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test_operator_decoders_image.py

Source:test_operator_decoders_image.py

1# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14from nvidia.dali import Pipeline, pipeline_def15import nvidia.dali.fn as fn16import nvidia.dali.ops as ops17import nvidia.dali.types as types18import math19import os20import random21import numpy as np22import glob23from nose import SkipTest24from nose.tools import assert_raises25from test_utils import check_batch26from test_utils import compare_pipelines27from test_utils import RandomDataIterator28from test_utils import get_dali_extra_path29from test_utils import check_output_pattern30from test_utils import to_array31def get_img_files(data_path, subdir='*', ext=None):32    if subdir is None:33        subdir = ''34    if ext:35        if isinstance(ext, (list, tuple)):36            files = []37            for e in ext:38                files += glob.glob(data_path + f"/{subdir}/*.{e}")39        else:40            files = glob.glob(data_path + f"/{subdir}/*.{ext}")41        return files42    else:43        files = glob.glob(data_path  + f"/{subdir}/*.*")44        txt_files = glob.glob(data_path  + f"/{subdir}/*.txt")45        return list(set(files) - set(txt_files))46@pipeline_def47def decoder_pipe(data_path, device, use_fast_idct=False, memory_stats=False):48    inputs, labels = fn.readers.file(file_root = data_path,49                                     shard_id = 0,50                                     num_shards = 1,51                                     name="Reader")52    decoded = fn.decoders.image(inputs, device = device, output_type = types.RGB, use_fast_idct=use_fast_idct,53                                memory_stats=memory_stats)54    return decoded, labels55test_data_root = get_dali_extra_path()56good_path = 'db/single'57missnamed_path = 'db/single/missnamed'58test_good_path = {'jpeg', 'mixed', 'png', 'tiff', 'pnm', 'bmp', 'jpeg2k'}59test_missnamed_path = {'jpeg', 'png', 'tiff', 'pnm', 'bmp'}60def run_decode(data_path, batch, device, threads, memory_stats=False):61    pipe = decoder_pipe(data_path=data_path, batch_size=batch, num_threads=threads, device_id=0, device=device, memory_stats=memory_stats, prefetch_queue_depth=1)62    pipe.build()63    iters = math.ceil(pipe.epoch_size("Reader") / batch)64    for _ in range(iters):65        pipe.run()66def test_image_decoder():67    def log(img_type, size, device, threads):68        pass69    for device in {'cpu', 'mixed'}:70        for batch_size in {1, 10}:71            for img_type in test_good_path:72                for threads in {1, random.choice([2, 3, 4])}:73                    data_path = os.path.join(test_data_root, good_path, img_type)74                    run_decode(data_path, batch_size, device, threads)75                    yield log, img_type, batch_size, device, threads76            for img_type in test_missnamed_path:77                for threads in {1, random.choice([2, 3, 4])}:78                    data_path = os.path.join(test_data_root, missnamed_path, img_type)79                    run_decode(data_path, batch_size, device, threads)80                    yield log, img_type, batch_size, device, threads81# TODO(januszl): check padding behavior82@pipeline_def83def create_decoder_slice_pipeline(data_path, device):84    jpegs, _ = fn.readers.file(file_root = data_path,85                               shard_id = 0,86                               num_shards = 1,87                               name = "Reader")88    anchor = fn.random.uniform(range=[0.05, 0.15], shape=(2,))89    shape = fn.random.uniform(range=[0.5, 0.7], shape=(2,))90    images_sliced_1 = fn.decoders.image_slice(jpegs,91                                              anchor,92                                              shape,93                                              device = device,94                                              hw_decoder_load = 0.7,95                                              output_type = types.RGB,96                                              axes = (0, 1))97    images = fn.decoders.image(jpegs,98                               device = device,99                               hw_decoder_load = 0.7,100                               output_type = types.RGB)101    images_sliced_2 = fn.slice(images,102                               anchor,103                               shape,104                               axes = (0, 1))105    return images_sliced_1, images_sliced_2106# TODO(januszl): check padding behavior107@pipeline_def108def create_decoder_crop_pipeline(data_path, device):109    jpegs, _ = fn.readers.file(file_root = data_path,110                               shard_id = 0,111                               num_shards = 1,112                               name = "Reader")113    crop_pos_x = fn.random.uniform(range=[0.1, 0.9])114    crop_pos_y = fn.random.uniform(range=[0.1, 0.9])115    w = 242116    h = 230117    images_crop_1 = fn.decoders.image_crop(jpegs,118                                           device = device,119                                           output_type = types.RGB,120                                           hw_decoder_load = 0.7,121                                           crop = (w, h),122                                           crop_pos_x = crop_pos_x,123                                           crop_pos_y = crop_pos_y)124    images = fn.decoders.image(jpegs,125                               device = device,126                               hw_decoder_load = 0.7,127                               output_type = types.RGB)128    images_crop_2 = fn.crop(images,129                            crop = (w, h),130                            crop_pos_x = crop_pos_x,131                            crop_pos_y = crop_pos_y)132    return images_crop_1, images_crop_2133@pipeline_def134def create_decoder_random_crop_pipeline(data_path, device):135    seed = 1234136    jpegs, _ = fn.readers.file(file_root = data_path,137                               shard_id = 0,138                               num_shards = 1,139                               name = "Reader")140    w = 242141    h = 230142    images_random_crop_1 = fn.decoders.image_random_crop(jpegs,143                                                         device = device,144                                                         output_type = types.RGB,145                                                         hw_decoder_load = 0.7,146                                                         seed = seed)147    images_random_crop_1 = fn.resize(images_random_crop_1, size = (w, h))148    images = fn.decoders.image(jpegs,149                               device = device,150                               hw_decoder_load = 0.7,151                               output_type = types.RGB)152    images_random_crop_2 = fn.random_resized_crop(images, size = (w, h), seed = seed)153    return images_random_crop_1, images_random_crop_2154def run_decode_fused(test_fun, path, img_type, batch, device, threads, validation_fun):155    data_path = os.path.join(test_data_root, path, img_type)156    pipe = test_fun(data_path=data_path, batch_size=batch, num_threads=threads, device_id=0, device=device, prefetch_queue_depth=1)157    pipe.build()158    iters = math.ceil(pipe.epoch_size("Reader") / batch)159    for _ in range(iters):160        out_1, out_2 = pipe.run()161        for img_1, img_2 in zip(out_1, out_2):162            img_1 = to_array(img_1)163            img_2 = to_array(img_2)164            assert validation_fun(img_1, img_2)165def test_image_decoder_fused():166    threads = 4167    batch_size = 10168    for test_fun in [create_decoder_slice_pipeline, create_decoder_crop_pipeline, create_decoder_random_crop_pipeline]:169        if test_fun == create_decoder_random_crop_pipeline:170            # random_resized_crop can properly handle border as it has pixels that are cropped out, while171            # plain resize folowing image_decoder_random_crop cannot do that and must duplicate the border pixels172            validation_fun = lambda x, y: np.mean(np.abs(x - y) < 0.5)173        else:174            validation_fun = lambda x, y: np.allclose(x, y)175        for device in {'cpu', 'mixed'}:176            for img_type in test_good_path:177                yield run_decode_fused, test_fun, good_path, img_type, batch_size, device, threads, validation_fun178def check_FastDCT_body(batch_size, img_type, device):179    data_path = os.path.join(test_data_root, good_path, img_type)180    compare_pipelines(decoder_pipe(data_path=data_path, batch_size=batch_size, num_threads=3,181                                   device_id=0, device=device, use_fast_idct=False),182                      decoder_pipe(data_path=data_path, batch_size=batch_size, num_threads=3,183                                   device_id=0, device='cpu', use_fast_idct=True),184                      # average difference should be no bigger by off-by-3185                      batch_size=batch_size, N_iterations=3, eps=3)186def test_FastDCT():187    for device in {'cpu', 'mixed'}:188        for batch_size in {1, 8}:189            for img_type in test_good_path:190              yield check_FastDCT_body, batch_size, img_type, device191def test_image_decoder_memory_stats():192    device = 'mixed'193    img_type = 'jpeg'194    def check(img_type, size, device, threads):195        data_path = os.path.join(test_data_root, good_path, img_type)196        # largest allocation should match our (in this case) memory padding settings197        # (assuming no reallocation was needed here as the hint is big enough)198        pattern = 'Device memory: \d+ allocations, largest = 16777216 bytes\n' + \199                  'Host \(pinned\) memory: \d+ allocations, largest = 8388608 bytes\n'200        with check_output_pattern(pattern):201            run_decode(data_path, size, device, threads, memory_stats=True)202    for size in {1, 10}:203        for threads in {1, random.choice([2, 3, 4])}:204            yield check, img_type, size, device, threads205batch_size_test = 16206@pipeline_def(batch_size=batch_size_test, device_id=0, num_threads=4)207def img_decoder_pipe(device, out_type, files):208    encoded, _ = fn.readers.file(files=files)209    decoded = fn.decoders.image(encoded, device=device, output_type=out_type)210    return decoded211def _testimpl_image_decoder_consistency(img_out_type, file_fmt, path, subdir='*', ext=None):212    eps = 1213    if file_fmt == 'jpeg' or file_fmt == 'mixed':214        eps = 4215    if ((file_fmt == 'jpeg2k' or file_fmt == 'mixed') and img_out_type == types.YCbCr):216        eps = 6217    files = get_img_files(os.path.join(test_data_root, path), subdir=subdir, ext=ext)218    compare_pipelines(img_decoder_pipe("cpu", out_type=img_out_type, files=files),219                      img_decoder_pipe("mixed", out_type=img_out_type, files=files),220                      batch_size=batch_size_test, N_iterations=3,221                      eps=eps)222def test_image_decoder_consistency():223    for out_img_type in [types.RGB, types.BGR, types.YCbCr, types.GRAY, types.ANY_DATA]:224        for file_fmt in test_good_path:225            path = os.path.join(good_path, file_fmt)226            yield _testimpl_image_decoder_consistency, out_img_type, file_fmt, path227        for file_fmt, path, ext in [("tiff", "db/single/multichannel/tiff_multichannel", 'tif'),228                                    ("jpeg2k", "db/single/multichannel/with_alpha", 'jp2'),229                                    ("png", "db/single/multichannel/with_alpha", 'png')]:230            subdir = None  # In those paths the images are not organized in subdirs231            yield _testimpl_image_decoder_consistency, out_img_type, file_fmt, path, subdir, ext232def _testimpl_image_decoder_tiff_with_alpha_16bit(device, out_type, path, ext):233    @pipeline_def(batch_size=1, device_id=0, num_threads=1)234    def pipe(device, out_type, files):235        encoded, _ = fn.readers.file(files=files)236        decoded = fn.decoders.image(encoded, device=device, output_type=out_type)237        peeked_shape = fn.peek_image_shape(encoded)238        return decoded, peeked_shape239    files = get_img_files(os.path.join(test_data_root, path), ext=ext, subdir=None)240    pipe = pipe(device, out_type=out_type, files=files)241    pipe.build()242    out, shape = pipe.run()243    if device == 'mixed':244        out = out.as_cpu()245    out = np.array(out[0])246    shape = np.array(shape[0])247    expected_channels = 4 if out_type == types.ANY_DATA else \248                        1 if out_type == types.GRAY else \249                        3250    assert out.shape[2] == expected_channels, \251        f"Expected {expected_channels} but got {out.shape[2]}"252def test_image_decoder_tiff_with_alpha_16bit():253    for device in ['cpu', 'mixed']:254        for out_type in [types.RGB, types.BGR, types.YCbCr, types.ANY_DATA]:255            path = "db/single/multichannel/with_alpha_16bit"256            for ext in [("png", "tiff", "jp2")]:257                yield _testimpl_image_decoder_tiff_with_alpha_16bit, device, out_type, path, ext258@pipeline_def(batch_size=batch_size_test, device_id=0, num_threads=4)259def decoder_pipe_with_name(decoder_op, file_root, device, use_fast_idct):260    encoded, _ = fn.readers.file(file_root=file_root)261    decoded = decoder_op(encoded, device=device, output_type=types.RGB, use_fast_idct=use_fast_idct,262                         seed=42)263    return decoded264def check_image_decoder_alias(new_op, old_op, file_root, device, use_fast_idct):265    new_pipe = decoder_pipe_with_name(new_op, file_root, device, use_fast_idct)266    legacy_pipe = decoder_pipe_with_name(old_op, file_root, device, use_fast_idct)267    compare_pipelines(new_pipe, legacy_pipe, batch_size=batch_size_test, N_iterations=3)268def test_image_decoder_alias():269    data_path = os.path.join(test_data_root, good_path, "jpeg")270    for new_op, old_op in [(fn.decoders.image, fn.image_decoder),271                           (fn.decoders.image_crop, fn.image_decoder_crop),272                           (fn.decoders.image_random_crop, fn.image_decoder_random_crop)]:273        for device in ["cpu", "mixed"]:274            for use_fast_idct in [True, False]:275                yield check_image_decoder_alias, new_op, old_op, data_path, device, use_fast_idct276@pipeline_def(batch_size=batch_size_test, device_id=0, num_threads=4)277def decoder_slice_pipe(decoder_op, file_root, device, use_fast_idct):278    encoded, _ = fn.readers.file(file_root=file_root)279    start = types.Constant(np.array([0., 0.]))280    end = types.Constant(np.array([0.5, 0.5]))281    decoded = decoder_op(encoded, start, end, device=device,282                         output_type=types.RGB, use_fast_idct=use_fast_idct)283    return decoded284def check_image_decoder_slice_alias(new_op, old_op, file_root, device, use_fast_idct):285    new_pipe = decoder_slice_pipe(new_op, file_root, device, use_fast_idct)286    legacy_pipe = decoder_slice_pipe(old_op, file_root, device, use_fast_idct)287    compare_pipelines(new_pipe, legacy_pipe, batch_size=batch_size_test, N_iterations=3)288def test_image_decoder_slice_alias():289    data_path = os.path.join(test_data_root, good_path, "jpeg")290    new_op, old_op = fn.decoders.image_slice, fn.image_decoder_slice291    for device in ["cpu", "mixed"]:292        for use_fast_idct in [True, False]:...

test_operator_decoder.py

Source:test_operator_decoder.py

1# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14from __future__ import print_function15from nvidia.dali.pipeline import Pipeline16import nvidia.dali.ops as ops17import nvidia.dali.types as types18import os19from test_utils import check_batch20from test_utils import compare_pipelines21from test_utils import RandomDataIterator22from test_utils import get_dali_extra_path23class DecoderPipeline(Pipeline):24    def __init__(self, data_path, batch_size, num_threads, device_id, device, use_fast_idct=False):25        super(DecoderPipeline, self).__init__(batch_size, num_threads, device_id, prefetch_queue_depth=1)26        self.input = ops.FileReader(file_root = data_path,27                                    shard_id = 0,28                                    num_shards = 1)29        self.decode = ops.ImageDecoder(device = device, output_type = types.RGB, use_fast_idct=use_fast_idct)30    def define_graph(self):31        inputs, labels = self.input(name="Reader")32        output = self.decode(inputs)33        return (output, labels)34test_data_root = get_dali_extra_path()35good_path = 'db/single'36missnamed_path = 'db/single/missnamed'37test_good_path = {'jpeg', 'mixed', 'png', 'tiff', 'pnm', 'bmp'}38test_missnamed_path = {'jpeg', 'png', 'tiff', 'pnm', 'bmp'}39def run_decode(data_path, batch, device, threads):40    pipe = DecoderPipeline(data_path=data_path, batch_size=batch, num_threads=threads, device_id=0, device=device)41    pipe.build()42    iters = pipe.epoch_size("Reader")43    for _ in range(iters):44        pipe.run()45def test_image_decoder():46    for device in {'cpu', 'mixed'}:47        for threads in {1, 2, 3, 4}:48            for size in {1, 10}:49                for img_type in test_good_path:50                    data_path = os.path.join(test_data_root, good_path, img_type)51                    run_decode(data_path, size, device, threads)52                    yield check, img_type, size, device, threads53def test_missnamed_host_decoder():54    for decoder in {'cpu', 'mixed'}:55        for threads in {1, 2, 3, 4}:56            for size in {1, 10}:57                for img_type in test_missnamed_path:58                    data_path = os.path.join(test_data_root, missnamed_path, img_type)59                    run_decode(data_path, size, decoder, threads)60                    yield check, img_type, size, decoder, threads61def check(img_type, size, device, threads):62    pass63class DecoderPipelineFastIDC(Pipeline):64    def __init__(self, data_path, batch_size, num_threads, use_fast_idct=False):65        super(DecoderPipelineFastIDC, self).__init__(batch_size, num_threads, 0, prefetch_queue_depth=1)66        self.input = ops.FileReader(file_root = data_path,67                                    shard_id = 0,68                                    num_shards = 1)69        self.decode = ops.ImageDecoder(device = 'cpu', output_type = types.RGB, use_fast_idct=use_fast_idct)70    def define_graph(self):71        inputs, labels = self.input(name="Reader")72        output = self.decode(inputs)73        return (output, labels)74def check_FastDCT_body(batch_size, img_type, device):75    data_path = os.path.join(test_data_root, good_path, img_type)76    compare_pipelines(DecoderPipeline(data_path=data_path, batch_size=batch_size, num_threads=3,77                                      device_id=0, device=device, use_fast_idct=False),78                      DecoderPipeline(data_path=data_path, batch_size=batch_size, num_threads=3,79                                      device_id=0, device='cpu', use_fast_idct=True),80                      # average difference should be no bigger by off-by-381                      batch_size=batch_size, N_iterations=3, eps=3)82def check_FastDCT():83    for device in {'cpu', 'mixed'}:84        for batch_size in {1, 8}:85            for img_type in test_good_path:...

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