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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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