def get_estimator(data_dir=None,
model_dir=tempfile.mkdtemp(),
epochs=200,
batch_size_per_gpu=32,
train_steps_per_epoch=None,
eval_steps_per_epoch=None):
num_device = get_num_devices()
train_ds, val_ds = mscoco.load_data(root_dir=data_dir)
train_ds = PreMosaicDataset(mscoco_ds=train_ds)
batch_size = num_device * batch_size_per_gpu
pipeline = fe.Pipeline(
train_data=train_ds,
eval_data=val_ds,
ops=[
ReadImage(inputs=("image1", "image2", "image3", "image4"),
outputs=("image1", "image2", "image3", "image4"),
mode="train"),
ReadImage(inputs="image", outputs="image", mode="eval"),
LongestMaxSize(max_size=640,
image_in="image1",
bbox_in="bbox1",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train"),
LongestMaxSize(max_size=640,
image_in="image2",
bbox_in="bbox2",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train"),
LongestMaxSize(max_size=640,
image_in="image3",
bbox_in="bbox3",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train"),
LongestMaxSize(max_size=640,
image_in="image4",
bbox_in="bbox4",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train"),
LongestMaxSize(max_size=640,
image_in="image",
bbox_in="bbox",
bbox_params=BboxParams("coco", min_area=1.0),
mode="eval"),
PadIfNeeded(min_height=640,
min_width=640,
image_in="image",
bbox_in="bbox",
bbox_params=BboxParams("coco", min_area=1.0),
mode="eval",
border_mode=cv2.BORDER_CONSTANT,
value=(114, 114, 114)),
CombineMosaic(inputs=("image1", "image2", "image3", "image4",
"bbox1", "bbox2", "bbox3", "bbox4"),
outputs=("image", "bbox"),
mode="train"),
CenterCrop(height=640,
width=640,
image_in="image",
bbox_in="bbox",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train"),
Sometimes(
HorizontalFlip(image_in="image",
bbox_in="bbox",
bbox_params=BboxParams("coco", min_area=1.0),
mode="train")),
HSVAugment(inputs="image", outputs="image", mode="train"),
ToArray(inputs="bbox", outputs="bbox", dtype="float32"),
CategoryID2ClassID(inputs="bbox", outputs="bbox"),
GTBox(inputs="bbox",
outputs=("gt_sbbox", "gt_mbbox", "gt_lbbox"),
image_size=640),
Delete(keys=("image1", "image2", "image3", "image4", "bbox1",
"bbox2", "bbox3", "bbox4", "bbox"),
mode="train"),
Delete(keys="image_id", mode="eval"),
Batch(batch_size=batch_size, pad_value=0)
])
init_lr = 1e-2 / 64 * batch_size
model = fe.build(
lambda: YoloV5(w=640, h=640, c=3),
optimizer_fn=lambda x: torch.optim.SGD(
x, lr=init_lr, momentum=0.937, weight_decay=0.0005, nesterov=True),
mixed_precision=True)
network = fe.Network(ops=[
RescaleTranspose(inputs="image", outputs="image"),
ModelOp(model=model,
inputs="image",
outputs=("pred_s", "pred_m", "pred_l")),
DecodePred(inputs=("pred_s", "pred_m", "pred_l"),
outputs=("pred_s", "pred_m", "pred_l")),
ComputeLoss(inputs=("pred_s", "gt_sbbox"),
outputs=("sbbox_loss", "sconf_loss", "scls_loss")),
ComputeLoss(inputs=("pred_m", "gt_mbbox"),
outputs=("mbbox_loss", "mconf_loss", "mcls_loss")),
ComputeLoss(inputs=("pred_l", "gt_lbbox"),
outputs=("lbbox_loss", "lconf_loss", "lcls_loss")),
Average(inputs=("sbbox_loss", "mbbox_loss", "lbbox_loss"),
outputs="bbox_loss"),
Average(inputs=("sconf_loss", "mconf_loss", "lconf_loss"),
outputs="conf_loss"),
Average(inputs=("scls_loss", "mcls_loss", "lcls_loss"),
outputs="cls_loss"),
Average(inputs=("bbox_loss", "conf_loss", "cls_loss"),
outputs="total_loss"),
PredictBox(width=640,
height=640,
inputs=("pred_s", "pred_m", "pred_l"),
outputs="box_pred",
mode="eval"),
UpdateOp(model=model, loss_name="total_loss")
])
traces = [
MeanAveragePrecision(num_classes=80,
true_key='bbox',
pred_key='box_pred',
mode="eval"),
BestModelSaver(model=model,
save_dir=model_dir,
metric='mAP',
save_best_mode="max")
]
lr_schedule = {
1:
LRScheduler(model=model,
lr_fn=lambda step: lr_schedule_warmup(
step,
train_steps_epoch=np.ceil(len(train_ds) / batch_size),
init_lr=init_lr)),
4:
LRScheduler(model=model,
lr_fn=lambda epoch: cosine_decay(epoch,
cycle_length=epochs - 3,
init_lr=init_lr,
min_lr=init_lr / 100,
start=4))
}
traces.append(EpochScheduler(lr_schedule))
estimator = fe.Estimator(
pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces,
monitor_names=["bbox_loss", "conf_loss", "cls_loss"],
train_steps_per_epoch=train_steps_per_epoch,
eval_steps_per_epoch=eval_steps_per_epoch)
return estimator
def __init__(self, inputs, outputs, mode=None):
super().__init__(inputs=inputs, outputs=outputs, mode=mode)
self.num_device = get_num_devices()
def get_estimator(epochs=150,
batch_size=32,
save_dir=tempfile.mkdtemp(),
train_steps_per_epoch=None,
eval_steps_per_epoch=None):
# step 1: prepare dataset
train_data, eval_data = load_data()
pipeline = fe.Pipeline(train_data=train_data,
eval_data=eval_data,
batch_size=batch_size * get_num_devices(),
ops=[
Normalize(inputs="x",
outputs="x",
mean=(0.4914, 0.4822, 0.4465),
std=(0.2471, 0.2435, 0.2616)),
PadIfNeeded(min_height=40,
min_width=40,
image_in="x",
image_out="x",
mode="train"),
RandomCrop(32,
32,
image_in="x",
image_out="x",
mode="train"),
Sometimes(
HorizontalFlip(image_in="x",
image_out="x",
mode="train")),
CoarseDropout(inputs="x",
outputs="x",
mode="train",
max_holes=1)
])
# step 2: prepare network
model = fe.build(
model_fn=lambda: pyramidnet_cifar(inputs_shape=(32, 32, 3),
depth=272,
alpha=200,
num_classes=10,
bottleneck=True),
optimizer_fn=lambda: tfa.optimizers.SGDW(
weight_decay=0.0001, lr=0.1, momentum=0.9))
network = fe.Network(ops=[
ModelOp(model=model, inputs="x", outputs="y_pred"),
CrossEntropy(inputs=("y_pred", "y"), outputs="ce", from_logits=True),
UpdateOp(model=model, loss_name="ce")
])
# step 3 prepare estimator
traces = [
Accuracy(true_key="y", pred_key="y_pred"),
LRScheduler(model=model, lr_fn=lr_schedule),
BestModelSaver(model=model,
save_dir=save_dir,
metric="accuracy",
save_best_mode="max")
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces,
train_steps_per_epoch=train_steps_per_epoch,
eval_steps_per_epoch=eval_steps_per_epoch)
return estimator
def get_estimator(target_size=128,
epochs=55,
save_dir=tempfile.mkdtemp(),
max_train_steps_per_epoch=None,
data_dir=None):
# assert growth parameters
num_grow = np.log2(target_size) - 2
assert num_grow >= 1 and num_grow % 1 == 0, "need exponential of 2 and greater than 8 as target size"
num_phases = int(2 * num_grow + 1)
assert epochs % num_phases == 0, "epoch must be multiple of {} for size {}".format(
num_phases, target_size)
num_grow, phase_length = int(num_grow), int(epochs / num_phases)
event_epoch = [1, 1 + phase_length] + [
phase_length * (2 * i + 1) + 1 for i in range(1, num_grow)
]
event_size = [4] + [2**(i + 3) for i in range(num_grow)]
# set up data schedules
dataset = nih_chestxray.load_data(root_dir=data_dir)
resize_map = {
epoch: Resize(image_in="x", image_out="x", height=size, width=size)
for (epoch, size) in zip(event_epoch, event_size)
}
resize_low_res_map1 = {
epoch: Resize(image_in="x",
image_out="x_low_res",
height=size // 2,
width=size // 2)
for (epoch, size) in zip(event_epoch, event_size)
}
resize_low_res_map2 = {
epoch: Resize(image_in="x_low_res",
image_out="x_low_res",
height=size,
width=size)
for (epoch, size) in zip(event_epoch, event_size)
}
batch_size_map = {
epoch: 512 // size * get_num_devices() if size <= 128 else 4 *
get_num_devices()
for (epoch, size) in zip(event_epoch, event_size)
}
batch_scheduler = EpochScheduler(epoch_dict=batch_size_map)
pipeline = fe.Pipeline(
batch_size=batch_scheduler,
train_data=dataset,
drop_last=True,
ops=[
ReadImage(inputs="x", outputs="x", color_flag='gray'),
EpochScheduler(epoch_dict=resize_map),
EpochScheduler(epoch_dict=resize_low_res_map1),
EpochScheduler(epoch_dict=resize_low_res_map2),
Normalize(inputs=["x", "x_low_res"],
outputs=["x", "x_low_res"],
mean=1.0,
std=1.0,
max_pixel_value=127.5),
LambdaOp(fn=lambda: np.random.normal(size=[512]).astype('float32'),
outputs="z")
])
# now model schedule
fade_in_alpha = tf.Variable(initial_value=1.0,
dtype='float32',
trainable=False)
d_models = fe.build(
model_fn=lambda: build_D(fade_in_alpha,
target_resolution=int(np.log2(target_size)),
num_channels=1),
optimizer_fn=[
lambda: Adam(0.001, beta_1=0.0, beta_2=0.99, epsilon=1e-8)
] * len(event_size),
model_name=["d_{}".format(size) for size in event_size])
g_models = fe.build(
model_fn=lambda: build_G(fade_in_alpha,
target_resolution=int(np.log2(target_size)),
num_channels=1),
optimizer_fn=[
lambda: Adam(0.001, beta_1=0.0, beta_2=0.99, epsilon=1e-8)
] * len(event_size) + [None],
model_name=["g_{}".format(size) for size in event_size] + ["G"])
fake_img_map = {
epoch: ModelOp(inputs="z", outputs="x_fake", model=model)
for (epoch, model) in zip(event_epoch, g_models[:-1])
}
fake_score_map = {
epoch: ModelOp(inputs="x_fake", outputs="fake_score", model=model)
for (epoch, model) in zip(event_epoch, d_models)
}
real_score_map = {
epoch: ModelOp(inputs="x_blend", outputs="real_score", model=model)
for (epoch, model) in zip(event_epoch, d_models)
}
interp_score_map = {
epoch: ModelOp(inputs="x_interp", outputs="interp_score", model=model)
for (epoch, model) in zip(event_epoch, d_models)
}
g_update_map = {
epoch: UpdateOp(loss_name="gloss", model=model)
for (epoch, model) in zip(event_epoch, g_models[:-1])
}
d_update_map = {
epoch: UpdateOp(loss_name="dloss", model=model)
for (epoch, model) in zip(event_epoch, d_models)
}
network = fe.Network(ops=[
EpochScheduler(fake_img_map),
EpochScheduler(fake_score_map),
ImageBlender(
alpha=fade_in_alpha, inputs=("x", "x_low_res"), outputs="x_blend"),
EpochScheduler(real_score_map),
Interpolate(inputs=("x_fake", "x"), outputs="x_interp"),
EpochScheduler(interp_score_map),
GradientPenalty(inputs=("x_interp", "interp_score"), outputs="gp"),
GLoss(inputs="fake_score", outputs="gloss"),
DLoss(inputs=("real_score", "fake_score", "gp"), outputs="dloss"),
EpochScheduler(g_update_map),
EpochScheduler(d_update_map)
])
traces = [
AlphaController(alpha=fade_in_alpha,
fade_start_epochs=event_epoch[1:],
duration=phase_length,
batch_scheduler=batch_scheduler,
num_examples=len(dataset)),
ModelSaver(model=g_models[-1],
save_dir=save_dir,
frequency=phase_length),
ImageSaving(epoch_model_map={
epoch - 1: model
for (epoch,
model) in zip(event_epoch[1:] + [epochs + 1], g_models[:-1])
},
save_dir=save_dir)
]
estimator = fe.Estimator(
pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces,
max_train_steps_per_epoch=max_train_steps_per_epoch)
return estimator
def get_estimator(data_dir=None,
epochs=12,
batch_size_per_gpu=4,
im_size=1344,
model_dir=tempfile.mkdtemp(),
train_steps_per_epoch=None,
eval_steps_per_epoch=None):
assert im_size % 32 == 0, "im_size must be a multiple of 32"
num_device = get_num_devices()
train_ds, val_ds = mscoco.load_data(root_dir=data_dir, load_masks=True)
batch_size = num_device * batch_size_per_gpu
pipeline = fe.Pipeline(
train_data=train_ds,
eval_data=val_ds,
test_data=val_ds,
ops=[
ReadImage(inputs="image", outputs="image"),
MergeMask(inputs="mask", outputs="mask"),
GetImageSize(inputs="image", outputs="imsize", mode="test"),
LongestMaxSize(max_size=im_size,
image_in="image",
mask_in="mask",
bbox_in="bbox",
bbox_params="coco"),
RemoveIf(fn=lambda x: len(x) == 0, inputs="bbox"),
PadIfNeeded(min_height=im_size,
min_width=im_size,
image_in="image",
mask_in="mask",
bbox_in="bbox",
bbox_params="coco",
border_mode=cv2.BORDER_CONSTANT,
value=0),
Sometimes(
HorizontalFlip(image_in="image",
mask_in="mask",
bbox_in="bbox",
bbox_params="coco",
mode="train")),
Resize(height=im_size // 4, width=im_size // 4,
image_in='mask'), # downscale mask for memory efficiency
Gt2Target(inputs=("mask", "bbox"),
outputs=("gt_match", "mask", "classes")),
Delete(keys="bbox"),
Delete(keys="image_id", mode="!test"),
Batch(batch_size=batch_size, pad_value=0)
],
num_process=8 * num_device)
init_lr = 1e-2 / 16 * batch_size
model = fe.build(
model_fn=SoloV2,
optimizer_fn=lambda x: torch.optim.SGD(x, lr=init_lr, momentum=0.9))
network = fe.Network(ops=[
Normalize(inputs="image",
outputs="image",
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
Permute(inputs="image", outputs='image'),
ModelOp(model=model,
inputs="image",
outputs=("feat_seg", "feat_cls_list", "feat_kernel_list")),
LambdaOp(fn=lambda x: x,
inputs="feat_cls_list",
outputs=("cls1", "cls2", "cls3", "cls4", "cls5")),
LambdaOp(fn=lambda x: x,
inputs="feat_kernel_list",
outputs=("k1", "k2", "k3", "k4", "k5")),
Solov2Loss(0,
40,
inputs=("mask", "classes", "gt_match", "feat_seg", "cls1",
"k1"),
outputs=("l_c1", "l_s1")),
Solov2Loss(1,
36,
inputs=("mask", "classes", "gt_match", "feat_seg", "cls2",
"k2"),
outputs=("l_c2", "l_s2")),
Solov2Loss(2,
24,
inputs=("mask", "classes", "gt_match", "feat_seg", "cls3",
"k3"),
outputs=("l_c3", "l_s3")),
Solov2Loss(3,
16,
inputs=("mask", "classes", "gt_match", "feat_seg", "cls4",
"k4"),
outputs=("l_c4", "l_s4")),
Solov2Loss(4,
12,
inputs=("mask", "classes", "gt_match", "feat_seg", "cls5",
"k5"),
outputs=("l_c5", "l_s5")),
CombineLoss(inputs=("l_c1", "l_s1", "l_c2", "l_s2", "l_c3", "l_s3",
"l_c4", "l_s4", "l_c5", "l_s5"),
outputs=("total_loss", "cls_loss", "seg_loss")),
L2Regularizaton(inputs="total_loss",
outputs="total_loss_l2",
model=model,
beta=1e-5,
mode="train"),
UpdateOp(model=model, loss_name="total_loss_l2"),
PointsNMS(inputs="feat_cls_list", outputs="feat_cls_list",
mode="test"),
Predict(inputs=("feat_seg", "feat_cls_list", "feat_kernel_list"),
outputs=("seg_preds", "cate_scores", "cate_labels"),
mode="test")
])
train_steps_epoch = int(np.ceil(len(train_ds) / batch_size))
lr_schedule = {
1:
LRScheduler(
model=model,
lr_fn=lambda step: lr_schedule_warmup(step, init_lr=init_lr)),
2:
LRScheduler(
model=model,
lr_fn=lambda step: cosine_decay(step,
cycle_length=train_steps_epoch *
(epochs - 1),
init_lr=init_lr,
min_lr=init_lr / 100,
start=train_steps_epoch))
}
traces = [
EpochScheduler(lr_schedule),
COCOMaskmAP(data_dir=val_ds.root_dir,
inputs=("seg_preds", "cate_scores", "cate_labels",
"image_id", "imsize"),
mode="test"),
BestModelSaver(model=model, save_dir=model_dir, metric="total_loss")
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=epochs,
traces=traces,
monitor_names=("cls_loss", "seg_loss"),
train_steps_per_epoch=train_steps_per_epoch,
eval_steps_per_epoch=eval_steps_per_epoch)
return estimator
