when train in windows,the error:AttributeError: 'LayerParameter' object has no attribute 'min_size' about pelee

[Belinda-great]

The SSD is https://github.com/conner99/caffe. The train_voc.py is:`from future import print_function
import argparse
import caffe
from caffe import layers as L
from caffe import params as P
from caffe.proto import caffe_pb2

from caffe.model_libs import CreateAnnotatedDataLayer, check_if_exist, make_if_not_exist
from feature_extractor import CreateMultiBoxHead
from google.protobuf import text_format

from feature_extractor import VGG_RUN, VGG_SSD, Pelee

import math
import os
import shutil
import stat
import subprocess
import sys

model_meta = {
'pelee':Pelee,
'ssd':VGG_SSD,
'run':VGG_RUN
}

Default_weights_file = "F:/data/people/pelee_voc/models/peleenet_inet_acc7243.caffemodel"

parser = argparse.ArgumentParser(description='Pelee Training')

parser.add_argument('--arch', '-a', metavar='ARCH', default='pelee',
choices=model_meta.keys(),
help='model architecture: ' +
' | '.join(model_meta.keys()) +
' (default: pelee)')
parser.add_argument('--lr', '--learning-rate', default=0.005, type=float,
metavar='LR', help='initial learning rate (default: 0.005)')
parser.add_argument('--weight-decay', '--wd', default=0.0005, type=float,
metavar='W', help='weight decay (default: 5e-4)')
parser.add_argument('-b', '--batch-size', default=32, type=int,
metavar='N', help='mini-batch size (default: 32)')
parser.add_argument('-k', '--kernel-size', default=1, type=int,
metavar='K', help='kernel size for CreateMultiBoxHead (default: 1)')
parser.add_argument('--image-size', default=304, type=int,
metavar='IMAGE_SIZE', help='image size (default: 304)')
parser.add_argument('--weights', default=Default_weights_file, type=str,
metavar='WEIGHTS', help='initial weights file (default: {})'.format(Default_weights_file))
parser.add_argument('--run-later', dest='run_soon', action='store_false',
help='start training later after generating all files')
parser.add_argument('--step-value', '-s', nargs='+', type=int, default=[80000, 100000, 120000],
metavar='S', help='step value (default: [80000, 100000, 120000])')
parser.add_argument('--posfix', '-p', metavar='POSFIX', default='', type=str)
parser.set_defaults(run_soon=True)

args = parser.parse_args()

print( 'args:',args)

NetBuilder = model_meta[args.arch]
mbox_source_layers = NetBuilder.mbox_source_layers
model_prefix = '{}{}'.format(args.arch, args.posfix)

Modify the following parameters accordingly

The directory which contains the caffe code.

We assume you are running the script at the CAFFE_ROOT.

caffe_root = os.getcwd()

Set true if you want to start training right after generating all files.

run_soon = True

run_soon = args.run_soon

Set true if you want to load from most recently saved snapshot.

Otherwise, we will load from the pretrain_model defined below.

resume_training = True

If true, Remove old model files.

remove_old_models = False

pretrain_model = args.weights

The database file for training data. Created by data/VOC0712/create_data.sh

train_data = "F:/data/people/mydata_trainval_lmdb"

The database file for testing data. Created by data/VOC0712/create_data.sh

test_data = "F:/data/people/mydata_test_lmdb"

Specify the batch sampler.

resize_width = args.image_size
resize_height = args.image_size
resize = "{}x{}".format(resize_width, resize_height)
batch_sampler = [
{
'sampler': {
},
'max_trials': 1,
'max_sample': 1,
},
{
'sampler': {
'min_scale': 0.3,
'max_scale': 1.0,
'min_aspect_ratio': 0.5,
'max_aspect_ratio': 2.0,
},
'sample_constraint': {
'min_jaccard_overlap': 0.1,
},
'max_trials': 50,
'max_sample': 1,
},
{
'sampler': {
'min_scale': 0.3,
'max_scale': 1.0,
'min_aspect_ratio': 0.5,
'max_aspect_ratio': 2.0,
},
'sample_constraint': {
'min_jaccard_overlap': 0.3,
},
'max_trials': 50,
'max_sample': 1,
},
{
'sampler': {
'min_scale': 0.3,
'max_scale': 1.0,
'min_aspect_ratio': 0.5,
'max_aspect_ratio': 2.0,
},
'sample_constraint': {
'min_jaccard_overlap': 0.5,
},
'max_trials': 50,
'max_sample': 1,
},
{
'sampler': {
'min_scale': 0.3,
'max_scale': 1.0,
'min_aspect_ratio': 0.5,
'max_aspect_ratio': 2.0,
},
'sample_constraint': {
'min_jaccard_overlap': 0.7,
},
'max_trials': 50,
'max_sample': 1,
},
{
'sampler': {
'min_scale': 0.3,
'max_scale': 1.0,
'min_aspect_ratio': 0.5,
'max_aspect_ratio': 2.0,
},
'sample_constraint': {
'min_jaccard_overlap': 0.9,
},
'max_trials': 50,
'max_sample': 1,
},
{
'sampler': {
'min_scale': 0.3,
'max_scale': 1.0,
'min_aspect_ratio': 0.5,
'max_aspect_ratio': 2.0,
},
'sample_constraint': {
'max_jaccard_overlap': 1.0,
},
'max_trials': 50,
'max_sample': 1,
},
]

train_transform_param = {
'mirror': True,
'scale': 0.017,
'mean_value': [103.94,116.78,123.68],
'resize_param': {
'prob': 1,
'resize_mode': P.Resize.WARP,
'height': resize_height,
'width': resize_width,
'interp_mode': [
P.Resize.LINEAR,
P.Resize.AREA,
P.Resize.NEAREST,
P.Resize.CUBIC,
P.Resize.LANCZOS4,
],
},
'emit_constraint': {
'emit_type': caffe_pb2.EmitConstraint.CENTER,
}
}
test_transform_param = {
'scale': 0.017,
'mean_value': [103.94,116.78,123.68],
'resize_param': {
'prob': 1,
'resize_mode': P.Resize.WARP,
'height': resize_height,
'width': resize_width,
'interp_mode': [P.Resize.LINEAR],
},
}

Modify the job name if you want.

job_name = "SSD_{}".format(resize)

The name of the model. Modify it if you want.

model_name = "{}_{}".format(model_prefix, job_name)

Directory which stores the model .prototxt file.

save_dir = "F:/data/people/pelee_voc/models/{}".format(job_name)

Directory which stores the snapshot of models.

snapshot_dir = "F:/data/people/pelee_voc/models/{}".format(job_name)

Directory which stores the job script and log file.

job_dir = "F:/work/caffe-ssd-microsoft_peleenet/jobs/{}".format(job_name)

Directory which stores the detection results.

output_result_dir = "{}/Main".format( job_name)

model definition files.

train_net_file = "{}/train.prototxt".format(save_dir)
test_net_file = "{}/test.prototxt".format(save_dir)
deploy_net_file = "{}/deploy.prototxt".format(save_dir)
solver_file = "{}/solver.prototxt".format(save_dir)

snapshot prefix.

snapshot_prefix = "{}/{}".format(snapshot_dir, model_name)

job script path.

##job_file = "{}/{}.sh".format(job_dir, model_name)
job_file = "{}/{}_train.bat".format(job_dir, model_name)

Stores the test image names and sizes. Created by data/VOC0712/create_list.sh

name_size_file = "F:/data/people/test_name_size.txt"

pretrain_model = "F:/data/people/pelee_voc/models/pelee_304x304_acc7094.caffemodel"

Stores LabelMapItem.

label_map_file = "F:/data/people/labelmap_mydata.prototxt"

MultiBoxLoss parameters.

num_classes = 2
share_location = True
background_label_id=0
train_on_diff_gt = True
normalization_mode = P.Loss.VALID
code_type = P.PriorBox.CENTER_SIZE
neg_pos_ratio = 3.
loc_weight = (neg_pos_ratio + 1.) / 4.
mining_type = 1 ##P.MultiBoxLoss.MAX_NEGATIVE
multibox_loss_param = {
'loc_loss_type': P.MultiBoxLoss.SMOOTH_L1,
'conf_loss_type': P.MultiBoxLoss.SOFTMAX,
'loc_weight': loc_weight,
'num_classes': num_classes,
'share_location': share_location,
'match_type': P.MultiBoxLoss.PER_PREDICTION,
'overlap_threshold': 0.5,
'use_prior_for_matching': True,
'background_label_id': background_label_id,
'use_difficult_gt': train_on_diff_gt,
'mining_type': mining_type,
'neg_pos_ratio': neg_pos_ratio,
'neg_overlap': 0.5,
'code_type': code_type,
}
loss_param = {
'normalization': normalization_mode,
}

parameters for generating priors.

minimum dimension of input image

min_dim = args.image_size

in percent %

min_ratio = 15 ###20
max_ratio = 95 ##90
step = int(math.floor((max_ratio - min_ratio) / (len(mbox_source_layers) - 2)))
min_sizes = []
max_sizes = []
for ratio in xrange(min_ratio, max_ratio + 1, step):
min_sizes.append(min_dim * ratio / 100.)
max_sizes.append(min_dim * (ratio + step) / 100.)
min_sizes = [min_dim * 7 / 100.] + min_sizes #10
max_sizes = [min_dim * 20 / 100.] + max_sizes

aspect_ratios = [[2,3], [2, 3], [2, 3], [2, 3], [2,3], [2,3]]
normalizations = None #[-1, -1, -1, -1, -1, -1]

variance used to encode/decode prior bboxes.

if code_type == P.PriorBox.CENTER_SIZE:
prior_variance = [0.1, 0.1, 0.2, 0.2]
else:
prior_variance = [0.1]
flip = True
clip = False

Solver parameters.

Defining which GPUs to use.

gpus = "0"
gpulist = gpus.split(",")
num_gpus = len(gpulist)

Divide the mini-batch to different GPUs.

batch_size = 4 ####32
accum_batch_size = args.batch_size
iter_size = accum_batch_size / batch_size
solver_mode = P.Solver.CPU
device_id = 0
batch_size_per_device = batch_size
if num_gpus > 0:
batch_size_per_device = int(math.ceil(float(batch_size) / num_gpus))
iter_size = int(math.ceil(float(accum_batch_size) / (batch_size_per_device * num_gpus)))
solver_mode = P.Solver.GPU
device_id = int(gpulist[0])

Evaluate on whole test set.

num_test_image = 4952
test_batch_size = 1
test_iter = num_test_image / test_batch_size

solver_param = {

'base_lr': args.lr,
'weight_decay': args.weight_decay,
'lr_policy': "multistep",
'stepvalue': args.step_value,
'gamma': 0.1,
'momentum': 0.9,
'iter_size': iter_size,
'max_iter': args.step_value[-1],
'snapshot': 2000,
'display': 10,
'average_loss': 10,
'type': "SGD",
'solver_mode': solver_mode,
'device_id': device_id,
'debug_info': False,
'snapshot_after_train': True,
# Test parameters
'test_iter': [test_iter],
'test_interval': 2000,
'eval_type': "detection",
'ap_version': "11point",
'test_initialization': False,
}

parameters for generating detection output.

det_out_param = {
'num_classes': num_classes,
'share_location': share_location,
'background_label_id': background_label_id,
'nms_param': {'nms_threshold': 0.45, 'top_k': 400},
'save_output_param': {
'output_directory': output_result_dir,
'output_name_prefix': "comp4_det_test_",
'output_format': "VOC",
'label_map_file': label_map_file,
'name_size_file': name_size_file,
'num_test_image': num_test_image,
},
'keep_top_k': 200,
'confidence_threshold': 0.01,
'code_type': code_type,
}

parameters for evaluating detection results.

det_eval_param = {
'num_classes': num_classes,
'background_label_id': background_label_id,
'overlap_threshold': 0.5,
'evaluate_difficult_gt': False,
'name_size_file': name_size_file,
}

Hopefully you don't need to change the following

Check file.

check_if_exist(train_data)
check_if_exist(test_data)
check_if_exist(label_map_file)
check_if_exist(pretrain_model)
make_if_not_exist(save_dir)
make_if_not_exist(job_dir)
make_if_not_exist(snapshot_dir)

Create train net.

net = caffe.NetSpec()
net.data, net.label = CreateAnnotatedDataLayer(train_data, batch_size=batch_size_per_device,
train=True, output_label=True, label_map_file=label_map_file,
transform_param=train_transform_param, batch_sampler=batch_sampler)

NetBuilder(net, from_layer='data')

Don't use batch norm for location/confidence prediction layers.

mbox_layers = CreateMultiBoxHead(net, data_layer='data', from_layers=mbox_source_layers,
use_batchnorm=False, min_sizes=min_sizes, max_sizes=max_sizes, normalizations=normalizations,
aspect_ratios=aspect_ratios, num_classes=num_classes, share_location=share_location,
flip=flip, clip=clip, prior_variance=prior_variance, kernel_size=args.kernel_size, pad=int(args.kernel_size/2))

Create the MultiBoxLossLayer.

name = "mbox_loss"
mbox_layers.append(net.label)
net[name] = L.MultiBoxLoss(*mbox_layers, multibox_loss_param=multibox_loss_param,
loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value('TRAIN')),
propagate_down=[True, True, False, False])

with open(train_net_file, 'w') as f:
print('name: "{}_train"'.format(model_name), file=f)
print(net.to_proto(), file=f)
shutil.copy(train_net_file, job_dir)

Create test net.

net = caffe.NetSpec()
net.data, net.label = CreateAnnotatedDataLayer(test_data, batch_size=test_batch_size,
train=False, output_label=True, label_map_file=label_map_file,
transform_param=test_transform_param)

NetBuilder(net, from_layer='data')

Don't use batch norm for location/confidence prediction layers.

mbox_layers = CreateMultiBoxHead(net, data_layer='data', from_layers=mbox_source_layers,
use_batchnorm=False, min_sizes=min_sizes, max_sizes=max_sizes,normalizations=normalizations,
aspect_ratios=aspect_ratios, num_classes=num_classes, share_location=share_location,
flip=flip, clip=clip, prior_variance=prior_variance, kernel_size=args.kernel_size, pad=int(args.kernel_size/2))

conf_name = "mbox_conf"
if multibox_loss_param["conf_loss_type"] == P.MultiBoxLoss.SOFTMAX:
reshape_name = "{}_reshape".format(conf_name)
net[reshape_name] = L.Reshape(net[conf_name], shape=dict(dim=[0, -1, num_classes]))
softmax_name = "{}_softmax".format(conf_name)
net[softmax_name] = L.Softmax(net[reshape_name], axis=2)
flatten_name = "{}_flatten".format(conf_name)
net[flatten_name] = L.Flatten(net[softmax_name], axis=1)
mbox_layers[1] = net[flatten_name]
elif multibox_loss_param["conf_loss_type"] == P.MultiBoxLoss.LOGISTIC:
sigmoid_name = "{}_sigmoid".format(conf_name)
net[sigmoid_name] = L.Sigmoid(net[conf_name])
mbox_layers[1] = net[sigmoid_name]

net.detection_out = L.DetectionOutput(*mbox_layers,
detection_output_param=det_out_param,
include=dict(phase=caffe_pb2.Phase.Value('TEST')))
net.detection_eval = L.DetectionEvaluate(net.detection_out, net.label,
detection_evaluate_param=det_eval_param,
include=dict(phase=caffe_pb2.Phase.Value('TEST')))

with open(test_net_file, 'w') as f:
print('name: "{}_test"'.format(model_name), file=f)
print(net.to_proto(), file=f)
shutil.copy(test_net_file, job_dir)

Create deploy net.

Remove the first and last layer from test net.

deploy_net = net
with open(deploy_net_file, 'w') as f:
net_param = deploy_net.to_proto()
# Remove the first (AnnotatedData) and last (DetectionEvaluate) layer from test net.
del net_param.layer[0]
del net_param.layer[-1]
net_param.name = '{}_deploy'.format(model_name)
net_param.input.extend(['data'])
net_param.input_shape.extend([
caffe_pb2.BlobShape(dim=[1, 3, resize_height, resize_width])])
print(net_param, file=f)
shutil.copy(deploy_net_file, job_dir)

# Create deploy net for CoreML.

coreml_deploy_net_file = "{}/coreml_deploy.prototxt".format(save_dir)

net = caffe.NetSpec()

net.data, net.label = CreateAnnotatedDataLayer(test_data, batch_size=test_batch_size,

train=False, output_label=True, label_map_file=label_map_file,

transform_param=test_transform_param)

NetBuilder(net, from_layer='data')

conf_layers, loc_layers = CreateMultiBoxHeadForCoreML(net, data_layer='data', from_layers=mbox_source_layers,

use_batchnorm=use_batchnorm, min_sizes=min_sizes, max_sizes=max_sizes,normalizations=normalizations,

aspect_ratios=aspect_ratios, num_classes=num_classes, share_location=share_location,

flip=flip, clip=clip, prior_variance=prior_variance, kernel_size=args.kernel_size, pad=int(args.kernel_size/2))

with open(coreml_deploy_net_file, 'w') as f:

net_param = net.to_proto()

# Remove the first (AnnotatedData) from test net.

del net_param.layer[0]

net_param.name = '{}_{}_coreml'.format(model_prefix,resize)

net_param.input.extend(['data'])

net_param.input_shape.extend([

caffe_pb2.BlobShape(dim=[1, 3, resize_height, resize_width])])

print(net_param, file=f)

shutil.copy(deploy_net_file, job_dir)

Create solver.

solver = caffe_pb2.SolverParameter(
train_net=train_net_file,
test_net=[test_net_file],
snapshot_prefix=snapshot_prefix,
**solver_param)

with open(solver_file, 'w') as f:
print(solver, file=f)
shutil.copy(solver_file, job_dir)

max_iter = 0

Find most recent snapshot.

for file in os.listdir(snapshot_dir):
if file.endswith(".solverstate"):
basename = os.path.splitext(file)[0]
iter = int(basename.split("{}iter".format(model_name))[1])
if iter > max_iter:
max_iter = iter

train_src_param = '--weights="{}" \\n'.format(pretrain_model)
if resume_training:
if max_iter > 0:
train_src_param = '--snapshot="{}iter{}.solverstate" \\n'.format(snapshot_prefix, max_iter)

if remove_old_models:

Remove any snapshots smaller than max_iter.

for file in os.listdir(snapshot_dir):
if file.endswith(".solverstate"):
basename = os.path.splitext(file)[0]
iter = int(basename.split("{}iter".format(model_name))[1])
if max_iter > iter:
os.remove("{}/{}".format(snapshot_dir, file))
if file.endswith(".caffemodel"):
basename = os.path.splitext(file)[0]
iter = int(basename.split("{}iter".format(model_name))[1])
if max_iter > iter:
os.remove("{}/{}".format(snapshot_dir, file))

Create job file.

with open(job_file, 'w') as f:
f.write('cd {}\n'.format(caffe_root))
f.write('./build/tools/caffe train \\n')
f.write('--solver="{}" \\n'.format(solver_file))
f.write(train_src_param)
if solver_param['solver_mode'] == P.Solver.GPU:
f.write('--gpu {} 2>&1 | tee {}/{}.log\n'.format(gpus, job_dir, model_name))
else:
f.write('2>&1 | tee {}/{}.log\n'.format(job_dir, model_name))

Copy the python script to job_dir.

py_file = os.path.abspath(file)
shutil.copy(py_file, job_dir)

# # Run the job.

os.chmod(job_file, stat.S_IRWXU)
if run_soon:
subprocess.call(job_file, shell=True)
`

[ujsyehao]

You should clone weiliu's ssd-caffe version.

[Belinda-great]

@ujsyehao Thanks! I will try it.