diff --git a/docs/zh_CN/faq.md b/docs/zh_CN/faq.md index fe83d31ba03bf649aab1bff097d78874310fb4e6..e3037e45c7b0c19f39bdae4fb6558ea2eab19a8b 100644 --- a/docs/zh_CN/faq.md +++ b/docs/zh_CN/faq.md @@ -18,3 +18,32 @@ >> * Q: 评估和预测时，已经指定了预训练模型所在文件夹的地址，但是仍然无法导入参数，这么为什么呢？ * A: 加载预训练模型时，需要指定预训练模型的前缀，例如预训练模型参数所在的文件夹为`output/ResNet50_vd/19`，预训练模型参数的名称为`output/ResNet50_vd/19/ppcls.pdparams`，则`pretrained_model`参数需要指定为`output/ResNet50_vd/19/ppcls`，PaddleClas会自动补齐`.pdparams`的后缀。 + + +>> +* Q: 在评测`EfficientNetB0_small`模型时，为什么最终的精度始终比官网的低0.3%左右？ +* A: `EfficientNet`系列的网络在进行resize的时候，是使用`cubic插值方式`(resize参数的interpolation值设置为2)，而其他模型默认情况下为None，因此在训练和评估的时候需要显式地指定resiz的interpolation值。具体地，可以参考以下配置中预处理过程中ResizeImage的参数。 +``` +VALID: + batch_size: 16 + num_workers: 4 + file_list: "./dataset/ILSVRC2012/val_list.txt" + data_dir: "./dataset/ILSVRC2012/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - ResizeImage: + resize_short: 256 + interpolation: 2 + - CropImage: + size: 224 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: +``` diff --git a/ppcls/data/imaug/operators.py b/ppcls/data/imaug/operators.py index 98e89ca8307d44ee156deaaeb0bf64ffbd6db58a..a8454740f4f6047f16104b32c5a06b6f5cc1e327 100644 --- a/ppcls/data/imaug/operators.py +++ b/ppcls/data/imaug/operators.py @@ -22,7 +22,6 @@ from __future__ import unicode_literals import six import math import random -import functools import cv2 import numpy as np @@ -38,8 +37,8 @@ class DecodeImage(object): def __init__(self, to_rgb=True, to_np=False, channel_first=False): self.to_rgb = to_rgb - self.to_np = to_np #to numpy - self.channel_first = channel_first #only enabled when to_np is True + self.to_np = to_np # to numpy + self.channel_first = channel_first # only enabled when to_np is True def __call__(self, img): if six.PY2: @@ -64,7 +63,8 @@ class DecodeImage(object): class ResizeImage(object): """ resize image """ - def __init__(self, size=None, resize_short=None): + def __init__(self, size=None, resize_short=None, interpolation=-1): + self.interpolation = interpolation if interpolation >= 0 else None if resize_short is not None and resize_short > 0: self.resize_short = resize_short self.w = None @@ -86,8 +86,10 @@ class ResizeImage(object): else: w = self.w h = self.h - - return cv2.resize(img, (w, h)) + if self.interpolation is None: + return cv2.resize(img, (w, h)) + else: + return cv2.resize(img, (w, h), interpolation=self.interpolation) class CropImage(object): @@ -138,8 +140,7 @@ class RandCropImage(object): scale_max = min(scale[1], bound) scale_min = min(scale[0], bound) - target_area = img_w * img_h * random.uniform(\ - scale_min, scale_max) + target_area = img_w * img_h * random.uniform(scale_min, scale_max) target_size = math.sqrt(target_area) w = int(target_size * w) h = int(target_size * h) @@ -176,7 +177,8 @@ class NormalizeImage(object): """ def __init__(self, scale=None, mean=None, std=None, order='chw'): - if isinstance(scale, str): scale = eval(scale) + if isinstance(scale, str): + scale = eval(scale) self.scale = np.float32(scale if scale is not None else 1.0 / 255.0) mean = mean if mean is not None else [0.485, 0.456, 0.406] std = std if std is not None else [0.229, 0.224, 0.225] diff --git a/ppcls/modeling/architectures/__init__.py b/ppcls/modeling/architectures/__init__.py index 34be3ce2addc0a8320e4b3606e1d87f0873d2a47..b6b69a370b4bb56752715f6faed51501609609b5 100644 --- a/ppcls/modeling/architectures/__init__.py +++ b/ppcls/modeling/architectures/__init__.py @@ -36,7 +36,7 @@ from .densenet import DenseNet121, DenseNet161, DenseNet169, DenseNet201, DenseN from .squeezenet import SqueezeNet1_0, SqueezeNet1_1 from .darknet import DarkNet53 from .resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl, Fix_ResNeXt101_32x48d_wsl -from .efficientnet import EfficientNet, EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7 +from .efficientnet import EfficientNet, EfficientNetB0, EfficientNetB0_small, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7 from .res2net import Res2Net50_48w_2s, Res2Net50_26w_4s, Res2Net50_14w_8s, Res2Net50_26w_6s, Res2Net50_26w_8s, Res2Net101_26w_4s, Res2Net152_26w_4s from .res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_14w_8s, Res2Net50_vd_26w_6s, Res2Net50_vd_26w_8s, Res2Net101_vd_26w_4s, Res2Net152_vd_26w_4s, Res2Net200_vd_26w_4s from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C diff --git a/ppcls/modeling/architectures/efficientnet.py b/ppcls/modeling/architectures/efficientnet.py index 082a14226dcc9bcf670233ad2916da5501c576be..5952d4c9af833c04b10aeeb8f57fde89310e4242 100644 --- a/ppcls/modeling/architectures/efficientnet.py +++ b/ppcls/modeling/architectures/efficientnet.py @@ -1,16 +1,16 @@ -#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# copyright (c) 2020 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 +# 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. +# 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 @@ -192,15 +192,17 @@ class EfficientNet(): if is_test: return inputs keep_prob = 1.0 - prob - random_tensor = keep_prob + fluid.layers.uniform_random_batch_size_like( - inputs, [-1, 1, 1, 1], min=0., max=1.) + random_tensor = keep_prob + \ + fluid.layers.uniform_random_batch_size_like( + inputs, [-1, 1, 1, 1], min=0., max=1.) binary_tensor = fluid.layers.floor(random_tensor) output = inputs / keep_prob * binary_tensor return output def _expand_conv_norm(self, inputs, block_args, is_test, name=None): # Expansion phase - oup = block_args.input_filters * block_args.expand_ratio # number of output channels + oup = block_args.input_filters * \ + block_args.expand_ratio # number of output channels if block_args.expand_ratio != 1: conv = self.conv_bn_layer( @@ -222,7 +224,8 @@ class EfficientNet(): s = block_args.stride if isinstance(s, list) or isinstance(s, tuple): s = s[0] - oup = block_args.input_filters * block_args.expand_ratio # number of output channels + oup = block_args.input_filters * \ + block_args.expand_ratio # number of output channels conv = self.conv_bn_layer( inputs, @@ -285,7 +288,7 @@ class EfficientNet(): name=conv_name, use_bias=use_bias) - if use_bn == False: + if use_bn is False: return conv else: bn_name = name + bn_name @@ -325,7 +328,8 @@ class EfficientNet(): drop_connect_rate=None, name=None): # Expansion and Depthwise Convolution - oup = block_args.input_filters * block_args.expand_ratio # number of output channels + oup = block_args.input_filters * \ + block_args.expand_ratio # number of output channels has_se = self.use_se and (block_args.se_ratio is not None) and ( 0 < block_args.se_ratio <= 1) id_skip = block_args.id_skip # skip connection and drop connect @@ -346,8 +350,11 @@ class EfficientNet(): conv = self._project_conv_norm(conv, block_args, is_test, name) # Skip connection and drop connect - input_filters, output_filters = block_args.input_filters, block_args.output_filters - if id_skip and block_args.stride == 1 and input_filters == output_filters: + input_filters = block_args.input_filters + output_filters = block_args.output_filters + if id_skip and \ + block_args.stride == 1 and \ + input_filters == output_filters: if drop_connect_rate: conv = self._drop_connect(conv, drop_connect_rate, self.is_test) @@ -412,7 +419,8 @@ class EfficientNet(): num_repeat=round_repeats(block_args.num_repeat, self._global_params)) - # The first block needs to take care of stride and filter size increase. + # The first block needs to take care of stride, + # and filter size increase. drop_connect_rate = self._global_params.drop_connect_rate if drop_connect_rate: drop_connect_rate *= float(idx) / block_size @@ -440,7 +448,9 @@ class EfficientNet(): class BlockDecoder(object): - """ Block Decoder for readability, straight from the official TensorFlow repository """ + """ + Block Decoder, straight from the official TensorFlow repository. + """ @staticmethod def _decode_block_string(block_string): @@ -456,9 +466,10 @@ class BlockDecoder(object): options[key] = value # Check stride - assert ( - ('s' in options and len(options['s']) == 1) or - (len(options['s']) == 2 and options['s'][0] == options['s'][1])) + cond_1 = ('s' in options and len(options['s']) == 1) + cond_2 = ((len(options['s']) == 2) + and (options['s'][0] == options['s'][1])) + assert (cond_1 or cond_2) return BlockArgs( kernel_size=int(options['k']), @@ -487,10 +498,11 @@ class BlockDecoder(object): @staticmethod def decode(string_list): """ - Decodes a list of string notations to specify blocks inside the network. + Decode a list of string notations to specify blocks in the network. - :param string_list: a list of strings, each string is a notation of block - :return: a list of BlockArgs namedtuples of block args + string_list: list of strings, each string is a notation of block + return + list of BlockArgs namedtuples of block args """ assert isinstance(string_list, list) blocks_args = [] @@ -525,6 +537,19 @@ def EfficientNetB0(is_test=False, return model +def EfficientNetB0_small(is_test=False, + padding_type='DYNAMIC', + override_params=None, + use_se=False): + model = EfficientNet( + name='b0', + is_test=is_test, + padding_type=padding_type, + override_params=override_params, + use_se=use_se) + return model + + def EfficientNetB1(is_test=False, padding_type='SAME', override_params=None,