def test(args):
if args.dataset == 'coco':
import lib.coco_reader as reader
IMAGE_SIZE = [288, 384]
FLIP_PAIRS = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12],
[13, 14], [15, 16]]
args.kp_dim = 17
elif args.dataset == 'mpii':
import lib.mpii_reader as reader
IMAGE_SIZE = [384, 384]
FLIP_PAIRS = [[0, 5], [1, 4], [2, 3], [10, 15], [11, 14], [12, 13]]
args.kp_dim = 16
else:
raise ValueError('The dataset {} is not supported yet.'.format(
args.dataset))
print_arguments(args)
# Image and target
image = layers.data(name='image',
shape=[3, IMAGE_SIZE[1], IMAGE_SIZE[0]],
dtype='float32')
file_id = layers.data(name='file_id', shape=[
1,
], dtype='int')
# Build model
model = pose_resnet.ResNet(layers=50, kps_num=args.kp_dim, test_mode=True)
# Output
output = model.net(input=image, target=None, target_weight=None)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if args.checkpoint is not None:
fluid.io.load_persistables(exe, args.checkpoint)
# Dataloader
test_reader = paddle.batch(reader.test(), batch_size=args.batch_size)
feeder = fluid.DataFeeder(place=place, feed_list=[image, file_id])
test_exe = fluid.ParallelExecutor(
use_cuda=True if args.use_gpu else False,
main_program=fluid.default_main_program().clone(for_test=True),
loss_name=None)
fetch_list = [image.name, output.name]
for batch_id, data in enumerate(test_reader()):
print_immediately("Processing batch #%d" % batch_id)
num_images = len(data)
file_ids = []
for i in range(num_images):
file_ids.append(data[i][1])
input_image, out_heatmaps = test_exe.run(fetch_list=fetch_list,
feed=feeder.feed(data))
if args.flip_test:
# Flip all the images in a same batch
data_fliped = []
for i in range(num_images):
data_fliped.append((data[i][0][:, :, ::-1], data[i][1]))
# Inference again
_, output_flipped = test_exe.run(fetch_list=fetch_list,
feed=feeder.feed(data_fliped))
# Flip back
output_flipped = flip_back(output_flipped, FLIP_PAIRS)
# Feature is not aligned, shift flipped heatmap for higher accuracy
if args.shift_heatmap:
output_flipped[:, :, :, 1:] = \
output_flipped.copy()[:, :, :, 0:-1]
# Aggregate
out_heatmaps = (out_heatmaps + output_flipped) * 0.5
save_predict_results(input_image,
out_heatmaps,
file_ids,
fold_name='results')
def valid(args):
if args.dataset == 'coco':
import lib.coco_reader as reader
IMAGE_SIZE = [288, 384]
HEATMAP_SIZE = [72, 96]
FLIP_PAIRS = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12],
[13, 14], [15, 16]]
args.kp_dim = 17
args.total_images = 144406 # 149813
elif args.dataset == 'mpii':
import lib.mpii_reader as reader
IMAGE_SIZE = [384, 384]
HEATMAP_SIZE = [96, 96]
FLIP_PAIRS = [[0, 5], [1, 4], [2, 3], [10, 15], [11, 14], [12, 13]]
args.kp_dim = 16
args.total_images = 2958 # validation
else:
raise ValueError('The dataset {} is not supported yet.'.format(
args.dataset))
print_arguments(args)
# Image and target
image = layers.data(name='image',
shape=[3, IMAGE_SIZE[1], IMAGE_SIZE[0]],
dtype='float32')
target = layers.data(name='target',
shape=[args.kp_dim, HEATMAP_SIZE[1], HEATMAP_SIZE[0]],
dtype='float32')
target_weight = layers.data(name='target_weight',
shape=[args.kp_dim, 1],
dtype='float32')
center = layers.data(name='center', shape=[
2,
], dtype='float32')
scale = layers.data(name='scale', shape=[
2,
], dtype='float32')
score = layers.data(name='score', shape=[
1,
], dtype='float32')
# Build model
model = pose_resnet.ResNet(layers=50, kps_num=args.kp_dim)
# Output
loss, output = model.net(input=image,
target=target,
target_weight=target_weight)
# Parameters from model and arguments
params = {}
params["total_images"] = args.total_images
params["lr"] = args.lr
params["num_epochs"] = args.num_epochs
params["learning_strategy"] = {}
params["learning_strategy"]["batch_size"] = args.batch_size
params["learning_strategy"]["name"] = args.lr_strategy
if args.with_mem_opt:
fluid.memory_optimize(
fluid.default_main_program(),
skip_opt_set=[loss.name, output.name, target.name])
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
args.pretrained_model = './pretrained/resnet_50/115'
if args.pretrained_model:
def if_exist(var):
exist_flag = os.path.exists(
os.path.join(args.pretrained_model, var.name))
return exist_flag
fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
if args.checkpoint is not None:
fluid.io.load_persistables(exe, args.checkpoint)
# Dataloader
valid_reader = paddle.batch(reader.valid(), batch_size=args.batch_size)
feeder = fluid.DataFeeder(
place=place,
feed_list=[image, target, target_weight, center, scale, score])
valid_exe = fluid.ParallelExecutor(
use_cuda=True if args.use_gpu else False,
main_program=fluid.default_main_program().clone(for_test=False),
loss_name=loss.name)
fetch_list = [image.name, loss.name, output.name, target.name]
# For validation
acc = AverageMeter()
idx = 0
num_samples = args.total_images
all_preds = np.zeros((num_samples, args.kp_dim, 3), dtype=np.float32)
all_boxes = np.zeros((num_samples, 6))
for batch_id, data in enumerate(valid_reader()):
num_images = len(data)
centers = []
scales = []
scores = []
for i in range(num_images):
centers.append(data[i][3])
scales.append(data[i][4])
scores.append(data[i][5])
input_image, loss, out_heatmaps, target_heatmaps = valid_exe.run(
fetch_list=fetch_list, feed=feeder.feed(data))
if args.flip_test:
# Flip all the images in a same batch
data_fliped = []
for i in range(num_images):
# Input, target, target_weight, c, s, score
data_fliped.append((
# np.flip(input_image, 3)[i],
data[i][0][:, :, ::-1],
data[i][1],
data[i][2],
data[i][3],
data[i][4],
data[i][5]))
# Inference again
_, _, output_flipped, _ = valid_exe.run(
fetch_list=fetch_list, feed=feeder.feed(data_fliped))
# Flip back
output_flipped = flip_back(output_flipped, FLIP_PAIRS)
# Feature is not aligned, shift flipped heatmap for higher accuracy
if args.shift_heatmap:
output_flipped[:, :, :, 1:] = \
output_flipped.copy()[:, :, :, 0:-1]
# Aggregate
# out_heatmaps.shape: size[b, args.kp_dim, 96, 96]
out_heatmaps = (out_heatmaps + output_flipped) * 0.5
loss = np.mean(np.array(loss))
# Accuracy
_, avg_acc, cnt, pred = accuracy(out_heatmaps, target_heatmaps)
acc.update(avg_acc, cnt)
# Current center, scale, score
centers = np.array(centers)
scales = np.array(scales)
scores = np.array(scores)
preds, maxvals = get_final_preds(args, out_heatmaps, centers, scales)
all_preds[idx:idx + num_images, :, 0:2] = preds[:, :, 0:2]
all_preds[idx:idx + num_images, :, 2:3] = maxvals
# Double check this all_boxes parts
all_boxes[idx:idx + num_images, 0:2] = centers[:, 0:2]
all_boxes[idx:idx + num_images, 2:4] = scales[:, 0:2]
all_boxes[idx:idx + num_images, 4] = np.prod(scales * 200, 1)
all_boxes[idx:idx + num_images, 5] = scores
# image_path.extend(meta['image'])
idx += num_images
print('Epoch [{:4d}] '
'Loss = {:.5f} '
'Acc = {:.5f}'.format(batch_id, loss, acc.avg))
if batch_id % 10 == 0:
save_batch_heatmaps(input_image,
out_heatmaps,
file_name='*****@*****.**',
normalize=True)
# Evaluate
args.DATAROOT = 'data/mpii'
args.TEST_SET = 'valid'
output_dir = ''
filenames = []
imgnums = []
image_path = []
name_values, perf_indicator = mpii_evaluate(args, all_preds, output_dir,
all_boxes, image_path,
filenames, imgnums)
print_name_value(name_values, perf_indicator)
def train(args):
if args.dataset == 'coco':
import lib.coco_reader as reader
IMAGE_SIZE = [288, 384]
HEATMAP_SIZE = [72, 96]
args.kp_dim = 17
args.total_images = 144406 # 149813
elif args.dataset == 'mpii':
import lib.mpii_reader as reader
IMAGE_SIZE = [384, 384]
HEATMAP_SIZE = [96, 96]
args.kp_dim = 16
args.total_images = 22246
else:
raise ValueError('The dataset {} is not supported yet.'.format(
args.dataset))
print_arguments(args)
# Image and target
image = layers.data(name='image',
shape=[3, IMAGE_SIZE[1], IMAGE_SIZE[0]],
dtype='float32')
target = layers.data(name='target',
shape=[args.kp_dim, HEATMAP_SIZE[1], HEATMAP_SIZE[0]],
dtype='float32')
target_weight = layers.data(name='target_weight',
shape=[args.kp_dim, 1],
dtype='float32')
# Build model
model = pose_resnet.ResNet(layers=50, kps_num=args.kp_dim)
# Output
loss, output = model.net(input=image,
target=target,
target_weight=target_weight)
# Parameters from model and arguments
params = {}
params["total_images"] = args.total_images
params["lr"] = args.lr
params["num_epochs"] = args.num_epochs
params["learning_strategy"] = {}
params["learning_strategy"]["batch_size"] = args.batch_size
params["learning_strategy"]["name"] = args.lr_strategy
# Initialize optimizer
optimizer = optimizer_setting(args, params)
optimizer.minimize(loss)
if args.with_mem_opt:
fluid.memory_optimize(
fluid.default_main_program(),
skip_opt_set=[loss.name, output.name, target.name])
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if args.pretrained_model:
def if_exist(var):
exist_flag = os.path.exists(
os.path.join(args.pretrained_model, var.name))
return exist_flag
fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
if args.checkpoint is not None:
fluid.io.load_persistables(exe, args.checkpoint)
# Dataloader
train_reader = paddle.batch(reader.train(), batch_size=args.batch_size)
feeder = fluid.DataFeeder(place=place,
feed_list=[image, target, target_weight])
train_exe = fluid.ParallelExecutor(
use_cuda=True if args.use_gpu else False, loss_name=loss.name)
fetch_list = [image.name, loss.name, output.name]
for pass_id in range(params["num_epochs"]):
for batch_id, data in enumerate(train_reader()):
current_lr = np.array(paddle.fluid.global_scope().find_var(
'learning_rate').get_tensor())
input_image, loss, out_heatmaps = train_exe.run(
fetch_list, feed=feeder.feed(data))
loss = np.mean(np.array(loss))
print_immediately('Epoch [{:4d}/{:3d}] LR: {:.10f} '
'Loss = {:.5f}'.format(batch_id, pass_id,
current_lr[0], loss))
if batch_id % 10 == 0:
save_batch_heatmaps(input_image,
out_heatmaps,
file_name='*****@*****.**',
normalize=True)
model_path = os.path.join(
args.model_save_dir + '/' + 'simplebase-{}'.format(args.dataset),
str(pass_id))
if not os.path.isdir(model_path):
os.makedirs(model_path)
fluid.io.save_persistables(exe, model_path)
def test(args):
if args.dataset == 'coco':
import lib.coco_reader as reader
IMAGE_SIZE = [288, 384]
# HEATMAP_SIZE = [72, 96]
FLIP_PAIRS = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12],
[13, 14], [15, 16]]
args.kp_dim = 17
args.total_images = 144406 # 149813
elif args.dataset == 'mpii':
import lib.mpii_reader as reader
IMAGE_SIZE = [384, 384]
# HEATMAP_SIZE = [96, 96]
FLIP_PAIRS = [[0, 5], [1, 4], [2, 3], [10, 15], [11, 14], [12, 13]]
args.kp_dim = 16
args.total_images = 2958 # validation
else:
raise ValueError('The dataset {} is not supported yet.'.format(
args.dataset))
print_arguments(args)
# Image and target
image = layers.data(name='image',
shape=[3, IMAGE_SIZE[1], IMAGE_SIZE[0]],
dtype='float32')
file_id = layers.data(name='file_id', shape=[
1,
], dtype='int')
# Build model
model = pose_resnet.ResNet(layers=50, kps_num=args.kp_dim, test_mode=True)
# Output
output = model.net(input=image, target=None, target_weight=None)
# Parameters from model and arguments
params = {}
params["total_images"] = args.total_images
params["lr"] = args.lr
params["num_epochs"] = args.num_epochs
params["learning_strategy"] = {}
params["learning_strategy"]["batch_size"] = args.batch_size
params["learning_strategy"]["name"] = args.lr_strategy
if args.with_mem_opt:
fluid.memory_optimize(fluid.default_main_program(),
skip_opt_set=[output.name])
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
args.pretrained_model = './pretrained/resnet_50/115'
if args.pretrained_model:
def if_exist(var):
exist_flag = os.path.exists(
os.path.join(args.pretrained_model, var.name))
return exist_flag
fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
if args.checkpoint is not None:
fluid.io.load_persistables(exe, args.checkpoint)
# Dataloader
test_reader = paddle.batch(reader.test(), batch_size=args.batch_size)
feeder = fluid.DataFeeder(place=place, feed_list=[image, file_id])
test_exe = fluid.ParallelExecutor(
use_cuda=True if args.use_gpu else False,
main_program=fluid.default_main_program().clone(for_test=False),
loss_name=None)
fetch_list = [image.name, output.name]
for batch_id, data in tqdm(enumerate(test_reader())):
num_images = len(data)
file_ids = []
for i in range(num_images):
file_ids.append(data[i][1])
input_image, out_heatmaps = test_exe.run(fetch_list=fetch_list,
feed=feeder.feed(data))
if args.flip_test:
# Flip all the images in a same batch
data_fliped = []
for i in range(num_images):
data_fliped.append((data[i][0][:, :, ::-1], data[i][1]))
# Inference again
_, output_flipped = test_exe.run(fetch_list=fetch_list,
feed=feeder.feed(data_fliped))
# Flip back
output_flipped = flip_back(output_flipped, FLIP_PAIRS)
# Feature is not aligned, shift flipped heatmap for higher accuracy
if args.shift_heatmap:
output_flipped[:, :, :, 1:] = \
output_flipped.copy()[:, :, :, 0:-1]
# Aggregate
out_heatmaps = (out_heatmaps + output_flipped) * 0.5
save_predict_results(input_image,
out_heatmaps,
file_ids,
fold_name='results')
