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seminar-breakout
Commit 536c7587 authored by Patrick Wieschollek's avatar Patrick Wieschollek Committed by Yuxin Wu
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rewrite VGG16 as well (#835)

parent 75c857db
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examples/CaffeModels/load-vgg16.py
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#!/usr/bin/env python #!/usr/bin/env python
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
# File: load-vgg16.py # File: load-vgg16.py
# Author: Yuxin Wu
from __future__ import print_function from __future__ import print_function
import cv2 import cv2
import tensorflow as tf import tensorflow as tf
import numpy as np import numpy as np
import os import os
import six
import argparse import argparse
from tensorpack import * from tensorpack import *
from tensorpack.tfutils.symbolic_functions import *
from tensorpack.tfutils.summary import *
from tensorpack.dataflow.dataset import ILSVRCMeta from tensorpack.dataflow.dataset import ILSVRCMeta
enable_argscope_for_module(tf.layers)
def tower_func(image): def tower_func(image):
with argscope(Conv2D, kernel_size=3, activation=tf.nn.relu): is_training = get_current_tower_context().is_training
logits = (LinearWrap(image)
.Conv2D('conv1_1', 64) with argscope([tf.layers.conv2d], kernel_size=3, activation=tf.nn.relu, padding='same'):
.Conv2D('conv1_2', 64) x = image
.MaxPooling('pool1', 2) x = tf.layers.conv2d(x, 64, name='conv1_1')
# 112 x = tf.layers.conv2d(x, 64, name='conv1_2')
.Conv2D('conv2_1', 128) x = tf.layers.max_pooling2d(x, 2, 2, name='pool1')
.Conv2D('conv2_2', 128)
.MaxPooling('pool2', 2) x = tf.layers.conv2d(x, 128, name='conv2_1')
# 56 x = tf.layers.conv2d(x, 128, name='conv2_2')
.Conv2D('conv3_1', 256) x = tf.layers.max_pooling2d(x, 2, 2, name='pool2')
.Conv2D('conv3_2', 256)
.Conv2D('conv3_3', 256) x = tf.layers.conv2d(x, 256, name='conv3_1')
.MaxPooling('pool3', 2) x = tf.layers.conv2d(x, 256, name='conv3_2')
# 28 x = tf.layers.conv2d(x, 256, name='conv3_3')
.Conv2D('conv4_1', 512) x = tf.layers.max_pooling2d(x, 2, 2, name='pool3')
.Conv2D('conv4_2', 512)
.Conv2D('conv4_3', 512) x = tf.layers.conv2d(x, 512, name='conv4_1')
.MaxPooling('pool4', 2) x = tf.layers.conv2d(x, 512, name='conv4_2')
# 14 x = tf.layers.conv2d(x, 512, name='conv4_3')
.Conv2D('conv5_1', 512) x = tf.layers.max_pooling2d(x, 2, 2, name='pool4')
.Conv2D('conv5_2', 512)
.Conv2D('conv5_3', 512) x = tf.layers.conv2d(x, 512, name='conv5_1')
.MaxPooling('pool5', 2) x = tf.layers.conv2d(x, 512, name='conv5_2')
# 7 x = tf.layers.conv2d(x, 512, name='conv5_3')
.FullyConnected('fc6', 4096, activation=tf.nn.relu) x = tf.layers.max_pooling2d(x, 2, 2, name='pool5')
.Dropout('drop0', 0.5) x = tf.layers.flatten(x, name='flatten')
.FullyConnected('fc7', 4096, activation=tf.nn.relu)
.Dropout('drop1', 0.5) x = tf.layers.dense(x, 4096, activation=tf.nn.relu, name='fc6')
.FullyConnected('fc8', 1000)()) x = tf.layers.dropout(x, rate=0.5, name='drop0', training=is_training)
x = tf.layers.dense(x, 4096, activation=tf.nn.relu, name='fc7')
x = tf.layers.dropout(x, rate=0.5, name='drop1', training=is_training)
logits = tf.layers.dense(x, 1000, activation=tf.identity, name='fc8')
tf.nn.softmax(logits, name='prob') tf.nn.softmax(logits, name='prob')
def run_test(path, input): def run_test(path, input):
param_dict = dict(np.load(path)) param_dict = dict(np.load(path))
param_dict = {k.replace('/W', '/kernel').replace('/b', '/bias'): v for k, v in six.iteritems(param_dict)}
predict_func = OfflinePredictor(PredictConfig( predict_func = OfflinePredictor(PredictConfig(
inputs_desc=[InputDesc(tf.float32, (None, 224, 224, 3), 'input')], inputs_desc=[InputDesc(tf.float32, (None, 224, 224, 3), 'input')],
tower_func=tower_func, tower_func=tower_func,
......
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