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load-resnet.py
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load-resnet.py
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#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# File: load-resnet.py
# Author: Eric Yujia Huang [email protected]
# Yuxin Wu <[email protected]>
import cv2
import tensorflow as tf
import argparse
import os
import re
import numpy as np
import six
from six.moves import zip
from tensorflow.contrib.layers import variance_scaling_initializer
from tensorpack import *
from tensorpack.utils import logger
from tensorpack.utils.stats import RatioCounter
from tensorpack.tfutils.symbolic_functions import *
from tensorpack.tfutils.summary import *
from tensorpack.dataflow.dataset import ILSVRCMeta
MODEL_DEPTH = None
class Model(ModelDesc):
def _get_inputs(self):
return [InputDesc(tf.float32, [None, 224, 224, 3], 'input'),
InputDesc(tf.int32, [None], 'label')]
def _build_graph(self, input_vars):
image, label = input_vars
def shortcut(l, n_in, n_out, stride):
if n_in != n_out:
l = Conv2D('convshortcut', l, n_out, 1, stride=stride)
return BatchNorm('bnshortcut', l)
else:
return l
def bottleneck(l, ch_out, stride, preact):
ch_in = l.get_shape().as_list()[-1]
input = l
if preact == 'both_preact':
l = tf.nn.relu(l, name='preact-relu')
input = l
l = Conv2D('conv1', l, ch_out, 1, stride=stride)
l = BatchNorm('bn1', l)
l = tf.nn.relu(l)
l = Conv2D('conv2', l, ch_out, 3)
l = BatchNorm('bn2', l)
l = tf.nn.relu(l)
l = Conv2D('conv3', l, ch_out * 4, 1)
l = BatchNorm('bn3', l) # put bn at the bottom
return l + shortcut(input, ch_in, ch_out * 4, stride)
def layer(l, layername, features, count, stride, first=False):
with tf.variable_scope(layername):
with tf.variable_scope('block0'):
l = bottleneck(l, features, stride,
'no_preact' if first else 'both_preact')
for i in range(1, count):
with tf.variable_scope('block{}'.format(i)):
l = bottleneck(l, features, 1, 'both_preact')
return l
cfg = {
50: ([3, 4, 6, 3]),
101: ([3, 4, 23, 3]),
152: ([3, 8, 36, 3])
}
defs = cfg[MODEL_DEPTH]
with argscope(Conv2D, nl=tf.identity, use_bias=False,
W_init=variance_scaling_initializer(mode='FAN_OUT')):
# tensorflow with padding=SAME will by default pad [2,3] here.
# but caffe conv with stride will pad [3,3]
image = tf.pad(image, [[0, 0], [3, 3], [3, 3], [0, 0]])
fc1000 = (LinearWrap(image)
.Conv2D('conv0', 64, 7, stride=2, nl=BNReLU, padding='VALID')
.MaxPooling('pool0', shape=3, stride=2, padding='SAME')
.apply(layer, 'group0', 64, defs[0], 1, first=True)
.apply(layer, 'group1', 128, defs[1], 2)
.apply(layer, 'group2', 256, defs[2], 2)
.apply(layer, 'group3', 512, defs[3], 2)
.tf.nn.relu()
.GlobalAvgPooling('gap')
.FullyConnected('fc1000', 1000, nl=tf.identity)())
prob = tf.nn.softmax(fc1000, name='prob')
nr_wrong = prediction_incorrect(fc1000, label, name='wrong-top1')
nr_wrong = prediction_incorrect(fc1000, label, 5, name='wrong-top5')
def get_inference_augmentor():
# load ResNet mean from Kaiming:
# from tensorpack.utils.loadcaffe import get_caffe_pb
# obj = get_caffe_pb().BlobProto()
# obj.ParseFromString(open('ResNet_mean.binaryproto').read())
# pp_mean_224 = np.array(obj.data).reshape(3, 224, 224).transpose(1,2,0)
meta = ILSVRCMeta()
pp_mean = meta.get_per_pixel_mean()
pp_mean_224 = pp_mean[16:-16, 16:-16, :]
transformers = imgaug.AugmentorList([
imgaug.ResizeShortestEdge(256),
imgaug.CenterCrop((224, 224)),
imgaug.MapImage(lambda x: x - pp_mean_224),
])
return transformers
def run_test(params, input):
pred_config = PredictConfig(
model=Model(),
session_init=DictRestore(params),
input_names=['input'],
output_names=['prob']
)
predict_func = OfflinePredictor(pred_config)
prepro = get_inference_augmentor()
im = cv2.imread(input).astype('float32')
im = prepro.augment(im)
im = np.reshape(im, (1, 224, 224, 3))
outputs = predict_func([im])
prob = outputs[0]
ret = prob[0].argsort()[-10:][::-1]
print(ret)
meta = ILSVRCMeta().get_synset_words_1000()
print([meta[k] for k in ret])
def eval_on_ILSVRC12(params, data_dir):
ds = dataset.ILSVRC12(data_dir, 'val', shuffle=False, dir_structure='train')
ds = AugmentImageComponent(ds, get_inference_augmentor())
ds = BatchData(ds, 128, remainder=True)
pred_config = PredictConfig(
model=Model(),
session_init=DictRestore(params),
input_names=['input', 'label'],
output_names=['wrong-top1', 'wrong-top5']
)
pred = SimpleDatasetPredictor(pred_config, ds)
acc1, acc5 = RatioCounter(), RatioCounter()
for o in pred.get_result():
batch_size = o[0].shape[0]
acc1.feed(o[0].sum(), batch_size)
acc5.feed(o[1].sum(), batch_size)
print("Top1 Error: {}".format(acc1.ratio))
print("Top5 Error: {}".format(acc5.ratio))
def name_conversion(caffe_layer_name):
""" Convert a caffe parameter name to a tensorflow parameter name as
defined in the above model """
# beginning & end mapping
NAME_MAP = {'bn_conv1/beta': 'conv0/bn/beta',
'bn_conv1/gamma': 'conv0/bn/gamma',
'bn_conv1/mean/EMA': 'conv0/bn/mean/EMA',
'bn_conv1/variance/EMA': 'conv0/bn/variance/EMA',
'conv1/W': 'conv0/W', 'conv1/b': 'conv0/b',
'fc1000/W': 'fc1000/W', 'fc1000/b': 'fc1000/b'}
if caffe_layer_name in NAME_MAP:
return NAME_MAP[caffe_layer_name]
s = re.search('([a-z]+)([0-9]+)([a-z]+)_', caffe_layer_name)
if s is None:
s = re.search('([a-z]+)([0-9]+)([a-z]+)([0-9]+)_', caffe_layer_name)
layer_block_part1 = s.group(3)
layer_block_part2 = s.group(4)
assert layer_block_part1 in ['a', 'b']
layer_block = 0 if layer_block_part1 == 'a' else int(layer_block_part2)
else:
layer_block = ord(s.group(3)) - ord('a')
layer_type = s.group(1)
layer_group = s.group(2)
layer_branch = int(re.search('_branch([0-9])', caffe_layer_name).group(1))
assert layer_branch in [1, 2]
if layer_branch == 2:
layer_id = re.search('_branch[0-9]([a-z])/', caffe_layer_name).group(1)
layer_id = ord(layer_id) - ord('a') + 1
TYPE_DICT = {'res': 'conv', 'bn': 'bn'}
tf_name = caffe_layer_name[caffe_layer_name.index('/'):]
layer_type = TYPE_DICT[layer_type] + \
(str(layer_id) if layer_branch == 2 else 'shortcut')
tf_name = 'group{}/block{}/{}'.format(
int(layer_group) - 2, layer_block, layer_type) + tf_name
return tf_name
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', required=True,
help='.npy model file generated by tensorpack.utils.loadcaffe')
parser.add_argument('-d', '--depth', help='resnet depth', required=True, type=int, choices=[50, 101, 152])
parser.add_argument('--input', help='an input image')
parser.add_argument('--eval', help='ILSVRC dir to run validation on')
args = parser.parse_args()
assert args.input or args.eval, "Choose either input or eval!"
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
MODEL_DEPTH = args.depth
param = np.load(args.load, encoding='latin1').item()
resnet_param = {}
for k, v in six.iteritems(param):
try:
newname = name_conversion(k)
except:
logger.error("Exception when processing caffe layer {}".format(k))
raise
logger.info("Name Transform: " + k + ' --> ' + newname)
resnet_param[newname] = v
if args.eval:
eval_on_ILSVRC12(resnet_param, args.eval)
else:
run_test(resnet_param, args.input)