From d3f4d45e65eab371f6ebebd34f3b1d1cada9de1d Mon Sep 17 00:00:00 2001 From: wuxun-zhang Date: Wed, 17 Jul 2019 14:12:51 +0800 Subject: [PATCH 01/10] add quantized FCN model --- docs/tutorials/deployment/int8_inference.py | 14 +- gluoncv/model_zoo/model_zoo.py | 2 + .../fcn_resnet101_coco_int8-symbol.json | 18622 ++++++++++++++++ .../fcn_resnet101_voc_int8-symbol.json | 18622 ++++++++++++++++ gluoncv/model_zoo/quantized/quantized.py | 5 +- scripts/segmentation/eval_segmentation.py | 172 + scripts/segmentation/test.py | 81 - .../{train.py => train_segmentation.py} | 0 8 files changed, 37432 insertions(+), 86 deletions(-) create mode 100755 gluoncv/model_zoo/quantized/fcn_resnet101_coco_int8-symbol.json create mode 100755 gluoncv/model_zoo/quantized/fcn_resnet101_voc_int8-symbol.json create mode 100644 scripts/segmentation/eval_segmentation.py delete mode 100644 scripts/segmentation/test.py rename scripts/segmentation/{train.py => train_segmentation.py} (100%) diff --git a/docs/tutorials/deployment/int8_inference.py b/docs/tutorials/deployment/int8_inference.py index 9da5f3af81..45ba4e3a70 100644 --- a/docs/tutorials/deployment/int8_inference.py +++ b/docs/tutorials/deployment/int8_inference.py @@ -11,13 +11,14 @@ GluonCV delivered some quantized models to improve the performance and reduce the deployment costs for the computer vision inference tasks. In real production, there are two main benefits of lower precision (INT8). First, the computation can be accelerated by the low precision instruction, like Intel Vector Neural Network Instruction (VNNI). Second, lower precision data type would save the memory bandwidth and allow for better cache locality and save the power. The new feature can get up to 4X performance speedup in the latest `AWS EC2 C5 instances `_ under the `Intel Deep Learning Boost (VNNI) `_ enabled hardware with less than 0.5% accuracy drop. -Please checkout `verify_pretrained.py `_ for imagenet inference -and `eval_ssd.py `_ for SSD inference. +Please checkout `verify_pretrained.py `_ for imagenet inference, +`eval_ssd.py `_ for SSD inference, and `eval_segmentation.py `_ +for FCN inference. Performance ----------- -GluonCV supports some quantized classification models and detection models. +GluonCV supports some quantized classification models, detection models and segmentation models. For the throughput, the target is to achieve the maximum machine efficiency to combine the inference requests together and get the results by one iteration. From the bar-chart, it is clearly that the fusion and quantization approach improved the throughput from 3.22X to 7.24X for selected models. Below CPU performance is collected with dummy input from AWS EC2 C5.24xlarge instance with 24 physical cores. @@ -41,8 +42,13 @@ +-----------------------+----------+------------+------------------+------------------+---------+-----------------+-----------------+ | SSD-mobilenet1.0 512* | VOC | 224 | 65.97 | 212.59 | 3.22 | 75.42 | 74.70 | +-----------------------+----------+------------+------------------+------------------+---------+-----------------+-----------------+ +| FCN_resnet101 | VOC | 1 | 5.09 | 21.12 | 4.15 | 97.97% | 95.71% | ++-----------------------+----------+------------+------------------+------------------+---------+-----------------+-----------------+ +| FCN_resnet101 | COCO | 1 | 5.10 | 25.98 | 5.09 | 91.28% | 90.10% | ++-----------------------+----------+------------+------------------+------------------+---------+-----------------+-----------------+ + -Quantized SSD models are evaluated with ``nms_thresh=0.45``, ``nms_topk=200``. +Quantized SSD models are evaluated with ``nms_thresh=0.45``, ``nms_topk=200``. For FCN models, the accuracy metric is pixAcc. Demo usage for SSD ------------------ diff --git a/gluoncv/model_zoo/model_zoo.py b/gluoncv/model_zoo/model_zoo.py index 63c0d6a370..9884954939 100644 --- a/gluoncv/model_zoo/model_zoo.py +++ b/gluoncv/model_zoo/model_zoo.py @@ -219,6 +219,8 @@ 'alpha_pose_resnet101_v1b_coco': alpha_pose_resnet101_v1b_coco, 'vgg16_ucf101': vgg16_ucf101, 'inceptionv3_ucf101': inceptionv3_ucf101, + 'fcn_resnet101_voc_int8': fcn_resnet101_voc_int8, + 'fcn_resnet101_coco_int8':fcn_resnet101_coco_int8, } diff --git a/gluoncv/model_zoo/quantized/fcn_resnet101_coco_int8-symbol.json b/gluoncv/model_zoo/quantized/fcn_resnet101_coco_int8-symbol.json new file mode 100755 index 0000000000..8bc31f7c62 --- /dev/null +++ b/gluoncv/model_zoo/quantized/fcn_resnet101_coco_int8-symbol.json @@ -0,0 +1,18622 @@ +{ + "nodes": [ + { + "op": "null", + "name": "data", + "inputs": [] + }, + { + "op": "_contrib_quantize_v2", + "name": "data_quantize", + "attrs": { + "max_calib_range": "2.640000", + "min_calib_range": 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[[652, 0, 0], [666, 0, 0]], + "attrs": {"mxnet_version": ["int", 10500]} +} \ No newline at end of file diff --git a/gluoncv/model_zoo/quantized/quantized.py b/gluoncv/model_zoo/quantized/quantized.py index fd9a686070..6bc341c7c3 100644 --- a/gluoncv/model_zoo/quantized/quantized.py +++ b/gluoncv/model_zoo/quantized/quantized.py @@ -8,7 +8,8 @@ __all__ = ['mobilenet1_0_int8', 'resnet50_v1_int8', 'ssd_300_vgg16_atrous_voc_int8', 'ssd_512_mobilenet1_0_voc_int8', - 'ssd_512_resnet50_v1_voc_int8', 'ssd_512_vgg16_atrous_voc_int8'] + 'ssd_512_resnet50_v1_voc_int8', 'ssd_512_vgg16_atrous_voc_int8', + 'fcn_resnet101_voc_int8', 'fcn_resnet101_coco_int8'] def _not_impl(*args, **kwargs): raise NotImplementedError("Not yet implemented for quantized models") @@ -73,3 +74,5 @@ def func(pretrained=False, tag=None, root='~/.mxnet/models', ctx=cpu(0), **kwarg _create_quantized_models('ssd_512_mobilenet1_0_voc_int8', 'ssd0_') _create_quantized_models('ssd_512_resnet50_v1_voc_int8', 'ssd0_') _create_quantized_models('ssd_512_vgg16_atrous_voc_int8', 'ssd0_') +_create_quantized_models('fcn_resnet101_voc_int8', 'fcn0_') +_create_quantized_models('fcn_resnet101_coco_int8', 'fcn0_') diff --git a/scripts/segmentation/eval_segmentation.py b/scripts/segmentation/eval_segmentation.py new file mode 100644 index 0000000000..d9e6b6a0db --- /dev/null +++ b/scripts/segmentation/eval_segmentation.py @@ -0,0 +1,172 @@ +import os +from tqdm import tqdm +import numpy as np +import argparse +import time +import sys + +import mxnet as mx +from mxnet import gluon +from mxnet.gluon.data.vision import transforms + +import gluoncv +from gluoncv.model_zoo.segbase import * +from gluoncv.model_zoo import get_model +from gluoncv.data import get_segmentation_dataset, ms_batchify_fn +from gluoncv.utils.viz import get_color_pallete + +def parse_args(): + parser = argparse.ArgumentParser(description='Validation on Segmentation model') + # model and dataset + parser.add_argument('--model', type=str, default='fcn', + help='model name (default: fcn)') + parser.add_argument('--backbone', type=str, default='resnet101', + help='base network') + parser.add_argument('--base-size', type=int, default=520, + help='base image size') + parser.add_argument('--crop-size', type=int, default=480, + help='crop image size') + parser.add_argument('--mode', type=str, default='val', + help='val, testval') + parser.add_argument('--dataset', type=str, default='pascal_voc', + help='dataset used for validation [pascal_voc, pascal_aug, coco, ade20k]') + parser.add_argument('--quantized', action='store_true', + help='whether to use quantized model') + parser.add_argument('--batch-size', type=int, default=16) + parser.add_argument('--num-batches', type=int, default=100) + parser.add_argument('--workers', type=int, default=4, + help='number of workers for data loading') + parser.add_argument('--pretrained', action="store_true", + help='whether to use pretrained params') + parser.add_argument('--ngpus', type=int, default=0) + parser.add_argument('--aux', action='store_true', default=False, + help='Auxiliary loss') + # synchronized Batch Normalization + parser.add_argument('--syncbn', action='store_true', default=False, + help='using Synchronized Cross-GPU BatchNorm') + parser.add_argument('--resume', type=str, default=None, + help='put the path to resuming file if needed') + # evaluation only + parser.add_argument('--eval', action='store_true', default=False, + help='evaluation only') + + args = parser.parse_args() + + args.ctx = [mx.cpu(0)] + args.ctx = [mx.gpu(i) for i in range(args.ngpus)] if args.ngpus > 0 else args.ctx + + args.norm_layer = mx.gluon.contrib.nn.SyncBatchNorm if args.syncbn \ + else mx.gluon.nn.BatchNorm + args.norm_kwargs = {'num_devices': args.ngpus} if args.syncbn else {} + return args + + +def test(args, model): + # output folder + outdir = 'outdir' + if not os.path.exists(outdir): + os.makedirs(outdir) + # image transform + input_transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize([.485, .456, .406], [.229, .224, .225]), + ]) + data_kwargs = {'transform': input_transform, 'base_size': args.base_size, + 'crop_size': args.crop_size} + # get dataset + if args.eval: + testset = get_segmentation_dataset( + args.dataset, split='val', mode=args.mode, **data_kwargs) + else: + testset = get_segmentation_dataset( + args.dataset, split='test', mode=args.mode, **data_kwargs) + size = len(testset) + # get dataloader + test_data = gluon.data.DataLoader( + testset, args.batch_size, last_batch='keep', shuffle=False, num_workers=args.workers) + + print(model) + if not args.eval: + evaluator = MultiEvalModel(model, testset.num_class, ctx_list=args.ctx) + metric = gluoncv.utils.metrics.SegmentationMetric(testset.num_class) + + tbar = tqdm(test_data) + metric.reset() + tic = time.time() + for i, (batch, dsts) in enumerate(tbar): + if args.eval: + targets = mx.gluon.utils.split_and_load(dsts, ctx_list=args.ctx, even_split=False) + data = mx.gluon.utils.split_and_load(batch, ctx_list=args.ctx, batch_axis=0, even_split=False) + outputs = None + for x in data: + output = model.forward(x)[0] + outputs = output if outputs is None else nd.concat(outputs, output, axis=0) + outputs = [outputs] + metric.update(targets, outputs) + pixAcc, mIoU = metric.get() + tbar.set_description( 'pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU)) + else: + im_paths = dsts + predicts = evaluator.parallel_forward(batch) + for predict, impath in zip(predicts, im_paths): + predict = mx.nd.squeeze(mx.nd.argmax(predict[0], 1)).asnumpy() + \ + testset.pred_offset + mask = get_color_pallete(predict, args.dataset) + outname = os.path.splitext(impath)[0] + '.png' + mask.save(os.path.join(outdir, outname)) + speed = size / (time.time() - tic) + print('Inference speed with batchsize %d is %.2f img/sec' % (args.batch_size, speed)) + + +if __name__ == "__main__": + args = parse_args() + + withQuantization = False + model_prefix = args.model + '_' + args.backbone + if 'pascal' in args.dataset: + model_prefix += '_voc' + withQuantization = True if (args.backbone in ['resnet101'] and args.ngpus == 0) else withQuantization + elif args.dataset == 'coco': + model_prefix += '_coco' + withQuantization = True if args.backbone in ['resnet101'] else withQuantization + elif args.dataset == 'ade20k': + model_prefix += 'ade' + elif args.dataset == 'citys': + model_prefix += 'citys' + else: + raise ValueError('Unsupported dataset {} used'.format(args.dataset)) + + if withQuantization and args.quantized: + model_prefix += '_int8' + + if args.quantized and args.mode != 'val': + raise ValueError("Currently, %s mode or is not supported by quantized model." % args.mode) + + if args.quantized and args.eval == False: + raise ValueError("Currently, only evaluation is supported by quantized model.") + + # create network + if args.pretrained: + model = get_model(model_prefix, pretrained=True) + model.collect_params().reset_ctx(ctx = args.ctx) + else: + model = get_segmentation_model(model=args.model, dataset=args.dataset, ctx=args.ctx, + backbone=args.backbone, norm_layer=args.norm_layer, + norm_kwargs=args.norm_kwargs, aux=args.aux, + base_size=args.base_size, crop_size=args.crop_size) + # load local pretrained weight + assert args.resume is not None, '=> Please provide the checkpoint using --resume' + if os.path.isfile(args.resume): + model.load_parameters(args.resume, ctx=args.ctx) + else: + raise RuntimeError("=> no checkpoint found at '{}'" \ + .format(args.resume)) + + print("Successfully loaded %s model" % model_prefix) + if '_int8' in model_prefix: + model.hybridize(static_alloc=True, static_shape=True) + else: + model.hybridize() + + print('Testing model: ', args.resume) + test(args, model) diff --git a/scripts/segmentation/test.py b/scripts/segmentation/test.py deleted file mode 100644 index 3cb5a6396f..0000000000 --- a/scripts/segmentation/test.py +++ /dev/null @@ -1,81 +0,0 @@ -import os -from tqdm import tqdm -import numpy as np - -import mxnet as mx -from mxnet import gluon -from mxnet.gluon.data.vision import transforms - -import gluoncv -from gluoncv.model_zoo.segbase import * -from gluoncv.model_zoo import get_model -from gluoncv.data import get_segmentation_dataset, ms_batchify_fn -from gluoncv.utils.viz import get_color_pallete - -from train import parse_args - -def test(args): - # output folder - outdir = 'outdir' - if not os.path.exists(outdir): - os.makedirs(outdir) - # image transform - input_transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([.485, .456, .406], [.229, .224, .225]), - ]) - # dataset and dataloader - if args.eval: - testset = get_segmentation_dataset( - args.dataset, split='val', mode='testval', transform=input_transform) - total_inter, total_union, total_correct, total_label = \ - np.int64(0), np.int64(0), np.int64(0), np.int64(0) - else: - testset = get_segmentation_dataset( - args.dataset, split='test', mode='test', transform=input_transform) - test_data = gluon.data.DataLoader( - testset, args.test_batch_size, shuffle=False, last_batch='keep', - batchify_fn=ms_batchify_fn, num_workers=args.workers) - # create network - if args.model_zoo is not None: - model = get_model(args.model_zoo, pretrained=True) - else: - model = get_segmentation_model(model=args.model, dataset=args.dataset, ctx=args.ctx, - backbone=args.backbone, norm_layer=args.norm_layer, - norm_kwargs=args.norm_kwargs, aux=args.aux, - base_size=args.base_size, crop_size=args.crop_size) - # load pretrained weight - assert args.resume is not None, '=> Please provide the checkpoint using --resume' - if os.path.isfile(args.resume): - model.load_parameters(args.resume, ctx=args.ctx) - else: - raise RuntimeError("=> no checkpoint found at '{}'" \ - .format(args.resume)) - print(model) - evaluator = MultiEvalModel(model, testset.num_class, ctx_list=args.ctx) - metric = gluoncv.utils.metrics.SegmentationMetric(testset.num_class) - - tbar = tqdm(test_data) - for i, (data, dsts) in enumerate(tbar): - if args.eval: - predicts = [pred[0] for pred in evaluator.parallel_forward(data)] - targets = [target.as_in_context(predicts[0].context) \ - for target in dsts] - metric.update(targets, predicts) - pixAcc, mIoU = metric.get() - tbar.set_description( 'pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU)) - else: - im_paths = dsts - predicts = evaluator.parallel_forward(data) - for predict, impath in zip(predicts, im_paths): - predict = mx.nd.squeeze(mx.nd.argmax(predict[0], 1)).asnumpy() + \ - testset.pred_offset - mask = get_color_pallete(predict, args.dataset) - outname = os.path.splitext(impath)[0] + '.png' - mask.save(os.path.join(outdir, outname)) - -if __name__ == "__main__": - args = parse_args() - args.test_batch_size = args.ngpus - print('Testing model: ', args.resume) - test(args) diff --git a/scripts/segmentation/train.py b/scripts/segmentation/train_segmentation.py similarity index 100% rename from scripts/segmentation/train.py rename to scripts/segmentation/train_segmentation.py From c3599c215d95f4b655f256d1c02e1c782ff5be15 Mon Sep 17 00:00:00 2001 From: wuxun-zhang Date: Tue, 23 Jul 2019 21:21:23 +0800 Subject: [PATCH 02/10] address comments --- docs/tutorials/deployment/int8_inference.py | 2 +- .../{eval_segmentation.py => test.py} | 60 +++++++++++++------ .../{train_segmentation.py => train.py} | 0 tests/unittests/test_model_zoo.py | 9 +++ 4 files changed, 52 insertions(+), 19 deletions(-) rename scripts/segmentation/{eval_segmentation.py => test.py} (78%) rename scripts/segmentation/{train_segmentation.py => train.py} (100%) diff --git a/docs/tutorials/deployment/int8_inference.py b/docs/tutorials/deployment/int8_inference.py index 45ba4e3a70..f96be50e0d 100644 --- a/docs/tutorials/deployment/int8_inference.py +++ b/docs/tutorials/deployment/int8_inference.py @@ -12,7 +12,7 @@ GluonCV delivered some quantized models to improve the performance and reduce the deployment costs for the computer vision inference tasks. In real production, there are two main benefits of lower precision (INT8). First, the computation can be accelerated by the low precision instruction, like Intel Vector Neural Network Instruction (VNNI). Second, lower precision data type would save the memory bandwidth and allow for better cache locality and save the power. The new feature can get up to 4X performance speedup in the latest `AWS EC2 C5 instances `_ under the `Intel Deep Learning Boost (VNNI) `_ enabled hardware with less than 0.5% accuracy drop. Please checkout `verify_pretrained.py `_ for imagenet inference, -`eval_ssd.py `_ for SSD inference, and `eval_segmentation.py `_ +`eval_ssd.py `_ for SSD inference, and `test.py `_ for FCN inference. Performance diff --git a/scripts/segmentation/eval_segmentation.py b/scripts/segmentation/test.py similarity index 78% rename from scripts/segmentation/eval_segmentation.py rename to scripts/segmentation/test.py index d9e6b6a0db..6ad712fecd 100644 --- a/scripts/segmentation/eval_segmentation.py +++ b/scripts/segmentation/test.py @@ -6,7 +6,7 @@ import sys import mxnet as mx -from mxnet import gluon +from mxnet import gluon, ndarray as nd from mxnet.gluon.data.vision import transforms import gluoncv @@ -22,6 +22,8 @@ def parse_args(): help='model name (default: fcn)') parser.add_argument('--backbone', type=str, default='resnet101', help='base network') + parser.add_argument('--image-shape', type=int, default=480, + help='image shape') parser.add_argument('--base-size', type=int, default=520, help='base image size') parser.add_argument('--crop-size', type=int, default=480, @@ -33,7 +35,8 @@ def parse_args(): parser.add_argument('--quantized', action='store_true', help='whether to use quantized model') parser.add_argument('--batch-size', type=int, default=16) - parser.add_argument('--num-batches', type=int, default=100) + parser.add_argument('--num-iterations', type=int, default=100, + help='number of benchmarking iterations.') parser.add_argument('--workers', type=int, default=4, help='number of workers for data loading') parser.add_argument('--pretrained', action="store_true", @@ -49,6 +52,9 @@ def parse_args(): # evaluation only parser.add_argument('--eval', action='store_true', default=False, help='evaluation only') + # dummy benchmark + parser.add_argument('--benchmark', action='store_true', default=False, + help='whether to use dummy data for benchmark') args = parser.parse_args() @@ -81,13 +87,13 @@ def test(args, model): testset = get_segmentation_dataset( args.dataset, split='test', mode=args.mode, **data_kwargs) size = len(testset) + # get dataloader + batchify_fn = ms_batchify_fn if args.mode == 'test' else None test_data = gluon.data.DataLoader( - testset, args.batch_size, last_batch='keep', shuffle=False, num_workers=args.workers) + testset, args.batch_size, batchify_fn=batchify_fn, last_batch='keep', shuffle=False, num_workers=args.workers) print(model) - if not args.eval: - evaluator = MultiEvalModel(model, testset.num_class, ctx_list=args.ctx) metric = gluoncv.utils.metrics.SegmentationMetric(testset.num_class) tbar = tqdm(test_data) @@ -99,17 +105,18 @@ def test(args, model): data = mx.gluon.utils.split_and_load(batch, ctx_list=args.ctx, batch_axis=0, even_split=False) outputs = None for x in data: - output = model.forward(x)[0] + output = model.forward(x) outputs = output if outputs is None else nd.concat(outputs, output, axis=0) - outputs = [outputs] metric.update(targets, outputs) pixAcc, mIoU = metric.get() tbar.set_description( 'pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU)) else: - im_paths = dsts - predicts = evaluator.parallel_forward(batch) - for predict, impath in zip(predicts, im_paths): - predict = mx.nd.squeeze(mx.nd.argmax(predict[0], 1)).asnumpy() + \ + for data, impath in zip(batch, dsts): + data = data.as_in_context(args.ctx[0]) + if len(data.shape) < 4: + data = nd.expand_dims(data, axis=0) + predict = model.forward(data)[0] + predict = mx.nd.squeeze(mx.nd.argmax(predict, 1)).asnumpy() + \ testset.pred_offset mask = get_color_pallete(predict, args.dataset) outname = os.path.splitext(impath)[0] + '.png' @@ -118,6 +125,26 @@ def test(args, model): print('Inference speed with batchsize %d is %.2f img/sec' % (args.batch_size, speed)) +def benchmarking(args, model): + print('-----benchmarking on %s -----' % args.model) + bs = args.batch_size + num_iterations = args.num_iterations + input_shape = (bs, 3, args.image_shape, args.image_shape) + size = num_iterations * bs + data = [mx.random.uniform(-1.0, 1.0, shape=input_shape, ctx=args.ctx[0], dtype='float32')] + dry_run = 5 + with tqdm(total=size+dry_run*bs) as pbar: + for n in range(dry_run + num_iterations): + if n == dry_run: + tic = time.time() + outputs = model.forward(data[0]) + for output in outputs: + output.wait_to_read() + pbar.update(bs) + speed = size / (time.time() - tic) + print('Throughput is %f imgs/sec' % speed) + + if __name__ == "__main__": args = parse_args() @@ -139,12 +166,6 @@ def test(args, model): if withQuantization and args.quantized: model_prefix += '_int8' - if args.quantized and args.mode != 'val': - raise ValueError("Currently, %s mode or is not supported by quantized model." % args.mode) - - if args.quantized and args.eval == False: - raise ValueError("Currently, only evaluation is supported by quantized model.") - # create network if args.pretrained: model = get_model(model_prefix, pretrained=True) @@ -169,4 +190,7 @@ def test(args, model): model.hybridize() print('Testing model: ', args.resume) - test(args, model) + if not args.benchmark: + test(args, model) + else: + benchmarking(args, model) diff --git a/scripts/segmentation/train_segmentation.py b/scripts/segmentation/train.py similarity index 100% rename from scripts/segmentation/train_segmentation.py rename to scripts/segmentation/train.py diff --git a/tests/unittests/test_model_zoo.py b/tests/unittests/test_model_zoo.py index 9ae60d1404..6c117a85c7 100644 --- a/tests/unittests/test_model_zoo.py +++ b/tests/unittests/test_model_zoo.py @@ -411,6 +411,15 @@ def test_quantized_ssd_models(): _test_model_list(model_list, ctx, x) +@unittest.skip("temporarily disabled to fallback to non-mkl version") +@with_cpu(0) +def test_quantized_fcn_models(): + model_list = ['fcn_resnet101_voc_int8', 'fcn_resnet101_coco_int8-symbol'] + ctx = mx.context.current_context() + x = mx.random.uniform(shape=(1, 3, 480, 480), ctx=ctx) + _test_model_list(model_list, ctx, x) + + if __name__ == '__main__': import nose From cd03df0850be7d4c86d099facc414418219897d5 Mon Sep 17 00:00:00 2001 From: wuxun-zhang Date: Wed, 24 Jul 2019 14:40:34 +0800 Subject: [PATCH 03/10] add separate test function for quantized model --- scripts/segmentation/test.py | 122 ++++++++++++++++++++---------- tests/unittests/test_model_zoo.py | 2 +- 2 files changed, 81 insertions(+), 43 deletions(-) diff --git a/scripts/segmentation/test.py b/scripts/segmentation/test.py index 6ad712fecd..a8bb89026f 100644 --- a/scripts/segmentation/test.py +++ b/scripts/segmentation/test.py @@ -67,77 +67,93 @@ def parse_args(): return args -def test(args, model): - # output folder - outdir = 'outdir' - if not os.path.exists(outdir): - os.makedirs(outdir) - # image transform - input_transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([.485, .456, .406], [.229, .224, .225]), - ]) - data_kwargs = {'transform': input_transform, 'base_size': args.base_size, - 'crop_size': args.crop_size} - # get dataset - if args.eval: - testset = get_segmentation_dataset( - args.dataset, split='val', mode=args.mode, **data_kwargs) - else: - testset = get_segmentation_dataset( - args.dataset, split='test', mode=args.mode, **data_kwargs) +def test(model, is_eval, batch_size, testset, metric, num_class, ctx_list, num_workers, outdir): size = len(testset) - - # get dataloader - batchify_fn = ms_batchify_fn if args.mode == 'test' else None test_data = gluon.data.DataLoader( - testset, args.batch_size, batchify_fn=batchify_fn, last_batch='keep', shuffle=False, num_workers=args.workers) + testset, batch_size, batchify_fn=ms_batchify_fn, last_batch='keep', shuffle=False, num_workers=num_workers) + print(model) + evaluator = MultiEvalModel(model, num_class, ctx_list=ctx_list) + tbar = tqdm(test_data) + metric.reset() + tic = time.time() + for i, (data, dsts) in enumerate(tbar): + if is_eval: + predicts = [pred for pred in evaluator.parallel_forward(data)] + targets = [target.as_in_context(predicts[0].context) \ + for target in dsts] + metric.update(targets, predicts) + pixAcc, mIoU = metric.get() + tbar.set_description('pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU)) + else: + im_paths = dsts + predicts = evaluator.parallel_forward(data) + for predict, impath in zip(predicts, im_paths): + predict = [predict] + predict = mx.nd.squeeze(mx.nd.argmax(predict[0], 1)).asnumpy() + \ + testset.pred_offset + mask = get_color_pallete(predict, testset) + outname = os.path.splitext(impath)[0] + '.png' + mask.save(os.path.join(outdir, outname)) + speed = size / (time.time() - tic) + print('Inference speed with batchsize %d is %.2f img/sec' % (batch_size, speed)) + + +def test_quantization(model, is_eval, batch_size, testset, metric, num_class, ctx_list, num_workers, outdir): + model.hybridize(static_alloc=True, static_shape=True) + size = len(testset) + batchify_fn = ms_batchify_fn if testset.mode == 'test' else None + test_data = gluon.data.DataLoader( + testset, batch_size, batchify_fn=batchify_fn, last_batch='keep', shuffle=False, num_workers=num_workers) print(model) - metric = gluoncv.utils.metrics.SegmentationMetric(testset.num_class) tbar = tqdm(test_data) metric.reset() tic = time.time() for i, (batch, dsts) in enumerate(tbar): - if args.eval: - targets = mx.gluon.utils.split_and_load(dsts, ctx_list=args.ctx, even_split=False) - data = mx.gluon.utils.split_and_load(batch, ctx_list=args.ctx, batch_axis=0, even_split=False) + if is_eval: + targets = mx.gluon.utils.split_and_load(dsts, ctx_list=ctx_list, even_split=False) + data = mx.gluon.utils.split_and_load(batch, ctx_list=ctx_list, batch_axis=0, even_split=False) outputs = None for x in data: - output = model.forward(x) + output = model(x) outputs = output if outputs is None else nd.concat(outputs, output, axis=0) metric.update(targets, outputs) pixAcc, mIoU = metric.get() - tbar.set_description( 'pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU)) + tbar.set_description('pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU)) else: for data, impath in zip(batch, dsts): - data = data.as_in_context(args.ctx[0]) + data = data.as_in_context(ctx_list[0]) if len(data.shape) < 4: data = nd.expand_dims(data, axis=0) - predict = model.forward(data)[0] + predict = model(data)[0] predict = mx.nd.squeeze(mx.nd.argmax(predict, 1)).asnumpy() + \ testset.pred_offset - mask = get_color_pallete(predict, args.dataset) + mask = get_color_pallete(predict, testset) outname = os.path.splitext(impath)[0] + '.png' mask.save(os.path.join(outdir, outname)) speed = size / (time.time() - tic) - print('Inference speed with batchsize %d is %.2f img/sec' % (args.batch_size, speed)) + print('Inference speed with batchsize %d is %.2f img/sec' % (batch_size, speed)) def benchmarking(args, model): print('-----benchmarking on %s -----' % args.model) + if args.quantized: + model.hybridize(static_alloc=True, static_shape=True) + else: + model.hybridize() + bs = args.batch_size num_iterations = args.num_iterations input_shape = (bs, 3, args.image_shape, args.image_shape) size = num_iterations * bs - data = [mx.random.uniform(-1.0, 1.0, shape=input_shape, ctx=args.ctx[0], dtype='float32')] + data = mx.random.uniform(-1.0, 1.0, shape=input_shape, ctx=args.ctx[0], dtype='float32') dry_run = 5 with tqdm(total=size+dry_run*bs) as pbar: for n in range(dry_run + num_iterations): if n == dry_run: tic = time.time() - outputs = model.forward(data[0]) + outputs = model(data) for output in outputs: output.wait_to_read() pbar.update(bs) @@ -166,10 +182,15 @@ def benchmarking(args, model): if withQuantization and args.quantized: model_prefix += '_int8' + # output folder + outdir = 'outdir' + if not os.path.exists(outdir): + os.makedirs(outdir) + # create network if args.pretrained: model = get_model(model_prefix, pretrained=True) - model.collect_params().reset_ctx(ctx = args.ctx) + model.collect_params().reset_ctx(ctx=args.ctx) else: model = get_segmentation_model(model=args.model, dataset=args.dataset, ctx=args.ctx, backbone=args.backbone, norm_layer=args.norm_layer, @@ -182,15 +203,32 @@ def benchmarking(args, model): else: raise RuntimeError("=> no checkpoint found at '{}'" \ .format(args.resume)) - print("Successfully loaded %s model" % model_prefix) - if '_int8' in model_prefix: - model.hybridize(static_alloc=True, static_shape=True) - else: - model.hybridize() + # image transform + input_transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize([.485, .456, .406], [.229, .224, .225]), + ]) + data_kwargs = {'transform': input_transform, 'base_size': args.base_size, + 'crop_size': args.crop_size} + # get dataset + if args.eval: + test_dataset = get_segmentation_dataset( + args.dataset, split='val', mode=args.mode, **data_kwargs) + else: + test_dataset = get_segmentation_dataset( + args.dataset, split='test', mode=args.mode, **data_kwargs) + + metric = gluoncv.utils.metrics.SegmentationMetric(test_dataset.num_class) print('Testing model: ', args.resume) + if not args.benchmark: - test(args, model) + if '_int8' in model_prefix: + test_quantization(model, args.eval, args.batch_size, test_dataset, metric, + test_dataset.num_class, args.ctx, args.workers, outdir) + else: + test(model, args.eval, args.batch_size, test_dataset, metric, + test_dataset.num_class, args.ctx, args.workers, outdir) else: benchmarking(args, model) diff --git a/tests/unittests/test_model_zoo.py b/tests/unittests/test_model_zoo.py index 6c117a85c7..f114c35e38 100644 --- a/tests/unittests/test_model_zoo.py +++ b/tests/unittests/test_model_zoo.py @@ -414,7 +414,7 @@ def test_quantized_ssd_models(): @unittest.skip("temporarily disabled to fallback to non-mkl version") @with_cpu(0) def test_quantized_fcn_models(): - model_list = ['fcn_resnet101_voc_int8', 'fcn_resnet101_coco_int8-symbol'] + model_list = ['fcn_resnet101_voc_int8', 'fcn_resnet101_coco_int8'] ctx = mx.context.current_context() x = mx.random.uniform(shape=(1, 3, 480, 480), ctx=ctx) _test_model_list(model_list, ctx, x) From 5682cbe2311a79a806f526c233c1001aa3166420 Mon Sep 17 00:00:00 2001 From: wuxun-zhang Date: Fri, 26 Jul 2019 14:21:45 +0800 Subject: [PATCH 04/10] address comments --- scripts/segmentation/test.py | 92 ++++++++++++++++++++---------------- 1 file changed, 50 insertions(+), 42 deletions(-) diff --git a/scripts/segmentation/test.py b/scripts/segmentation/test.py index a8bb89026f..9ba72e6b9c 100644 --- a/scripts/segmentation/test.py +++ b/scripts/segmentation/test.py @@ -41,7 +41,9 @@ def parse_args(): help='number of workers for data loading') parser.add_argument('--pretrained', action="store_true", help='whether to use pretrained params') - parser.add_argument('--ngpus', type=int, default=0) + parser.add_argument('--ngpus', type=int, + default=len(mx.test_utils.list_gpus()), + help='number of GPUs (default: 4)') parser.add_argument('--aux', action='store_true', default=False, help='Auxiliary loss') # synchronized Batch Normalization @@ -67,19 +69,31 @@ def parse_args(): return args -def test(model, is_eval, batch_size, testset, metric, num_class, ctx_list, num_workers, outdir): - size = len(testset) +def test(model, args, input_transform): + # output folder + outdir = 'outdir' + if not os.path.exists(outdir): + os.makedirs(outdir) + # get dataset + if args.eval: + testset = get_segmentation_dataset( + args.dataset, split='val', mode='testval', transform=input_transform) + total_inter, total_union, total_correct, total_label = \ + np.int64(0), np.int64(0), np.int64(0), np.int64(0) + else: + testset = get_segmentation_dataset( + args.dataset, split='test', mode='test', transform=input_transform) test_data = gluon.data.DataLoader( - testset, batch_size, batchify_fn=ms_batchify_fn, last_batch='keep', shuffle=False, num_workers=num_workers) + testset, args.batch_size, shuffle=False, last_batch='keep', + batchify_fn=ms_batchify_fn, num_workers=args.workers) print(model) - evaluator = MultiEvalModel(model, num_class, ctx_list=ctx_list) + evaluator = MultiEvalModel(model, testset.num_class, ctx_list=args.ctx) + metric = gluoncv.utils.metrics.SegmentationMetric(testset.num_class) tbar = tqdm(test_data) - metric.reset() - tic = time.time() for i, (data, dsts) in enumerate(tbar): - if is_eval: - predicts = [pred for pred in evaluator.parallel_forward(data)] + if args.eval: + predicts = [pred[0] for pred in evaluator.parallel_forward(data)] targets = [target.as_in_context(predicts[0].context) \ for target in dsts] metric.update(targets, predicts) @@ -89,31 +103,43 @@ def test(model, is_eval, batch_size, testset, metric, num_class, ctx_list, num_w im_paths = dsts predicts = evaluator.parallel_forward(data) for predict, impath in zip(predicts, im_paths): - predict = [predict] predict = mx.nd.squeeze(mx.nd.argmax(predict[0], 1)).asnumpy() + \ testset.pred_offset - mask = get_color_pallete(predict, testset) + mask = get_color_pallete(predict, args.dataset) outname = os.path.splitext(impath)[0] + '.png' mask.save(os.path.join(outdir, outname)) - speed = size / (time.time() - tic) - print('Inference speed with batchsize %d is %.2f img/sec' % (batch_size, speed)) -def test_quantization(model, is_eval, batch_size, testset, metric, num_class, ctx_list, num_workers, outdir): +def test_quantization(model, args, input_transform): + # output folder + outdir = 'outdir_int8' + if not os.path.exists(outdir): + os.makedirs(outdir) + # hybridize model.hybridize(static_alloc=True, static_shape=True) + + # get dataset + if args.eval: + testset = get_segmentation_dataset( + args.dataset, split='val', mode=args.mode, transform=input_transform) + else: + testset = get_segmentation_dataset( + args.dataset, split='test', mode=args.mode, transform=input_transform) size = len(testset) batchify_fn = ms_batchify_fn if testset.mode == 'test' else None test_data = gluon.data.DataLoader( - testset, batch_size, batchify_fn=batchify_fn, last_batch='keep', shuffle=False, num_workers=num_workers) + testset, args.batch_size, batchify_fn=batchify_fn, last_batch='keep', + shuffle=False, num_workers=args.workers) print(model) + metric = gluoncv.utils.metrics.SegmentationMetric(testset.num_class) tbar = tqdm(test_data) metric.reset() tic = time.time() for i, (batch, dsts) in enumerate(tbar): - if is_eval: - targets = mx.gluon.utils.split_and_load(dsts, ctx_list=ctx_list, even_split=False) - data = mx.gluon.utils.split_and_load(batch, ctx_list=ctx_list, batch_axis=0, even_split=False) + if args.eval: + targets = mx.gluon.utils.split_and_load(dsts, ctx_list=args.ctx, even_split=False) + data = mx.gluon.utils.split_and_load(batch, ctx_list=args.ctx, batch_axis=0, even_split=False) outputs = None for x in data: output = model(x) @@ -123,17 +149,17 @@ def test_quantization(model, is_eval, batch_size, testset, metric, num_class, ct tbar.set_description('pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU)) else: for data, impath in zip(batch, dsts): - data = data.as_in_context(ctx_list[0]) + data = data.as_in_context(args.ctx[0]) if len(data.shape) < 4: data = nd.expand_dims(data, axis=0) predict = model(data)[0] predict = mx.nd.squeeze(mx.nd.argmax(predict, 1)).asnumpy() + \ testset.pred_offset - mask = get_color_pallete(predict, testset) + mask = get_color_pallete(predict, args.dataset) outname = os.path.splitext(impath)[0] + '.png' mask.save(os.path.join(outdir, outname)) speed = size / (time.time() - tic) - print('Inference speed with batchsize %d is %.2f img/sec' % (batch_size, speed)) + print('Inference speed with batchsize %d is %.2f img/sec' % (args.batch_size, speed)) def benchmarking(args, model): @@ -182,11 +208,6 @@ def benchmarking(args, model): if withQuantization and args.quantized: model_prefix += '_int8' - # output folder - outdir = 'outdir' - if not os.path.exists(outdir): - os.makedirs(outdir) - # create network if args.pretrained: model = get_model(model_prefix, pretrained=True) @@ -205,30 +226,17 @@ def benchmarking(args, model): .format(args.resume)) print("Successfully loaded %s model" % model_prefix) + print('Testing model: ', args.resume) # image transform input_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize([.485, .456, .406], [.229, .224, .225]), ]) - data_kwargs = {'transform': input_transform, 'base_size': args.base_size, - 'crop_size': args.crop_size} - # get dataset - if args.eval: - test_dataset = get_segmentation_dataset( - args.dataset, split='val', mode=args.mode, **data_kwargs) - else: - test_dataset = get_segmentation_dataset( - args.dataset, split='test', mode=args.mode, **data_kwargs) - - metric = gluoncv.utils.metrics.SegmentationMetric(test_dataset.num_class) - print('Testing model: ', args.resume) if not args.benchmark: if '_int8' in model_prefix: - test_quantization(model, args.eval, args.batch_size, test_dataset, metric, - test_dataset.num_class, args.ctx, args.workers, outdir) + test_quantization(model, args, input_transform) else: - test(model, args.eval, args.batch_size, test_dataset, metric, - test_dataset.num_class, args.ctx, args.workers, outdir) + test(model, args, input_transform) else: benchmarking(args, model) From 1a5693668ef8132485fcfe9c33168e32df49912f Mon Sep 17 00:00:00 2001 From: wuxun-zhang Date: Fri, 2 Aug 2019 09:50:37 +0800 Subject: [PATCH 05/10] adopt compressed json --- .../fcn_resnet101_coco_int8-symbol.json | 18622 ---------------- .../fcn_resnet101_voc_int8-symbol.json | 18622 ---------------- gluoncv/model_zoo/quantized/quantized.py | 15 +- gluoncv/utils/compress_json.py | 56 + scripts/segmentation/test.py | 12 +- 5 files changed, 74 insertions(+), 37253 deletions(-) delete mode 100755 gluoncv/model_zoo/quantized/fcn_resnet101_coco_int8-symbol.json delete mode 100755 gluoncv/model_zoo/quantized/fcn_resnet101_voc_int8-symbol.json create mode 100644 gluoncv/utils/compress_json.py diff --git a/gluoncv/model_zoo/quantized/fcn_resnet101_coco_int8-symbol.json b/gluoncv/model_zoo/quantized/fcn_resnet101_coco_int8-symbol.json deleted file mode 100755 index 8bc31f7c62..0000000000 --- a/gluoncv/model_zoo/quantized/fcn_resnet101_coco_int8-symbol.json +++ /dev/null @@ -1,18622 +0,0 @@ -{ - "nodes": [ - { - "op": "null", - "name": "data", - "inputs": [] - }, - { - "op": 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[[652, 0, 0], [666, 0, 0]], - "attrs": {"mxnet_version": ["int", 10500]} -} \ No newline at end of file diff --git a/gluoncv/model_zoo/quantized/quantized.py b/gluoncv/model_zoo/quantized/quantized.py index 6bc341c7c3..6ce902a0ec 100644 --- a/gluoncv/model_zoo/quantized/quantized.py +++ b/gluoncv/model_zoo/quantized/quantized.py @@ -5,6 +5,7 @@ import mxnet as mx from mxnet.context import cpu from mxnet.gluon import SymbolBlock +from gluoncv.utils.compress_json import get_compressed_model __all__ = ['mobilenet1_0_int8', 'resnet50_v1_int8', 'ssd_300_vgg16_atrous_voc_int8', 'ssd_512_mobilenet1_0_voc_int8', @@ -36,18 +37,24 @@ def func(pretrained=False, tag=None, root='~/.mxnet/models', ctx=cpu(0), **kwarg curr_dir = os.path.abspath(os.path.dirname(__file__)) model_name = name.replace('mobilenet1_', 'mobilenet1.') model_name = model_name.replace('mobilenet0_', 'mobilenet0.') - json_file = os.path.join(curr_dir, '{}-symbol.json'.format(model_name)) - base_name = '_'.join(model_name.split('_')[:-1]) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") + import tempfile + if "fcn" in model_name: + model_json = get_compressed_model(model_name) + with tempfile.NamedTemporaryFile('w') as tmpf: + tmpf.write(model_json) + sym_net = SymbolBlock.imports(tmpf.name, ['data'], None, ctx=ctx) + else: + json_file = os.path.join(curr_dir, '{}-symbol.json'.format(model_name)) + sym_net = SymbolBlock.imports(json_file, ['data'], None, ctx=ctx) + base_name = '_'.join(model_name.split('_')[:-1]) param_file = get_model_file(base_name, tag=tag, root=root) if pretrained else None net = get_model('_'.join(model_name.split('_')[:-1]), prefix=sym_prefix) classes = getattr(net, 'classes', []) - sym_net = SymbolBlock.imports(json_file, ['data'], None, ctx=ctx) if param_file: # directly imports weights saved by save_parameters is not applicable # so we hack it by load and export once to a temporary params file - import tempfile net.load_params(param_file) net.hybridize() if '512' in base_name: diff --git a/gluoncv/utils/compress_json.py b/gluoncv/utils/compress_json.py new file mode 100644 index 0000000000..8ccb817d22 --- /dev/null +++ b/gluoncv/utils/compress_json.py @@ -0,0 +1,56 @@ +"""Encode/Decode helper function for compressed quantized models""" +import json +import zlib +import base64 + +_compressed_int8_json = { + 'fcn_resnet101_voc_int8': b'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', + 'fcn_resnet101_coco_int8': b'eJztnU9v5EiS5e/9KQp56ga6AvT/7nvbmcFiT3NYLLCHRkNQVaqrEp2lrMlUVvXMoL/7RoSS5hYMF8kgGcHnDBMaXaKLYroe3dwtfrTn/O8/fPfdu+dP75++vPsf3/1lf/Ddd/99/P9986df923vnr9+/Pjuz9+1jc+Pvzwdmt8/vjyy5g/Pv359OV7jr8emf377UediDz9+en75/OGHh//4+vj88uG/nh5+029cnE5hP398efl8+Efaq+6bfnn8x8OPjx/3l/z8+PzT8bf1zttm/5V/8XDeh+fued/rnVIhNfbkxE9fXx5e/vPX4xmPX18+vfv2o3+W/ti/NH/+7vC/v/b+1W9I+Lcfn5uHz09fnp9eflNfDtL81jz8/vThp59f+v/mh4f3hx4+PByucvpnPjx8/Pzwy9ePL68/VLvuj7/8/Nj+5h+9/fN35vV/f+qe9vLp8+NPTw9v/zu/v+/8O306LSDPD48vP/78/OnzL83DT4+//PI4Q6IPzx++9fvT837gz9GvKt1+eHpZRrb/evr86Y50+/z1+fnD808Pvzw9Pot+0/X77fGzRO3Dl/1A+vvH98/Pxxm/IGS78L3vnPrww/PD448vD02/iIUV0e6MTt6ooRWx2TXN2cpJ3Tmc8fL569PJT3//8PLzoVdv//CHZ/pZ71Kqvi2lf/7uLzp/a/K3Nn/rjt+qw7c+f7u/gvp2wv6/+rgu53/qy9cffvr8+OvPOdPhd+f1DpwkQqVT2I08DYN8DZbDNIWfd4dJ+/XPzqnT/tWenOIKnfnX/eU/ffz68uHT86V9+tvv7wu/cj6c6UfvP3x8fDle74+H+/yn89/en/T3p8/PTx+PJ51lNXTSx8f/3Cd5h5P+/V//9/8rnvL86eGHD4+Hnrz7v51Bnc/5+svD3z58fHn6fDjN2zdP+unzp69HuVTxlF8f3w/8VV/2KfP71z99P6j1n951TunerXKK+hoVLFt969cXGXnddG31UOjkQUj94fkFYr/26/YVuvUvh3/o3/f/0KS+XTx/PP7jw5e3g/Dp19cfPn3fuOIJf/vwj2+fO/an/a/Hj1/Kk8Ivn355en75+svrapqK53z98vTw08dPPzx+3Ccej0cFv13xorgeuUg2eZGcGPj/88eXD789XjbJf376+HXwJr07ZDPtZ+3Db7zr/4tD+a9gR389+f13j59/eqA1vTlmB3up9hLtpfGdcw/nPXz+9PvDry+f8+ma/8afv9v3QB1+oDq//PPT4/vXPsazPrb9WwgRqJsjghYTVPSZQwkjmKibMIJpugkjWEY/YQSLMALVL2KRESRrVByk5qsygpBTmpi/TewTDv+0oxga0Pn7kDFBWB0TvIF4HvafUPd/9eqfBnjGgQQQlAAEOuUigPB60kUfNFYGCAoNICgwgKBAAYICBggT5g8BCLcGCIM3SQDCBICgbwoQlI5VEgQNQxAOClYlHApCqE44NIZQrYAYEGFt+eZTBN2vYrH2zihvXICmCIqlO4rlO4olPMqz7wMDCZF9b1jBgQFFCQoJJWhAlKA3hxL2E4+whL6sbvVY6GRLSP3BZAkamCVMmECEJdyaJQzeJGEJOXHsujTeP/z66dPHffT15pAd1Q+/0ikBKSWR3wbf4eziaO5dR3vWz7eXsneHf4tu9T6JPfvhYZ45pAb76DkMtP1Zv+3T15Ph82ZR3hspJ39GlVjamNq0cf4Hob0WT5+/qJWMJEfQc6414ifGViixlCymHwrzqVU/NPRTu44YBKj6MhJzMQBSOxuM1ubkj0IDQJp9DNXcd8LSWM3yWG0z9NHMe6KbvJIfvl8ZAL2VgKEQoFKCgkKCTvt2dSL09kmgxSWHUNgOEHorA4QJETS3ykDKgtw/GGBU6KOAI2hw1N4xcbPQjxcsRuFLrrhaLpg3xN0yVz/BFfP0E1yxrI6CKxbBFbZfxCKu0Em7BrteRbNaFM0sMJp5YDR7wGCajCgMM8EcrkO4wq+OK95AToi0As0Cc9q3rdWviBVmXAIIEyJo1piBjAW5f7i0QiwzFdEKsc6c9G9hWnFbC412/hVXVFpdgWOlOShZpYAovKJaAdGARfVCYhCLOmR8/+n35zXK8o4LR0V1ea86AVbl1TTMAIvyKpVP1oxFZZQV491syO0vBtx6F3yySZ/8QeWXIRgfGnNauDeCcE+puTMMXBsGri07xzJwbdnuTVJzd1GHznMPFIbNe7a1erv9LDMXYW+r4K6c2oFEB1qxXW8mgts7GHR91kMB1wuAa3Z0E9T7JuY9R9HjXI3zygvevz8+nnYTMrBogm78UAY2tcSAJWBnP/v105eXL/s1p7cM4cvrCBxM4ejX/mLycGKNptRoS42u1Jhf5MEaQ6HRFv8hTWnhaaPuNlpTONO223e0I2m1hPKNyhaUfLL0GAQlozztm+SUdMo2c8q3HjTBxAjaLh8Dz0WQ+weTWxb6KNnlzcoiZn5SuSqXefr49MvvH/aC7lPFCePp08vLx6fnpx///pv6oXl4+PXj1y/9nSxthr5KrcniW6uk8l8z8QPIn7+LA59BTv6ZptM11TnWnWPTObadY9c59p3j0P33zjrQ7YHqdkGZt6XJH5toa/yrl8eYm5t5Xp90VvGQ82wBMTCPOet0URiY55y16of2oLN2HTGedFbv5gn9IhbdPN75Jg4+7FzVzWNZXmxZYmyZy8ey3M6yHWdtYt/b/CD08D2em6dFpojsygCzK7M5diX7j4xLAmFCpJNdIfYLG12ZCtDVhHlG0NValGXwZomjZwaysLL/yPh5wwqxmKmfEIt5+gmxWFZHIRaLEIvYL2J5u1SdYrhacdAixMKxD0mObZfqWPLkWPbk2Hapjm2X6ljptlu/dPsN6oRIKywwrbCy/widcif7j1hUWmFBaYUFpxW2AloxYZ4RWrEWrRi8WUIrZtAKJ/uPXDBxOBhcsbb5dLKAKLyiWgHRgEX1QmIQi7VlXMbTlPqVLLvKG2vj1bZNXcHT5HL5BWsMpcZYakyFRp83YmWNqtBoSz4p5wtOJedLniZb8jRZbE9TRCQtDpi0uM3VhYinaWTyChMjnaQQsV/YqMVVgFomTDSCWq40QaxSV7mgl0lV5WUaHPniZWJfd+Bl8uJlumDh8DCorc6KDA9D2mrVDw201a4jBmervjJINf0qFkuDTNNof5JQw5UGeZYme5Yne5Yoe54p511+/uID+z5v33P8Hq80qGWliNDKA0MrvzloJWamcVkgTIh00ivEfmEzK18Bs5owzwizWguzDN4sKQ+awSyCmJnGzxtBkMVM/QRZzNNPkMWyOgqyWAZZqH4Vy26m5BuH7Wby7OUTnr18IrBPT4G5nAJ7+UQwDFlEhiwiIrI4cidEXhGAeUUQOxOdcid2poDKKwIorwjgvCJUwCsmzDPCK9biFYM3S3jFDF4Rxc50wcQRYYDF2u6HyQKiEItqBURDFtULicEs1pZxGTuT0v1SFsCF2ZmkTLjaxrEr+JlCyVEUSm9eCiXnUyg5n0LeipY1pkKjL70hKpRcSsEW/Ey+9DYnqgtpsxA01qIQWUsEZi1xc7UhYmgamb7CxEgnLUTsFzZsiRXAlgkTjcCWmxuarlhbuaChSVdlaBoc+WJoYl+bNjTpk9fA3wa2KR0rdDTpszeVA3w4P0hZpYAotK1aAdFoW/VCYtC2tWVcoETIXEza1C4m11hsV1Nkn5UjKwWKLIeOLImObMPjyDY8jmzD4wi54TEhUyx0dZqqYKGrtm9bQ1f76UjQ1ZhcECZGOjkWYr8A0dVbGQESujrpo6CrCuqE9JG2DN4sqROagS7U7dHF8f9qcjaxmUMJupgroKCLmQIKulhYSEEXC6EL2y9j2d3kknMnbgo8dMHeIh35QyjmeorM9ZTyFsV/SYqhC8/QhUdEF0f+hIgtFDC2UJtzNy2ALUbZmw7FZ3ViC4WKLRQotlDg2EJVgC0mTDSCLdbCFoM3S7DFDGyhb4ot3KEC5ZVd1FlxoWGwxUHKKgVEwRbVCoiGLaoXEgNb1CHj+0+/P69Rq3dcOSqq1XvVCbBSr6ZhBlinV6l8smYsKqOsGO9mg27l+yUsQm5/MMIO1ud9vz9Rm2ROLbOXWWH7VDmpwUsMXicGr1XDEVvD8LVq2K5dUoZ3MXM6z0FQaDbv2dZK8JzSb561ZAleNSy7nOKBRAda+V1vRoLbOxiGfdZDIdgLEGx2dBPm+ybvPWfSo3jvQnuSuIszMb0zIamUhjKxivYkSaVdQZIpNZZ2L0ml3UtSfqMMawyFRtUU/6XSViNJFzYlOf7+2anHVuhtSSxKUll6KIKSVp72TRJLOuVOvB0aJLV842kOYr+g0sv+Zw8YCWahj5JiomxLMuLDAOb2JPp0e5Kmku1JXitPBiNAtidhX3ewPYm5vcfntWKmikeeZyuIgXnoubYlYLKAKI89qxUQ7cFn9UJiPPpcW8YFPD5hwuNPFVxwJ5YCOI+Palh6rBrHDzw/CPwg7927P0j8IG/L+3qA+3zUIaIsA4yyzOZQlmxTMjInhImRTq6F2C9slGUqQFkTJhpBWWtRl8GbJX6fGQjDyjYlI5SimcMKwpgroCCMmQIKwlhYSEEYCyGM2C9jAWHskxzbpHi1dxktgzAU/7CkFD/Q/MDwA7bNqlKOH7AK7+MBHsI48ihEfGGB8YWV7UryKVt+G/N5PggTI508C7Ff2PjCVoAvJkw0gi/WwheDN0vwxQx84WS7khFK0czhYPDF2l7VyQKi4ItqBUTDF9ULiYEv1pZxIetT6peybH3yzpiTV6lWbn1SqvSWZaVKr1lWKhZbU6lV5w1ceasqtTYlV9WxZ+e2JuWLFqjSK5xz0UibkaCBl4gIXhwweHGbqxsRC9TITBYmRjoZImK/sMGLqwC8TJhoBLzc/M3M1yy8XND7pKryPg0OffE+sa878D558T6NUIqWDg9D3tauM5gsIAp5q1ZANPJWvZAY5G1tGecXDummX8ay9ylqb7Dfb6Q0Lw/SvDxIc1eU5q4onfcI2h8EfqBZ4ZDWiIVDhFERGZYHZlh+cwxLvE8jc0KYGOnkWoj9wmZYvgKGNWGiEYa1FnUZvFlSPDQDYQTxPo1QimaOIAhjroCCMGYKKAhjYSEFYSyEMFS/jEWE0fiY4ulfhYcwImcT/H0Whn+MMtwVZdj7LJQxHGFEjjAiIsI48ihEfBGA8UUQ71M+5U68TwEVXwRQfBHA8UWoAF9MmGgEX6yFLwZvluCLGfgiivdphFI0c0QYfLG2VWKygCj4oloB0fBF9UJi4Iu1ZVzG+6R1v5Rl75NNtrnaDrRreJ9M0XdkSq9zUqbokzJFn5TJe9ry1lRqZXUjnZ6d+5mMLXmfWK1IpxXZ+6QVIniJwOAlbq5uRLxPIzNZmBjpZIiI/cIGL7EC8DJhohHwcnvv0xULLxf0PumqvE+DQ1+8T+zrDrxPSbxPI5SipSPBkLe16wwmC4hC3qoVEI28VS8kBnlbW8YFCofMxdRN7XTTJH+SVuMVDln+adny8iDLXVGWu6Is3zTZ8k2TLd802UJumkwYFZFhJWCGlTbHsMT7NDInhImRTq6F2C9shpUqYFgTJhphWGtRl8GbJcVDMxCGasT8NEIqmjr2egnEmKugUIy5CgrGWFpJ4RgLcQzbL2ORY7gQlDlJdwA5Bn9LteVvqbbcGmW5Nco1DF04xTmG5xzDI3KMI5RCZBiUsyBCjH3nxAJFp9yJBarNCmHCpJttQXYMG2Sc5gagJGPKbCMoYy2UMXy3hGXMYRlKnFAjpMqTh4JhGWs7J6YriMIy6lUQjWXUryQGy1hbx4XcUK5fyqIbqknOn6ZPtbuhXNGJ5EyxteicckXnlMu73PLWUGpllSSdnp07nJwuuaFY9UinFdoNZSEpjEKmMGpztSTihxqbz8KESTdPhOwYOIVRNVCYCbONUJibm6KuWZG5oCnKVGWKGh774opiX5t2RZljyN22okgfPkFU54oyefHAKSg6SFmlgCgMrloB0RBc9UJiELi1ZVygmsj3y1isJvI6WX/yYRmvmsjxmiHHa4Y8/zTtuV/K8+2UPd9O2fHtlB3kdsqEU7FY1mnKgoWy2r5tjWTtpyUhWWNyQpgY6eRaiP0CxFhvZQZIFOukjwKxKiglMkfsIpVEJ/1bGGHctpDoiDCO/1eTKYrNHDh1RGt/4pksoCCMmQIKwlhYSEEYCyGMMAFhNNEkd/pXwSEMz19d7fmrqz23SnlulfKRI4zEDyxDGN4iIowjj0LEF5iVOG3ftmaHWgBfbNEOdZ4PwsRIJ89C7Bc2voAtwjnpo+CLavDF4kUjgi/Ymqtvu6dLo+03flFpCYaG4RdHLatUEAVg1KsgGsGoX0kMhFGJju8//f68RgHf6/JRUQXfq1CA9XtVDTTA8r1a9ZOFY1kdZdlYAH3r1K9hAXubnYneG3PyFxWw9/f7PytEZU83DLvMONunSqc6L3CaHTjNDiencZodTzb+kuq8ixHUeTaCArd5z7ZWmXeYc+ay7W2V5pWTPZDwQCvL681McHsHw7TPeihEewGizY5uwoDf5L/njHoU/11oH5M4JR9TprleGcIa+5iE4h4iQRVbi3uehOKeJyG/sYa3ulJrLP9rxb1JQlPax+RwhfNzowLfxySgZJelhyUo+eVp3yTDzKdsM8N862kUTJB0HvIg9gsqz+x/IIGRaRb6KLkmyg4mIz4VYG5kYk43Mmkq2cjktSRlMAJkHxP2dQf7mJjbm4Ben4XW8Rj0bAkxMA9C1zYNTBYQ5UlotQKiPQqtXkiMZ6FryzjfBWSafhnLr0VKqWkGH4eu6wKK/CXOkb/EOXJ/UOT+oJg3/d0fBH6g2aPSqJEflUZEmGWAYZbZHMySjUxG5oQwMdLJtRD7hc2yTAUsa8JEIyxrLewyeLPECTSDYVjZyGSEUjRzWEEYcwUUhDFTQEEYCwspCGMhhKH6ZSxvZGJUcCdZFSDC4GXcke/FmvjHqMT3Yk18L9bE92KNvNo7QlZ7H3kUIr6wwPjCzsIXB5S1wY1M3v6zNoEvLCq+sKD4woLjC1sBvpgw0Qi+WAtfDN4swRcz8IWTjUzGSEVTh4PhF6u7VycriAIw6lUQjWDUryQGwlhdx2WcUEb3a1ngGG7XROfj1d4ps4YTKhXf0pyKb2lOvtgaiq15i1femkqtseiwSrbkbkq26IQqvv2ZKkfatASNvihE+uKA6YvbXPGIOKHGprMwQdLJEhH7hY1fXAX4ZcJMI/jl9u9yvmL55YIWKFWVBWpw6IsFin3dgQXKiwVqjFS0dngY/rZ2ucFkAVHwW7UCotG36oXEgG9ry7hA/ZDpl7G4A5GPqmlON3pEqx/SDfu4rBvFDzQ/MPwg7xe0P3D8IO8D9HqAVz9EIBWRYnlgiuU3R7HEAjUyJ4SJkU6uhdgvbIjlK4BYEyYagVhrYZfBmyU1RDMYRhAL1AilaOYIgjDmCigIY6aAgjAWFlIQxkIIw/bLWLRAGW1iwt7FRTees4nADyI/YOYo/e0xxCu1UIojDM8RhkdEGEcehYgvAjC+CGKByqfciQUqoOKLAIovAji+CBXgiwkTjeCLtfDF4M0SfDEDX0SxQI2RiqaOCMMvVrdLTFYQBWDUqyAawahfSQyEsbqOC1mgXL+WBY6RdtpFF04sC5VboLQq2Y+0Kr3gSauSXUqrkl1Kq7y/LW8NpVZWPNLp2Zmt6dh6ZoHiBSOdVmgLlEWkLxGYvsTNFY+IBWpsOgsTJJ0sEbFf2PglVoBfJsw0gl9uboG6ZvnlghYoXZUFanDoiwWKfd2BBSqJBWqMVLR2JBj+tna5wWQBUfBbtQKi0bfqhcSAb2vLuED9kO+XscDd9M40ugnx5K+Cqx9SvEpI8Sohzc1RmpujNNtCWWu2hfLxalQ/pCC3UCaQikixEjDFSpujWGKBGpkTwsRIJ9dC7Bc2xEoVQKwJE41ArLWwy+DNkhqiGQxDNeKBGiEVTR17vQRizFVQKMZcBQVjLK2kcIyFOEa4mGM0u6ictieZEx7H0JYDCscPuENKc4eUjpxjJH5gGcfQFpFjHKEUIsOgnAURYuw7J04oOuVOnFBtVggTJt1sC7Jj2CDjNDcAJRlTZhtBGWuhjOG7JSxjDstQYogao1WePRQMzFjdPTFdQhSaUbGEaDhjA1Ji8IzVhVzIFBX7tSyaolQKUV9tc5c1TFGmaEgyqthaNFCZooHK5B1veasrtbJykk7Pzo1OpimZolgJSacV2hQVIFGMQkYxanMFJWKLGp3UwsRJN1eE7Bg4i1E1sJgJ042wmNt7o65Yl7mgN8pU5Y0aHvtijmJfd2COUlrcUWO0ysuHhoFxa1chTFcQhcXVqyAaiqtfSQwSt7aOC1QWpX4Ziy9nj8m7CP6SKMPrhwyvHzLcO2W4d8ryHZYt32HZ8B2WDeQOy0RVIZGWRkZaenNISzxSYzNDmDDpZlyQHQMnWroGojVhthGitRqDGbxbUl00B2gYcUqNkCpPHkZ4xmwFhWfMVVB4xtJKCs9YhmfYpl/G4hujgotGh5O/Co5nWP5qa8tfbW25h8pyD5X1nGcEfqAZz7AakWcc4RQkyzDILMOIUyqfci9OKQPLMgwqyzDoLMPUwDImzDbCMlZjGYN3S1jGHJZhxSk1Rqs8e1gYmLG6pWK6hCg0o2IJ0XDGBqTE4BmrC7mMU8qqfi1LUMPvtLbxelRjDauULdqUbCq1uqKtyhVtVS7vjMtbTamV1ZV0enZuf7KxZJVitSSdVmSrlG0gWYxFZjF2c3UlYpUandXCxEk3WYTsGDiMsTXAmAnTjcCY21ulrliguaBVytZllRoc+2KVYl/3YJVyYpUao1VePhwMjVu7DGG6gigwrl4F0Vhc/UpioLi1dVygtEhfTOH0zhulrvcS92VKixwvIHK8gMhxE5XjJirHN2F2fBNmxzdhdpCbMBNWhURaDhlpuc0hLbFKjc0MYcKkm3FBdgycaLkaiNaE2UaI1moMZvBuSXnRHKDhxSo1Qqo8eXjhGbMVFJ4xV0HhGUsrKTxjIZ5h+mUsWqV8Sk1ztf2Xl+EZnn1u0p6/AttzE5XnJipvGcLwjh80jGf4BpFnHOEUJMvwyCzDi1Uqn3IvVikPyzI8Ksvw6CzD18AyJsw2wjJWYxmDd0tYxhyWEcQqNUarPHsEGJixuqdiuoQoNKNiCdFwxgakxOAZqwu5kFXK9mtZghph15jG+JMsq3arlC/alHwothZtVb5oqwp5i1zeqkqtrK6k07Nz+5P3JasUqyXptEJbpQwkiwnILCZsrq5ErFKjs1qYOOkmi5AdA4cxoQYYM2G6ERhzc6vUNQs0F7RKubqsUoNjX6xS7OserFJRrFJjtMrLR4ShcWuXIUxXEAXG1asgGourX0kMFLe2jguUFrl+GQsULu688VHFk78KrrQo8AKiwAuIAjdRBW6iCnwX5sB3YQ58F+YAuQszYVVIpBWRkVbcHNISq9TYzBAmTLoZF2THwIlWrIFoTZhthGitxmAG75aUF80BGkmsUiOkypNHEp4xW0HhGXMVFJ6xtJLCMxbiGb5fxgLP8LuotVKnfxUez+Dvwg78XdiRm6giN1FFzRBGNJxnRM4zIiLPOMIpSJaRkFlGEqtUPuVerFIJlmUkVJaR0FlGqoFlTJhthGWsxjIG75awjBksQzdilRqjFc0ee8FQYMbqnorpEqLQjIolRMMZG5ASg2esLuRCVqnQr2VpP1uzi16neLUNbdewSsWiTSm6YmvRVhWLtqqYt8jlranUyupKOj07tz9FW7JKsVqSTiu0VcojshjKuRBZzL5zW6srEavU6KwWJk66ySJkx7BhzGliAwpjpkw3AmNub5W6YoHmglYpX5VVanjsi1WKfd2BVUorsUqN0SovHwqGxq1dhjBdQRQYV6+CaCyufiUxUNzaOi5QWhT7ZSyWFvkYGn2S8eKVFiVeQJR4AVHiJqrETVSJ78Kc+C7Mie/CnCB3YSasCom0FDLSUptDWmKVGpsZwoRJN+OC7Bg40VI1EK0Js40QrdUYzODdkvKiOUBDi1VqhFR58tDCM2YrKDxjroLCM5ZWUnjGQjwj9ctY5BnapXj6gQmQZ/B3YSf+LuzETVSJmahMk3dL3h8ozjM85xkekWcc4RQky9DILEOLVSqfcidWqTYrhAmTbrYF2TFwlqFrYBkTZhthGauxjMG7JSxjDsswYpUao1WePQwMzFjdUzFdQhSaUbGEaDhjA1Ji8IzVhVzGKuWafi1LVim7a4x3ZktvlTJNyaZkGlNsLdmqTFOyVZkmb5HLW0OhldeVdHp2Zn86tp5bpVgtSacV2iqVIFmMQWYxZnN1JWKVGp3VwsRJN1mE7Bg4jDE1wJgJ043AmNtbpa5YoLmgVSrUZZUaHPtilWJf92CVsmKVGqNVXj4sDI1buwxhuoIoMK5eBdFYXP1KYqC4tXWcX1rkVL+M5bdKuWCVvhaEW6S0yDSsgMg0vIBINfxA8QO2C7NRbBfm49UUXQ1yF2bCqpBIyyIjLbs5pCVWqbGZIUyYdDMuyI6BEy1bA9GaMNsI0VqNwQzeLSkvmgM0nFilRkiVJw8nPGO2gsIz5iooPGNpJYVnLMQzdL+MRZ6hQwj+akVFy/AMZTmocPzA84PADyLnGYkfWMYzlEXkGUc4BckyHDLLcGKVyqfci1XKwbIMh8oyHDrLcDWwjAmzjbCM1VjG4N0SljGHZXixSo3RKs8eHgZmrO6pmC4hCs2oWEI0nLEBKTF4xupCLmSVMhdDDW12KUVvtvRWKaOLNiWtiq1FW5Uu2qp03iKXt7pSK6sr6fTs3Cqlm4JViteSdFqRrVJOQ7IYj8xi/ObqSsQqNTqrhYmTbrII2TFwGONrgDETphuBMTe3Sl2zQHNBq1Ssyyo1OPbFKsW+7sEqFcQqNUarvHwEGBq3dhnCdAVRYFy9CqKxuPqVxEBxa+u4QGmR7ZexuAtzsiEk7F2YjeYFRJoXEGluotLcRGX4LsyG7cJ8vBqVFmnIXZgJq0IirYCMtMLmkJZYpcZmhjBh0s24IDsGTrRCDURrwmwjRGs1BjN4t6S8aA7QiGKVGiFVnjyi8IzZCgrPmKug8IyllRSesRDPcP0yFniGUjvXOO3AvVKG50zG8APuojLcRWU8BxqBH2gGNIxGBBpHOgUJMyIyzIjilcqn3ItXKsLCjIgKMyI6zIg1wIwJs43AjNVgxuDdEpgxB2Yk8UqN0SrPHgmGZqxuqpguIQrOqFhCNJ6xASkxgMbqQi7klfL9Wpa8UnoXnFNNvBbVWMMrZYo+JZNKrbboq7JFX5XNe+TyVlNqZYUlnZ6d+59MLHmlWDFJpxXaK+UgWUxCZjFpc4Ul4pUandXCxEk3WYTsGDiMSTXAmAnTjcCY23ulrlihuaBXKtXllRoc++KVYl934JUyjXilxmhFy8deMBQat3YdwnQFUWBcvQqisbj6lcRAcWvruEBtUbicwu2Ut9ac/lVwpUWWFxBZXkBkuYvKcheV5dswW74Ns+XbMFvIbZgJqyIiLUpcEJHWvnNbQ1rilRqbGcKESTfjguwYNtE6zQ9AidaU2UaI1loMZvhuSXnRHKChxCs1Qqo8eSjhGbMVFJ4xV0HhGUsrKTxjIZ4R+2Us8wylfbzaBszL8AzHPjcZx1+G7Xgy5biJylmGMJzjBw3jGa5B5BlHOAXJMhQyy1Bilcqn3IlVqs0KYcKkm21BdgycZagaWMaE2UZYxmosY/BuCcuYwzK0WKXGaJVnDw0DM1b3VEyXEIVmVCwhGs7YgJQYPGN1IReySqV+LctWKR9s4i+kWpZqrGGVckWbkgvF1qKtyhVtVT7vkctbVamV1ZV0enZuf3K+ZJVitSSdVmirVIRkMRqZxejN1ZWIVWp0VgsTJ91kEbJj4DBG1wBjJkw3AmNub5W6YoHmglYp1VTllRoe/OKVYl/34JUy4pUao1VePwwMjlu7DmG6gig0rl4F0WBc/UpisLi1dZxfW+SbfhlL+zDvVPDGnGTPeLVFnmfNnlcQee6i8txF5fk2zJ5vw+z5Nswechtm4qqQTMsgMy2zOaYlXqmxmSFMmHQzLsiOgSMtUwPSmjDbCNJajcEM3i2pL5oDNKx4pUZIlScPKzxjtoLCM+YqKDxjaSWFZyzEM1S/jAWe0exibLQG90p5/jZsz9+GHbiLKnAXVdAMYQTDeUbkPCMi8owjnIJkGRaZZVjxSuVT7sUrZWFZhkVlGRadZdgaWMaE2UZYxmosY/BuCcuYwzKceKXGaJVnDwcDM1Y3VUyXEIVmVCwhGs7YgJQYPGN1IZfxSnl9MdTQeueijelqVGMNr1Qo+pSCK7YWfVWh6KsKeY9c3ppKrayupNOzc/9TsCWvFKsl6bQie6W8gmQxDpnFuM3VlYhXanRWCxMn3WQRsmPgMMbVAGMmTDcCY27vlbpigeaSXilVl1dqcPCLV4p93YNXyotXaoxWef3wMDhu7TqE6Qqi0Lh6FUSDcfUricHi1tZxgdoi0y9jAcOZXWO9UScpL15tUeQVRJFXEEWeT0fuoop8H+bI92GOfB/mCLkPM3FVSKblkZmW3xzTEq/U2MwQJky6GRdkx8CRlq8BaU2YbQRprcZgBu+W1BfNARpBvFIjpMqTRxCeMVtB4RlzFRSesbSSwjMW4hm2X8bi3i/OG5OutgPzQjyDvw078rdhR+6iitxFlRqGMJLiPMNznuERecYRTkGyjIDMMoJ4pfIp9+KVCrAsI6CyjIDOMkINLGPCbCMsYzWWMXi3hGXMYRlRvFJjtMqzR4SBGaubKqZLiEIzKpYQDWdsQEoMnrG6kAt5pVy/lmWvlNFG23jyp1XulUpFn1IyxdairyoVfVUp75HLW0OpldWVdHp27n9KuuSVYrUknVZor5SFZDERmcXEzdWViFdqdFYLEyfdZBGyY+AwJtYAYyZMNwJjbu6VumaB5pJeKV2XV2pw8ItXin3dg1cqiVdqjFZ5/UgwOG7tOoTpCqLQuHoVRINx9SuJweLW1nGB2iLfL2MBw8XdobLIg3ulEq8gSqyCyDYNP1D8gO3DbBu+D3Pi+zAnyH2YiatCMq2EzLTS5piWeKXGZoYwYdLNuCA7Bo60Ug1Ia8JsI0hrNQYzeLekvmgG0LCNeKVGSEWTx14v4RlzFRSeMVdB4RlLKyk8YyGeES7mGWnno/bgXinbWA4qHD/w/CDwg8h5RuIHeR/k1wM8nnGEU4gsg3IWRJax75x4peiUO/FKtVkhTJh0sy3IjmGzjNPcAJRlTJlthGWsxTKG75awjDksQ4lXaoxWefZQMDBjdVPFdAlRaEbFEqLhjA1IicEzVhdyIa9U7Ney7JXSyaXm5KNT5V4pq/LnLd6qiq0lX5VVJV+VVXmPXN7qCq28rqTTszP/07H13CvFakk6rdBeqQDJYhQyi1GbqysRr9TorBYmTrrJImTHwGGMqgHGTJhuBMbc3it1xQLNJb1Spiqv1PDgF68U+7oDr5TV4pUao1VePzQMjlu7DmG6gig0rl4F0WBc/UpisLi1dVygtij1y1jAcH4XQ2j01XYsWqa2SPEKIsUriFTkB9xFpdk+zFazfZiPV6PaIgW5DzNxVUimpZGZlt4c0xKv1NjMECZMuhkXZMfAkZauAWlNmG0Eaa3GYAbvltQXzQEaRrxSI6TKk4cRnjFbQeEZcxUUnrG0ksIzluEZoemXsfReqX2Xg/MqnPxZcEBDa04qeNakuY1KcxuV9hxoBH6gGdDQGhFoHOkUJMwwyDDDiFkqn3IvZikDCzMMKsww6DDD1AAzJsw2AjNWgxmDd0tgxhyYYcUsNUarPHtYGJqxuqtiuoQoOKNiCdF4xgakxAAaqwu5jFkqqH4t3zBLhWD8SRJVu1lKF41KOpVaTdFYZYrGKpM3yeWtptTKCks6PTs3S+lYMEvxYpJOK7JZKjSQLMYisxi7ucISMUuNzmph4qSbLEJ2DBzG2BpgzITpRmDM7c1SV6zQXNIsZesySw0OfjFLsa97MEs5MUuN0SqvHw4Gx61diDBdQRQaV6+CaDCufiUxWNzaOi5QXKQvxnBup0OT1El6jVdbZHgFkeEVRIbbqAy3URm+EbPhGzEbvhGzgdyImbgqJNNyyEzLbY5piVlqbGYIEybdjAuyY+BIy9WAtCbMNoK0VmMwg3dL6ovmAA0vZqkRUuXJwwvPmK2g8Iy5CgrPWFpJ4RkL8QzTL2PZLBVCShHcLGXZBydr+fuwLbdRWZ5PWcsYhnX8oGFAwzaIQONIpyBhhkeGGV7MUvmUezFLeViY4VFhhkeHGb4GmDFhthGYsRrMGLxbAjPmwIwgZqkxWuXZI8DQjNVdFdMlRMEZFUuIxjM2ICUG0FhdyIXMUrZfy5JZKu50o+OptaF2s5QtGpVsKLYWjVW2aKxyeZdc3qpKraywpNOzcwOU9SWzFCsm6bRCm6UMJIsJyCwmbK6wRMxSo7NamDjpJouQHQOHMaEGGDNhuhEYc3Oz1DUrNJc0S7m6zFKDg1/MUuzrHsxSUcxSY7TK60eEwXFrFyJMVxCFxtWrIBqMq19JDBa3to4LFBe5fhkLGC7tvNHe6pO/Cq62yPEKIsfzZsdtVI7bqBzfiNnxjZgd34jZQW7ETFwVkmlFZKYVN8e0xCw1NjOECZNuxgXZMXCkFWtAWhNmG0FaqzGYwbsl9UVzgEYSs9QIqfLkkYRnzFZQeMZcBYVnLK2k8IyFeIbvl7FsltLaRHXyiQkQaPAXYjv+QmzPbVSe26i8ZgzDGw40IgcaERFoHOkUJMxIyDAjiVkqn3IvZqkECzMSKsxI6DAj1QAzJsw2AjNWgxmDd0tgxgyY4RoxS43RimaPvWAoNGN1V8V0CVFwRsUSovGMDUiJATRWF3Ihs1To17JsllLRaHO1PW3XMEv5olHJu2Jr0Vjli8Yqn3fJ5a2p1MoKSzo9OzdAeVsyS7Fikk4rtFnKI7IYyrkQWcy+c1srLBGz1OisFiZOuskiZMewYcxpYgMKY6ZMNwJjbm+WumKF5pJmKV+VWWp48ItZin3dgVnKKTFLjdEqrx8KBsetXYgwXUEUGlevgmgwrn4lMVjc2jouUFwUL8Zwh52YnbHqansWLVNcFHgJUeAlRIH7qAJPqQPfiTnwnZgD34k5QO7ETGAVEmopZKilNge1xC01NjWECZNuygXZMXCmpWpgWhNmG2Faq0GYwbslBUZziIYWt9QIqfLkoQVozFZQgMZcBQVoLK2kAI2FgEbql7EENMwuKBsM+KulAn8jduBvxA7cRxW4jyo2jGFExYGG50DDIwKNI56ChBkaGWZocUvlU+7ELdWmhTBh0k23IDsGDjN0DTBjwmwjMGM1mDF4twRmzIEZRtxSY7TKs4eBoRmr2yqmS4iCMyqWEI1nbEBKDKCxupDLuKVi069lgWoYu1NBBWdO/rTK3VKx6FSKpthadFbForMq5m1yeWsotbLCkk7Pzh1QUZfcUqyYpNMK7ZZKkCzGILMYs7nCEnFLjc5qYeKkmyxCdgwcxpgaYMyE6UZgzO3dUles0FzSLRXqcksNDn5xS7Gve3BLWXFLjdEqrx8WBsetXYgwXUEUGlevgmgwrn4lMVjc2jrOLy6Kql/GUnGR3kVrGwVeXBR5CVHkJUSJ+6gS91ElvhVz4lsxR74Vc4TcipnAKiTUsshQy24OaolbamxqCBMm3ZQLsmPgTMvWwLQmzDbCtFaDMIN3SwqM5hANJ26pEVLlycMJ0JitoACNuQoK0FhaSQEaCwEN3S9jCWjYXeN0MuAvy078ldiJvxI7cR9V4j6qFDnQSPzAMqCRLCLQOOIpSJjhkGGGE7dUPuVe3FIOFmY4VJjh0GGGqwFmTJhtBGasBjMG75bAjDkww4tbaoxWefbwMDRjdVvFdAlRcEbFEqLxjA1IiQE0VhdyIbeU6deyQDVss/MqGHO1N2av4JZyTcmp5BpVbC05q1xTcla5Jm+Ty1tdoZUXlnR6duaAOraeu6VYMUmnFdktFTUki/HILMZvrrBE3FKjs1qYOOkmi5AdA4cxvgYYM2G6ERhzc7fUNSs0l3RLxbrcUoODX9xS7Ose3FJB3FJjtMrrR4DBcWsXIkxXEIXG1asgGoyrX0kMFre2jgsUF9l+GctuKa1MSFfbtGiR4iLXsBIi1wR+EPkB81E5xbZidoptxXy8mqLfgdyKmcAqJNQKyFArbA5qiVtqbGoIEybdlAuyY+BMK9TAtCbMNsK0VoMwg3dLCozmEI0obqkRUuXJIwrQmK2gAI25CgrQWFpJARoLAQ3XL2MBaPidVyklbLOUU5qDCsMPeNqkHD/wnGcEfqAZz1AakWcc6RQky4jILCOKWSqfci9mqQjLMiIqy4joLCPWwDImzDbCMlZjGYN3S1jGHJaRxCw1Rqs8eyQYmLG6q2K6hCg0o2IJ0XDGBqTE4BmrC7mQWcr3a1k2Sznrnd+UWUoVjUoqlVp10Vili8YqnXfJ5a2m1MrqSjo9OzdLqVgwS/Fakk4rtFnKQbKYhMxi0ubqSsQsNTqrhYmTbrII2TFwGJNqgDETphuBMbc3S12xQHNJs1Sqyyw1OPjFLMW+7sAs5RsxS43RitaPvWAoOG7tOoTpCqLQuHoVRINx9SuJweLW1nGB2qLQL2N5J2bjrIpXw3DLFBdpniBrXkKkuY1KcxuVZjsxO534AduJ+XiAV1xEYBURalHmggi19p3bGtQSs9TY1BAmTLopF2THsJnWaYIAyrSmzDbCtNaCMMN3SwqM5hANJWapEVLlyUMJ0JitoACNuQoK0FhaSQEaCwGNeDnQ8LvYaBVOqgDwgIZhH5ycUfyA+6gM91EZyxiGcfygYUDDNIhA44inIGGGQoYZStxS+ZQ7cUu1aSFMmHTTLciOgcMMVQPMmDDbCMxYDWYM3i2BGXNghha31Bit8uyhYWjG6raK6RKi4IyKJUTjGRuQEgNorC7kQm6p1K9lgWo4tQsuKX2S+dTuljJFp5IJxdais8oUnVU275LLW1WplRWWdHp27oAyvuSWYsUknVZot1SEZDEamcXozRWWiFtqdFYLEyfdZBGyY+AwRtcAYyZMNwJjbu+WumKF5oJuKd1U5ZYaHvzilmJf9+CWMuKWGqNVXj8MDI5buxBhuoIoNK5eBdFgXP1KYrC4tXWcX1yUmn4Z33BLmWBtOPmz4IqLLC8hsryEyPLU2XIfleVbMVu+FbPlWzFbyK2YCaxCQi2DDLXM5qCWuKXGpoYwYdJNuSA7Bs60TA1Ma8JsI0xrNQgzeLekwGgO0bDilhohVZ48rACN2QoK0JiroACNpZUUoLEQ0FD9MhaARthFbZQ9ya0AeQZ/IbblL8R23EbluI3KaYYwnOE8I3KeERF5xpFOQbIMi8wyrJil8in3YpaysCzDorIMi84ybA0sY8JsIyxjNZYxeLeEZcxhGU7MUmO0yrOHg4EZq7sqpkuIQjMqlhANZ2xASgyesbqQy5il+Guvx5qlmp1xsWmu9sLsNcxSrmhUcq7YWjRWuaKxyuVdcnlrKrWyupJOz84NUM6WzFKslqTTimyWSgqSxThkFuM2V1ciZqnRWS1MnHSTRciOgcMYVwOMmTDdCIy5vVnqigWaS5qlVF1mqcHBL2Yp9nUPZikvZqkxWuX1w8PguLXrEKYriELj6lUQDcbVryQGi1tbxwVqi8zFGC7tnHaNiSd/FVxtkecVRJ5XEHnuovLcReX5Rsyeb8Ts+UbMHnIjZuKqkEzLIzMtvzmmJV6psZkhTJh0My7IjoEjLV8D0pow2wjSWo3BDN4tqS+aAzSCeKVGSJUnjyA8Y7aCwjPmKig8Y2klhWcsxDNsv4wFnhF3QXsTzMlfhccz+PuwPX8ftucuKs9dVKFhCCMozjM85xkekWcc4RQkywjILCOIVyqfci9eqQDLMgIqywjoLCPUwDImzDbCMlZjGYN3S1jGHJYRxSs1Rqs8e0QYmLG6qWK6hCg0o2IJ0XDGBqTE4BmrC7mQV8r1a1mAGtbskvbJX+112Wt4pULRpxRMsbXoqwpFX1XIe+Ty1lBqZXUlnZ6d+5+CLnmlWC1JpxXaK2UhWUxEZjFxc3Ul4pUandXCxEk3WYTsGDiMiTXAmAnTjcCYm3ulrlmguaRXStfllRoc/OKVYl+b9krZY8w1N6Vx7qBHfVYpm5ePBgbGHbSsUkAUFFetgGggrnohMTDc2jIuUFbk+2UsEDi/U8oGc/pXwZUVBV48FHjxUOQGqsgNVJFvwRz5FsyBb8EcILdgJqSKhbNOcxYsmtX2bWswaz8tCcsakxPCxEgn10LsFyDIeiszQOJYJ30UjFVBTZE9cpfBmyUlRTMYhro9wzj+X03uKDZzKEEYcwUUhDFTQEEYCwspCGMhhBH6ZSwgDLNTyjQWHGFEnhZF/u7ryD1TkXumYuQII/EDyxBGtIgI48ijEPGFAsYXanO+qAXwxRZ9Uef5IEyMdPIsxH5h4wtVAb6YMNEIvlgLXwzeLMEXM/CFvu3mLs3hpZ+vhRh1lmBoGH5x1LJKBVEARr0KohGM+pXEQBiV6Pj+0+/Pa1TwvS4fNZXwvSoFWMBX1UgDrN+rVT9ZOZbVUdaNBdh3Sv0aFri33ne30S4Omme/P5xpnHEnH8wvtM/26dIp0EscaCcOtNPJaQxo++ZkEzAp0LuYQp0nJCh8m/dsa8V5h1lnLt7eVnVeOd0DCQ+0yrze3AS3dzBY+6yHArUXgNrs6CYY+E0EfI6pRyHghbYziVMyMqVicGEoI6tpO5NU3EokqWJrceuTVNz6JOXX2PBWV2j1TflfK25RkprCdibHK5yde2yF3s4koKSXpQcmKAnmad8kxcynbDPFfOuJFEyQdB70IPYLKtHsfyiBkWoW+ijJJso+JiM+FmBuZ2JPtzNpKtnN5LUsZTACZDMT9nUHm5mY2xuBvj0PreJJ6NkSYmCeha5tHJgsIMrD0GoFRHsaWr2QGI9D15ZxvhNIcTw2EsC5nU1Bnb6JBc4K5BuWNvvG8APLD3jy3OTNf/cHgR/kTX1fD3AflkZEmmWAaZbZHM2S3UxGJoUwMdJJthD7hQ2zTAUwa8JEIzBrLe4yeLPEDjQDYljZzWSEUjRzWGEYcwUUhjFTQGEYCwspDGMphqH6dSzuyBpVCObkYxIgw4gcTrAdWb1q+IHiB2xHVq8MZxiRMwzIgu9XIoUIMCwwwLCzAMY+PbQb3M/k7T9rEwDDogIMCwowLDjAsBUAjAkTjQCMtQDG4M0SgDEDYDjZz2SMVDR1OBiCsbqHdbKCKAijXgXRGEb9SmJAjNV1XMYNpRrdL2ZpY1a/S01U6STpqdwO5VXpjc1eld7Y7JUvtoZia97rlbemUmtTslkde3ZucVK2aIcqvQk6V4+0iQkcgFGIAMYBAxi3uQoS8UONzWhhgqSTKCL2C5vAuAoIzISZRgjM7d/rfMUazAWNUKoqI9Tg0BcjFPu6AyOUFyPUGKlo7fAwCG7tmoPJAqIQuGoFRANw1QuJwd/WlnGJIiJzMXpTdheUc8qc/F1wVUSa1wppXiukuUdKc4+UzvsG7Q8cP2hYFZFuEKuIMk1FBFkeGGT5zYEssUKNTAthYqSTbiH2C5tj+Qo41oSJRjjWWuRl8GZJJdEMjBHECjVCKZo5glCMuQIKxZgpoFCMhYUUirEUxbD9Ohbf7Ny4xoO/2dlrz+lE4AfcJKW5Sco0jFsYxSGG5xDDI0KMVyaFCDACMMAIYoXKp9yJFSqgAowACjACOMAIFQCMCRONAIy1AMbgzRKAMQNgRLFCjZGKpo4IQzBWt01MVhAFYdSrIBrDqF9JDIixuo5LWaFcv5glkuF2yUbnr7Yz7RpWKFO0IZnS2568KdqmTNE2ZfJet7w1lFpZBUmnZ+f2JqNLVihWNdJpxbZCWUQAE4EBTNxcBYlYocZmtDBB0kkUEfuFTWBiBQRmwkwjBObmVqirFmEu6IXSVXmhBse+eKHY16a8UA/7/z/8C83Dz//5wz53+PL0H1/3082Hx4/NyYvpF6NxI01RyOVEfZqxF9jdEMshlyEsoOQN8dzGlVwB092JojfDdch6LlFy5PtlLL3CfedCVAb8DVKGFxYZXlhkuaPKckeV5bsvW777suG7LxvI3Zczel2Xeo1NcdblX8O9vDoJq68U6e0/qx4Qdkk2CRhBnTQNu4cAmOzytGJNYDayt4LOAIuX+u7dgeEM3jYpY8qJ7X4J3q8oPzy8f2oTxkJy+2ZKW/41nh1a/hZRa1lyZ9vNIHs7+G+fP/16WKInfJh5//qrneFQyr0f/3G8Ve/+ePoZ48evhz/509/+VozGd7++hp8ayI+tO7k1Qzei1fPhNz33j/6Wnj4UbtENPoDs/3EKssevL5+GZPJjZJqD/NQtkd9hAwn46rthxY6p7/o8Za9mpR//VRMuDjtldsmqxqqhuPve7HzQxpxuanz+yX8g8rhNyPJP8/bk03zgk3dY/ZP5lKkQMDfmMxNs9w7TADZGUPUU1Lyd13cqasofIjDrafbf6gXSZH1JdvxmMmxumAzvJ/jhLNhxEur41lCuGZMFU8/+5cPHD89Pj5//z9OX/b+m/+2tlf2Hb+d9fj3vfdO/CP38mhjtf9vG02X09w/vX35uf9C/jji1VAanEB7aandwvzaHLb5qeGhb0gzvoe1B1MqVBHlouwElwR7abkhRiIe2a+u5xEPb2C9jcZ8I2zRJ25M/C+2hrQv8Y57jW1o6Duwcp7suWyH2B9kB8Xq1NqM5HsA+tE0gz2yHMhyMD1tv93Jrz2z3c5U8s70smQSMIKxnthdkP3X0FOSZ7UBv5Zkt8DPb0r2TZ7b04xs8s40jaNVJcsgL8lxc8JltaSyAPLN1p0aPBZ/ZDv3Rs5/Zzvr8ceEzW9+MkWkO8bv5M9tX6lfBM9u3FZNntjM//asmXRx2yu5sDCrFobj73u60S008Laq49Jmt59XUnr+fwvMP84fTFD9AemY7cioEzI2Bntn2TwPYGEGe2bJT5JntLZ/ZpuEs2HMQ6nnloh9VuTj7ma3qX4QWeWbrO8WJf2hv+OmNPv6wtf+2Jt/WX9z6ils/cesjjt/+m779l/zD5Bsmv7Bqr6Hai6j2Kqq1Iav2err9dd3+um67otvr6PY6uv11Tb/e9se0/THtBU17HdNex7TXMW1/THtB017QtBe07QVte0Hb9tC217HtdWx7Hdtex7bXce2vu/bXXdsxR2K3F3TtdVx7Hddex7f98e0FfXsd317H011rO+bbC/r2OqG9TmivE9qOhfaCob1OaK8T2uuEtmOhvWBsrxPb68T2OrHtWGwvGNvrxPY6sb1OajuW2gum9jqpvU5qr5PajqX2golGaB6iNEYbGqQNjdKGRmdDw7Oh8dlE+o6ul4d6HuvK0Hc07PO4z+OdBrxSdD1N/dM5iOh6NPwVjX+lqacUCopCQFEMKAoCZainFA+K4kBRICiKBEWhoCgWFMWAsjnK6XqWekqRoSgiFIWEophQjnpK4aEoLJTLEwhdz1FPKUYUxYai4FAUHcpTTylQlM8zEl2PQkR56ilFi6IoURQmiuJEBeophYwKebKjK1PUKAobFanPFC+KAkZRxKhI14t59qSrUNQoChuVqKcUQYoiR1HoKIodRcGjKHo0xYymmNENzc0NTc4UR5qiR1P0aIoeTSuGpjjSFD2aokdT9GhaNjTFkabo0RQ9mq0h1NO8iuTVg6JH54UkryQUR5qiR5u8KNH1aDnRFEeaokdT9GiKHk1LiaY40javcnQ9ih5NC4umONIUPZqiR1P0aFpdNMWRpujRFD2aokfTEqMpjjRFj6bo0RQ9mtYZTXGkKXo0RY+m6NG02GiKI03Royl6dMgrPPWUokdTzGiKGU2rjqZlR1McaYoeTdGjY04eqKcUR5qiR1P0aIoeTQuQpjjSFD065WwkpyOUj1AcGYoeQ9FjKHoMrUKG4shQ9BiV8xu6Hq1ChuLIUPQYih5D0WNoFTIUR4aix1D0GIoeQ6uQoTgyFD2GosfkRCxnYiwVo+vlZCxnYzkdy/kYRY+h6DEUPYZWIWNzlkfXo+gxFD2GViFDcWQoegxFj6HoMS6njXRlih5D0WMoegytQobiyFD0GIoeQ9FjaBUyFEeGosdQ9BiKHkOrkKE4MhQ9JuTElq5Ha4+hODIUPYaix1D0GFqFDMWRoegxFD2GosfQKmQojgxFj6HoMRQ9hlYhQ3FkKHosRY+l6LG0ClmKI0vRYyl6LEWPpVXIUhxZih5L0WMpeiytQpbiyFL0WIoeS9FjaRWyFEeWosdS9FiKHkurkKU4shQ9lqLHUvRYWoUsxZGl6LEUPZaix9IqZPPnmfw5Jn+QoeixtArZ/Okmf6rJH2soeiytQpbiyFL0WIoeS9FjaRWyFEeWosdS9FiKHkurkKU4shQ9lqLHUvRYWoUsxZGl6LEUPZaix9IqZCmOLEWPpeixFD2WViFLcWQpeixFj6XosbQKWYojS9FjKXosRY+lVchSHFmKHkvRYyl6bMofPemzJ0WPo+hxFD2OViFHceQoehxFj6PocbQKOYojR9HjKHocRY+jVchRHDmKHkfR4yh6HK1CjuLIUfQ4ih5H0eNoFXIUR46ix5n8uZuuR6uQozhyFD2OosdR9DhahRzFkaPocRQ9jqLH0SrkKI5cpgIUPS4DgkwIMiLIaICix2VKkDEBxZGj6HEUPY6ix9Eq5CiOHEWPo+hxFD2OViFHceQoehxFj6PocbQKOYojR9HjKHocRY+jVchRHDmKHkfR4yh6HK1CjuLIUfQ4ih5H0eNoFXIUR47iyFEcOYojR3HkaT3yFD2eosdT9HhahTzFkafo8RQ9nqLH0yrkKY48RY+n6PEUPZ5WIU9x5Cl6PEWPp+jxtAp5iiNP0eMpejxFj6dVyFMceYoeT9HjKXo8rUKe4shT9HiKHk/R42kV8hRHntYZT5HiKRY8rSmeosJTVHiKCk/j3vvDloXfGGhxO0b69Pztv2/hT8JW3/6bGU77TaYh7Tdv08/80fUi+pmT9/YbSt7GsM4xZDPnA+03PWQzLwbtN2MQZ54cKEJowNDdp5tKd7L9hn6LQGQPx2yvUwCahC/bf30Ux7wIX7bXmUwtaUzRXDOaWk5glXS9AUJJ1yuyyswlqS8TCCVdr59V0lAeopH0uyUuyRgk9WU0jbycQdL1BsgjXa/IIG/LGzMf7KeMdL0ib8xskSbA0ZTxcrZI1xsgivlRVIktZo5IfRlLFCdwRLreBKKYOWJmhvS7o+nh5cyQrjdACul6JWbI+CD1ZTQpvJwP0vUGqCBdr8gHMwukvoymgpezwLz89xNAul6RBWbuR30ZTQAv5345QemnfXS9IvfL2Q31ZSztm8D46HoDZI+uV2R8medRX0aTvct5Hl1vgOLR9Uo8j7E76stoinc5u6MUc4DY5XS1xO4ypyMmMprYXc7pMlfrp3N0vSKny0yO+jKazl3O5HLZQj+Jo+sVmVzmb9SXsSRuAn/LhRX91I2uV+Rv+bML9WU0dbuctdHHoQHCRh+ASqyNcTX6MDWasF3O1eh6AzSNrlfkapmhUV9G07TLGVpmXv3kLH/0LDG0zMuoL6PJ2eW8jK43QMnoekVeltkY9WUsJZvFxkocjDgOze1lNpY/5RPbGU3ELudgdL0B+kXXK3EwxryoL6Pp1+XMi65HY7zMweh6NO49jfsBIkZzu8/1ZBm7ZO6SwQsjL3S9zF5o3Hsa457GuKe53dPc7mncexr3nsa4pzHuaW73NLd7Gveexr2nMe5pjHua2z3N7Z7Gvadx72mMexrjnuZ2T3O7p3EfaNwHGuOBxniguT3Q3B5o3Aca94HGeKAxHmhuDzS3Bxr3gcZ9oDEeaIwHmtsDze2Bxn2gcR9ojAca44Hm9kBze6BxH2jcBxrjgcZ4oLk90NweaNwHGveBxnigMR5obg80twca94HGfaAxHmiMB5rbA83tgcZ9oHEfaIwHGuOB5vZAc3ugcR9o3Aca4yEDxlw7mYsnM3XM2DFTRlY3SdfLlZM07gON+0BjPNAYDzS3B5rbA437QOM+0BgPNMYDze2B5vZA4z7QuA807kPK18tslOAozfeRxn2kMR5pjEea2yPN7ZHGfaRxH2mMRxrjkeb2SHN7pHEfadxHGuORxnikuT3S3B5p3Eca95HGeKQxHmlujzS3Rxr3kcZ9pDEeaYxHmtsjze2Rxn2kcR9pjEca45Hm9khze6RxH2ncRxrjkcZ4pLk90tweadxHGveRxnikMR5pRo80siON7EgjO9LsHWlkRxrZkUZ2pJEdM1XPWD1z9YzRcz1wJuo0Z0caz5HGc6SxG2nsxhT+QE93svfguCjmWn/yRL+elz3Iv/zj+enl4benz18O7pf9b7778HywJqpmn8r/9Z9/+Of/BwskOOg=' +} + +def encode_json(json_file, is_print=False): + r""" Encode json string to compressed base64 string. + + Parameters + ---------- + json_file : str + String value represents the path to json file. + is_print : bool + Boolean value controls whether to print the encoded base64 string. + """ + with open(json_file, encoding='utf-8') as fh: + data = fh.read() + zipped_str = zlib.compress(data.encode('utf-8')) + b64_str = base64.b64encode(zipped_str) + if is_print: + print(b64_str) + return b64_str + +def decode_b64(b64_str, is_print=False): + r""" Decode b64 string to json format + + Parameters + --------- + b64_str: str + String value represents the compressed base64 string. + is_print : bool + Boolean value controls whether to print the decoded json string. + """ + json_str = zlib.decompress(base64.b64decode(b64_str)).decode('utf-8') + if is_print: + print(json_str) + return json_str + +def get_compressed_model(model_name): + r""" Get compressed (INT8) models from existing `_compressed_int8_json` dict + + Parameters + ---------- + model_name: str + String value represents the name of compressed (INT8) model. + """ + b64_str = _compressed_int8_json.get(model_name, None) + if b64_str: + return decode_b64(b64_str) + raise ValueError('Model: {} is not found. Available compressed models are:\n{}'.format(model_name, '\n'.join(list(_compressed_int8_json.keys())))) + diff --git a/scripts/segmentation/test.py b/scripts/segmentation/test.py index 9ba72e6b9c..a6a6181b60 100644 --- a/scripts/segmentation/test.py +++ b/scripts/segmentation/test.py @@ -14,6 +14,7 @@ from gluoncv.model_zoo import get_model from gluoncv.data import get_segmentation_dataset, ms_batchify_fn from gluoncv.utils.viz import get_color_pallete +from gluoncv.utils.compress_json import get_compressed_model def parse_args(): parser = argparse.ArgumentParser(description='Validation on Segmentation model') @@ -162,7 +163,7 @@ def test_quantization(model, args, input_transform): print('Inference speed with batchsize %d is %.2f img/sec' % (args.batch_size, speed)) -def benchmarking(args, model): +def benchmarking(model, args): print('-----benchmarking on %s -----' % args.model) if args.quantized: model.hybridize(static_alloc=True, static_shape=True) @@ -197,7 +198,7 @@ def benchmarking(args, model): withQuantization = True if (args.backbone in ['resnet101'] and args.ngpus == 0) else withQuantization elif args.dataset == 'coco': model_prefix += '_coco' - withQuantization = True if args.backbone in ['resnet101'] else withQuantization + withQuantization = True if (args.backbone in ['resnet101'] and args.ngpus == 0) else withQuantization elif args.dataset == 'ade20k': model_prefix += 'ade' elif args.dataset == 'citys': @@ -208,11 +209,12 @@ def benchmarking(args, model): if withQuantization and args.quantized: model_prefix += '_int8' - # create network + # create network if args.pretrained: model = get_model(model_prefix, pretrained=True) model.collect_params().reset_ctx(ctx=args.ctx) else: + assert "_in8" not in model_prefix, "Currently, Int8 models are not supported when pretrained=False" model = get_segmentation_model(model=args.model, dataset=args.dataset, ctx=args.ctx, backbone=args.backbone, norm_layer=args.norm_layer, norm_kwargs=args.norm_kwargs, aux=args.aux, @@ -224,8 +226,8 @@ def benchmarking(args, model): else: raise RuntimeError("=> no checkpoint found at '{}'" \ .format(args.resume)) - print("Successfully loaded %s model" % model_prefix) + print("Successfully loaded %s model" % model_prefix) print('Testing model: ', args.resume) # image transform input_transform = transforms.Compose([ @@ -239,4 +241,4 @@ def benchmarking(args, model): else: test(model, args, input_transform) else: - benchmarking(args, model) + benchmarking(model, args) From 99e874957f1336eb0d1391806c50c2dbaf671cd4 Mon Sep 17 00:00:00 2001 From: wuxun-zhang Date: Fri, 2 Aug 2019 10:09:26 +0800 Subject: [PATCH 06/10] fix pylint --- gluoncv/utils/compress_json.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/gluoncv/utils/compress_json.py b/gluoncv/utils/compress_json.py index 8ccb817d22..5da0093c74 100644 --- a/gluoncv/utils/compress_json.py +++ b/gluoncv/utils/compress_json.py @@ -1,5 +1,5 @@ +# pylint: disable=line-too-long """Encode/Decode helper function for compressed quantized models""" -import json import zlib import base64 @@ -27,7 +27,7 @@ def encode_json(json_file, is_print=False): return b64_str def decode_b64(b64_str, is_print=False): - r""" Decode b64 string to json format + r""" Decode b64 string to json format Parameters --------- @@ -53,4 +53,3 @@ def get_compressed_model(model_name): if b64_str: return decode_b64(b64_str) raise ValueError('Model: {} is not found. Available compressed models are:\n{}'.format(model_name, '\n'.join(list(_compressed_int8_json.keys())))) - From 317331884091bd973d3b553854756aee93be5ff1 Mon Sep 17 00:00:00 2001 From: wuxun-zhang Date: Sun, 4 Aug 2019 15:35:15 +0800 Subject: [PATCH 07/10] fix comments --- gluoncv/model_zoo/quantized/quantized.py | 8 +++++++- gluoncv/utils/compress_json.py | 16 +++++++--------- scripts/segmentation/test.py | 3 +-- 3 files changed, 15 insertions(+), 12 deletions(-) diff --git a/gluoncv/model_zoo/quantized/quantized.py b/gluoncv/model_zoo/quantized/quantized.py index 6ce902a0ec..19b176d3bf 100644 --- a/gluoncv/model_zoo/quantized/quantized.py +++ b/gluoncv/model_zoo/quantized/quantized.py @@ -1,4 +1,5 @@ # pylint: skip-file +# pylint: disable=line-too-long """Create quantized model from JSON files...""" import os import warnings @@ -12,6 +13,11 @@ 'ssd_512_resnet50_v1_voc_int8', 'ssd_512_vgg16_atrous_voc_int8', 'fcn_resnet101_voc_int8', 'fcn_resnet101_coco_int8'] +_compressed_int8_json = { + 'fcn_resnet101_voc_int8': 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', + 'fcn_resnet101_coco_int8': b'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' +} + def _not_impl(*args, **kwargs): raise NotImplementedError("Not yet implemented for quantized models") @@ -41,7 +47,7 @@ def func(pretrained=False, tag=None, root='~/.mxnet/models', ctx=cpu(0), **kwarg warnings.simplefilter("always") import tempfile if "fcn" in model_name: - model_json = get_compressed_model(model_name) + model_json = get_compressed_model(model_name, _compressed_int8_json) with tempfile.NamedTemporaryFile('w') as tmpf: tmpf.write(model_json) sym_net = SymbolBlock.imports(tmpf.name, ['data'], None, ctx=ctx) diff --git a/gluoncv/utils/compress_json.py b/gluoncv/utils/compress_json.py index 5da0093c74..055c30ed84 100644 --- a/gluoncv/utils/compress_json.py +++ b/gluoncv/utils/compress_json.py @@ -3,11 +3,6 @@ import zlib import base64 -_compressed_int8_json = { - 'fcn_resnet101_voc_int8': b'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', - 'fcn_resnet101_coco_int8': b'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' -} - def encode_json(json_file, is_print=False): r""" Encode json string to compressed base64 string. @@ -41,15 +36,18 @@ def decode_b64(b64_str, is_print=False): print(json_str) return json_str -def get_compressed_model(model_name): - r""" Get compressed (INT8) models from existing `_compressed_int8_json` dict +def get_compressed_model(model_name, compressed_json): + r""" Get compressed (INT8) models from existing `compressed_json` dict Parameters ---------- model_name: str String value represents the name of compressed (INT8) model. + compressed_json : dict + Dictionary's key represents the name of (INT8) model, and dictionary's value + represents the compressed json string of (INT8) model. """ - b64_str = _compressed_int8_json.get(model_name, None) + b64_str = compressed_json.get(model_name, None) if b64_str: return decode_b64(b64_str) - raise ValueError('Model: {} is not found. Available compressed models are:\n{}'.format(model_name, '\n'.join(list(_compressed_int8_json.keys())))) + raise ValueError('Model: {} is not found. Available compressed models are:\n{}'.format(model_name, '\n'.join(list(compressed_json.keys())))) diff --git a/scripts/segmentation/test.py b/scripts/segmentation/test.py index a6a6181b60..2809ba6c2d 100644 --- a/scripts/segmentation/test.py +++ b/scripts/segmentation/test.py @@ -14,7 +14,6 @@ from gluoncv.model_zoo import get_model from gluoncv.data import get_segmentation_dataset, ms_batchify_fn from gluoncv.utils.viz import get_color_pallete -from gluoncv.utils.compress_json import get_compressed_model def parse_args(): parser = argparse.ArgumentParser(description='Validation on Segmentation model') @@ -164,7 +163,6 @@ def test_quantization(model, args, input_transform): def benchmarking(model, args): - print('-----benchmarking on %s -----' % args.model) if args.quantized: model.hybridize(static_alloc=True, static_shape=True) else: @@ -241,4 +239,5 @@ def benchmarking(model, args): else: test(model, args, input_transform) else: + print('-----benchmarking on %s -----' % model_prefix) benchmarking(model, args) From c5fd833e2c8b69bd5cc0cbee976d58381548ffb0 Mon Sep 17 00:00:00 2001 From: wuxun-zhang Date: Sun, 4 Aug 2019 20:57:15 +0800 Subject: [PATCH 08/10] enable fcn ut --- tests/unittests/test_model_zoo.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/unittests/test_model_zoo.py b/tests/unittests/test_model_zoo.py index f114c35e38..b6407f2e1c 100644 --- a/tests/unittests/test_model_zoo.py +++ b/tests/unittests/test_model_zoo.py @@ -411,7 +411,6 @@ def test_quantized_ssd_models(): _test_model_list(model_list, ctx, x) -@unittest.skip("temporarily disabled to fallback to non-mkl version") @with_cpu(0) def test_quantized_fcn_models(): model_list = ['fcn_resnet101_voc_int8', 'fcn_resnet101_coco_int8'] From e7e4adb78b09d28fd7b22308e78f023e8a4d0808 Mon Sep 17 00:00:00 2001 From: wuxun-zhang Date: Sun, 4 Aug 2019 22:18:26 +0800 Subject: [PATCH 09/10] trigger CI From d81fec6fad5ddf2c13bab23486c60468b7c452f8 Mon Sep 17 00:00:00 2001 From: wuxun-zhang Date: Mon, 5 Aug 2019 14:25:42 +0800 Subject: [PATCH 10/10] fix python2 issue --- gluoncv/model_zoo/quantized/quantized.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/gluoncv/model_zoo/quantized/quantized.py b/gluoncv/model_zoo/quantized/quantized.py index 19b176d3bf..d00cf8d432 100644 --- a/gluoncv/model_zoo/quantized/quantized.py +++ b/gluoncv/model_zoo/quantized/quantized.py @@ -48,9 +48,15 @@ def func(pretrained=False, tag=None, root='~/.mxnet/models', ctx=cpu(0), **kwarg import tempfile if "fcn" in model_name: model_json = get_compressed_model(model_name, _compressed_int8_json) - with tempfile.NamedTemporaryFile('w') as tmpf: - tmpf.write(model_json) - sym_net = SymbolBlock.imports(tmpf.name, ['data'], None, ctx=ctx) + fd, path = tempfile.mkstemp(dir=curr_dir, suffix='.json', text=True) + with open(path, 'w') as f: + f.write(model_json) + sym_net = SymbolBlock.imports(path, ['data'], None, ctx=ctx) + os.close(fd) + try: + os.remove(path) + except: + pass else: json_file = os.path.join(curr_dir, '{}-symbol.json'.format(model_name)) sym_net = SymbolBlock.imports(json_file, ['data'], None, ctx=ctx)