From cb817fd97225467049ce6e1ea011d47feceb8f74 Mon Sep 17 00:00:00 2001 From: jm12138 <2286040843@qq.com> Date: Fri, 4 Nov 2022 17:15:58 +0800 Subject: [PATCH] update fix_resnext101_32x48d_wsl_imagenet (#2042) --- .../README.md | 7 +- .../README_en.md | 7 +- .../data_feed.py | 2 - .../module.py | 136 +++++----------- .../processor.py | 5 +- .../resnext101_wsl.py | 147 ------------------ .../test.py | 63 ++++++++ 7 files changed, 115 insertions(+), 252 deletions(-) delete mode 100644 modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/resnext101_wsl.py create mode 100644 modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/test.py diff --git a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/README.md b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/README.md index ef750cec..f583346b 100644 --- a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/README.md +++ b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/README.md @@ -129,6 +129,11 @@ * 1.0.0 初始发布 + +* 1.1.0 + + 移除 Fluid API + - ```shell - $ hub install fix_resnext101_32x48d_wsl_imagenet==1.0.0 + $ hub install fix_resnext101_32x48d_wsl_imagenet==1.1.0 ``` diff --git a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/README_en.md b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/README_en.md index 2634e00e..74516516 100644 --- a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/README_en.md +++ b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/README_en.md @@ -129,6 +129,11 @@ * 1.0.0 First release + +* 1.1.0 + + Remove Fluid API + - ```shell - $ hub install fix_resnext101_32x48d_wsl_imagenet==1.0.0 + $ hub install fix_resnext101_32x48d_wsl_imagenet==1.1.0 ``` diff --git a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/data_feed.py b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/data_feed.py index 99a0855f..95ba8337 100644 --- a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/data_feed.py +++ b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/data_feed.py @@ -1,9 +1,7 @@ -# coding=utf-8 import os import time from collections import OrderedDict -import cv2 import numpy as np from PIL import Image diff --git a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/module.py b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/module.py index 686431e0..cf5a139e 100644 --- a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/module.py +++ b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/module.py @@ -1,21 +1,20 @@ -# coding=utf-8 from __future__ import absolute_import from __future__ import division -import ast import argparse +import ast import os import numpy as np -import paddle.fluid as fluid -import paddlehub as hub -from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor -from paddlehub.module.module import moduleinfo, runnable, serving -from paddlehub.common.paddle_helper import add_vars_prefix +from paddle.inference import Config +from paddle.inference import create_predictor -from fix_resnext101_32x48d_wsl_imagenet.processor import postprocess, base64_to_cv2 -from fix_resnext101_32x48d_wsl_imagenet.data_feed import reader -from fix_resnext101_32x48d_wsl_imagenet.resnext101_wsl import Fix_ResNeXt101_32x48d_wsl +from .data_feed import reader +from .processor import base64_to_cv2 +from .processor import postprocess +from paddlehub.module.module import moduleinfo +from paddlehub.module.module import runnable +from paddlehub.module.module import serving @moduleinfo( @@ -24,10 +23,11 @@ from fix_resnext101_32x48d_wsl_imagenet.resnext101_wsl import Fix_ResNeXt101_32x author="paddlepaddle", author_email="paddle-dev@baidu.com", summary="fix_resnext101_32x48d_wsl is a image classfication model, this module is trained with imagenet datasets.", - version="1.0.0") -class FixResnext10132x48dwslImagenet(hub.Module): - def _initialize(self): - self.default_pretrained_model_path = os.path.join(self.directory, "model") + version="1.1.0") +class FixResnext10132x48dwslImagenet: + + def __init__(self): + self.default_pretrained_model_path = os.path.join(self.directory, "model", "model") label_file = os.path.join(self.directory, "label_list.txt") with open(label_file, 'r', encoding='utf-8') as file: self.label_list = file.read().split("\n")[:-1] @@ -51,10 +51,12 @@ class FixResnext10132x48dwslImagenet(hub.Module): """ predictor config setting """ - cpu_config = AnalysisConfig(self.default_pretrained_model_path) + model = self.default_pretrained_model_path + '.pdmodel' + params = self.default_pretrained_model_path + '.pdiparams' + cpu_config = Config(model, params) cpu_config.disable_glog_info() cpu_config.disable_gpu() - self.cpu_predictor = create_paddle_predictor(cpu_config) + self.cpu_predictor = create_predictor(cpu_config) try: _places = os.environ["CUDA_VISIBLE_DEVICES"] @@ -63,58 +65,10 @@ class FixResnext10132x48dwslImagenet(hub.Module): except: use_gpu = False if use_gpu: - gpu_config = AnalysisConfig(self.default_pretrained_model_path) + gpu_config = Config(model, params) gpu_config.disable_glog_info() gpu_config.enable_use_gpu(memory_pool_init_size_mb=1000, device_id=0) - self.gpu_predictor = create_paddle_predictor(gpu_config) - - def context(self, trainable=True, pretrained=True): - """context for transfer learning. - - Args: - trainable (bool): Set parameters in program to be trainable. - pretrained (bool) : Whether to load pretrained model. - - Returns: - inputs (dict): key is 'image', corresponding vaule is image tensor. - outputs (dict): key is : - 'classification', corresponding value is the result of classification. - 'feature_map', corresponding value is the result of the layer before the fully connected layer. - context_prog (fluid.Program): program for transfer learning. - """ - context_prog = fluid.Program() - startup_prog = fluid.Program() - with fluid.program_guard(context_prog, startup_prog): - with fluid.unique_name.guard(): - image = fluid.layers.data(name="image", shape=[3, 224, 224], dtype="float32") - resnet_vd = Fix_ResNeXt101_32x48d_wsl() - output, feature_map = resnet_vd.net(input=image, class_dim=len(self.label_list)) - - name_prefix = '@HUB_{}@'.format(self.name) - inputs = {'image': name_prefix + image.name} - outputs = {'classification': name_prefix + output.name, 'feature_map': name_prefix + feature_map.name} - add_vars_prefix(context_prog, name_prefix) - add_vars_prefix(startup_prog, name_prefix) - global_vars = context_prog.global_block().vars - inputs = {key: global_vars[value] for key, value in inputs.items()} - outputs = {key: global_vars[value] for key, value in outputs.items()} - - place = fluid.CPUPlace() - exe = fluid.Executor(place) - # pretrained - if pretrained: - - def _if_exist(var): - b = os.path.exists(os.path.join(self.default_pretrained_model_path, var.name)) - return b - - fluid.io.load_vars(exe, self.default_pretrained_model_path, context_prog, predicate=_if_exist) - else: - exe.run(startup_prog) - # trainable - for param in context_prog.global_block().iter_parameters(): - param.trainable = trainable - return inputs, outputs, context_prog + self.gpu_predictor = create_predictor(gpu_config) def classification(self, images=None, paths=None, batch_size=1, use_gpu=False, top_k=1): """ @@ -136,7 +90,7 @@ class FixResnext10132x48dwslImagenet(hub.Module): int(_places[0]) except: raise RuntimeError( - "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES as cuda_device_id." + "Attempt to use GPU for prediction, but environment variable CUDA_VISIBLE_DEVICES was not set correctly." ) if not self.predictor_set: @@ -161,32 +115,19 @@ class FixResnext10132x48dwslImagenet(hub.Module): pass # feed batch image batch_image = np.array([data['image'] for data in batch_data]) - batch_image = PaddleTensor(batch_image.copy()) - predictor_output = self.gpu_predictor.run([batch_image]) if use_gpu else self.cpu_predictor.run( - [batch_image]) - out = postprocess(data_out=predictor_output[0].as_ndarray(), label_list=self.label_list, top_k=top_k) + + predictor = self.gpu_predictor if use_gpu else self.cpu_predictor + input_names = predictor.get_input_names() + input_handle = predictor.get_input_handle(input_names[0]) + input_handle.copy_from_cpu(batch_image.copy()) + predictor.run() + output_names = predictor.get_output_names() + output_handle = predictor.get_output_handle(output_names[0]) + + out = postprocess(data_out=output_handle.copy_to_cpu(), label_list=self.label_list, top_k=top_k) res += out return res - def save_inference_model(self, dirname, model_filename=None, params_filename=None, combined=True): - if combined: - model_filename = "__model__" if not model_filename else model_filename - params_filename = "__params__" if not params_filename else params_filename - place = fluid.CPUPlace() - exe = fluid.Executor(place) - - program, feeded_var_names, target_vars = fluid.io.load_inference_model( - dirname=self.default_pretrained_model_path, executor=exe) - - fluid.io.save_inference_model( - dirname=dirname, - main_program=program, - executor=exe, - feeded_var_names=feeded_var_names, - target_vars=target_vars, - model_filename=model_filename, - params_filename=params_filename) - @serving def serving_method(self, images, **kwargs): """ @@ -201,11 +142,10 @@ class FixResnext10132x48dwslImagenet(hub.Module): """ Run as a command. """ - self.parser = argparse.ArgumentParser( - description="Run the {} module.".format(self.name), - prog='hub run {}'.format(self.name), - usage='%(prog)s', - add_help=True) + self.parser = argparse.ArgumentParser(description="Run the {} module.".format(self.name), + prog='hub run {}'.format(self.name), + usage='%(prog)s', + add_help=True) self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required") self.arg_config_group = self.parser.add_argument_group( title="Config options", description="Run configuration for controlling module behavior, not required.") @@ -219,8 +159,10 @@ class FixResnext10132x48dwslImagenet(hub.Module): """ Add the command config options. """ - self.arg_config_group.add_argument( - '--use_gpu', type=ast.literal_eval, default=False, help="whether use GPU or not.") + self.arg_config_group.add_argument('--use_gpu', + type=ast.literal_eval, + default=False, + help="whether use GPU or not.") self.arg_config_group.add_argument('--batch_size', type=ast.literal_eval, default=1, help="batch size.") self.arg_config_group.add_argument('--top_k', type=ast.literal_eval, default=1, help="Return top k results.") diff --git a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/processor.py b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/processor.py index fa8cbb50..dde1248f 100644 --- a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/processor.py +++ b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/processor.py @@ -1,12 +1,10 @@ -# coding=utf-8 from __future__ import absolute_import from __future__ import division from __future__ import print_function import base64 -import cv2 -import os +import cv2 import numpy as np @@ -18,7 +16,6 @@ def base64_to_cv2(b64str): def softmax(x): - orig_shape = x.shape if len(x.shape) > 1: tmp = np.max(x, axis=1) x -= tmp.reshape((x.shape[0], 1)) diff --git a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/resnext101_wsl.py b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/resnext101_wsl.py deleted file mode 100644 index 3c01d8bd..00000000 --- a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/resnext101_wsl.py +++ /dev/null @@ -1,147 +0,0 @@ -#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. -# -#Licensed under the Apache License, Version 2.0 (the "License"); -#you may not use this file except in compliance with the License. -#You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -#Unless required by applicable law or agreed to in writing, software -#distributed under the License is distributed on an "AS IS" BASIS, -#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -#See the License for the specific language governing permissions and -#limitations under the License. -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -import paddle -import paddle.fluid as fluid -import math -from paddle.fluid.param_attr import ParamAttr - -__all__ = [ - "ResNeXt101_32x8d_wsl", "ResNeXt101_32x16d_wsl", "ResNeXt101_32x32d_wsl", "ResNeXt101_32x48d_wsl", - "Fix_ResNeXt101_32x48d_wsl" -] - - -class ResNeXt101_wsl(): - def __init__(self, layers=101, cardinality=32, width=48): - self.layers = layers - self.cardinality = cardinality - self.width = width - - def net(self, input, class_dim=1000): - layers = self.layers - cardinality = self.cardinality - width = self.width - - depth = [3, 4, 23, 3] - base_width = cardinality * width - num_filters = [base_width * i for i in [1, 2, 4, 8]] - - conv = self.conv_bn_layer( - input=input, num_filters=64, filter_size=7, stride=2, act='relu', name="conv1") #debug - conv = fluid.layers.pool2d(input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max') - - for block in range(len(depth)): - for i in range(depth[block]): - conv_name = 'layer' + str(block + 1) + "." + str(i) - conv = self.bottleneck_block( - input=conv, - num_filters=num_filters[block], - stride=2 if i == 0 and block != 0 else 1, - cardinality=cardinality, - name=conv_name) - - pool = fluid.layers.pool2d(input=conv, pool_type='avg', global_pooling=True) - stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0) - out = fluid.layers.fc( - input=pool, - size=class_dim, - param_attr=fluid.param_attr.ParamAttr(initializer=fluid.initializer.Uniform(-stdv, stdv), name='fc.weight'), - bias_attr=fluid.param_attr.ParamAttr(name='fc.bias')) - return out, pool - - def conv_bn_layer(self, input, num_filters, filter_size, stride=1, groups=1, act=None, name=None): - if "downsample" in name: - conv_name = name + '.0' - else: - conv_name = name - conv = fluid.layers.conv2d( - input=input, - num_filters=num_filters, - filter_size=filter_size, - stride=stride, - padding=(filter_size - 1) // 2, - groups=groups, - act=None, - param_attr=ParamAttr(name=conv_name + ".weight"), - bias_attr=False) - if "downsample" in name: - bn_name = name[:9] + 'downsample' + '.1' - else: - if "conv1" == name: - bn_name = 'bn' + name[-1] - else: - bn_name = (name[:10] if name[7:9].isdigit() else name[:9]) + 'bn' + name[-1] - return fluid.layers.batch_norm( - input=conv, - act=act, - param_attr=ParamAttr(name=bn_name + '.weight'), - bias_attr=ParamAttr(bn_name + '.bias'), - moving_mean_name=bn_name + '.running_mean', - moving_variance_name=bn_name + '.running_var', - ) - - def shortcut(self, input, ch_out, stride, name): - ch_in = input.shape[1] - if ch_in != ch_out or stride != 1: - return self.conv_bn_layer(input, ch_out, 1, stride, name=name) - else: - return input - - def bottleneck_block(self, input, num_filters, stride, cardinality, name): - cardinality = self.cardinality - width = self.width - conv0 = self.conv_bn_layer( - input=input, num_filters=num_filters, filter_size=1, act='relu', name=name + ".conv1") - conv1 = self.conv_bn_layer( - input=conv0, - num_filters=num_filters, - filter_size=3, - stride=stride, - groups=cardinality, - act='relu', - name=name + ".conv2") - conv2 = self.conv_bn_layer( - input=conv1, num_filters=num_filters // (width // 8), filter_size=1, act=None, name=name + ".conv3") - - short = self.shortcut(input, num_filters // (width // 8), stride, name=name + ".downsample") - - return fluid.layers.elementwise_add(x=short, y=conv2, act='relu') - - -def ResNeXt101_32x8d_wsl(): - model = ResNeXt101_wsl(cardinality=32, width=8) - return model - - -def ResNeXt101_32x16d_wsl(): - model = ResNeXt101_wsl(cardinality=32, width=16) - return model - - -def ResNeXt101_32x32d_wsl(): - model = ResNeXt101_wsl(cardinality=32, width=32) - return model - - -def ResNeXt101_32x48d_wsl(): - model = ResNeXt101_wsl(cardinality=32, width=48) - return model - - -def Fix_ResNeXt101_32x48d_wsl(): - model = ResNeXt101_wsl(cardinality=32, width=48) - return model diff --git a/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/test.py b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/test.py new file mode 100644 index 00000000..a42f2a32 --- /dev/null +++ b/modules/image/classification/fix_resnext101_32x48d_wsl_imagenet/test.py @@ -0,0 +1,63 @@ +import os +import shutil +import unittest + +import cv2 +import requests + +import paddlehub as hub + +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + + +class TestHubModule(unittest.TestCase): + + @classmethod + def setUpClass(cls) -> None: + img_url = 'https://unsplash.com/photos/brFsZ7qszSY/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8OHx8ZG9nfGVufDB8fHx8MTY2MzA1ODQ1MQ&force=true&w=640' + if not os.path.exists('tests'): + os.makedirs('tests') + response = requests.get(img_url) + assert response.status_code == 200, 'Network Error.' + with open('tests/test.jpg', 'wb') as f: + f.write(response.content) + cls.module = hub.Module(name="fix_resnext101_32x48d_wsl_imagenet") + + @classmethod + def tearDownClass(cls) -> None: + shutil.rmtree('tests') + shutil.rmtree('inference') + + def test_classification1(self): + results = self.module.classification(paths=['tests/test.jpg']) + data = results[0] + self.assertTrue('Pembroke' in data) + self.assertTrue(data['Pembroke'] > 0.5) + + def test_classification2(self): + results = self.module.classification(images=[cv2.imread('tests/test.jpg')]) + data = results[0] + self.assertTrue('Pembroke' in data) + self.assertTrue(data['Pembroke'] > 0.5) + + def test_classification3(self): + results = self.module.classification(images=[cv2.imread('tests/test.jpg')], use_gpu=True) + data = results[0] + self.assertTrue('Pembroke' in data) + self.assertTrue(data['Pembroke'] > 0.5) + + def test_classification4(self): + self.assertRaises(AssertionError, self.module.classification, paths=['no.jpg']) + + def test_classification5(self): + self.assertRaises(TypeError, self.module.classification, images=['tests/test.jpg']) + + def test_save_inference_model(self): + self.module.save_inference_model('./inference/model') + + self.assertTrue(os.path.exists('./inference/model.pdmodel')) + self.assertTrue(os.path.exists('./inference/model.pdiparams')) + + +if __name__ == "__main__": + unittest.main() -- GitLab