from __future__ import print_function

import _init_paths
import caffe
from caffe.model_libs import *
from google.protobuf import text_format

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


# Add extra layers on top of a "base" network (e.g. VGGNet or Inception).
def AddExtraLayers(net, use_batchnorm=True, lr_mult=1):
    use_relu = True

    # Add additional convolutional layers.
    # 19 x 19
    from_layer = net.keys()[-1]

    # TODO(weiliu89): Construct the name using the last layer to avoid duplication.
    # 10 x 10
    out_layer = "conv6_1"
    ConvBNLayer(
        net,
        from_layer,
        out_layer,
        use_batchnorm,
        use_relu,
        256,
        1,
        0,
        1,
        lr_mult=lr_mult)

    from_layer = out_layer
    out_layer = "conv6_2"
    ConvBNLayer(
        net,
        from_layer,
        out_layer,
        use_batchnorm,
        use_relu,
        512,
        3,
        1,
        2,
        lr_mult=lr_mult)

    # 5 x 5
    from_layer = out_layer
    out_layer = "conv7_1"
    ConvBNLayer(
        net,
        from_layer,
        out_layer,
        use_batchnorm,
        use_relu,
        128,
        1,
        0,
        1,
        lr_mult=lr_mult)

    from_layer = out_layer
    out_layer = "conv7_2"
    ConvBNLayer(
        net,
        from_layer,
        out_layer,
        use_batchnorm,
        use_relu,
        256,
        3,
        1,
        2,
        lr_mult=lr_mult)

    # 3 x 3
    from_layer = out_layer
    out_layer = "conv8_1"
    ConvBNLayer(
        net,
        from_layer,
        out_layer,
        use_batchnorm,
        use_relu,
        128,
        1,
        0,
        1,
        lr_mult=lr_mult)

    from_layer = out_layer
    out_layer = "conv8_2"
    ConvBNLayer(
        net,
        from_layer,
        out_layer,
        use_batchnorm,
        use_relu,
        256,
        3,
        0,
        1,
        lr_mult=lr_mult)

    # 1 x 1
    from_layer = out_layer
    out_layer = "conv9_1"
    ConvBNLayer(
        net,
        from_layer,
        out_layer,
        use_batchnorm,
        use_relu,
        128,
        1,
        0,
        1,
        lr_mult=lr_mult)

    from_layer = out_layer
    out_layer = "conv9_2"
    ConvBNLayer(
        net,
        from_layer,
        out_layer,
        use_batchnorm,
        use_relu,
        256,
        3,
        0,
        1,
        lr_mult=lr_mult)

    return net


### Modify the following parameters accordingly ###
# Notice: we do evaluation by setting the solver parameters approximately.
# The reason that we do not use ./build/tools/caffe test ... is because it
# only supports testing for classification problem now.
# 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

YEAR = sys.argv[1]
# The database file for training data. Created by data/VOC{}/create_data.sh
train_data = "data/VOC{}/lmdb/VOC{}_trainval_lmdb".format(YEAR, YEAR)
# The database file for testing data. Created by data/VOC{}/create_data.sh
test_data = "data/VOC{}/lmdb/VOC{}_test_lmdb".format(YEAR, YEAR)
# Specify the batch sampler.
resize_width = 300
resize_height = 300
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,
    'mean_value': [104, 117, 123],
    '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,
        ],
    },
    'distort_param': {
        'brightness_prob': 0.5,
        'brightness_delta': 32,
        'contrast_prob': 0.5,
        'contrast_lower': 0.5,
        'contrast_upper': 1.5,
        'hue_prob': 0.5,
        'hue_delta': 18,
        'saturation_prob': 0.5,
        'saturation_lower': 0.5,
        'saturation_upper': 1.5,
        'random_order_prob': 0.0,
    },
    'expand_param': {
        'prob': 0.5,
        'max_expand_ratio': 4.0,
    },
    'emit_constraint': {
        'emit_type': caffe_pb2.EmitConstraint.CENTER,
    }
}
test_transform_param = {
    'mean_value': [104, 117, 123],
    'resize_param': {
        'prob': 1,
        'resize_mode': P.Resize.WARP,
        'height': resize_height,
        'width': resize_width,
        'interp_mode': [P.Resize.LINEAR],
    },
}

# If true, use batch norm for all newly added layers.
# Currently only the non batch norm version has been tested.
use_batchnorm = False
lr_mult = 1
# Use different initial learning rate.
if use_batchnorm:
    base_lr = 0.0004
else:
    # A learning rate for batch_size = 1, num_gpus = 1.
    base_lr = 0.00004

# The job name should be same as the name used in examples/ssd/ssd_pascal.py.
# job_name = "SSD_{}".format(resize)
job_name = sys.argv[2]
# The name of the model. Modify it if you want.
# model_name = "VGG_VOC{}_{}".format(YEAR,job_name)
model_name = "VGG_VOC{}".format(YEAR)

# Directory which stores the model .prototxt file.
save_dir = "output/{}_score_test".format(job_name)
# Directory which stores the snapshot of trained models.
snapshot_dir = "output/{}".format(job_name)
# Directory which stores the job script and log file.
job_dir = "experiments/logs/{}_score_test".format(job_name)
# Directory which stores the detection results.
output_result_dir = "data/VOCdevkit{}/results/VOC{}/Main/{}_score_test/".format(
    YEAR, YEAR, 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)

# Find most recent snapshot.
max_iter = 0
for file in os.listdir(snapshot_dir):
    if file.endswith(".caffemodel"):
        basename = os.path.splitext(file)[0]
        iter = int(basename.split("{}_iter_".format(model_name))[1])
        if iter > max_iter:
            max_iter = iter

if max_iter == 0:
    print("Cannot find snapshot in {}".format(snapshot_dir))
    sys.exit()

# Stores the test image names and sizes. Created by data/VOC{}/create_list.sh
name_size_file = "data/VOC{}/test_name_size.txt".format(YEAR)
# The resume model.
pretrain_model = "{}_iter_{}.caffemodel".format(snapshot_prefix, max_iter)
# Stores LabelMapItem.
label_map_file = "data/VOC{}/labelmap_voc.prototxt".format(YEAR)

# MultiBoxLoss parameters.
num_classes = 21
share_location = True
background_label_id = 0
train_on_diff_gt = True
normalization_mode = P.Loss.VALID
code_type = P.PriorBox.CENTER_SIZE
ignore_cross_boundary_bbox = False
mining_type = P.MultiBoxLoss.MAX_NEGATIVE
neg_pos_ratio = 3.
loc_weight = (neg_pos_ratio + 1.) / 4.
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,
    'ignore_cross_boundary_bbox': ignore_cross_boundary_bbox,
}
loss_param = {
    'normalization': normalization_mode,
}

# parameters for generating priors.
# minimum dimension of input image
min_dim = 300
# conv4_3 ==> 38 x 38
# fc7 ==> 19 x 19
# conv6_2 ==> 10 x 10
# conv7_2 ==> 5 x 5
# conv8_2 ==> 3 x 3
# conv9_2 ==> 1 x 1
mbox_source_layers = [
    'conv4_3', 'fc7', 'conv6_2', 'conv7_2', 'conv8_2', 'conv9_2'
]
# in percent %
min_ratio = 20
max_ratio = 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 * 10 / 100.] + min_sizes
max_sizes = [min_dim * 20 / 100.] + max_sizes
steps = [8, 16, 32, 64, 100, 300]
aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
# L2 normalize conv4_3.
normalizations = [20, -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"
gpus = sys.argv[3]
gpulist = gpus.split(",")
num_gpus = len(gpulist)

# The number does not matter since we do not do training with this script.
batch_size = 1
accum_batch_size = 1
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])

if normalization_mode == P.Loss.NONE:
    base_lr /= batch_size_per_device
elif normalization_mode == P.Loss.VALID:
    base_lr *= 25. / loc_weight
elif normalization_mode == P.Loss.FULL:
    # Roughly there are 2000 prior bboxes per image.
    # TODO(weiliu89): Estimate the exact # of priors.
    base_lr *= 2000.

# Evaluate on whole test set.
if int(YEAR) == 2007:
    num_test_image = 4952
elif int(YEAR) == 2010:
    num_test_image = 9637
elif int(YEAR) == 2012:
    num_test_image = 10991
else:
    print('unknow year: {}'.format(YEAR))
    exit(0)
test_batch_size = 8
# Ideally test_batch_size should be divisible by num_test_image,
# otherwise mAP will be slightly off the true value.
test_iter = int(math.ceil(float(num_test_image) / test_batch_size))

solver_param = {
    # Train parameters
    'base_lr': base_lr,
    'weight_decay': 0.0005,
    'lr_policy': "multistep",
    'stepvalue': [80000, 100000, 120000],
    'gamma': 0.1,
    'momentum': 0.9,
    'iter_size': iter_size,
    'max_iter': 0,
    'snapshot': 0,
    'display': 10,
    'average_loss': 10,
    # 'type': "SGD",
    'solver_mode': solver_mode,
    'device_id': device_id,
    'debug_info': False,
    'snapshot_after_train': False,
    # Test parameters
    'test_iter': [test_iter],
    'test_interval': 10000,
    'eval_type': "detection",
    'ap_version': "11point",
    'test_initialization': True,
    'show_per_class_result': True,
}

# 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
    },
    # To test the inference speed, we need comment save_out_param,
    # as the output IO will influence the speed.
    'save_output_param': {
        'output_directory': output_result_dir,
        'output_name_prefix': "comp3_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,
    # 'visualize': True,
    'visualize': False,
}

# 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)

VGGNetBody(
    net,
    from_layer='data',
    fully_conv=True,
    reduced=True,
    dilated=True,
    dropout=False)

AddExtraLayers(net, use_batchnorm, lr_mult=lr_mult)

mbox_layers = CreateMultiBoxHead(
    net,
    data_layer='data',
    from_layers=mbox_source_layers,
    use_batchnorm=use_batchnorm,
    min_sizes=min_sizes,
    max_sizes=max_sizes,
    aspect_ratios=aspect_ratios,
    steps=steps,
    normalizations=normalizations,
    num_classes=num_classes,
    share_location=share_location,
    flip=flip,
    clip=clip,
    prior_variance=prior_variance,
    kernel_size=3,
    pad=1,
    lr_mult=lr_mult)

# 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)

VGGNetBody(
    net,
    from_layer='data',
    fully_conv=True,
    reduced=True,
    dilated=True,
    dropout=False)

AddExtraLayers(net, use_batchnorm, lr_mult=lr_mult)

mbox_layers = CreateMultiBoxHead(
    net,
    data_layer='data',
    from_layers=mbox_source_layers,
    use_batchnorm=use_batchnorm,
    min_sizes=min_sizes,
    max_sizes=max_sizes,
    aspect_ratios=aspect_ratios,
    steps=steps,
    normalizations=normalizations,
    num_classes=num_classes,
    share_location=share_location,
    flip=flip,
    clip=clip,
    prior_variance=prior_variance,
    kernel_size=3,
    pad=1,
    lr_mult=lr_mult)

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]

mbox_layers.append(net.data)
net.detection_out = L.DetectionOutput(
    *mbox_layers,
    detection_output_param=det_out_param,
    transform_param=test_transform_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 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)

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