def __init__(self, batch_size, num_threads, device_id, **kwargs):
super().__init__(batch_size, num_threads, device_id, **kwargs)
self.oversampling = kwargs["oversampling"]
self.crop_shape = types.Constant(np.array(self.patch_size),
dtype=types.INT64)
self.crop_shape_float = types.Constant(np.array(self.patch_size),
dtype=types.FLOAT)
def __init__(self, batch_size, num_threads, device_id, **kwargs):
super(TrainPipeline, self).__init__(batch_size, num_threads, device_id)
self.dim = kwargs["dim"]
self.oversampling = kwargs["oversampling"]
self.input_x = get_numpy_reader(
num_shards=kwargs["num_device"],
files=kwargs["imgs"],
seed=kwargs["seed"],
shard_id=device_id,
shuffle=True,
)
self.input_y = get_numpy_reader(
num_shards=kwargs["num_device"],
files=kwargs["lbls"],
seed=kwargs["seed"],
shard_id=device_id,
shuffle=True,
)
self.patch_size = kwargs["patch_size"]
if self.dim == 2:
self.patch_size = [kwargs["batch_size_2d"]] + self.patch_size
self.crop_shape = types.Constant(np.array(self.patch_size), dtype=types.INT64)
self.crop_shape_float = types.Constant(np.array(self.patch_size), dtype=types.FLOAT)
shard_id = int(os.getenv("LOCAL_RANK", "0"))
if kwargs['set_aug_seed']:
aug_seed = kwargs['seed'] + shard_id
self.aug_seed_kwargs = {'seed': aug_seed}
print("TrainPipeline augmentation seed: ", aug_seed)
else:
self.aug_seed_kwargs = {}
print("TrainPipeline WO augmentation seed")
self.augment = kwargs['augment']
def __init__(self, batch_size, num_threads, device_id, **kwargs):
super(TFRecordTrain, self).__init__(batch_size, num_threads, device_id)
self.dim = kwargs["dim"]
self.seed = kwargs["seed"]
self.oversampling = kwargs["oversampling"]
self.input = ops.TFRecordReader(
path=kwargs["tfrecords"],
index_path=kwargs["tfrecords_idx"],
features={
"X_shape": tfrec.FixedLenFeature([self.dim + 1], tfrec.int64, 0),
"Y_shape": tfrec.FixedLenFeature([self.dim + 1], tfrec.int64, 0),
"X": tfrec.VarLenFeature([], tfrec.float32, 0.0),
"Y": tfrec.FixedLenFeature([], tfrec.string, ""),
"fname": tfrec.FixedLenFeature([], tfrec.string, ""),
},
num_shards=kwargs["gpus"],
shard_id=device_id,
random_shuffle=True,
pad_last_batch=True,
read_ahead=True,
seed=self.seed,
)
self.patch_size = kwargs["patch_size"]
self.crop_shape = types.Constant(np.array(self.patch_size), dtype=types.INT64)
self.crop_shape_float = types.Constant(np.array(self.patch_size), dtype=types.FLOAT)
self.layout = "CDHW" if self.dim == 3 else "CHW"
self.axis_name = "DHW" if self.dim == 3 else "HW"
def __init__(self, batch_size, num_threads, device_id, **kwargs):
super(TrainPipeline, self).__init__(batch_size, num_threads, device_id)
self.dim = kwargs["dim"]
self.oversampling = kwargs["oversampling"]
self.input_x = get_numpy_reader(
num_shards=kwargs["gpus"],
files=kwargs["imgs"],
seed=kwargs["seed"],
shard_id=device_id,
shuffle=True,
)
self.input_y = get_numpy_reader(
num_shards=kwargs["gpus"],
files=kwargs["lbls"],
seed=kwargs["seed"],
shard_id=device_id,
shuffle=True,
)
self.patch_size = kwargs["patch_size"]
if self.dim == 2:
self.patch_size = [kwargs["batch_size_2d"]] + self.patch_size
self.crop_shape = types.Constant(np.array(self.patch_size),
dtype=types.INT64)
self.crop_shape_float = types.Constant(np.array(self.patch_size),
dtype=types.FLOAT)
def decoder_slice_pipe(decoder_op, file_root, device, use_fast_idct):
encoded, _ = fn.readers.file(file_root=file_root)
start = types.Constant(np.array([0., 0.]))
end = types.Constant(np.array([0.5, 0.5]))
decoded = decoder_op(encoded, start, end, device=device, output_type=types.RGB,
use_fast_idct=use_fast_idct)
return decoded
def define_graph(self):
device = self.device
return [
# no-op
ops.Reshape(device=device, shape=[1])(types.Constant(1.25)),
# flatten with reshape op
ops.Reshape(device=device)
(types.Constant(np.array([[1, 2], [3, 4]], dtype=np.uint16),
device=device),
shape=types.Constant([4]))
]
def __init__(self, params, num_threads, device_id):
super(DaliPipeline, self).__init__(params.batch_size,
num_threads,
device_id,
seed=12)
with h5py.File(params.data_path, 'r') as f:
# load hydro and clean up
Hydro = f['Hydro'][...]
self.Hydro = types.Constant(Hydro,
shape=Hydro.shape,
layout="DHWC",
device="cpu")
del Hydro
# load nbody and clean up
Nbody = f['Nbody'][...]
self.Nbody = types.Constant(Nbody,
shape=Nbody.shape,
layout="DHWC",
device="cpu")
del Nbody
#self.ndummy = np.zeros((20, 20, 20, 4), dtype=np.float32)
#self.hdummy = np.zeros((20, 20, 20, 5), dtype=np.float32)
#self.Nbody = types.Constant(self.ndummy, shape = self.ndummy.shape, layout = "DHWC", device="cpu")
#self.Hydro = types.Constant(self.hdummy, shape = self.hdummy.shape, layout = "DHWC", device="cpu")
#self.Nbody = ops.Constant(fdata = self.ndummy.flatten().tolist(), shape = self.ndummy.shape, layout = "DHWC", device = "cpu")
#self.Hydro = ops.Constant(fdata = self.hdummy.flatten().tolist(), shape = self.hdummy.shape, layout = "DHWC", device = "cpu")
self.do_rotate = True if params.rotate_input == 1 else False
print("Enable Rotation" if self.do_rotate else "Disable Rotation")
self.rng_angle = ops.Uniform(device="cpu", range=[-1.5, 2.5])
self.rng_pos = ops.Uniform(device="cpu", range=[0., 1.])
self.icast = ops.Cast(device="cpu", dtype=types.INT32)
self.fcast = ops.Cast(device="cpu", dtype=types.FLOAT)
self.crop = ops.Crop(device="cpu",
crop_d=params.data_size,
crop_h=params.data_size,
crop_w=params.data_size)
self.rotate1 = ops.Rotate(device="gpu",
axis=(1, 0, 0),
interp_type=types.INTERP_LINEAR)
self.rotate2 = ops.Rotate(device="gpu",
axis=(0, 1, 0),
interp_type=types.INTERP_LINEAR)
self.rotate3 = ops.Rotate(device="gpu",
axis=(0, 0, 1),
interp_type=types.INTERP_LINEAR)
self.transpose = ops.Transpose(device="gpu", perm=[3, 0, 1, 2])
def get_operand(self, operand, kind, operand_type):
if kind == "const":
return types.Constant(magic_number, np_types_to_dali[operand_type])
elif kind == "cpu":
return operand
elif kind == "gpu":
return operand.gpu()
def make_pipe():
image = fn.external_source(source=image_gen)
if device == "gpu":
image = image.gpu()
if get_dynamic_axes:
axes, rel_start, rel_shape = fn.external_source(
source=get_dynamic_axes, num_outputs=3)
else:
axes = types.Constant(np.array([0, 1], dtype=np.int32),
device="cpu")
rel_start = fn.random.uniform(range=(0.1, 0.2),
shape=(2, ),
dtype=types.FLOAT,
device=args_device)
rel_shape = fn.random.uniform(range=(0.4, 0.6),
shape=(2, ),
dtype=types.FLOAT,
device=args_device)
if args_device == "gpu":
sliced = fn.slice(image, rel_start, rel_shape, axes=axes)
return image, axes, rel_start, rel_shape, sliced
else:
sliced1 = fn.slice(image,
rel_start=rel_start,
rel_shape=rel_shape,
axes=axes)
sliced2 = fn.slice(image, rel_start, rel_shape, axes=axes)
return image, axes, rel_start, rel_shape, sliced1, sliced2
def pipe(device, interp_type, test_data=checkerboard, out_size=out_size):
data = types.Constant(test_data, device=device)
data = fn.expand_dims(data, axes=[2])
resized = fn.resize(data,
dtype=types.FLOAT,
min_filter=interp_type,
mag_filter=interp_type,
size=out_size)
resized = fn.squeeze(resized, axes=[2])
return resized
def test_variable_batch():
pipe = Pipeline(6, 1, 0)
batches = [
[np.array(1), np.array(2)],
[np.array(1)],
[np.array(1), np.array(2), np.array(3), np.array(4), np.array(5), np.array(5)]
]
dummy = fn.external_source(batches, cycle=True)
val = np.float32([[1,2],[3,4]])
pipe.set_outputs(types.Constant(val, device="cpu"), types.Constant(val, device="gpu"), dummy)
pipe.build()
for batch in batches:
cpu, gpu, _ = pipe.run()
assert len(cpu) == len(batch)
assert len(gpu) == len(batch)
gpu = gpu.as_cpu()
for i in range(len(batch)):
assert np.array_equal(cpu.at(i), val)
assert np.array_equal(gpu.at(i), val)
def create_dali_pipe(channel_first,
seq_len,
interp,
dtype,
w,
h,
batch_size=2):
pipe = dali.pipeline.Pipeline(batch_size, 1, 0, 0)
with pipe:
layout = "FCHW" if channel_first else "FHWC"
ext = fn.external_source(GetSequences(channel_first, seq_len,
batch_size),
layout=layout)
resize_cpu_out = fn.resize(ext,
resize_x=w,
resize_y=h,
interp_type=interp,
dtype=dtype,
save_attrs=True)
resize_gpu_out = fn.resize(ext.gpu(),
resize_x=w,
resize_y=h,
interp_type=interp,
minibatch_size=4,
dtype=dtype,
save_attrs=True)
dali_resized_cpu, size_cpu = resize_cpu_out
dali_resized_gpu, size_gpu = resize_gpu_out
# extract just HW part from the input shape
shape_anchor = np.array([2 if channel_first else 1], dtype=np.float32)
shape_shape = np.array([2], dtype=np.float32)
ext_size = fn.slice(fn.cast(fn.shapes(ext), dtype=types.INT32),
types.Constant(shape_anchor, device="cpu"),
types.Constant(shape_shape, device="cpu"),
normalized_anchor=False,
normalized_shape=False,
axes=[0])
pipe.set_outputs(dali_resized_cpu, dali_resized_gpu, ext_size,
size_cpu, size_gpu)
return pipe
def define_graph(self):
device = self.device
return [
types.Constant(device = device, value = (1.25,2.5,3)),
types.Constant(device = device, value = self.array([[[1,2]],[[3,4]]], dtype=self.dtype('int32'))),
types.Constant(device = device, value = self.array([0,1,2,3,4], dtype=self.dtype('uint8'))),
types.Constant(device = device, value = self.array([0.25,1.25,2.25,3.25,4.25], dtype=self.dtype('float16'))),
types.Constant(device = device, value = 5.5, shape=(100,100), name="large"),
types.Constant(device = device, value = -4, shape=(10,20)),
types.Constant(device = device, value = [False, True, False])
]
def define_graph(self):
self.jpegs, self.labels = self.input(name = "Reader")
images = self.decode(self.jpegs)
if self.warp.device == "gpu":
images = images.gpu()
if self.cast:
images = self.cast(images)
dynamic_size = types.Constant(np.array([240, 320], dtype=np.float32)) if self.use_dynamic_size else None
if self.use_input:
transform = self.transform_source()
outputs = self.warp(images, transform, size = dynamic_size)
else:
outputs = self.warp(images, size = dynamic_size)
return outputs
def nonsilent_pipe(data_arr=None, window_size=256, cutoff_value=-10, reference_power=None):
if data_arr is None:
raw, _ = fn.readers.file(files=audio_files)
audio, _ = fn.decoders.audio(raw, dtype=types.INT16, downmix=True)
else:
audio = types.Constant(device='cpu', value=data_arr)
begin_cpu, len_cpu = fn.nonsilent_region(
audio, cutoff_db=cutoff_value, window_length=window_size,
reference_power=reference_power,
)
begin_gpu, len_gpu = fn.nonsilent_region(
audio.gpu(), cutoff_db=cutoff_value, window_length=window_size,
reference_power=reference_power,
)
return begin_cpu, len_cpu, begin_gpu, len_gpu
def dali_pipe(data, label):
fdata = types.Constant(data)
flabel = types.Constant(label)
return fdata, flabel
def check_normal_distribution(device,
dtype,
shape=None,
use_shape_like_input=False,
variable_shape=False,
mean=0.0,
stddev=1.0,
variable_dist_params=False,
shape_gen_f=None,
niter=3,
batch_size=3,
device_id=0,
num_threads=3):
pipe = Pipeline(batch_size=batch_size,
device_id=device_id,
num_threads=num_threads,
seed=123456)
with pipe:
shape_like_in = None
shape_arg = None
assert shape is None or shape_gen_f is None
if variable_shape:
if shape_gen_f is None:
def shape_gen_f():
return random_shape(shape)
if use_shape_like_input:
shape_like_in = fn.external_source(
lambda: np.zeros(shape_gen_f()),
device=device,
batch=False)
shape_out = fn.shapes(shape_like_in)
else:
shape_arg = fn.external_source(shape_gen_f, batch=False)
shape_out = shape_arg
else:
if use_shape_like_input:
shape_like_in = np.zeros(shape)
else:
shape_arg = shape
# Can't make an empty list constant
shape_out = types.Constant(shape if shape is not None and shape !=
() else (1, ),
dtype=types.INT32)
mean_arg = None
stddev_arg = None
if variable_dist_params:
mean_arg = fn.external_source(lambda: np.array(
np.random.uniform(low=-100.0, high=100.0), dtype=np.float32),
device='cpu',
batch=False)
stddev_arg = fn.external_source(lambda: np.array(
np.random.uniform(low=1.0, high=100.0), dtype=np.float32),
device='cpu',
batch=False)
else:
mean_arg = mean
stddev_arg = stddev
inputs = [shape_like_in] if shape_like_in is not None else []
out = fn.random.normal(*inputs,
device=device,
shape=shape_arg,
mean=mean_arg,
stddev=stddev_arg,
dtype=dtype)
pipe.set_outputs(out, shape_out, mean_arg, stddev_arg)
pipe.build()
for i in range(niter):
outputs = pipe.run()
out, shapes, means, stddevs = tuple(outputs[i].as_cpu(
) if isinstance(outputs[i], TensorListGPU) else outputs[i]
for i in range(len(outputs)))
for sample_idx in range(batch_size):
sample = np.array(out[sample_idx])
if sample.shape == ():
continue
sample_shape = np.array(shapes[sample_idx])
mean = np.array(means[sample_idx])
stddev = np.array(stddevs[sample_idx])
assert (sample.shape == sample_shape
).all(), f"{sample.shape} != {sample_shape}"
data = sample.flatten()
data_len = len(data)
# Checking sanity of the data
if data_len >= 100 and dtype in [types.FLOAT, types.FLOAT64]:
# Empirical rule:
# ~68% of the observations within one standard deviation
# ~95% of the observations within one standard deviation
# ~99.7% of the observations within one standard deviation
within_1stddevs = np.where((data > (mean - 1 * stddev))
& (data < (mean + 1 * stddev)))
p1 = len(within_1stddevs[0]) / data_len
within_2stddevs = np.where((data > (mean - 2 * stddev))
& (data < (mean + 2 * stddev)))
p2 = len(within_2stddevs[0]) / data_len
within_3stddevs = np.where((data > (mean - 3 * stddev))
& (data < (mean + 3 * stddev)))
p3 = len(within_3stddevs[0]) / data_len
assert p3 > 0.9, f"{p3}" # leave some room
assert p2 > 0.8, f"{p2}" # leave some room
assert p1 > 0.5, f"{p1}" # leave some room
# It's not 100% mathematically correct, but makes do in case of this test
_, pvalues_anderson, _ = st.anderson(data, dist='norm')
assert pvalues_anderson[2] > 0.5
def pipe():
data = np.array([np.random.rand(1, 2) for i in range(10)])
label = np.array([np.random.rand(1, 3) for i in range(10)])
fdata = types.Constant(data)
flabel = types.Constant(label)
return fdata, flabel
def build_pipes(device, dim, batch_size, channel_first, mode, interp, dtype,
w_input, h_input, d_input, use_size_arg, use_size_input,
use_roi):
dali_pipe = Pipeline(batch_size=batch_size,
num_threads=8,
device_id=0,
seed=1234)
with dali_pipe:
if dim == 2:
files, labels = dali.fn.readers.caffe(path=db_2d_folder,
random_shuffle=True)
images_cpu = dali.fn.decoders.image(files, device="cpu")
else:
images_cpu = dali.fn.external_source(
source=random_3d_loader(batch_size), layout="DHWC")
images_hwc = images_cpu if device == "cpu" else images_cpu.gpu()
if channel_first:
images = dali.fn.transpose(
images_hwc,
perm=[3, 0, 1, 2] if dim == 3 else [2, 0, 1],
transpose_layout=True)
else:
images = images_hwc
roi_start = None
roi_end = None
w = None
h = None
d = None
size = None
minibatch_size = 2 if dim == 3 else 8
if use_roi:
# Calculate absolute RoI
in_size = fn.slice(fn.shapes(images_cpu),
types.Constant(0,
dtype=types.FLOAT,
device="cpu"),
types.Constant(dim,
dtype=types.FLOAT,
device="cpu"),
axes=[0],
normalized_shape=False)
roi_start = fn.random.uniform(range=(0, 0.4), shape=[dim
]) * in_size
roi_end = fn.random.uniform(range=(0.6, 1.0), shape=[dim
]) * in_size
size_range = (10, 200) if dim == 3 else (10, 1000)
if use_size_arg:
if use_size_input:
mask = fn.cast(fn.random.uniform(range=(0.8, 1.9),
shape=[dim]),
dtype=types.INT32)
size = fn.random.uniform(range=size_range, shape=[dim]) * mask
else:
size = [300, 400] if dim == 2 else [80, 100, 120]
resized = resize_dali(images,
channel_first,
dtype,
interp,
mode,
size,
None,
None,
None,
roi_start,
roi_end,
minibatch_size=minibatch_size,
max_size=max_size(dim))
else:
if w_input:
has_w = fn.random.coin_flip(probability=0.8)
w = fn.random.uniform(range=size_range) * has_w
else:
w = 320 # some fixed value
if h_input:
has_h = fn.random.coin_flip(probability=0.8)
h = fn.random.uniform(range=size_range) * has_h
else:
h = 240 # some other fixed value
if dim >= 3:
if d_input:
has_d = fn.random.coin_flip(probability=0.8)
d = fn.random.uniform(range=size_range) * has_d
else:
d = 31 # some other fixed value
resized = resize_dali(images,
channel_first,
dtype,
interp,
mode,
None,
w,
h,
d,
roi_start,
roi_end,
minibatch_size=minibatch_size,
max_size=max_size(dim))
outputs = [images, resized]
if roi_start is not None and roi_end is not None:
outputs += [roi_start, roi_end]
for x in (d, h, w, size):
if x is not None:
if isinstance(x, _DataNode):
outputs.append(x)
else:
outputs.append(
types.Constant(np.array(x, dtype=np.float32)))
dali_pipe.set_outputs(*outputs)
pil_pipe = Pipeline(batch_size=batch_size,
num_threads=8,
device_id=0,
exec_async=False,
exec_pipelined=False)
with pil_pipe:
images = fn.external_source(name="images",
layout=layout_str(dim, channel_first))
sizes = fn.external_source(name="size")
roi_start = fn.external_source(name="roi_start")
roi_end = fn.external_source(name="roi_end")
resized = resize_PIL(dim, channel_first, dtype, interp, images, sizes,
roi_start, roi_end)
resized = fn.reshape(resized, layout=layout_str(dim, channel_first))
pil_pipe.set_outputs(resized)
dali_pipe.build()
pil_pipe.build()
return dali_pipe, pil_pipe
def get_dali_pipe():
data = types.Constant(1)
return data
def get_dali_pipe(value):
data = types.Constant(value)
return data
def _prepare_graph(self, define_graph = None):
self._pipe = b.Pipeline(self._max_batch_size,
self._num_threads,
self._device_id,
self._seed,
self._exec_pipelined,
self._prefetch_queue_depth,
self._exec_async,
self._bytes_per_sample,
self._set_affinity,
self._max_streams,
self._default_cuda_stream_priority)
self._pipe.SetExecutionTypes(self._exec_pipelined, self._exec_separated, self._exec_async)
self._pipe.SetQueueSizes(self._cpu_queue_size, self._gpu_queue_size)
self._pipe.EnableExecutorMemoryStats(self._enable_memory_stats)
if define_graph is not None:
if self._graph_out is not None:
raise RuntimeError("Duplicate graph definition - `define_graph` argument "
"should not be specified when graph was defined with a call to `set_outputs`.")
else:
define_graph = self.define_graph
if self._graph_out:
outputs = self._graph_out
else:
with self:
outputs = define_graph()
if isinstance(outputs, tuple):
outputs = list(outputs)
elif not isinstance(outputs, list):
outputs = [outputs]
for i in range(len(outputs)):
if isinstance(outputs[i], types.ScalarConstant):
import nvidia.dali.ops
outputs[i] = nvidia.dali.ops._instantiate_constant_node("cpu", outputs[i])
elif not isinstance(outputs[i], DataNode):
outputs[i] = types.Constant(outputs[i], device="cpu")
_data_node._check(outputs[i])
# Backtrack to construct the graph
op_ids = set()
edges = deque(list(outputs) + self._sinks)
ops = []
while edges:
current_edge = edges.popleft()
source_op = current_edge.source
if source_op is None:
raise RuntimeError(
"Pipeline encountered "
"Edge with no source op.")
# To make sure we don't double count ops in
# the case that they produce more than one
# output, we keep track of the unique op ids
# for each op we encounter and only add the
# op if we have not already
if source_op.id not in op_ids:
op_ids.add(source_op.id)
source_op.check_args()
ops.append(source_op)
else:
# If the op was already added, we need to
# change its position to the top of the list.
# This ensures topological ordering of ops
# when adding to the backend pipeline
ops.remove(source_op)
ops.append(source_op)
for edge in source_op.inputs:
if isinstance(edge, list):
for e in edge:
edges.append(e)
else:
edges.append(edge)
# Add the ops to the graph and build the backend
related_logical_id = {}
self._ops = []
while ops:
op = ops.pop()
self._ops.append(op)
if op.relation_id not in related_logical_id:
related_logical_id[op.relation_id] = self._pipe.AddOperator(op.spec, op.name)
else:
self._pipe.AddOperator(op.spec, op.name, related_logical_id[op.relation_id])
self._prepared = True
self._setup_input_callbacks()
self._names_and_devices = [(e.name, e.device) for e in outputs]
