def image_decoder_slice_pipe(max_batch_size, input_data, device): pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0) encoded = fn.external_source(source=input_data, cycle=False, device='cpu') anch = fn.constant(fdata=.1) sh = fn.constant(fdata=.4) decoded = fn.decoders.image_slice(encoded, anch, sh, axes=0, device=device) pipe.set_outputs(decoded) return pipe
def pipe(max_batch_size, input_data, device): pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0) anch = fn.constant(fdata=.1, device='cpu') sh = fn.constant(fdata=.5, device='cpu') data = fn.external_source(source=input_data, cycle=False, device=device) processed = fn.slice(data, anch, sh, axes=0, device=device) pipe.set_outputs(processed) return pipe
def pipe(max_batch_size, input_data, device): pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0) # just to drive the variable batch size. batch_size_setter = fn.external_source(source=input_data, cycle=False, device=device) data = fn.constant(fdata=3.1415, shape=(10, 10), device=device) pipe.set_outputs(data, batch_size_setter) return pipe
def pipe(max_batch_size, input_data, device): pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0) shape = fn.external_source(source=input_data, cycle=False, device='cpu') data = fn.constant(fdata=3.1415, shape=shape, device=device) pipe.set_outputs(data) return pipe
def make_pipe(): fake_data = fn.constant(idata=0, shape=[10, 10, 3], dtype=types.FLOAT, device=device) axes = fn.random.uniform(range=wrong_axes_range, shape=(2,), dtype=types.INT32) rel_start = fn.random.uniform(range=[0.0, 0.3], shape=(2,), dtype=types.FLOAT) rel_shape = fn.random.uniform(range=[0.4, 0.6], shape=(2,), dtype=types.FLOAT) if named_args: sliced = fn.slice(fake_data, rel_start=rel_start, rel_shape=rel_shape, axes=axes) else: sliced = fn.slice(fake_data, rel_start, rel_shape, axes=axes) return sliced
def make_pipe(): fake_data = fn.constant(idata=0, shape=[10, 10, 3], dtype=types.FLOAT, device=device) axes = fn.random.uniform(range=wrong_axes_range, shape=(2, ), dtype=types.INT32) padded = fn.pad(fake_data, axes=axes) return padded
def dali_const_dataset(batch_size, sample_size, device_id): pipeline = Pipeline(batch_size, 4, device_id) const = fn.constant(device='gpu', fdata=sample_size * [1.]) pipeline.set_outputs(const) dali_dataset = dali_tf.DALIDataset( pipeline=pipeline, batch_size=batch_size, output_shapes=((batch_size, sample_size)), output_dtypes=(tf.float32), device_id=device_id) return dali_dataset
def make_pipe(): fake_data = fn.constant(idata=0, shape=[10, 10, 3], dtype=types.FLOAT, device="cpu") rel_start = fn.random.uniform(range=[0.0, 0.3], shape=(2, ), dtype=types.FLOAT, device="gpu") rel_shape = fn.random.uniform(range=[0.4, 0.6], shape=(2, ), dtype=types.FLOAT, device="gpu") sliced = fn.slice(fake_data, rel_start, rel_shape, device="cpu") return sliced
def nan_check_pipeline(source): return fn.constant(fdata=next(source))
def pipeline(): data = fn.constant(idata=255, shape=(10, 10, 3), dtype=type, device=device) return op(data)