.scale_loss,
reduce_master_grad = args.reduce_master_grad)
params_grads,
main_prog,
startup_prog,
args.scale_loss,
reduce_master_grad=args.reduce_master_grad)
optimizer.apply_gradients(master_params_grads)
utils.master_param_to_train_param(master_params_grads, params_grads, main_prog)
utils.master_param_to_train_param(master_params_grads,
params_grads, main_prog)
else:
optimizer.minimize(avg_cost)
......@@ -208,6 +219,7 @@ def test_single(exe, test_prog, args, pyreader, fetch_list):
test_avg_loss = np.mean(np.array(test_losses))
return test_avg_loss, np.mean(acc1.eval()), np.mean(acc5.eval())
def test_parallel(exe, test_prog, args, pyreader, fetch_list):
acc1 = fluid.metrics.Accuracy()
acc5 = fluid.metrics.Accuracy()
......@@ -231,16 +243,20 @@ def run_pserver(train_prog, startup_prog):
server_exe.run(startup_prog)
server_exe.run(train_prog)
def train_parallel(args):
train_prog = fluid.Program()
test_prog = fluid.Program()
startup_prog = fluid.Program()
train_pyreader, train_cost, train_acc1, train_acc5 = build_program(True, train_prog, startup_prog, args)
test_pyreader, test_cost, test_acc1, test_acc5 = build_program(False, test_prog, startup_prog, args)
train_pyreader, train_cost, train_acc1, train_acc5 = build_program(
True, train_prog, startup_prog, args)
test_pyreader, test_cost, test_acc1, test_acc5 = build_program(
False, test_prog, startup_prog, args)
if args.update_method == "pserver":
train_prog, startup_prog = pserver_prepare(args, train_prog, startup_prog)
train_prog, startup_prog = pserver_prepare(args, train_prog,
startup_prog)
elif args.update_method == "nccl2":
nccl2_prepare(args, startup_prog, main_prog=train_prog)
......@@ -253,15 +269,17 @@ def train_parallel(args):
gpu_id = 0
if os.getenv("FLAGS_selected_gpus"):
gpu_id = int(os.getenv("FLAGS_selected_gpus"))
place = core.CUDAPlace(gpu_id) if args.use_gpu else core.CPUPlace()
place = fluid.CUDAPlace(gpu_id) if args.use_gpu else fluid.CPUPlace()
startup_exe = fluid.Executor(place)
if args.multi_batch_repeat > 1:
append_bn_repeat_init_op(train_prog, startup_prog, args.multi_batch_repeat)
append_bn_repeat_init_op(train_prog, startup_prog,
args.multi_batch_repeat)
startup_exe.run(startup_prog)
if args.checkpoint:
fluid.io.load_persistables(startup_exe, args.checkpoint, main_program=train_prog)
fluid.io.load_persistables(
startup_exe, args.checkpoint, main_program=train_prog)
strategy = fluid.ExecutionStrategy()
strategy.num_threads = args.num_threads
......@@ -274,7 +292,8 @@ def train_parallel(args):
build_strategy = fluid.BuildStrategy()
build_strategy.enable_inplace = False
build_strategy.memory_optimize = False
build_strategy.enable_sequential_execution = bool(args.enable_sequential_execution)
build_strategy.enable_sequential_execution = bool(
args.enable_sequential_execution)
if args.reduce_strategy == "reduce":
build_strategy.reduce_strategy = fluid.BuildStrategy(
......@@ -324,9 +343,11 @@ def train_parallel(args):
# 1. MP mode, batch size for current process should be args.batch_size / GPUs
# 2. SP/PG mode, batch size for each process should be original args.batch_size
if os.getenv("FLAGS_selected_gpus"):
steps_per_pass = args.total_images / (args.batch_size / get_device_num()) / args.dist_env["num_trainers"]
steps_per_pass = args.total_images / (
args.batch_size / get_device_num()) / args.dist_env["num_trainers"]
else:
steps_per_pass = args.total_images / args.batch_size / args.dist_env["num_trainers"]
steps_per_pass = args.total_images / args.batch_size / args.dist_env[
"num_trainers"]
for pass_id in range(args.num_epochs):
num_samples = 0
......@@ -339,9 +360,11 @@ def train_parallel(args):
if batch_id % 30 == 0:
fetch_ret = exe.run(fetch_list)
fetched_data = [np.mean(np.array(d)) for d in fetch_ret]
print("Pass [%d/%d], batch [%d/%d], loss %s, acc1: %s, acc5: %s, avg batch time %.4f" %
(pass_id, args.num_epochs, batch_id, steps_per_pass, fetched_data[0], fetched_data[1],
fetched_data[2], (time.time()-start_time) / batch_id))
print(
"Pass [%d/%d], batch [%d/%d], loss %s, acc1: %s, acc5: %s, avg batch time %.4f"
% (pass_id, args.num_epochs, batch_id, steps_per_pass,
fetched_data[0], fetched_data[1], fetched_data[2],
(time.time() - start_time) / batch_id))
else:
fetch_ret = exe.run([])
except fluid.core.EOFException:
......@@ -359,7 +382,8 @@ def train_parallel(args):
if args.multi_batch_repeat > 1:
copyback_repeat_bn_params(train_prog)
test_fetch_list = [test_cost.name, test_acc1.name, test_acc5.name]
test_ret = test_single(startup_exe, test_prog, args, test_pyreader,test_fetch_list)
test_ret = test_single(startup_exe, test_prog, args, test_pyreader,
test_fetch_list)
# NOTE: switch to below line if you use ParallelExecutor to run test.
# test_ret = test_parallel(test_exe, test_prog, args, test_pyreader,test_fetch_list)
print("Pass: %d, Test Loss %s, test acc1: %s, test acc5: %s\n" %
......@@ -369,7 +393,8 @@ def train_parallel(args):
print("saving model to ", model_path)
if not os.path.isdir(model_path):
os.makedirs(model_path)
fluid.io.save_persistables(startup_exe, model_path, main_program=train_prog)
fluid.io.save_persistables(
startup_exe, model_path, main_program=train_prog)
train_pyreader.reset()
startup_exe.close()
print("total train time: ", time.time() - over_all_start)
......@@ -397,6 +422,6 @@ def main():
args.dist_env = dist_env()
train_parallel(args)
if __name__ == "__main__":
main()
PaddleCV/image_classification/fast_imagenet/train.py
浏览文件 @ e64bd9c4
......@@ -23,7 +23,6 @@ import torchvision_reader
import torch
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.profiler as profiler
import paddle.fluid.transpiler.distribute_transpiler as distribute_transpiler
......@@ -34,6 +33,7 @@ import functools
from models.fast_imagenet import FastImageNet, lr_decay
import utils
def parse_args():
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
......@@ -61,6 +61,7 @@ def parse_args():
args = parser.parse_args()
return args
def get_device_num():
import subprocess
visible_device = os.getenv('CUDA_VISIBLE_DEVICES')
......@@ -71,8 +72,10 @@ def get_device_num():
['nvidia-smi', '-L']).decode().count('\n')
return device_num
DEVICE_NUM = get_device_num()
def test_parallel(exe, test_args, args, test_reader, feeder, bs):
acc_evaluators = []
for i in xrange(len(test_args[2])):
......@@ -86,18 +89,26 @@ def test_parallel(exe, test_args, args, test_reader, feeder, bs):
ret_result = [np.mean(np.array(ret)) for ret in acc_rets]
print("Test batch: [%d], acc_rets: [%s]" % (batch_id, ret_result))
for i, e in enumerate(acc_evaluators):
e.update(
value=np.array(acc_rets[i]), weight=bs)
e.update(value=np.array(acc_rets[i]), weight=bs)
num_samples = batch_id * bs * DEVICE_NUM
print_train_time(start_ts, time.time(), num_samples)
return [e.eval() for e in acc_evaluators]
def build_program(args, is_train, main_prog, startup_prog, py_reader_startup_prog, sz, trn_dir, bs, min_scale, rect_val=False):
def build_program(args,
is_train,
main_prog,
startup_prog,
py_reader_startup_prog,
sz,
trn_dir,
bs,
min_scale,
rect_val=False):
dshape=[3, sz, sz]
class_dim=1000
dshape = [3, sz, sz]
class_dim = 1000
pyreader = None
with fluid.program_guard(main_prog, startup_prog):
with fluid.unique_name.guard():
......@@ -108,23 +119,33 @@ def build_program(args, is_train, main_prog, startup_prog, py_reader_startup_pro
capacity=bs * DEVICE_NUM,
shapes=([-1] + dshape, (-1, 1)),
dtypes=('uint8', 'int64'),
name="train_reader_" + str(sz) if is_train else "test_reader_" + str(sz),
name="train_reader_" + str(sz)
if is_train else "test_reader_" + str(sz),
use_double_buffer=True)
input, label = fluid.layers.read_file(pyreader)
else:
input = fluid.layers.data(name="image", shape=[3, 244, 244], dtype="uint8")
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
input = fluid.layers.data(
name="image", shape=[3, 244, 244], dtype="uint8")
label = fluid.layers.data(
name="label", shape=[1], dtype="int64")
cast_img_type = "float16" if args.fp16 else "float32"
cast = fluid.layers.cast(input, cast_img_type)
img_mean = fluid.layers.create_global_var([3, 1, 1], 0.0, cast_img_type, name="img_mean", persistable=True)
img_std = fluid.layers.create_global_var([3, 1, 1], 0.0, cast_img_type, name="img_std", persistable=True)
img_mean = fluid.layers.create_global_var(
[3, 1, 1],
0.0,
cast_img_type,
name="img_mean",
persistable=True)
img_std = fluid.layers.create_global_var(
[3, 1, 1], 0.0, cast_img_type, name="img_std", persistable=True)
# image = (image - (mean * 255.0)) / (std * 255.0)
t1 = fluid.layers.elementwise_sub(cast, img_mean, axis=1)
t2 = fluid.layers.elementwise_div(t1, img_std, axis=1)
model = FastImageNet(is_train=is_train)
predict = model.net(t2, class_dim=class_dim, img_size=sz)
cost, pred = fluid.layers.softmax_with_cross_entropy(predict, label, return_softmax=True)
cost, pred = fluid.layers.softmax_with_cross_entropy(
predict, label, return_softmax=True)
if args.scale_loss > 1:
avg_cost = fluid.layers.mean(x=cost) * float(args.scale_loss)
else:
......@@ -139,22 +160,29 @@ def build_program(args, is_train, main_prog, startup_prog, py_reader_startup_pro
total_images = args.total_images
lr = args.lr
epochs = [(0,7), (7,13), (13, 22), (22, 25), (25, 28)]
bs_epoch = [bs*DEVICE_NUM for bs in [224, 224, 96, 96, 50]]
bs_scale = [bs*1.0 / bs_epoch[0] for bs in bs_epoch]
lrs = [(lr, lr*2), (lr*2, lr/4), (lr*bs_scale[2], lr/10*bs_scale[2]), (lr/10*bs_scale[2], lr/100*bs_scale[2]), (lr/100*bs_scale[4], lr/1000*bs_scale[4]), lr/1000*bs_scale[4]]
epochs = [(0, 7), (7, 13), (13, 22), (22, 25), (25, 28)]
bs_epoch = [bs * DEVICE_NUM for bs in [224, 224, 96, 96, 50]]
bs_scale = [bs * 1.0 / bs_epoch[0] for bs in bs_epoch]
lrs = [(lr, lr * 2), (lr * 2, lr / 4),
(lr * bs_scale[2], lr / 10 * bs_scale[2]),
(lr / 10 * bs_scale[2], lr / 100 * bs_scale[2]),
(lr / 100 * bs_scale[4], lr / 1000 * bs_scale[4]),
lr / 1000 * bs_scale[4]]
boundaries, values = lr_decay(lrs, epochs, bs_epoch, total_images)
boundaries, values = lr_decay(lrs, epochs, bs_epoch,
total_images)
optimizer = fluid.optimizer.Momentum(
learning_rate=fluid.layers.piecewise_decay(boundaries=boundaries, values=values),
learning_rate=fluid.layers.piecewise_decay(
boundaries=boundaries, values=values),
momentum=0.9)
if args.fp16:
params_grads = optimizer.backward(avg_cost)
master_params_grads = utils.create_master_params_grads(
params_grads, main_prog, startup_prog, args.scale_loss)
optimizer.apply_gradients(master_params_grads)
utils.master_param_to_train_param(master_params_grads, params_grads, main_prog)
utils.master_param_to_train_param(master_params_grads,
params_grads, main_prog)
else:
optimizer.minimize(avg_cost)
......@@ -164,35 +192,67 @@ def build_program(args, is_train, main_prog, startup_prog, py_reader_startup_pro
return avg_cost, optimizer, [batch_acc1, batch_acc5], pyreader
def refresh_program(args, epoch, sz, trn_dir, bs, val_bs, need_update_start_prog=False, min_scale=0.08, rect_val=False):
print('refresh program: epoch: [%d], image size: [%d], trn_dir: [%s], batch_size:[%d]' % (epoch, sz, trn_dir, bs))
def refresh_program(args,
epoch,
sz,
trn_dir,
bs,
val_bs,
need_update_start_prog=False,
min_scale=0.08,
rect_val=False):
print(
'refresh program: epoch: [%d], image size: [%d], trn_dir: [%s], batch_size:[%d]'
% (epoch, sz, trn_dir, bs))
train_prog = fluid.Program()
test_prog = fluid.Program()
startup_prog = fluid.Program()
py_reader_startup_prog = fluid.Program()
train_args = build_program(args, True, train_prog, startup_prog, py_reader_startup_prog, sz, trn_dir, bs, min_scale)
test_args = build_program(args, False, test_prog, startup_prog, py_reader_startup_prog, sz, trn_dir, val_bs, min_scale, rect_val=rect_val)
place = core.CUDAPlace(0)
train_args = build_program(args, True, train_prog, startup_prog,
py_reader_startup_prog, sz, trn_dir, bs,
min_scale)
test_args = build_program(
args,
False,
test_prog,
startup_prog,
py_reader_startup_prog,
sz,
trn_dir,
val_bs,
min_scale,
rect_val=rect_val)
place = fluid.CUDAPlace(0)
startup_exe = fluid.Executor(place)
startup_exe.run(py_reader_startup_prog)
if need_update_start_prog:
startup_exe.run(startup_prog)
conv2d_w_vars = [var for var in startup_prog.global_block().vars.values() if var.name.startswith('conv2d_')]
conv2d_w_vars = [
var for var in startup_prog.global_block().vars.values()
if var.name.startswith('conv2d_')
]
for var in conv2d_w_vars:
torch_w = torch.empty(var.shape)
kaiming_np = torch.nn.init.kaiming_normal_(torch_w, mode='fan_out', nonlinearity='relu').numpy()
kaiming_np = torch.nn.init.kaiming_normal_(
torch_w, mode='fan_out', nonlinearity='relu').numpy()
tensor = fluid.global_scope().find_var(var.name).get_tensor()
if args.fp16:
tensor.set(np.array(kaiming_np, dtype="float16").view(np.uint16), place)
tensor.set(np.array(
kaiming_np, dtype="float16").view(np.uint16),
place)
else:
tensor.set(np.array(kaiming_np, dtype="float32"), place)
np_tensors = {}
np_tensors["img_mean"] = np.array([0.485 * 255.0, 0.456 * 255.0, 0.406 * 255.0]).astype("float16" if args.fp16 else "float32").reshape((3, 1, 1))
np_tensors["img_std"] = np.array([0.229 * 255.0, 0.224 * 255.0, 0.225 * 255.0]).astype("float16" if args.fp16 else "float32").reshape((3, 1, 1))
np_tensors["img_mean"] = np.array(
[0.485 * 255.0, 0.456 * 255.0, 0.406 * 255.0]).astype(
"float16" if args.fp16 else "float32").reshape((3, 1, 1))
np_tensors["img_std"] = np.array(
[0.229 * 255.0, 0.224 * 255.0, 0.225 * 255.0]).astype(
"float16" if args.fp16 else "float32").reshape((3, 1, 1))
for vname, np_tensor in np_tensors.items():
var = fluid.global_scope().find_var(vname)
if args.fp16:
......@@ -200,13 +260,13 @@ def refresh_program(args, epoch, sz, trn_dir, bs, val_bs, need_update_start_prog
else:
var.get_tensor().set(np_tensor, place)
strategy = fluid.ExecutionStrategy()
strategy.num_threads = args.num_threads
strategy.allow_op_delay = False
strategy.num_iteration_per_drop_scope = 1
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy().ReduceStrategy.AllReduce
build_strategy.reduce_strategy = fluid.BuildStrategy(
).ReduceStrategy.AllReduce
avg_loss = train_args[0]
train_exe = fluid.ParallelExecutor(
......@@ -220,14 +280,25 @@ def refresh_program(args, epoch, sz, trn_dir, bs, val_bs, need_update_start_prog
return train_args, test_args, test_prog, train_exe, test_exe
def prepare_reader(epoch_id, train_py_reader, train_bs, val_bs, trn_dir, img_dim, min_scale, rect_val):
def prepare_reader(epoch_id, train_py_reader, train_bs, val_bs, trn_dir,
img_dim, min_scale, rect_val):
train_reader = torchvision_reader.train(
traindir="/data/imagenet/%strain" % trn_dir, sz=img_dim, min_scale=min_scale, shuffle_seed=epoch_id+1)
train_py_reader.decorate_paddle_reader(paddle.batch(train_reader, batch_size=train_bs))
traindir="/data/imagenet/%strain" % trn_dir,
sz=img_dim,
min_scale=min_scale,
shuffle_seed=epoch_id + 1)
train_py_reader.decorate_paddle_reader(
paddle.batch(
train_reader, batch_size=train_bs))
test_reader = torchvision_reader.test(
valdir="/data/imagenet/%svalidation" % trn_dir, bs=val_bs*DEVICE_NUM, sz=img_dim, rect_val=rect_val)
test_batched_reader = paddle.batch(test_reader, batch_size=val_bs * DEVICE_NUM)
valdir="/data/imagenet/%svalidation" % trn_dir,
bs=val_bs * DEVICE_NUM,
sz=img_dim,
rect_val=rect_val)
test_batched_reader = paddle.batch(
test_reader, batch_size=val_bs * DEVICE_NUM)
return test_batched_reader
......@@ -244,27 +315,49 @@ def train_parallel(args):
bs = 224
val_bs = 64
trn_dir = "sz/160/"
img_dim=128
min_scale=0.08
rect_val=False
img_dim = 128
min_scale = 0.08
rect_val = False
for epoch_id in range(args.num_epochs):
# refresh program
if epoch_id == 0:
train_args, test_args, test_prog, exe, test_exe = refresh_program(args, epoch_id, sz=img_dim, trn_dir=trn_dir, bs=bs, val_bs=val_bs, need_update_start_prog=True)
train_args, test_args, test_prog, exe, test_exe = refresh_program(
args,
epoch_id,
sz=img_dim,
trn_dir=trn_dir,
bs=bs,
val_bs=val_bs,
need_update_start_prog=True)
elif epoch_id == 13: #13
bs = 96
trn_dir="sz/352/"
img_dim=224
min_scale=0.087
train_args, test_args, test_prog, exe, test_exe = refresh_program(args, epoch_id, sz=img_dim, trn_dir=trn_dir, bs=bs, val_bs=val_bs, min_scale=min_scale)
trn_dir = "sz/352/"
img_dim = 224
min_scale = 0.087
train_args, test_args, test_prog, exe, test_exe = refresh_program(
args,
epoch_id,
sz=img_dim,
trn_dir=trn_dir,
bs=bs,
val_bs=val_bs,
min_scale=min_scale)
elif epoch_id == 25: #25
bs = 50
val_bs=8
trn_dir=""
img_dim=288
min_scale=0.5
rect_val=True
train_args, test_args, test_prog, exe, test_exe = refresh_program(args, epoch_id, sz=img_dim, trn_dir=trn_dir, bs=bs, val_bs=val_bs, min_scale=min_scale, rect_val=rect_val)
val_bs = 8
trn_dir = ""
img_dim = 288
min_scale = 0.5
rect_val = True
train_args, test_args, test_prog, exe, test_exe = refresh_program(
args,
epoch_id,
sz=img_dim,
trn_dir=trn_dir,
bs=bs,
val_bs=val_bs,
min_scale=min_scale,
rect_val=rect_val)
else:
pass
......@@ -273,7 +366,15 @@ def train_parallel(args):
iters = 0
start_time = time.time()
train_py_reader = train_args[3]
test_reader = prepare_reader(epoch_id, train_py_reader, bs, val_bs, trn_dir, img_dim=img_dim, min_scale=min_scale, rect_val=rect_val)
test_reader = prepare_reader(
epoch_id,
train_py_reader,
bs,
val_bs,
trn_dir,
img_dim=img_dim,
min_scale=min_scale,
rect_val=rect_val)
train_py_reader.start() # start pyreader
batch_start_time = time.time()
while True:
......@@ -304,20 +405,31 @@ def train_parallel(args):
if should_print:
fetched_data = [np.mean(np.array(d)) for d in fetch_ret]
print("Epoch %d, batch %d, loss %s, accucacys: %s, learning_rate %s, py_reader queue_size: %d, avg batch time: %0.4f secs" %
(epoch_id, iters, fetched_data[0], fetched_data[1:-1], fetched_data[-1], train_py_reader.queue.size(), (time.time() - batch_start_time)*1.0/args.log_period))
print(
"Epoch %d, batch %d, loss %s, accucacys: %s, learning_rate %s, py_reader queue_size: %d, avg batch time: %0.4f secs"
% (epoch_id, iters, fetched_data[0], fetched_data[1:-1],
fetched_data[-1], train_py_reader.queue.size(),
(time.time() - batch_start_time) * 1.0 /
args.log_period))
batch_start_time = time.time()
iters += 1
print_train_time(start_time, time.time(), num_samples)
feed_list = [test_prog.global_block().var(varname) for varname in ("image", "label")]
test_feeder = fluid.DataFeeder(feed_list=feed_list, place=fluid.CUDAPlace(0))
test_ret = test_parallel(test_exe, test_args, args, test_reader, test_feeder, val_bs)
feed_list = [
test_prog.global_block().var(varname)
for varname in ("image", "label")
]
test_feeder = fluid.DataFeeder(
feed_list=feed_list, place=fluid.CUDAPlace(0))
test_ret = test_parallel(test_exe, test_args, args, test_reader,
test_feeder, val_bs)
test_acc1, test_acc5 = [np.mean(np.array(v)) for v in test_ret]
print("Epoch: %d, Test Accuracy: %s, Spend %.2f hours\n" %
(epoch_id, [test_acc1, test_acc5], (time.time() - over_all_start) / 3600))
(epoch_id, [test_acc1, test_acc5],
(time.time() - over_all_start) / 3600))
if np.mean(np.array(test_ret[1])) > args.best_acc5:
print("Achieve the best top-1 acc %f, top-5 acc: %f" % (test_acc1, test_acc5))
print("Achieve the best top-1 acc %f, top-5 acc: %f" %
(test_acc1, test_acc5))
break
print("total train time: ", time.time() - over_all_start)
......
PaddleRL/DeepQNetwork/play.py
浏览文件 @ e64bd9c4
......@@ -44,7 +44,7 @@ if __name__ == '__main__':
place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
inference_scope = fluid.Scope()
with fluid.scope_guard(inference_scope):
[predict_program, feed_names,
fetch_targets] = fluid.io.load_inference_model(args.model_path, exe)
......
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