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seminar-breakout
Commit dc13638b authored by Yuxin Wu's avatar Yuxin Wu
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remove inceptionv3

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examples/Inception/inception-bn.py
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...@@ -16,6 +16,7 @@ from tensorpack.utils.gpu import get_nr_gpu ...@@ -16,6 +16,7 @@ from tensorpack.utils.gpu import get_nr_gpu
from imagenet_utils import fbresnet_augmentor, get_imagenet_dataflow from imagenet_utils import fbresnet_augmentor, get_imagenet_dataflow
# Change them if using different number of GPUs.
TOTAL_BATCH_SIZE = 64 * 6 TOTAL_BATCH_SIZE = 64 * 6
NR_GPU = 6 NR_GPU = 6
BATCH_SIZE = TOTAL_BATCH_SIZE // NR_GPU BATCH_SIZE = TOTAL_BATCH_SIZE // NR_GPU
......
examples/Inception/inceptionv3.py deleted 100755 → 0
View file @ 2e058ee1
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# File: inceptionv3.py
# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
import cv2
import argparse
import os
import tensorflow as tf
import multiprocessing
from tensorpack import *
from tensorpack.tfutils.symbolic_functions import prediction_incorrect
from tensorpack.tfutils.summary import add_moving_summary
from tensorpack.dataflow import dataset
"""
InceptionV3 on ILSVRC12.
See "Rethinking the Inception Architecture for Computer Vision", arxiv:1512.00567
This config follows the official inceptionv3 setup
(https://github.com/tensorflow/models/tree/master/inception/inception)
with much much fewer lines of code.
It reaches 74% single-crop validation accuracy, similar to the official code.
"""
TOTAL_BATCH_SIZE = 512
NR_GPU = None
BATCH_SIZE = None
INPUT_SHAPE = 299
class Model(ModelDesc):
def _get_inputs(self):
return [InputDesc(tf.float32, [None, INPUT_SHAPE, INPUT_SHAPE, 3], 'input'),
InputDesc(tf.int32, [None], 'label')]
def _build_graph(self, inputs):
image, label = inputs
image = image / 255.0 # ?
def proj_kk(l, k, ch_r, ch, stride=1):
l = Conv2D('conv{0}{0}r'.format(k), l, ch_r, 1)
return Conv2D('conv{0}{0}'.format(k), l, ch, k, stride=stride,
padding='VALID' if stride > 1 else 'SAME')
def proj_233(l, ch_r, ch, stride=1):
l = Conv2D('conv233r', l, ch_r, 1)
l = Conv2D('conv233a', l, ch, 3)
return Conv2D('conv233b', l, ch, 3, stride=stride,
padding='VALID' if stride > 1 else 'SAME')
def pool_proj(l, ch, pool_type):
if pool_type == 'max':
l = MaxPooling('maxpool', l, 3, 1)
else:
l = AvgPooling('maxpool', l, 3, 1, padding='SAME')
return Conv2D('poolproj', l, ch, 1)
def proj_77(l, ch_r, ch):
return (LinearWrap(l)
.Conv2D('conv77r', ch_r, 1)
.Conv2D('conv77a', ch_r, [1, 7])
.Conv2D('conv77b', ch, [7, 1])())
def proj_277(l, ch_r, ch):
return (LinearWrap(l)
.Conv2D('conv277r', ch_r, 1)
.Conv2D('conv277aa', ch_r, [7, 1])
.Conv2D('conv277ab', ch_r, [1, 7])
.Conv2D('conv277ba', ch_r, [7, 1])
.Conv2D('conv277bb', ch, [1, 7])())
with argscope(Conv2D, nl=BNReLU, use_bias=False),\
argscope(BatchNorm, decay=0.9997, epsilon=1e-3):
l = (LinearWrap(image)
.Conv2D('conv0', 32, 3, stride=2, padding='VALID') # 299
.Conv2D('conv1', 32, 3, padding='VALID') # 149
.Conv2D('conv2', 64, 3, padding='SAME') # 147
.MaxPooling('pool2', 3, 2)
.Conv2D('conv3', 80, 1, padding='SAME') # 73
.Conv2D('conv4', 192, 3, padding='VALID') # 71
.MaxPooling('pool4', 3, 2)()) # 35
with tf.variable_scope('incep-35-256a'):
l = tf.concat([
Conv2D('conv11', l, 64, 1),
proj_kk(l, 5, 48, 64),
proj_233(l, 64, 96),
pool_proj(l, 32, 'avg')
], 3, name='concat')
with tf.variable_scope('incep-35-288a'):
l = tf.concat([
Conv2D('conv11', l, 64, 1),
proj_kk(l, 5, 48, 64),
proj_233(l, 64, 96),
pool_proj(l, 64, 'avg')
], 3, name='concat')
with tf.variable_scope('incep-35-288b'):
l = tf.concat([
Conv2D('conv11', l, 64, 1),
proj_kk(l, 5, 48, 64),
proj_233(l, 64, 96),
pool_proj(l, 64, 'avg')
], 3, name='concat')
# 35x35x288
with tf.variable_scope('incep-17-768a'):
l = tf.concat([
Conv2D('conv3x3', l, 384, 3, stride=2, padding='VALID'),
proj_233(l, 64, 96, stride=2),
MaxPooling('maxpool', l, 3, 2)
], 3, name='concat')
with tf.variable_scope('incep-17-768b'):
l = tf.concat([
Conv2D('conv11', l, 192, 1),
proj_77(l, 128, 192),
proj_277(l, 128, 192),
pool_proj(l, 192, 'avg')
], 3, name='concat')
for x in ['c', 'd']:
with tf.variable_scope('incep-17-768{}'.format(x)):
l = tf.concat([
Conv2D('conv11', l, 192, 1),
proj_77(l, 160, 192),
proj_277(l, 160, 192),
pool_proj(l, 192, 'avg')
], 3, name='concat')
with tf.variable_scope('incep-17-768e'):
l = tf.concat([
Conv2D('conv11', l, 192, 1),
proj_77(l, 192, 192),
proj_277(l, 192, 192),
pool_proj(l, 192, 'avg')
], 3, name='concat')
# 17x17x768
with tf.variable_scope('br1'):
br1 = AvgPooling('avgpool', l, 5, 3, padding='VALID')
br1 = Conv2D('conv11', br1, 128, 1)
shape = br1.get_shape().as_list()
br1 = Conv2D('convout', br1, 768, shape[1:3], padding='VALID')
br1 = FullyConnected('fc', br1, 1000, nl=tf.identity)
with tf.variable_scope('incep-17-1280a'):
l = tf.concat([
proj_kk(l, 3, 192, 320, stride=2),
Conv2D('conv73', proj_77(l, 192, 192), 192, 3, stride=2, padding='VALID'),
MaxPooling('maxpool', l, 3, 2)
], 3, name='concat')
for x in ['a', 'b']:
with tf.variable_scope('incep-8-2048{}'.format(x)):
br11 = Conv2D('conv11', l, 320, 1)
br33 = Conv2D('conv133r', l, 384, 1)
br33 = tf.concat([
Conv2D('conv133a', br33, 384, [1, 3]),
Conv2D('conv133b', br33, 384, [3, 1])
], 3, name='conv133')
br233 = proj_kk(l, 3, 448, 384)
br233 = tf.concat([
Conv2D('conv233a', br233, 384, [1, 3]),
Conv2D('conv233b', br233, 384, [3, 1]),
], 3, name='conv233')
l = tf.concat([
br11, br33, br233,
pool_proj(l, 192, 'avg')
], 3, name='concat')
l = GlobalAvgPooling('gap', l)
# 1x1x2048
l = Dropout('drop', l, 0.8)
logits = FullyConnected('linear', l, out_dim=1000, nl=tf.identity)
loss1 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=br1, labels=label)
loss1 = tf.reduce_mean(loss1, name='loss1')
loss2 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
loss2 = tf.reduce_mean(loss2, name='loss2')
wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
# weight decay on all W of fc layers
wd_w = tf.train.exponential_decay(0.00004, get_global_step_var(),
80000, 0.7, True)
wd_cost = tf.multiply(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='l2_regularize_loss')
self.cost = tf.add_n([0.4 * loss1, loss2, wd_cost], name='cost')
add_moving_summary(loss1, loss2, wd_cost, self.cost)
def _get_optimizer(self):
lr = tf.get_variable('learning_rate', initializer=0.045, trainable=False)
return tf.train.AdamOptimizer(lr, epsilon=1e-3)
def get_data(train_or_test):
isTrain = train_or_test == 'train'
ds = dataset.ILSVRC12(args.data, train_or_test,
shuffle=True if isTrain else False)
meta = dataset.ILSVRCMeta()
pp_mean = meta.get_per_pixel_mean()
pp_mean_299 = cv2.resize(pp_mean, (299, 299))
if isTrain:
class Resize(imgaug.ImageAugmentor):
def __init__(self):
self._init(locals())
def _augment(self, img, _):
h, w = img.shape[:2]
size = 299
scale = self.rng.randint(size, 340) * 1.0 / min(h, w)
scaleX = scale * self.rng.uniform(0.85, 1.15)
scaleY = scale * self.rng.uniform(0.85, 1.15)
desSize = map(int, (max(size, min(w, scaleX * w)),
max(size, min(h, scaleY * h))))
dst = cv2.resize(img, tuple(desSize), interpolation=cv2.INTER_CUBIC)
return dst
augmentors = [
Resize(),
imgaug.Rotation(max_deg=10),
imgaug.RandomApplyAug(imgaug.GaussianBlur(3), 0.5),
imgaug.Brightness(30, True),
imgaug.Gamma(),
imgaug.Contrast((0.8, 1.2), True),
imgaug.RandomCrop((299, 299)),
imgaug.RandomApplyAug(imgaug.JpegNoise(), 0.8),
imgaug.RandomApplyAug(imgaug.GaussianDeform(
[(0.2, 0.2), (0.2, 0.8), (0.8, 0.8), (0.8, 0.2)],
(299, 299), 0.2, 3), 0.1),
imgaug.Flip(horiz=True),
imgaug.MapImage(lambda x: x - pp_mean_299),
]
else:
def resize_func(im):
h, w = im.shape[:2]
scale = 340.0 / min(h, w)
desSize = map(int, (max(299, min(w, scale * w)),
max(299, min(h, scale * h))))
im = cv2.resize(im, tuple(desSize), interpolation=cv2.INTER_CUBIC)
return im
augmentors = [
imgaug.MapImage(resize_func),
imgaug.CenterCrop((299, 299)),
imgaug.MapImage(lambda x: x - pp_mean_299),
]
ds = AugmentImageComponent(ds, augmentors, copy=False)
ds = BatchData(ds, BATCH_SIZE, remainder=not isTrain)
if isTrain:
ds = PrefetchDataZMQ(ds, min(12, multiprocessing.cpu_count()))
return ds
def get_config():
# prepare dataset
dataset_train = get_data('train')
dataset_val = get_data('val')
return TrainConfig(
dataflow=dataset_train,
callbacks=[
ModelSaver(),
InferenceRunner(dataset_val, [
ClassificationError('wrong-top1', 'val-error-top1'),
ClassificationError('wrong-top5', 'val-error-top5')]),
ScheduledHyperParamSetter('learning_rate',
[(5, 0.03), (9, 0.01), (12, 0.006),
(17, 0.003), (22, 1e-3), (36, 2e-4),
(41, 8e-5), (48, 1e-5), (53, 2e-6)]),
HumanHyperParamSetter('learning_rate')
],
model=Model(),
steps_per_epoch=5000,
max_epoch=100,
)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.', required=True)
parser.add_argument('--data', help='ILSVRC dataset dir')
parser.add_argument('--load', help='load model')
args = parser.parse_args()
logger.auto_set_dir()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
NR_GPU = len(args.gpu.split(','))
BATCH_SIZE = TOTAL_BATCH_SIZE // NR_GPU
config = get_config()
if args.load:
config.session_init = SaverRestore(args.load)
launch_train_with_config(config, SyncMultiGPUTrainer(NR_GPU))
examples/README.md
View file @ dc13638b
...@@ -22,7 +22,7 @@ See [Unawareness of Deep Learning Mistakes](https://medium.com/@ppwwyyxx/unaware ...@@ -22,7 +22,7 @@ See [Unawareness of Deep Learning Mistakes](https://medium.com/@ppwwyyxx/unaware
| [Train Faster-RCNN / Mask-RCNN on COCO](FasterRCNN) | reproduce paper | | [Train Faster-RCNN / Mask-RCNN on COCO](FasterRCNN) | reproduce paper |
| [DoReFa-Net: training binary / low-bitwidth CNN on ImageNet](DoReFa-Net) | reproduce paper | | [DoReFa-Net: training binary / low-bitwidth CNN on ImageNet](DoReFa-Net) | reproduce paper |
| [Generative Adversarial Network(GAN) variants](GAN), including DCGAN, InfoGAN, <br/> Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN | visually reproduce | | [Generative Adversarial Network(GAN) variants](GAN), including DCGAN, InfoGAN, <br/> Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN | visually reproduce |
| [Inception-BN and InceptionV3](Inception) | reproduce reference code | | [Inception-BN](Inception) | reproduce reference code |
| [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](HED) | visually reproduce | | [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](HED) | visually reproduce |
| [Spatial Transformer Networks on MNIST addition](SpatialTransformer) | reproduce paper | | [Spatial Transformer Networks on MNIST addition](SpatialTransformer) | reproduce paper |
| [Visualize CNN saliency maps](Saliency) | visually reproduce | | [Visualize CNN saliency maps](Saliency) | visually reproduce |
......
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