2020-1-capstone-design1 / PET_Project1

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김성주
95f7f316 1 parent 09bd413c

ipynb version for google colab & fixed more errors

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Showing 6 changed files with 2019 additions and 29 deletions
......@@ -24,10 +24,10 @@ anchor_path = data_path + 'yolo_anchors.txt' # The path of the anchor txt file.
class_name_path = data_path + 'classes.txt' # The path of the class names.
### Training releated numbers
batch_size = 6
batch_size = 10
img_size = [416, 416] # Images will be resized to `img_size` and fed to the network, size format: [width, height]
letterbox_resize = True # Whether to use the letterbox resize, i.e., keep the original aspect ratio in the resized image.
total_epoches = 50
total_epoches = 20
train_evaluation_step = 10 # Evaluate on the training batch after some steps.
val_evaluation_epoch = 2 # Evaluate on the whole validation dataset after some epochs. Set to None to evaluate every epoch.
save_epoch = 5 # Save the model after some epochs.
......@@ -73,7 +73,7 @@ use_label_smooth = True # Whether to use class label smoothing strategy.
use_focal_loss = True # Whether to apply focal loss on the conf loss.
use_mix_up = True # Whether to use mix up data augmentation strategy.
use_warm_up = True # whether to use warm up strategy to prevent from gradient exploding.
warm_up_epoch = 3 # Warm up training epoches. Set to a larger value if gradient explodes.
warm_up_epoch = 2 # Warm up training epoches. Set to a larger value if gradient explodes.
### some constants in validation
# nms
......
......@@ -2,8 +2,13 @@ changes from https://github.com/wizyoung/YOLOv3_TensorFlow
by Seongju Kim, kareus1@khu.ac.kr
I only tested in colab environment yet (2020.05.16),
so let me know if there are some errors/problems in python code version
(##last changed: 2020.05.16)
1] changed TextLineDataset to TFRecordDataset. (also changed data parsing in data utils and eval utils)
2] fixed restore-does-not-exist problem in train/eval mode
3] fixed saver to save the parameter only when save-optimizer option is true
4] changed parameter 'mode' to bool value 'is_training' in data util functions (string value 'mode' is passed as byte string, so functions do not evaluate if-clauses as expected. ex) 'train' != b'train')
5] wrote TFRecord binary iterator, which runs without tf session (references: https://github.com/pgmmpk/tfrecord )
\ No newline at end of file
5] wrote TFRecord binary iterator, which runs without tf session (references: https://github.com/pgmmpk/tfrecord )
6] removed logging/tenorboard summary code. (I will add it later if necessary)
\ No newline at end of file
......
......@@ -9,22 +9,20 @@ import random
PY_VERSION = sys.version_info[0]
iter_cnt = 0
FEATURE_DESCRIPTION = {
'index': tf.FixedLenFeature([], tf.int64),
'image': tf.FixedLenFeature([], tf.string),
'width': tf.FixedLenFeature([], tf.int64),
'height': tf.FixedLenFeature([], tf.int64),
'boxes': tf.VarLenFeature(tf.int64)
}
def _parse_tfrecord(data):
example = tf.train.Example()
example.ParseFromString(data)
features = example.features.feature
return features
def parse_tfrecord(data):
# tfrecord parser for TFRecordDataset (raw data)
features = tf.parse_single_example(data, FEATURE_DESCRIPTION)
index = int(features['index'])
encoded_image = np.frombuffer(features['image'], dtype = np.uint8)
width = int(features['width'])
height = int(features['height'])
boxes = features['boxes'].eval()
features = _parse_tfrecord(data)
index = features['index'].int64_list.value[0]
encoded_image = np.frombuffer(features['image'].bytes_list.value[0], dtype = np.uint8)
width = features['width'].int64_list.value[0]
height = features['height'].int64_list.value[0]
boxes = features['boxes'].int64_list.value
assert len(boxes) % 5 == 0, 'Annotation error occured in box array.'
box_cnt = len(boxes) // 5
......@@ -33,7 +31,7 @@ def parse_tfrecord(data):
labels = []
for i in range(box_cnt):
label, x_min, y_min, x_max, y_max = int(boxes[i * 5]), float(boxes[i * 5 + 1]), float(boxes[i * 5 + 2]), float(boxes[i * 5 + 3]) ## do we need to change int to float? is there float rectangle sample?
label, x_min, y_min, x_max, y_max = int(boxes[i * 5]), float(boxes[i * 5 + 1]), float(boxes[i * 5 + 2]), float(boxes[i * 5 + 3]), float(boxes[i * 5 + 4]) ## do we need to change int to float? is there float rectangle sample?
aligned_boxes.append([x_min, y_min, x_max, y_max])
labels.append(label)
......
......@@ -99,6 +99,8 @@ with tf.Session() as sess:
sess.run([tf.global_variables_initializer()])
if os.path.exists(args.restore_path):
saver_to_restore.restore(sess, args.restore_path)
else:
raise ValueError('there is no model to evaluate. You should move/create the checkpoint file to restore path')
print('\nStart evaluation...\n')
......
......@@ -22,18 +22,18 @@ pred_scores_flag = tf.placeholder(tf.float32, [1, None, None])
gpu_nms_op = gpu_nms(pred_boxes_flag, pred_scores_flag, args.class_num, args.nms_topk, args.score_threshold, args.nms_threshold)
### tf.data pipeline
train_dataset = tf.data.TFRecordDataset(filenames=train_file, compression_type='GZIP')
train_dataset = train_dataset.shuffle(train_img_cnt)
train_dataset = train_dataset.batch(batch_size)
train_dataset = tf.data.TFRecordDataset(filenames=args.train_file, compression_type='GZIP')
train_dataset = train_dataset.shuffle(args.train_img_cnt)
train_dataset = train_dataset.batch(args.batch_size)
train_dataset = train_dataset.map(
lambda x: tf.py_func(get_batch_data,
inp=[x, args.class_num, args.img_size, args.anchors, True, args.multi_scale_train, args.use_mix_up, args.letterbox_resize],
Tout=[tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]),
num_parallel_calls=args.num_threads
)
train_dataset = train_dataset.prefetch(prefetech_buffer)
train_dataset = train_dataset.prefetch(args.prefetech_buffer)
val_dataset = tf.data.TFRecordDataset(filenames=val_file, compression_type='GZIP')
val_dataset = tf.data.TFRecordDataset(filenames=args.val_file, compression_type='GZIP')
val_dataset = val_dataset.batch(1)
val_dataset = val_dataset.map(
lambda x: tf.py_func(get_batch_data,
......@@ -41,7 +41,7 @@ val_dataset = val_dataset.map(
Tout=[tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]),
num_parallel_calls=args.num_threads
)
val_dataset.prefetch(prefetech_buffer)
val_dataset.prefetch(args.prefetech_buffer)
iterator = tf.data.Iterator.from_structure(train_dataset.output_types, train_dataset.output_shapes)
train_init_op = iterator.make_initializer(train_dataset)
......@@ -71,13 +71,13 @@ saver_to_restore = tf.train.Saver(var_list=tf.contrib.framework.get_variables_to
update_vars = tf.contrib.framework.get_variables_to_restore(include=update_part)
global_step = tf.Variable(float(global_step), trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
global_step = tf.Variable(float(args.global_step), trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
if use_warm_up:
learning_rate = tf.cond(tf.less(global_step, train_batch_num * warm_up_epoch),
lambda: learning_rate_init * global_step / (train_batch_num * warm_up_epoch),
lambda: config_learning_rate(global_step - args.train_batch_num * args.warm_up_epoch))
lambda: config_learning_rate(args, global_step - args.train_batch_num * args.warm_up_epoch))
else:
learning_rate = config_learning_rate(global_step)
learning_rate = config_learning_rate(args, global_step)
optimizer = config_optimizer(args.optimizer_name, learning_rate)
......@@ -105,7 +105,7 @@ with tf.Session() as sess:
if os.path.exists(args.restore_path):
saver_to_restore.restore(sess, args.restore_path)
print('\nStart training...\n')
print('\nStart training...: Total epoches =', args.total_epoches, '\n')
best_mAP = -np.Inf
......@@ -163,7 +163,7 @@ with tf.Session() as sess:
# calc mAP
rec_total, prec_total, ap_total = AverageMeter(), AverageMeter(), AverageMeter()
gt_dict = parse_gt_rec(args.val_file, args.img_size, args.letterbox_resize)
gt_dict = parse_gt_rec(args.val_file, 'GZIP', args.img_size, args.letterbox_resize)
info = '======> Epoch: {}, global_step: {}, lr: {:.6g} <======\n'.format(epoch, __global_step, __lr)
......
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "yolov3.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "p0y3wIkfSuIT",
"colab_type": "code",
"outputId": "eeedd664-406a-43ff-aa5e-bd48963494c4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
}
},
"source": [
"%tensorflow_version 1.x\n",
"## Check your google colab/drive settings!!! (libraries, argument paths, ...)\n",
"from google.colab import drive\n",
"drive.mount('/content/gdrive')\n",
"\n",
"## variables for notebook\n",
"training = True\n",
"\n",
"##### changes\n",
"### changed some variable names because of argument conflicts\n",
"### last two parts are train, test mode code. you can switch the mode with above variable, 'training'\n",
"### there are some difficulties for separating train/eval code (making into functions), because of variable dependencies"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"TensorFlow 1.x selected.\n",
"Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Yh3RWBkgAjZx",
"colab_type": "code",
"colab": {}
},
"source": [
"## TFRecord utils here\n",
"import tensorflow as tf\n",
"from itertools import tee\n",
"\n",
"class TFRecordIterator:\n",
" def __init__(self, path, compression=None):\n",
" self._core = tf.python_io.tf_record_iterator(path, tf.python_io.TFRecordOptions(compression))\n",
" self._iterator = iter(self._core)\n",
" self._iterator, self._iterator_temp = tee(self._iterator)\n",
" self._total_cnt = sum(1 for _ in self._iterator_temp)\n",
"\n",
" def _read_value(self, feature):\n",
" if len(feature.int64_list.value) > 0:\n",
" return feature.int64_list.value\n",
"\n",
" if len(feature.bytes_list.value) > 0:\n",
" return feature.bytes_list.value\n",
"\n",
" if len(feature.float_list.value) > 0:\n",
" return feature.float_list.value\n",
"\n",
" return None\n",
"\n",
" def _read_features(self, features):\n",
" d = dict()\n",
" for data in features:\n",
" d[data] = self._read_value(features[data])\n",
" return d\n",
"\n",
" def __enter__(self):\n",
" return self\n",
"\n",
" def __exit__(self, exception_type, exception_value, traceback):\n",
" pass\n",
"\n",
" def __iter__(self):\n",
" return self\n",
"\n",
" def __next__(self):\n",
" record = next(self._iterator)\n",
" example = tf.train.Example()\n",
" example.ParseFromString(record)\n",
" return self._read_features(example.features.feature)\n",
"\n",
" def count(self):\n",
" return self._total_cnt\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "oCVOPE2XC3qE",
"colab_type": "code",
"colab": {}
},
"source": [
"## plot utils\n",
"from __future__ import division, print_function\n",
"\n",
"import cv2\n",
"import random\n",
"\n",
"def get_color_table(class_num, seed=2):\n",
" random.seed(seed)\n",
" color_table = {}\n",
" for i in range(class_num):\n",
" color_table[i] = [random.randint(0, 255) for _ in range(3)]\n",
" return color_table\n",
"\n",
"\n",
"def plot_one_box(img, coord, label=None, color=None, line_thickness=None):\n",
" tl = line_thickness or int(round(0.002 * max(img.shape[0:2]))) # line thickness\n",
" color = color or [random.randint(0, 255) for _ in range(3)]\n",
" c1, c2 = (int(coord[0]), int(coord[1])), (int(coord[2]), int(coord[3]))\n",
" cv2.rectangle(img, c1, c2, color, thickness=tl)\n",
" if label:\n",
" tf = max(tl - 1, 1) # font thickness\n",
" t_size = cv2.getTextSize(label, 0, fontScale=float(tl) / 3, thickness=tf)[0]\n",
" c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n",
" cv2.rectangle(img, c1, c2, color, -1) # filled\n",
" cv2.putText(img, label, (c1[0], c1[1] - 2), 0, float(tl) / 3, [0, 0, 0], thickness=tf, lineType=cv2.LINE_AA)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SY10K9LoDJOZ",
"colab_type": "code",
"colab": {}
},
"source": [
"## nms utils\n",
"import numpy as np\n",
"\n",
"def gpu_nms(boxes, scores, num_classes, max_boxes=50, score_thresh=0.5, nms_thresh=0.5):\n",
" boxes_list, label_list, score_list = [], [], []\n",
" max_boxes = tf.constant(max_boxes, dtype='int32')\n",
"\n",
" boxes = tf.reshape(boxes, [-1, 4]) # '-1' means we don't konw the exact number of boxes\n",
" score = tf.reshape(scores, [-1, num_classes])\n",
"\n",
" # Step 1: Create a filtering mask based on \"box_class_scores\" by using \"threshold\".\n",
" mask = tf.greater_equal(score, tf.constant(score_thresh))\n",
" # Step 2: Do non_max_suppression for each class\n",
" for i in range(num_classes):\n",
" # Step 3: Apply the mask to scores, boxes and pick them out\n",
" filter_boxes = tf.boolean_mask(boxes, mask[:,i])\n",
" filter_score = tf.boolean_mask(score[:,i], mask[:,i])\n",
" nms_indices = tf.image.non_max_suppression(boxes=filter_boxes,\n",
" scores=filter_score,\n",
" max_output_size=max_boxes,\n",
" iou_threshold=nms_thresh, name='nms_indices')\n",
" label_list.append(tf.ones_like(tf.gather(filter_score, nms_indices), 'int32')*i)\n",
" boxes_list.append(tf.gather(filter_boxes, nms_indices))\n",
" score_list.append(tf.gather(filter_score, nms_indices))\n",
"\n",
" boxes = tf.concat(boxes_list, axis=0)\n",
" score = tf.concat(score_list, axis=0)\n",
" label = tf.concat(label_list, axis=0)\n",
"\n",
" return boxes, score, label\n",
"\n",
"\n",
"def py_nms(boxes, scores, max_boxes=50, iou_thresh=0.5):\n",
" assert boxes.shape[1] == 4 and len(scores.shape) == 1\n",
"\n",
" x1 = boxes[:, 0]\n",
" y1 = boxes[:, 1]\n",
" x2 = boxes[:, 2]\n",
" y2 = boxes[:, 3]\n",
"\n",
" areas = (x2 - x1) * (y2 - y1)\n",
" order = scores.argsort()[::-1]\n",
"\n",
" keep = []\n",
" while order.size > 0:\n",
" i = order[0]\n",
" keep.append(i)\n",
" xx1 = np.maximum(x1[i], x1[order[1:]])\n",
" yy1 = np.maximum(y1[i], y1[order[1:]])\n",
" xx2 = np.minimum(x2[i], x2[order[1:]])\n",
" yy2 = np.minimum(y2[i], y2[order[1:]])\n",
"\n",
" w = np.maximum(0.0, xx2 - xx1 + 1)\n",
" h = np.maximum(0.0, yy2 - yy1 + 1)\n",
" inter = w * h\n",
" ovr = inter / (areas[i] + areas[order[1:]] - inter)\n",
"\n",
" inds = np.where(ovr <= iou_thresh)[0]\n",
" order = order[inds + 1]\n",
"\n",
" return keep[:max_boxes]\n",
"\n",
"\n",
"def cpu_nms(boxes, scores, num_classes, max_boxes=50, score_thresh=0.5, iou_thresh=0.5):\n",
" boxes = boxes.reshape(-1, 4)\n",
" scores = scores.reshape(-1, num_classes)\n",
" picked_boxes, picked_score, picked_label = [], [], []\n",
"\n",
" for i in range(num_classes):\n",
" indices = np.where(scores[:,i] >= score_thresh)\n",
" filter_boxes = boxes[indices]\n",
" filter_scores = scores[:,i][indices]\n",
" if len(filter_boxes) == 0: \n",
" continue\n",
"\n",
" indices = py_nms(filter_boxes, filter_scores,\n",
" max_boxes=max_boxes, iou_thresh=iou_thresh)\n",
" picked_boxes.append(filter_boxes[indices])\n",
" picked_score.append(filter_scores[indices])\n",
" picked_label.append(np.ones(len(indices), dtype='int32')*i)\n",
" if len(picked_boxes) == 0: \n",
" return None, None, None\n",
"\n",
" boxes = np.concatenate(picked_boxes, axis=0)\n",
" score = np.concatenate(picked_score, axis=0)\n",
" label = np.concatenate(picked_label, axis=0)\n",
"\n",
" return boxes, score, label"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Dg-ZKHmRDlPp",
"colab_type": "code",
"colab": {}
},
"source": [
"## misc utils\n",
"class AverageMeter(object):\n",
" def __init__(self):\n",
" self.reset()\n",
"\n",
" def reset(self):\n",
" self.val = 0\n",
" self.average = 0\n",
" self.sum = 0\n",
" self.count = 0\n",
"\n",
" def update(self, val, n=1):\n",
" self.val = val\n",
" self.sum += val * n\n",
" self.count += n\n",
" self.average = self.sum / float(self.count)\n",
"\n",
"\n",
"def parse_anchors(anchor_path):\n",
" anchors = np.reshape(np.asarray(open(anchor_path, 'r').read().split(','), np.float32), [-1, 2])\n",
" return anchors\n",
"\n",
"\n",
"def read_class_names(class_name_path):\n",
" names = {}\n",
" with open(class_name_path, 'r') as data:\n",
" for ID, name in enumerate(data):\n",
" names[ID] = name.strip('\\n')\n",
" return names\n",
"\n",
"\n",
"def shuffle_and_overwrite(file_name):\n",
" content = open(file_name, 'r').readlines()\n",
" random.shuffle(content)\n",
" with open(file_name, 'w') as f:\n",
" for line in content:\n",
" f.write(line)\n",
"\n",
"\n",
"def update_dict(ori_dict, new_dict):\n",
" if not ori_dict:\n",
" return new_dict\n",
" for key in ori_dict:\n",
" ori_dict[key] += new_dict[key]\n",
" return ori_dict\n",
"\n",
"\n",
"def list_add(ori_list, new_list):\n",
" for i in range(len(ori_list)):\n",
" ori_list[i] += new_list[i]\n",
" return ori_list\n",
"\n",
"\n",
"def load_weights(var_list, weights_file):\n",
" with open(weights_file, \"rb\") as fp:\n",
" np.fromfile(fp, dtype=np.int32, count=5)\n",
" weights = np.fromfile(fp, dtype=np.float32)\n",
"\n",
" ptr = 0\n",
" i = 0\n",
" assign_ops = []\n",
" while i < len(var_list) - 1:\n",
" var1 = var_list[i]\n",
" var2 = var_list[i + 1]\n",
" if 'Conv' in var1.name.split('/')[-2]:\n",
" if 'BatchNorm' in var2.name.split('/')[-2]:\n",
" gamma, beta, mean, var = var_list[i + 1:i + 5]\n",
" batch_norm_vars = [beta, gamma, mean, var]\n",
" for var in batch_norm_vars:\n",
" shape = var.shape.as_list()\n",
" num_params = np.prod(shape)\n",
" var_weights = weights[ptr:ptr + num_params].reshape(shape)\n",
" ptr += num_params\n",
" assign_ops.append(tf.assign(var, var_weights, validate_shape=True))\n",
" i += 4\n",
" elif 'Conv' in var2.name.split('/')[-2]:\n",
" # load biases\n",
" bias = var2\n",
" bias_shape = bias.shape.as_list()\n",
" bias_params = np.prod(bias_shape)\n",
" bias_weights = weights[ptr:ptr +\n",
" bias_params].reshape(bias_shape)\n",
" ptr += bias_params\n",
" assign_ops.append(tf.assign(bias, bias_weights, validate_shape=True))\n",
" i += 1\n",
"\n",
" shape = var1.shape.as_list()\n",
" num_params = np.prod(shape)\n",
"\n",
" var_weights = weights[ptr:ptr + num_params].reshape(\n",
" (shape[3], shape[2], shape[0], shape[1]))\n",
"\n",
" var_weights = np.transpose(var_weights, (2, 3, 1, 0))\n",
" ptr += num_params\n",
" assign_ops.append(\n",
" tf.assign(var1, var_weights, validate_shape=True))\n",
" i += 1\n",
"\n",
" return assign_ops\n",
"\n",
"\n",
"def config_learning_rate(global_step):\n",
" ## fixes for removing arg paramter\n",
" global lr_type, learning_rate_init, lr_decay_freq, lr_decay_factor, lr_lower_bound, total_epoches, use_warm_up, warm_up_epoch, train_batch_num, lr_lower_bound, pw_boundaries, pw_values\n",
"\n",
" if lr_type == 'exponential':\n",
" lr_tmp = tf.train.exponential_decay(learning_rate_init, global_step, lr_decay_freq,\n",
" lr_decay_factor, staircase=True, name='exponential_learning_rate')\n",
" return tf.maximum(lr_tmp, lr_lower_bound)\n",
" elif lr_type == 'cosine_decay':\n",
" train_steps = (total_epoches - float(use_warm_up) * warm_up_epoch) * train_batch_num\n",
" return lr_lower_bound + 0.5 * (learning_rate_init - lr_lower_bound) * \\\n",
" (1 + tf.cos(global_step / train_steps * np.pi))\n",
" elif lr_type == 'cosine_decay_restart':\n",
" return tf.train.cosine_decay_restarts(learning_rate_init, global_step, \n",
" lr_decay_freq, t_mul=2.0, m_mul=1.0, \n",
" name='cosine_decay_learning_rate_restart')\n",
" elif lr_type == 'fixed':\n",
" return tf.convert_to_tensor(learning_rate_init, name='fixed_learning_rate')\n",
" elif lr_type == 'piecewise':\n",
" return tf.train.piecewise_constant(global_step, boundaries=pw_boundaries, values=pw_values,\n",
" name='piecewise_learning_rate')\n",
" else:\n",
" raise ValueError('Unsupported learning rate type!')\n",
"\n",
"\n",
"def config_optimizer(optimizer_name, learning_rate, decay=0.9, momentum=0.9):\n",
" if optimizer_name == 'momentum':\n",
" return tf.train.MomentumOptimizer(learning_rate, momentum=momentum)\n",
" elif optimizer_name == 'rmsprop':\n",
" return tf.train.RMSPropOptimizer(learning_rate, decay=decay, momentum=momentum)\n",
" elif optimizer_name == 'adam':\n",
" return tf.train.AdamOptimizer(learning_rate)\n",
" elif optimizer_name == 'sgd':\n",
" return tf.train.GradientDescentOptimizer(learning_rate)\n",
" else:\n",
" raise ValueError('Unsupported optimizer type!')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YIlZhFLYD0d8",
"colab_type": "code",
"colab": {}
},
"source": [
"## data utils\n",
"\n",
"import sys\n",
"\n",
"PY_VERSION = sys.version_info[0]\n",
"iter_cnt = 0\n",
"\n",
"def _parse_tfrecord(data):\n",
" example = tf.train.Example()\n",
" example.ParseFromString(data)\n",
" features = example.features.feature\n",
" return features\n",
"\n",
"def parse_tfrecord(data):\n",
" # tfrecord parser for TFRecordDataset (raw data)\n",
" features = _parse_tfrecord(data)\n",
" index = features['index'].int64_list.value[0]\n",
" encoded_image = np.frombuffer(features['image'].bytes_list.value[0], dtype = np.uint8)\n",
" width = features['width'].int64_list.value[0]\n",
" height = features['height'].int64_list.value[0]\n",
" boxes = features['boxes'].int64_list.value\n",
"\n",
" assert len(boxes) % 5 == 0, 'Annotation error occured in box array.'\n",
" box_cnt = len(boxes) // 5\n",
"\n",
" aligned_boxes = []\n",
" labels = []\n",
"\n",
" for i in range(box_cnt):\n",
" label, x_min, y_min, x_max, y_max = int(boxes[i * 5]), float(boxes[i * 5 + 1]), float(boxes[i * 5 + 2]), float(boxes[i * 5 + 3]), float(boxes[i * 5 + 4]) ## do we need to change int to float? is there float rectangle sample?\n",
" aligned_boxes.append([x_min, y_min, x_max, y_max])\n",
" labels.append(label)\n",
"\n",
" aligned_boxes = np.asarray(aligned_boxes, np.float32)\n",
" labels = np.asarray(labels, np.int64)\n",
"\n",
" return index, encoded_image, aligned_boxes, labels, width, height\n",
"\n",
"def parse_record(features):\n",
" # tfrecord parser for TFRecordIterator (primitive data)\n",
"\n",
" index = int(features['index'][0])\n",
" encoded_image = np.frombuffer(features['image'][0], dtype = np.uint8)\n",
" width = int(features['width'][0])\n",
" height = int(features['height'][0])\n",
" boxes = features['boxes']\n",
"\n",
" assert len(boxes) % 5 == 0, 'Annotation error occured in box array.'\n",
" box_cnt = len(boxes) // 5\n",
"\n",
" aligned_boxes = []\n",
" labels = []\n",
"\n",
" for i in range(box_cnt):\n",
" label, x_min, y_min, x_max, y_max = int(boxes[i * 5]), float(boxes[i * 5 + 1]), float(boxes[i * 5 + 2]), float(boxes[i * 5 + 3]), float(boxes[i * 5 + 4])\n",
" aligned_boxes.append([x_min, y_min, x_max, y_max])\n",
" labels.append(label)\n",
"\n",
" aligned_boxes = np.asarray(aligned_boxes, np.float32)\n",
" labels = np.asarray(labels, np.int64)\n",
"\n",
" return index, encoded_image, aligned_boxes, labels, width, height\n",
"\n",
"def bbox_crop(bbox, crop_box=None, allow_outside_center=True):\n",
" bbox = bbox.copy()\n",
" if crop_box is None:\n",
" return bbox\n",
" if not len(crop_box) == 4:\n",
" raise ValueError(\n",
" \"Invalid crop_box parameter, requires length 4, given {}\".format(str(crop_box)))\n",
" if sum([int(c is None) for c in crop_box]) == 4:\n",
" return bbox\n",
"\n",
" l, t, w, h = crop_box\n",
"\n",
" left = l if l else 0\n",
" top = t if t else 0\n",
" right = left + (w if w else np.inf)\n",
" bottom = top + (h if h else np.inf)\n",
" crop_bbox = np.array((left, top, right, bottom))\n",
"\n",
" if allow_outside_center:\n",
" mask = np.ones(bbox.shape[0], dtype=bool)\n",
" else:\n",
" centers = (bbox[:, :2] + bbox[:, 2:4]) / 2\n",
" mask = np.logical_and(crop_bbox[:2] <= centers, centers < crop_bbox[2:]).all(axis=1)\n",
"\n",
" # transform borders\n",
" bbox[:, :2] = np.maximum(bbox[:, :2], crop_bbox[:2])\n",
" bbox[:, 2:4] = np.minimum(bbox[:, 2:4], crop_bbox[2:4])\n",
" bbox[:, :2] -= crop_bbox[:2]\n",
" bbox[:, 2:4] -= crop_bbox[:2]\n",
"\n",
" mask = np.logical_and(mask, (bbox[:, :2] < bbox[:, 2:4]).all(axis=1))\n",
" bbox = bbox[mask]\n",
" return bbox\n",
"\n",
"def bbox_iou(bbox_a, bbox_b, offset=0):\n",
" if bbox_a.shape[1] < 4 or bbox_b.shape[1] < 4:\n",
" raise IndexError(\"Bounding boxes axis 1 must have at least length 4\")\n",
"\n",
" tl = np.maximum(bbox_a[:, None, :2], bbox_b[:, :2])\n",
" br = np.minimum(bbox_a[:, None, 2:4], bbox_b[:, 2:4])\n",
"\n",
" area_i = np.prod(br - tl + offset, axis=2) * (tl < br).all(axis=2)\n",
" area_a = np.prod(bbox_a[:, 2:4] - bbox_a[:, :2] + offset, axis=1)\n",
" area_b = np.prod(bbox_b[:, 2:4] - bbox_b[:, :2] + offset, axis=1)\n",
" return area_i / (area_a[:, None] + area_b - area_i)\n",
"\n",
"\n",
"def random_crop_with_constraints(bbox, size, min_scale=0.3, max_scale=1,\n",
" max_aspect_ratio=2, constraints=None,\n",
" max_trial=50):\n",
" # default params in paper\n",
" if constraints is None:\n",
" constraints = (\n",
" (0.1, None),\n",
" (0.3, None),\n",
" (0.5, None),\n",
" (0.7, None),\n",
" (0.9, None),\n",
" (None, 1),\n",
" )\n",
"\n",
" w, h = size\n",
"\n",
" candidates = [(0, 0, w, h)]\n",
" for min_iou, max_iou in constraints:\n",
" min_iou = -np.inf if min_iou is None else min_iou\n",
" max_iou = np.inf if max_iou is None else max_iou\n",
"\n",
" for _ in range(max_trial):\n",
" scale = random.uniform(min_scale, max_scale)\n",
" aspect_ratio = random.uniform(\n",
" max(1 / max_aspect_ratio, scale * scale),\n",
" min(max_aspect_ratio, 1 / (scale * scale)))\n",
" crop_h = int(h * scale / np.sqrt(aspect_ratio))\n",
" crop_w = int(w * scale * np.sqrt(aspect_ratio))\n",
"\n",
" crop_t = random.randrange(h - crop_h)\n",
" crop_l = random.randrange(w - crop_w)\n",
" crop_bb = np.array((crop_l, crop_t, crop_l + crop_w, crop_t + crop_h))\n",
"\n",
" if len(bbox) == 0:\n",
" top, bottom = crop_t, crop_t + crop_h\n",
" left, right = crop_l, crop_l + crop_w\n",
" return bbox, (left, top, right-left, bottom-top)\n",
"\n",
" iou = bbox_iou(bbox, crop_bb[np.newaxis])\n",
" if min_iou <= iou.min() and iou.max() <= max_iou:\n",
" top, bottom = crop_t, crop_t + crop_h\n",
" left, right = crop_l, crop_l + crop_w\n",
" candidates.append((left, top, right-left, bottom-top))\n",
" break\n",
"\n",
" # random select one\n",
" while candidates:\n",
" crop = candidates.pop(np.random.randint(0, len(candidates)))\n",
" new_bbox = bbox_crop(bbox, crop, allow_outside_center=False)\n",
" if new_bbox.size < 1:\n",
" continue\n",
" new_crop = (crop[0], crop[1], crop[2], crop[3])\n",
" return new_bbox, new_crop\n",
" return bbox, (0, 0, w, h)\n",
"\n",
"\n",
"def random_color_distort(img, brightness_delta=32, hue_vari=18, sat_vari=0.5, val_vari=0.5):\n",
" def random_hue(img_hsv, hue_vari, p=0.5):\n",
" if np.random.uniform(0, 1) > p:\n",
" hue_delta = np.random.randint(-hue_vari, hue_vari)\n",
" img_hsv[:, :, 0] = (img_hsv[:, :, 0] + hue_delta) % 180\n",
" return img_hsv\n",
"\n",
" def random_saturation(img_hsv, sat_vari, p=0.5):\n",
" if np.random.uniform(0, 1) > p:\n",
" sat_mult = 1 + np.random.uniform(-sat_vari, sat_vari)\n",
" img_hsv[:, :, 1] *= sat_mult\n",
" return img_hsv\n",
"\n",
" def random_value(img_hsv, val_vari, p=0.5):\n",
" if np.random.uniform(0, 1) > p:\n",
" val_mult = 1 + np.random.uniform(-val_vari, val_vari)\n",
" img_hsv[:, :, 2] *= val_mult\n",
" return img_hsv\n",
"\n",
" def random_brightness(img, brightness_delta, p=0.5):\n",
" if np.random.uniform(0, 1) > p:\n",
" img = img.astype(np.float32)\n",
" brightness_delta = int(np.random.uniform(-brightness_delta, brightness_delta))\n",
" img = img + brightness_delta\n",
" return np.clip(img, 0, 255)\n",
"\n",
" # brightness\n",
" img = random_brightness(img, brightness_delta)\n",
" img = img.astype(np.uint8)\n",
"\n",
" # color jitter\n",
" img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV).astype(np.float32)\n",
"\n",
" if np.random.randint(0, 2):\n",
" img_hsv = random_value(img_hsv, val_vari)\n",
" img_hsv = random_saturation(img_hsv, sat_vari)\n",
" img_hsv = random_hue(img_hsv, hue_vari)\n",
" else:\n",
" img_hsv = random_saturation(img_hsv, sat_vari)\n",
" img_hsv = random_hue(img_hsv, hue_vari)\n",
" img_hsv = random_value(img_hsv, val_vari)\n",
"\n",
" img_hsv = np.clip(img_hsv, 0, 255)\n",
" img = cv2.cvtColor(img_hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)\n",
"\n",
" return img\n",
"\n",
"\n",
"def letterbox_resize(img, new_width, new_height, interp=0):\n",
" ori_height, ori_width = img.shape[:2]\n",
"\n",
" resize_ratio = min(new_width / ori_width, new_height / ori_height)\n",
"\n",
" resize_w = int(resize_ratio * ori_width)\n",
" resize_h = int(resize_ratio * ori_height)\n",
"\n",
" img = cv2.resize(img, (resize_w, resize_h), interpolation=interp)\n",
" image_padded = np.full((new_height, new_width, 3), 128, np.uint8)\n",
"\n",
" dw = int((new_width - resize_w) / 2)\n",
" dh = int((new_height - resize_h) / 2)\n",
"\n",
" image_padded[dh: resize_h + dh, dw: resize_w + dw, :] = img\n",
"\n",
" return image_padded, resize_ratio, dw, dh\n",
"\n",
"\n",
"def resize_with_bbox(img, bbox, new_width, new_height, interp=0, letterbox=False):\n",
" if letterbox:\n",
" image_padded, resize_ratio, dw, dh = letterbox_resize(img, new_width, new_height, interp)\n",
"\n",
" # xmin, xmax\n",
" bbox[:, [0, 2]] = bbox[:, [0, 2]] * resize_ratio + dw\n",
" # ymin, ymax\n",
" bbox[:, [1, 3]] = bbox[:, [1, 3]] * resize_ratio + dh\n",
"\n",
" return image_padded, bbox\n",
" else:\n",
" ori_height, ori_width = img.shape[:2]\n",
"\n",
" img = cv2.resize(img, (new_width, new_height), interpolation=interp)\n",
"\n",
" # xmin, xmax\n",
" bbox[:, [0, 2]] = bbox[:, [0, 2]] / ori_width * new_width\n",
" # ymin, ymax\n",
" bbox[:, [1, 3]] = bbox[:, [1, 3]] / ori_height * new_height\n",
"\n",
" return img, bbox\n",
"\n",
"\n",
"def random_flip(img, bbox, px=0, py=0):\n",
" height, width = img.shape[:2]\n",
" if np.random.uniform(0, 1) < px:\n",
" img = cv2.flip(img, 1)\n",
" xmax = width - bbox[:, 0]\n",
" xmin = width - bbox[:, 2]\n",
" bbox[:, 0] = xmin\n",
" bbox[:, 2] = xmax\n",
"\n",
" if np.random.uniform(0, 1) < py:\n",
" img = cv2.flip(img, 0)\n",
" ymax = height - bbox[:, 1]\n",
" ymin = height - bbox[:, 3]\n",
" bbox[:, 1] = ymin\n",
" bbox[:, 3] = ymax\n",
" return img, bbox\n",
"\n",
"\n",
"def random_expand(img, bbox, max_ratio=4, fill=0, keep_ratio=True):\n",
" h, w, c = img.shape\n",
" ratio_x = random.uniform(1, max_ratio)\n",
" if keep_ratio:\n",
" ratio_y = ratio_x\n",
" else:\n",
" ratio_y = random.uniform(1, max_ratio)\n",
"\n",
" oh, ow = int(h * ratio_y), int(w * ratio_x)\n",
" off_y = random.randint(0, oh - h)\n",
" off_x = random.randint(0, ow - w)\n",
"\n",
" dst = np.full(shape=(oh, ow, c), fill_value=fill, dtype=img.dtype)\n",
"\n",
" dst[off_y:off_y + h, off_x:off_x + w, :] = img\n",
"\n",
" # correct bbox\n",
" bbox[:, :2] += (off_x, off_y)\n",
" bbox[:, 2:4] += (off_x, off_y)\n",
"\n",
" return dst, bbox\n",
"\n",
"def process_box(boxes, labels, img_size, class_num, anchors):\n",
" anchors_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]\n",
"\n",
" # convert boxes form:\n",
" # shape: [N, 2]\n",
" # (x_center, y_center)\n",
" box_centers = (boxes[:, 0:2] + boxes[:, 2:4]) / 2\n",
" # (width, height)\n",
" box_sizes = boxes[:, 2:4] - boxes[:, 0:2]\n",
"\n",
" # [13, 13, 3, 5+num_class+1] `5` means coords and labels. `1` means mix up weight. \n",
" y_true_13 = np.zeros((img_size[1] // 32, img_size[0] // 32, 3, 6 + class_num), np.float32)\n",
" y_true_26 = np.zeros((img_size[1] // 16, img_size[0] // 16, 3, 6 + class_num), np.float32)\n",
" y_true_52 = np.zeros((img_size[1] // 8, img_size[0] // 8, 3, 6 + class_num), np.float32)\n",
"\n",
" # mix up weight default to 1.\n",
" y_true_13[..., -1] = 1.\n",
" y_true_26[..., -1] = 1.\n",
" y_true_52[..., -1] = 1.\n",
"\n",
" y_true = [y_true_13, y_true_26, y_true_52]\n",
"\n",
" # [N, 1, 2]\n",
" box_sizes = np.expand_dims(box_sizes, 1)\n",
" # broadcast tricks\n",
" # [N, 1, 2] & [9, 2] ==> [N, 9, 2]\n",
" mins = np.maximum(- box_sizes / 2, - anchors / 2)\n",
" maxs = np.minimum(box_sizes / 2, anchors / 2)\n",
" # [N, 9, 2]\n",
" whs = maxs - mins\n",
"\n",
" # [N, 9]\n",
" iou = (whs[:, :, 0] * whs[:, :, 1]) / (\n",
" box_sizes[:, :, 0] * box_sizes[:, :, 1] + anchors[:, 0] * anchors[:, 1] - whs[:, :, 0] * whs[:, :,\n",
" 1] + 1e-10)\n",
" # [N]\n",
" best_match_idx = np.argmax(iou, axis=1)\n",
"\n",
" ratio_dict = {1.: 8., 2.: 16., 3.: 32.}\n",
" for i, idx in enumerate(best_match_idx):\n",
" # idx: 0,1,2 ==> 2; 3,4,5 ==> 1; 6,7,8 ==> 0\n",
" feature_map_group = 2 - idx // 3\n",
" # scale ratio: 0,1,2 ==> 8; 3,4,5 ==> 16; 6,7,8 ==> 32\n",
" ratio = ratio_dict[np.ceil((idx + 1) / 3.)]\n",
" x = int(np.floor(box_centers[i, 0] / ratio))\n",
" y = int(np.floor(box_centers[i, 1] / ratio))\n",
" k = anchors_mask[feature_map_group].index(idx)\n",
" c = labels[i]\n",
" # print(feature_map_group, '|', y,x,k,c)\n",
"\n",
" y_true[feature_map_group][y, x, k, :2] = box_centers[i]\n",
" y_true[feature_map_group][y, x, k, 2:4] = box_sizes[i]\n",
" y_true[feature_map_group][y, x, k, 4] = 1.\n",
" y_true[feature_map_group][y, x, k, 5 + c] = 1.\n",
" y_true[feature_map_group][y, x, k, -1] = boxes[i, -1]\n",
"\n",
" return y_true_13, y_true_26, y_true_52\n",
"\n",
"\n",
"def parse_data(data, class_num, img_size, anchors, is_training, letterbox_resize):\n",
" \n",
" img_idx, encoded_img, boxes, labels, _, _ = parse_tfrecord(data)\n",
" img = cv2.imdecode(encoded_img, cv2.IMREAD_COLOR)\n",
" boxes = np.concatenate((boxes, np.full(shape=(boxes.shape[0], 1), fill_value=1., dtype=np.float32)), axis=-1)\n",
"\n",
" ## I erased mix-up method here\n",
"\n",
" if is_training:\n",
" # random color distortion\n",
" img = random_color_distort(img)\n",
"\n",
" # random expansion with prob 0.5\n",
" if np.random.uniform(0, 1) > 0.5:\n",
" img, boxes = random_expand(img, boxes, 4)\n",
"\n",
" # random cropping\n",
" h, w, _ = img.shape\n",
" boxes, crop = random_crop_with_constraints(boxes, (w, h))\n",
" x0, y0, w, h = crop\n",
" img = img[y0: y0+h, x0: x0+w]\n",
"\n",
" # resize with random interpolation\n",
" h, w, _ = img.shape\n",
" interp = np.random.randint(0, 5)\n",
" img, boxes = resize_with_bbox(img, boxes, img_size[0], img_size[1], interp=interp, letterbox=letterbox_resize)\n",
"\n",
" # random horizontal flip\n",
" h, w, _ = img.shape\n",
" img, boxes = random_flip(img, boxes, px=0.5)\n",
" else:\n",
" img, boxes = resize_with_bbox(img, boxes, img_size[0], img_size[1], interp=1, letterbox=letterbox_resize)\n",
"\n",
" img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32)\n",
"\n",
" # the input of yolo_v3 should be in range 0~1\n",
" img = img / 255.\n",
"\n",
" y_true_13, y_true_26, y_true_52 = process_box(boxes, labels, img_size, class_num, anchors)\n",
"\n",
" return img_idx, img, y_true_13, y_true_26, y_true_52\n",
"\n",
"\n",
"def get_batch_data(records, class_num, img_size, anchors, is_training, multi_scale=False, mix_up=False, letterbox_resize=True, interval=10):\n",
" global iter_cnt\n",
"\n",
" # multi_scale training\n",
" if multi_scale and is_training:\n",
" random.seed(iter_cnt // interval)\n",
" random_img_size = [[x * 32, x * 32] for x in range(10, 20)]\n",
" img_size = random.sample(random_img_size, 1)[0]\n",
" iter_cnt += 1\n",
"\n",
" img_idx_batch, img_batch, y_true_13_batch, y_true_26_batch, y_true_52_batch = [], [], [], [], []\n",
"\n",
" # deleted mix up strategy\n",
" \n",
" for data in records:\n",
" img_idx, img, y_true_13, y_true_26, y_true_52 = parse_data(data, class_num, img_size, anchors, is_training, letterbox_resize)\n",
"\n",
" img_idx_batch.append(img_idx)\n",
" img_batch.append(img)\n",
" y_true_13_batch.append(y_true_13)\n",
" y_true_26_batch.append(y_true_26)\n",
" y_true_52_batch.append(y_true_52)\n",
"\n",
" img_idx_batch, img_batch, y_true_13_batch, y_true_26_batch, y_true_52_batch = np.asarray(img_idx_batch, np.int64), np.asarray(img_batch), np.asarray(y_true_13_batch), np.asarray(y_true_26_batch), np.asarray(y_true_52_batch)\n",
"\n",
" return img_idx_batch, img_batch, y_true_13_batch, y_true_26_batch, y_true_52_batch"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sd9Pk3XgDqxt",
"colab_type": "code",
"colab": {}
},
"source": [
"## evaluation utils\n",
"\n",
"from collections import Counter\n",
"\n",
"def calc_iou(pred_boxes, true_boxes):\n",
" pred_boxes = np.expand_dims(pred_boxes, -2)\n",
" true_boxes = np.expand_dims(true_boxes, 0)\n",
"\n",
" intersect_mins = np.maximum(pred_boxes[..., :2], true_boxes[..., :2])\n",
" intersect_maxs = np.minimum(pred_boxes[..., 2:], true_boxes[..., 2:])\n",
" intersect_wh = np.maximum(intersect_maxs - intersect_mins, 0.)\n",
"\n",
" intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]\n",
" pred_box_wh = pred_boxes[..., 2:] - pred_boxes[..., :2]\n",
" pred_box_area = pred_box_wh[..., 0] * pred_box_wh[..., 1]\n",
" true_boxes_wh = true_boxes[..., 2:] - true_boxes[..., :2]\n",
" true_boxes_area = true_boxes_wh[..., 0] * true_boxes_wh[..., 1]\n",
"\n",
" iou = intersect_area / (pred_box_area + true_boxes_area - intersect_area + 1e-10)\n",
"\n",
" return iou\n",
"\n",
"\n",
"def evaluate_on_cpu(y_pred, y_true, num_classes, calc_now=True, max_boxes=50, score_thresh=0.5, iou_thresh=0.5):\n",
" num_images = y_true[0].shape[0]\n",
" true_labels_dict = {i: 0 for i in range(num_classes)}\n",
" pred_labels_dict = {i: 0 for i in range(num_classes)}\n",
" true_positive_dict = {i: 0 for i in range(num_classes)}\n",
"\n",
" for i in range(num_images):\n",
" true_labels_list, true_boxes_list = [], []\n",
" for j in range(3):\n",
" true_probs_temp = y_true[j][i][..., 5:-1]\n",
" true_boxes_temp = y_true[j][i][..., 0:4]\n",
"\n",
" object_mask = true_probs_temp.sum(axis=-1) > 0\n",
"\n",
" true_probs_temp = true_probs_temp[object_mask]\n",
" true_boxes_temp = true_boxes_temp[object_mask]\n",
"\n",
" true_labels_list += np.argmax(true_probs_temp, axis=-1).tolist()\n",
" true_boxes_list += true_boxes_temp.tolist()\n",
"\n",
" if len(true_labels_list) != 0:\n",
" for cls, count in Counter(true_labels_list).items():\n",
" true_labels_dict[cls] += count\n",
"\n",
" true_boxes = np.array(true_boxes_list)\n",
" box_centers, box_sizes = true_boxes[:, 0:2], true_boxes[:, 2:4]\n",
" true_boxes[:, 0:2] = box_centers - box_sizes / 2.\n",
" true_boxes[:, 2:4] = true_boxes[:, 0:2] + box_sizes\n",
"\n",
" pred_boxes = y_pred[0][i:i + 1]\n",
" pred_confs = y_pred[1][i:i + 1]\n",
" pred_probs = y_pred[2][i:i + 1]\n",
"\n",
" pred_boxes, pred_confs, pred_labels = cpu_nms(pred_boxes, pred_confs * pred_probs, num_classes, max_boxes=max_boxes, score_thresh=score_thresh, iou_thresh=iou_thresh)\n",
"\n",
" pred_labels_list = [] if pred_labels is None else pred_labels.tolist()\n",
" if pred_labels_list == []:\n",
" continue\n",
"\n",
" # calc iou\n",
" iou_matrix = calc_iou(pred_boxes, true_boxes)\n",
" max_iou_idx = np.argmax(iou_matrix, axis=-1)\n",
"\n",
" correct_idx = []\n",
" correct_conf = []\n",
"\n",
" for k in range(max_iou_idx.shape[0]):\n",
" pred_labels_dict[pred_labels_list[k]] += 1\n",
" match_idx = max_iou_idx[k] # V level\n",
" if iou_matrix[k, match_idx] > iou_thresh and true_labels_list[match_idx] == pred_labels_list[k]:\n",
" if match_idx not in correct_idx:\n",
" correct_idx.append(match_idx)\n",
" correct_conf.append(pred_confs[k])\n",
" else:\n",
" same_idx = correct_idx.index(match_idx)\n",
" if pred_confs[k] > correct_conf[same_idx]:\n",
" correct_idx.pop(same_idx)\n",
" correct_conf.pop(same_idx)\n",
" correct_idx.append(match_idx)\n",
" correct_conf.append(pred_confs[k])\n",
"\n",
" for t in correct_idx:\n",
" true_positive_dict[true_labels_list[t]] += 1\n",
"\n",
" if calc_now:\n",
" # avoid divided by 0\n",
" recall = sum(true_positive_dict.values()) / (sum(true_labels_dict.values()) + 1e-6)\n",
" precision = sum(true_positive_dict.values()) / (sum(pred_labels_dict.values()) + 1e-6)\n",
"\n",
" return recall, precision\n",
" else:\n",
" return true_positive_dict, true_labels_dict, pred_labels_dict\n",
"\n",
"\n",
"def evaluate_on_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, y_pred, y_true, num_classes, iou_thresh=0.5, calc_now=True):\n",
" num_images = y_true[0].shape[0]\n",
" true_labels_dict = {i: 0 for i in range(num_classes)}\n",
" pred_labels_dict = {i: 0 for i in range(num_classes)}\n",
" true_positive_dict = {i: 0 for i in range(num_classes)}\n",
"\n",
" for i in range(num_images):\n",
" true_labels_list, true_boxes_list = [], []\n",
" for j in range(3):\n",
" true_probs_temp = y_true[j][i][..., 5:-1]\n",
" true_boxes_temp = y_true[j][i][..., 0:4]\n",
"\n",
" object_mask = true_probs_temp.sum(axis=-1) > 0\n",
"\n",
" true_probs_temp = true_probs_temp[object_mask]\n",
" true_boxes_temp = true_boxes_temp[object_mask]\n",
"\n",
" true_labels_list += np.argmax(true_probs_temp, axis=-1).tolist()\n",
" true_boxes_list += true_boxes_temp.tolist()\n",
"\n",
" if len(true_labels_list) != 0:\n",
" for cls, count in Counter(true_labels_list).items():\n",
" true_labels_dict[cls] += count\n",
"\n",
" true_boxes = np.array(true_boxes_list)\n",
" box_centers, box_sizes = true_boxes[:, 0:2], true_boxes[:, 2:4]\n",
" true_boxes[:, 0:2] = box_centers - box_sizes / 2.\n",
" true_boxes[:, 2:4] = true_boxes[:, 0:2] + box_sizes\n",
"\n",
" pred_boxes = y_pred[0][i:i + 1]\n",
" pred_confs = y_pred[1][i:i + 1]\n",
" pred_probs = y_pred[2][i:i + 1]\n",
"\n",
" pred_boxes, pred_confs, pred_labels = sess.run(gpu_nms_op, feed_dict={pred_boxes_flag: pred_boxes, pred_scores_flag: pred_confs * pred_probs})\n",
"\n",
" pred_labels_list = [] if pred_labels is None else pred_labels.tolist()\n",
" if pred_labels_list == []:\n",
" continue\n",
"\n",
" # calc iou\n",
" iou_matrix = calc_iou(pred_boxes, true_boxes)\n",
" max_iou_idx = np.argmax(iou_matrix, axis=-1)\n",
"\n",
" correct_idx = []\n",
" correct_conf = []\n",
" for k in range(max_iou_idx.shape[0]):\n",
" pred_labels_dict[pred_labels_list[k]] += 1\n",
" match_idx = max_iou_idx[k] # V level\n",
" if iou_matrix[k, match_idx] > iou_thresh and true_labels_list[match_idx] == pred_labels_list[k]:\n",
" if match_idx not in correct_idx:\n",
" correct_idx.append(match_idx)\n",
" correct_conf.append(pred_confs[k])\n",
" else:\n",
" same_idx = correct_idx.index(match_idx)\n",
" if pred_confs[k] > correct_conf[same_idx]:\n",
" correct_idx.pop(same_idx)\n",
" correct_conf.pop(same_idx)\n",
" correct_idx.append(match_idx)\n",
" correct_conf.append(pred_confs[k])\n",
"\n",
" for t in correct_idx:\n",
" true_positive_dict[true_labels_list[t]] += 1\n",
"\n",
" if calc_now:\n",
" # avoid divided by 0\n",
" recall = sum(true_positive_dict.values()) / (sum(true_labels_dict.values()) + 1e-6)\n",
" precision = sum(true_positive_dict.values()) / (sum(pred_labels_dict.values()) + 1e-6)\n",
"\n",
" return recall, precision\n",
" else:\n",
" return true_positive_dict, true_labels_dict, pred_labels_dict\n",
"\n",
"\n",
"def get_preds_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, image_ids, y_pred):\n",
" image_id = image_ids[0]\n",
"\n",
" pred_boxes = y_pred[0][0:1]\n",
" pred_confs = y_pred[1][0:1]\n",
" pred_probs = y_pred[2][0:1]\n",
"\n",
" boxes, scores, labels = sess.run(gpu_nms_op, feed_dict={pred_boxes_flag: pred_boxes, pred_scores_flag: pred_confs * pred_probs})\n",
"\n",
" pred_content = []\n",
" for i in range(len(labels)):\n",
" x_min, y_min, x_max, y_max = boxes[i]\n",
" score = scores[i]\n",
" label = labels[i]\n",
" pred_content.append([image_id, x_min, y_min, x_max, y_max, score, label])\n",
"\n",
" return pred_content\n",
"\n",
"gt_dict = {} # key: img_id, value: gt object list\n",
"def parse_gt_rec(gt_filename, compression_type, target_img_size, letterbox_resize=True):\n",
" global gt_dict\n",
"\n",
" if not gt_dict:\n",
" new_width, new_height = target_img_size\n",
"\n",
" with TFRecordIterator(gt_filename, compression_type) as reader:\n",
" for data in reader:\n",
" img_id, image, boxes, labels, ori_width, ori_height = parse_record(data)\n",
"\n",
" objects = []\n",
" for i in range(len(labels)):\n",
" x_min, y_min, x_max, y_max = boxes[i]\n",
" label = labels[i]\n",
"\n",
" if letterbox_resize:\n",
" resize_ratio = min(new_width / ori_width, new_height / ori_height)\n",
"\n",
" resize_w = int(resize_ratio * ori_width)\n",
" resize_h = int(resize_ratio * ori_height)\n",
"\n",
" dw = int((new_width - resize_w) / 2)\n",
" dh = int((new_height - resize_h) / 2)\n",
"\n",
" objects.append([x_min * resize_ratio + dw,\n",
" y_min * resize_ratio + dh,\n",
" x_max * resize_ratio + dw,\n",
" y_max * resize_ratio + dh,\n",
" label])\n",
" else:\n",
" objects.append([x_min * new_width / ori_width,\n",
" y_min * new_height / ori_height,\n",
" x_max * new_width / ori_width,\n",
" y_max * new_height / ori_height,\n",
" label])\n",
" gt_dict[img_id] = objects\n",
" return gt_dict\n",
"\n",
"\n",
"# The following two functions are modified from FAIR's Detectron repo to calculate mAP:\n",
"# https://github.com/facebookresearch/Detectron/blob/master/detectron/datasets/voc_eval.py\n",
"def voc_ap(rec, prec, use_07_metric=False):\n",
" if use_07_metric:\n",
" ap = 0.\n",
" for t in np.arange(0., 1.1, 0.1):\n",
" if np.sum(rec >= t) == 0:\n",
" p = 0\n",
" else:\n",
" p = np.max(prec[rec >= t])\n",
" ap = ap + p / 11.\n",
" else:\n",
" mrec = np.concatenate(([0.], rec, [1.]))\n",
" mpre = np.concatenate(([0.], prec, [0.]))\n",
"\n",
" for i in range(mpre.size - 1, 0, -1):\n",
" mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])\n",
"\n",
" i = np.where(mrec[1:] != mrec[:-1])[0]\n",
"\n",
" ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])\n",
" return ap\n",
"\n",
"\n",
"def voc_eval(gt_dict, val_preds, classidx, iou_thres=0.5, use_07_metric=False):\n",
" # 1.obtain gt: extract all gt objects for this class\n",
" class_recs = {}\n",
" npos = 0\n",
" for img_id in gt_dict:\n",
" R = [obj for obj in gt_dict[img_id] if obj[-1] == classidx]\n",
" bbox = np.array([x[:4] for x in R])\n",
" det = [False] * len(R)\n",
" npos += len(R)\n",
" class_recs[img_id] = {'bbox': bbox, 'det': det}\n",
"\n",
" # 2. obtain pred results\n",
" pred = [x for x in val_preds if x[-1] == classidx]\n",
" img_ids = [x[0] for x in pred]\n",
" confidence = np.array([x[-2] for x in pred])\n",
" BB = np.array([[x[1], x[2], x[3], x[4]] for x in pred])\n",
"\n",
" # 3. sort by confidence\n",
" sorted_ind = np.argsort(-confidence)\n",
" try:\n",
" BB = BB[sorted_ind, :]\n",
" except:\n",
" print('no box, ignore')\n",
" return 1e-6, 1e-6, 0, 0, 0\n",
" img_ids = [img_ids[x] for x in sorted_ind]\n",
"\n",
" # 4. mark TPs and FPs\n",
" nd = len(img_ids)\n",
" tp = np.zeros(nd)\n",
" fp = np.zeros(nd)\n",
"\n",
" for d in range(nd):\n",
" R = class_recs[img_ids[d]]\n",
" bb = BB[d, :]\n",
" ovmax = -np.Inf\n",
" BBGT = R['bbox']\n",
"\n",
" if BBGT.size > 0:\n",
" ixmin = np.maximum(BBGT[:, 0], bb[0])\n",
" iymin = np.maximum(BBGT[:, 1], bb[1])\n",
" ixmax = np.minimum(BBGT[:, 2], bb[2])\n",
" iymax = np.minimum(BBGT[:, 3], bb[3])\n",
" iw = np.maximum(ixmax - ixmin + 1., 0.)\n",
" ih = np.maximum(iymax - iymin + 1., 0.)\n",
" inters = iw * ih\n",
"\n",
" uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + (BBGT[:, 2] - BBGT[:, 0] + 1.) * (\n",
" BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)\n",
"\n",
" overlaps = inters / uni\n",
" ovmax = np.max(overlaps)\n",
" jmax = np.argmax(overlaps)\n",
"\n",
" if ovmax > iou_thres:\n",
" # gt not matched yet\n",
" if not R['det'][jmax]:\n",
" tp[d] = 1.\n",
" R['det'][jmax] = 1\n",
" else:\n",
" fp[d] = 1.\n",
" else:\n",
" fp[d] = 1.\n",
"\n",
" fp = np.cumsum(fp)\n",
" tp = np.cumsum(tp)\n",
" rec = tp / float(npos)\n",
" # avoid divide by zero in case the first detection matches a difficult\n",
" prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)\n",
" ap = voc_ap(rec, prec, use_07_metric)\n",
"\n",
" # return rec, prec, ap\n",
" return npos, nd, tp[-1] / float(npos), tp[-1] / float(nd), ap"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "X4uQxNl0FRli",
"colab_type": "code",
"outputId": "c2b22c73-6195-4b80-d1b4-5ada76ef3da8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 161
}
},
"source": [
"## model\n",
"\n",
"slim = tf.contrib.slim\n",
"\n",
"def conv2d(inputs, filters, kernel_size, strides=1):\n",
" def _fixed_padding(inputs, kernel_size):\n",
" pad_total = kernel_size - 1\n",
" pad_beg = pad_total // 2\n",
" pad_end = pad_total - pad_beg\n",
"\n",
" padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\n",
" [pad_beg, pad_end], [0, 0]], mode='CONSTANT')\n",
" return padded_inputs\n",
" if strides > 1: \n",
" inputs = _fixed_padding(inputs, kernel_size)\n",
" inputs = slim.conv2d(inputs, filters, kernel_size, stride=strides,\n",
" padding=('SAME' if strides == 1 else 'VALID'))\n",
" return inputs\n",
"\n",
"def darknet53_body(inputs):\n",
" def res_block(inputs, filters):\n",
" shortcut = inputs\n",
" net = conv2d(inputs, filters * 1, 1)\n",
" net = conv2d(net, filters * 2, 3)\n",
"\n",
" net = net + shortcut\n",
"\n",
" return net\n",
" \n",
" # first two conv2d layers\n",
" net = conv2d(inputs, 32, 3, strides=1)\n",
" net = conv2d(net, 64, 3, strides=2)\n",
"\n",
" # res_block * 1\n",
" net = res_block(net, 32)\n",
"\n",
" net = conv2d(net, 128, 3, strides=2)\n",
"\n",
" # res_block * 2\n",
" for i in range(2):\n",
" net = res_block(net, 64)\n",
"\n",
" net = conv2d(net, 256, 3, strides=2)\n",
"\n",
" # res_block * 8\n",
" for i in range(8):\n",
" net = res_block(net, 128)\n",
"\n",
" route_1 = net\n",
" net = conv2d(net, 512, 3, strides=2)\n",
"\n",
" # res_block * 8\n",
" for i in range(8):\n",
" net = res_block(net, 256)\n",
"\n",
" route_2 = net\n",
" net = conv2d(net, 1024, 3, strides=2)\n",
"\n",
" # res_block * 4\n",
" for i in range(4):\n",
" net = res_block(net, 512)\n",
" route_3 = net\n",
"\n",
" return route_1, route_2, route_3\n",
"\n",
"\n",
"def yolo_block(inputs, filters):\n",
" net = conv2d(inputs, filters * 1, 1)\n",
" net = conv2d(net, filters * 2, 3)\n",
" net = conv2d(net, filters * 1, 1)\n",
" net = conv2d(net, filters * 2, 3)\n",
" net = conv2d(net, filters * 1, 1)\n",
" route = net\n",
" net = conv2d(net, filters * 2, 3)\n",
" return route, net\n",
"\n",
"\n",
"def upsample_layer(inputs, out_shape):\n",
" new_height, new_width = out_shape[1], out_shape[2]\n",
" # NOTE: here height is the first\n",
" inputs = tf.image.resize_nearest_neighbor(inputs, (new_height, new_width), name='upsampled')\n",
" return inputs\n",
"\n",
"class yolov3(object):\n",
"\n",
" def __init__(self, class_num, anchors, use_label_smooth=False, use_focal_loss=False, batch_norm_decay=0.999, weight_decay=5e-4, use_static_shape=True):\n",
" self.class_num = class_num\n",
" self.anchors = anchors\n",
" self.batch_norm_decay = batch_norm_decay\n",
" self.use_label_smooth = use_label_smooth\n",
" self.use_focal_loss = use_focal_loss\n",
" self.weight_decay = weight_decay\n",
" self.use_static_shape = use_static_shape\n",
"\n",
" def forward(self, inputs, is_training=False, reuse=False):\n",
" # the input size: [height, weight] format\n",
" self.img_size = tf.shape(inputs)[1:3]\n",
" print(\"Img size:\", self.img_size)\n",
"\t\t\n",
" batch_norm_params = {\n",
" 'decay': self.batch_norm_decay,\n",
" 'epsilon': 1e-05,\n",
" 'scale': True,\n",
" 'is_training': is_training,\n",
" 'fused': None,\n",
" }\n",
"\n",
" with slim.arg_scope([slim.conv2d, slim.batch_norm], reuse=reuse):\n",
" with slim.arg_scope([slim.conv2d], \n",
" normalizer_fn=slim.batch_norm,\n",
" normalizer_params=batch_norm_params,\n",
" biases_initializer=None,\n",
" activation_fn=lambda x: tf.nn.leaky_relu(x, alpha=0.1),\n",
" weights_regularizer=slim.l2_regularizer(self.weight_decay)):\n",
"\n",
" with tf.variable_scope('darknet53_body'):\n",
" route_1, route_2, route_3 = darknet53_body(inputs)\n",
"\n",
" with tf.variable_scope('yolov3_head'):\n",
" inter1, net = yolo_block(route_3, 512)\n",
" feature_map_1 = slim.conv2d(net, 3 * (5 + self.class_num), 1,\n",
" stride=1, normalizer_fn=None,\n",
" activation_fn=None, biases_initializer=tf.zeros_initializer())\n",
" feature_map_1 = tf.identity(feature_map_1, name='feature_map_1')\n",
"\n",
" inter1 = conv2d(inter1, 256, 1)\n",
" inter1 = upsample_layer(inter1, route_2.get_shape().as_list() if self.use_static_shape else tf.shape(route_2))\n",
" concat1 = tf.concat([inter1, route_2], axis=3)\n",
"\n",
" inter2, net = yolo_block(concat1, 256)\n",
" feature_map_2 = slim.conv2d(net, 3 * (5 + self.class_num), 1,\n",
" stride=1, normalizer_fn=None,\n",
" activation_fn=None, biases_initializer=tf.zeros_initializer())\n",
" feature_map_2 = tf.identity(feature_map_2, name='feature_map_2')\n",
"\n",
" inter2 = conv2d(inter2, 128, 1)\n",
" inter2 = upsample_layer(inter2, route_1.get_shape().as_list() if self.use_static_shape else tf.shape(route_1))\n",
" concat2 = tf.concat([inter2, route_1], axis=3)\n",
"\n",
" _, feature_map_3 = yolo_block(concat2, 128)\n",
" feature_map_3 = slim.conv2d(feature_map_3, 3 * (5 + self.class_num), 1,\n",
" stride=1, normalizer_fn=None,\n",
" activation_fn=None, biases_initializer=tf.zeros_initializer())\n",
" feature_map_3 = tf.identity(feature_map_3, name='feature_map_3')\n",
"\n",
" return feature_map_1, feature_map_2, feature_map_3\n",
"\n",
" def reorg_layer(self, feature_map, anchors):\t\n",
" # size : [h, w] format\n",
" grid_size = feature_map.get_shape().as_list()[1:3] if self.use_static_shape else tf.shape(feature_map)[1:3] # [13, 13]\n",
" ratio = tf.cast(self.img_size / grid_size, tf.float32)\n",
"\t\t\n",
" # anchor : [w, h] format\n",
" rescaled_anchors = [(anchor[0] / ratio[1], anchor[1] / ratio[0]) for anchor in anchors]\n",
"\n",
" feature_map = tf.reshape(feature_map, [-1, grid_size[0], grid_size[1], 3, 5 + self.class_num])\n",
"\t\t\n",
" box_centers, box_sizes, conf_logits, prob_logits = tf.split(feature_map, [2, 2, 1, self.class_num], axis=-1)\n",
" box_centers = tf.nn.sigmoid(box_centers)\n",
"\n",
" grid_x = tf.range(grid_size[1], dtype=tf.int32)\n",
" grid_y = tf.range(grid_size[0], dtype=tf.int32)\n",
" grid_x, grid_y = tf.meshgrid(grid_x, grid_y)\n",
" x_offset = tf.reshape(grid_x, (-1, 1))\n",
" y_offset = tf.reshape(grid_y, (-1, 1))\n",
" x_y_offset = tf.concat([x_offset, y_offset], axis=-1)\n",
"\t\t\n",
" x_y_offset = tf.cast(tf.reshape(x_y_offset, [grid_size[0], grid_size[1], 1, 2]), tf.float32)\n",
"\n",
" box_centers = box_centers + x_y_offset\n",
" box_centers = box_centers * ratio[::-1]\n",
"\n",
" box_sizes = tf.exp(box_sizes) * rescaled_anchors\n",
" box_sizes = box_sizes * ratio[::-1]\n",
"\n",
" boxes = tf.concat([box_centers, box_sizes], axis=-1)\n",
"\n",
" return x_y_offset, boxes, conf_logits, prob_logits\n",
" \n",
" def predict(self, feature_maps):\n",
" feature_map_1, feature_map_2, feature_map_3 = feature_maps\n",
"\n",
" feature_map_anchors = [(feature_map_1, self.anchors[6:9]),\n",
" (feature_map_2, self.anchors[3:6]),\n",
" (feature_map_3, self.anchors[0:3])]\n",
" reorg_results = [self.reorg_layer(feature_map, anchors) for (feature_map, anchors) in feature_map_anchors]\n",
"\n",
" def _reshape_logit(result):\n",
" x_y_offset, boxes, conf_logits, prob_logits = result\n",
" grid_size = x_y_offset.get_shape().as_list()[:2] if self.use_static_shape else tf.shape(x_y_offset)[:2]\n",
" boxes = tf.reshape(boxes, [-1, grid_size[0] * grid_size[1] * 3, 4])\n",
" conf_logits = tf.reshape(conf_logits, [-1, grid_size[0] * grid_size[1] * 3, 1])\n",
" prob_logits = tf.reshape(prob_logits, [-1, grid_size[0] * grid_size[1] * 3, self.class_num])\n",
" return boxes, conf_logits, prob_logits\n",
"\n",
" boxes_list, confs_list, probs_list = [], [], []\n",
"\t\t\n",
" for result in reorg_results:\n",
" boxes, conf_logits, prob_logits = _reshape_logit(result)\n",
" confs = tf.sigmoid(conf_logits)\n",
" probs = tf.sigmoid(prob_logits)\n",
" boxes_list.append(boxes)\n",
" confs_list.append(confs)\n",
" probs_list.append(probs)\n",
" \n",
" boxes = tf.concat(boxes_list, axis=1)\n",
" confs = tf.concat(confs_list, axis=1)\n",
" probs = tf.concat(probs_list, axis=1)\n",
"\n",
" center_x, center_y, width, height = tf.split(boxes, [1, 1, 1, 1], axis=-1)\n",
" x_min = center_x - width / 2\n",
" y_min = center_y - height / 2\n",
" x_max = center_x + width / 2\n",
" y_max = center_y + height / 2\n",
"\n",
" boxes = tf.concat([x_min, y_min, x_max, y_max], axis=-1)\n",
"\n",
" return boxes, confs, probs\n",
" \n",
" def loss_layer(self, feature_map_i, y_true, anchors):\n",
" grid_size = tf.shape(feature_map_i)[1:3]\n",
" ratio = tf.cast(self.img_size / grid_size, tf.float32)\n",
" # N: batch_size\n",
" N = tf.cast(tf.shape(feature_map_i)[0], tf.float32)\n",
"\n",
" x_y_offset, pred_boxes, pred_conf_logits, pred_prob_logits = self.reorg_layer(feature_map_i, anchors)\n",
"\n",
"\t\t### mask\n",
" object_mask = y_true[..., 4:5]\n",
" ignore_mask = tf.TensorArray(tf.float32, size=0, dynamic_size=True)\n",
"\t\t\n",
" def loop_cond(idx, ignore_mask):\n",
" return tf.less(idx, tf.cast(N, tf.int32))\n",
"\t\t\t\n",
" def loop_body(idx, ignore_mask):\n",
" valid_true_boxes = tf.boolean_mask(y_true[idx, ..., 0:4], tf.cast(object_mask[idx, ..., 0], 'bool'))\n",
"\t\t\t\n",
" iou = self.box_iou(pred_boxes[idx], valid_true_boxes)\t\t\t\n",
" best_iou = tf.reduce_max(iou, axis=-1)\n",
"\t\t\t\n",
" ignore_mask_tmp = tf.cast(best_iou < 0.5, tf.float32)\n",
"\t\t\t\n",
" ignore_mask = ignore_mask.write(idx, ignore_mask_tmp)\n",
" return idx + 1, ignore_mask\n",
"\t\t\t\n",
" _, ignore_mask = tf.while_loop(cond=loop_cond, body=loop_body, loop_vars=[0, ignore_mask])\n",
" ignore_mask = ignore_mask.stack()\n",
" ignore_mask = tf.expand_dims(ignore_mask, -1)\n",
"\n",
" pred_box_xy = pred_boxes[..., 0:2]\n",
" pred_box_wh = pred_boxes[..., 2:4]\n",
"\n",
" true_xy = y_true[..., 0:2] / ratio[::-1] - x_y_offset\n",
" pred_xy = pred_box_xy / ratio[::-1] - x_y_offset\n",
"\n",
" true_tw_th = y_true[..., 2:4] / anchors\n",
" pred_tw_th = pred_box_wh / anchors\n",
"\t\t\n",
" true_tw_th = tf.where(condition=tf.equal(true_tw_th, 0),\n",
" x=tf.ones_like(true_tw_th), y=true_tw_th)\n",
" pred_tw_th = tf.where(condition=tf.equal(pred_tw_th, 0),\n",
" x=tf.ones_like(pred_tw_th), y=pred_tw_th)\n",
" true_tw_th = tf.log(tf.clip_by_value(true_tw_th, 1e-9, 1e9))\n",
" pred_tw_th = tf.log(tf.clip_by_value(pred_tw_th, 1e-9, 1e9))\n",
"\n",
" box_loss_scale = 2. - (y_true[..., 2:3] / tf.cast(self.img_size[1], tf.float32)) * (y_true[..., 3:4] / tf.cast(self.img_size[0], tf.float32))\n",
"\n",
" ### loss\n",
"\t\t\n",
" mix_w = y_true[..., -1:]\n",
"\t\t\n",
" xy_loss = tf.reduce_sum(tf.square(true_xy - pred_xy) * object_mask * box_loss_scale * mix_w) / N\n",
" wh_loss = tf.reduce_sum(tf.square(true_tw_th - pred_tw_th) * object_mask * box_loss_scale * mix_w) / N\n",
"\n",
" conf_pos_mask = object_mask\n",
" conf_neg_mask = (1 - object_mask) * ignore_mask\n",
" conf_loss_pos = conf_pos_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=object_mask, logits=pred_conf_logits)\n",
" conf_loss_neg = conf_neg_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=object_mask, logits=pred_conf_logits)\n",
"\t\t\n",
" conf_loss = conf_loss_pos + conf_loss_neg\n",
"\n",
" if self.use_focal_loss:\n",
" alpha = 1.0\n",
" gamma = 2.0\n",
" focal_mask = alpha * tf.pow(tf.abs(object_mask - tf.sigmoid(pred_conf_logits)), gamma)\n",
" conf_loss *= focal_mask\n",
" conf_loss = tf.reduce_sum(conf_loss * mix_w) / N\n",
"\n",
" if self.use_label_smooth:\n",
" delta = 0.01\n",
" label_target = (1 - delta) * y_true[..., 5:-1] + delta * 1. / self.class_num\n",
" else:\n",
" label_target = y_true[..., 5:-1]\n",
"\t\t\t\n",
" class_loss = object_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=label_target, logits=pred_prob_logits) * mix_w\n",
" class_loss = tf.reduce_sum(class_loss) / N\n",
"\n",
" return xy_loss, wh_loss, conf_loss, class_loss\n",
" \n",
"\n",
" def box_iou(self, pred_boxes, valid_true_boxes):\n",
" pred_box_xy = pred_boxes[..., 0:2]\n",
" pred_box_wh = pred_boxes[..., 2:4]\n",
"\n",
" pred_box_xy = tf.expand_dims(pred_box_xy, -2)\n",
" pred_box_wh = tf.expand_dims(pred_box_wh, -2)\n",
"\n",
" true_box_xy = valid_true_boxes[:, 0:2]\n",
" true_box_wh = valid_true_boxes[:, 2:4]\n",
"\n",
" intersect_mins = tf.maximum(pred_box_xy - pred_box_wh / 2.,\n",
" true_box_xy - true_box_wh / 2.)\n",
" intersect_maxs = tf.minimum(pred_box_xy + pred_box_wh / 2.,\n",
" true_box_xy + true_box_wh / 2.)\n",
" intersect_wh = tf.maximum(intersect_maxs - intersect_mins, 0.)\n",
"\n",
" intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]\n",
" pred_box_area = pred_box_wh[..., 0] * pred_box_wh[..., 1]\n",
" true_box_area = true_box_wh[..., 0] * true_box_wh[..., 1]\n",
" true_box_area = tf.expand_dims(true_box_area, axis=0)\n",
"\n",
" iou = intersect_area / (pred_box_area + true_box_area - intersect_area + 1e-10)\n",
"\n",
" return iou\n",
"\n",
" \n",
" def compute_loss(self, y_pred, y_true):\n",
" loss_xy, loss_wh, loss_conf, loss_class = 0., 0., 0., 0.\n",
" anchor_group = [self.anchors[6:9], self.anchors[3:6], self.anchors[0:3]]\n",
"\n",
" for i in range(len(y_pred)):\n",
" result = self.loss_layer(y_pred[i], y_true[i], anchor_group[i])\n",
" loss_xy += result[0]\n",
" loss_wh += result[1]\n",
" loss_conf += result[2]\n",
" loss_class += result[3]\n",
" total_loss = loss_xy + loss_wh + loss_conf + loss_class\n",
" return [total_loss, loss_xy, loss_wh, loss_conf, loss_class]"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:\n",
"The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
"For more information, please see:\n",
" * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
" * https://github.com/tensorflow/addons\n",
" * https://github.com/tensorflow/io (for I/O related ops)\n",
"If you depend on functionality not listed there, please file an issue.\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Nlddq-K7AJin",
"colab_type": "code",
"outputId": "c5baed55-0d4e-4c65-fa7d-340b27baf8f9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 89
}
},
"source": [
"## arguments\n",
"\n",
"import math\n",
"\n",
"\n",
"### Some paths\n",
"\n",
"data_path = '/content/gdrive/My Drive/yolo/data/'\n",
"train_file = data_path + 'train.tfrecord' # The path of the training txt file.\n",
"val_file = data_path + 'val.tfrecord' # The path of the validation txt file.\n",
"restore_path = data_path + 'darknet_weights/yolov3.ckpt' # The path of the weights to restore.\n",
"save_dir = '/content/gdrive/My Drive/yolo/checkpoint/' # The directory of the weights to save.\n",
"\n",
"### we are not using tensorboard logs in this code\n",
"\n",
"log_dir = data_path + 'logs/' # The directory to store the tensorboard log files.\n",
"progress_log_path = data_path + 'progress.log' # The path to record the training progress.\n",
"\n",
"anchor_path = data_path + 'yolo_anchors.txt' # The path of the anchor txt file.\n",
"class_name_path = data_path + 'classes.txt' # The path of the class names.\n",
"\n",
"### Training releated numbers\n",
"batch_size = 4\n",
"img_size = [416, 416] # Images will be resized to `img_size` and fed to the network, size format: [width, height]\n",
"letterbox_resizing = True # Whether to use the letterbox resize, i.e., keep the original aspect ratio in the resized image.\n",
"total_epoches = 10\n",
"train_evaluation_step = 10 # Evaluate on the training batch after some steps.\n",
"val_evaluation_epoch = 2 # Evaluate on the whole validation dataset after some epochs. Set to None to evaluate every epoch.\n",
"save_epoch = 5 # Save the model after some epochs.\n",
"batch_norm_decay = 0.99 # decay in bn ops\n",
"weight_decay = 5e-4 # l2 weight decay\n",
"current_global_step = 0 # used when resuming training\n",
"\n",
"### tf.data parameters\n",
"num_threads = 10 # Number of threads for image processing used in tf.data pipeline.\n",
"prefetech_buffer = 5 # Prefetech_buffer used in tf.data pipeline.\n",
"\n",
"### Learning rate and optimizer\n",
"optimizer_name = 'momentum' # Chosen from [sgd, momentum, adam, rmsprop]\n",
"save_optimizer = True # Whether to save the optimizer parameters into the checkpoint file.\n",
"learning_rate_init = 1e-4\n",
"lr_type = 'piecewise' # Chosen from [fixed, exponential, cosine_decay, cosine_decay_restart, piecewise]\n",
"lr_decay_epoch = 5 # Epochs after which learning rate decays. Int or float. Used when chosen `exponential` and `cosine_decay_restart` lr_type.\n",
"lr_decay_factor = 0.96 # The learning rate decay factor. Used when chosen `exponential` lr_type.\n",
"lr_lower_bound = 1e-6 # The minimum learning rate.\n",
"# only used in piecewise lr type\n",
"pw_boundaries = [30, 50] # epoch based boundaries\n",
"pw_values = [learning_rate_init, 3e-5, 1e-5]\n",
"\n",
"### Load and finetune\n",
"# Choose the parts you want to restore the weights. List form.\n",
"# restore_include: None, restore_exclude: None => restore the whole model\n",
"# restore_include: None, restore_exclude: scope => restore the whole model except `scope`\n",
"# restore_include: scope1, restore_exclude: scope2 => if scope1 contains scope2, restore scope1 and not restore scope2 (scope1 - scope2)\n",
"# choise 1: only restore the darknet body\n",
"# restore_include = ['yolov3/darknet53_body']\n",
"# restore_exclude = None\n",
"# choise 2: restore all layers except the last 3 conv2d layers in 3 scale\n",
"restore_include = None\n",
"restore_exclude = ['yolov3/yolov3_head/Conv_14', 'yolov3/yolov3_head/Conv_6', 'yolov3/yolov3_head/Conv_22']\n",
"# Choose the parts you want to finetune. List form.\n",
"# Set to None to train the whole model.\n",
"\n",
"update_part = ['yolov3/yolov3_head']\n",
"\n",
"### other training strategies\n",
"multi_scale_train = True # Whether to apply multi-scale training strategy. Image size varies from [320, 320] to [640, 640] by default.\n",
"use_label_smooth = True # Whether to use class label smoothing strategy.\n",
"use_focal_loss = True # Whether to apply focal loss on the conf loss.\n",
"use_mix_up = True # Whether to use mix up data augmentation strategy. \n",
"use_warm_up = True # whether to use warm up strategy to prevent from gradient exploding.\n",
"warm_up_epoch = 2 # Warm up training epoches. Set to a larger value if gradient explodes.\n",
"\n",
"### some constants in validation\n",
"# nms\n",
"nms_threshold = 0.45 # iou threshold in nms operation\n",
"score_threshold = 0.01 # threshold of the probability of the classes in nms operation, i.e. score = pred_confs * pred_probs. set lower for higher recall.\n",
"nms_topk = 150 # keep at most nms_topk outputs after nms\n",
"# mAP eval\n",
"eval_threshold = 0.5 # the iou threshold applied in mAP evaluation\n",
"use_voc_07_metric = False # whether to use voc 2007 evaluation metric, i.e. the 11-point metric\n",
"\n",
"### parse some params\n",
"anchors = parse_anchors(anchor_path)\n",
"classes = read_class_names(class_name_path)\n",
"class_num = len(classes)\n",
"train_img_cnt = TFRecordIterator(train_file, 'GZIP').count()\n",
"val_img_cnt = TFRecordIterator(val_file, 'GZIP').count()\n",
"train_batch_num = int(math.ceil(float(train_img_cnt) / batch_size))\n",
"\n",
"lr_decay_freq = int(train_batch_num * lr_decay_epoch)\n",
"pw_boundaries = [float(i) * train_batch_num + current_global_step for i in pw_boundaries]\n"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From <ipython-input-2-ea7f0591b13c>:7: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use eager execution and: \n",
"`tf.data.TFRecordDataset(path)`\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NagT2oNZFf0q",
"colab_type": "code",
"colab": {}
},
"source": [
"## train\n",
"\n",
"import os\n",
"from tqdm import trange\n",
"\n",
"if training:\n",
" is_training = tf.placeholder(tf.bool, name=\"phase_train\")\n",
" handle_flag = tf.placeholder(tf.string, [], name='iterator_handle_flag')\n",
"\n",
" pred_boxes_flag = tf.placeholder(tf.float32, [1, None, None])\n",
" pred_scores_flag = tf.placeholder(tf.float32, [1, None, None])\n",
" gpu_nms_op = gpu_nms(pred_boxes_flag, pred_scores_flag, class_num, nms_topk, score_threshold, nms_threshold)\n",
"\n",
" ### tf.data pipeline\n",
" train_dataset = tf.data.TFRecordDataset(filenames=train_file, compression_type='GZIP')\n",
" train_dataset = train_dataset.shuffle(train_img_cnt)\n",
" train_dataset = train_dataset.batch(batch_size)\n",
" train_dataset = train_dataset.map(\n",
" lambda x: tf.py_func(get_batch_data,\n",
" inp=[x, class_num, img_size, anchors, True, multi_scale_train, use_mix_up, letterbox_resizing],\n",
" Tout=[tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]),\n",
" num_parallel_calls=num_threads\n",
" )\n",
" train_dataset = train_dataset.prefetch(prefetech_buffer)\n",
"\n",
" val_dataset = tf.data.TFRecordDataset(filenames=val_file, compression_type='GZIP')\n",
" val_dataset = val_dataset.batch(1)\n",
" val_dataset = val_dataset.map(\n",
" lambda x: tf.py_func(get_batch_data,\n",
" inp=[x, class_num, img_size, anchors, False, False, False, letterbox_resizing],\n",
" Tout=[tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]),\n",
" num_parallel_calls=num_threads\n",
" )\n",
" val_dataset.prefetch(prefetech_buffer)\n",
"\n",
" iterator = tf.data.Iterator.from_structure(train_dataset.output_types, train_dataset.output_shapes)\n",
" train_init_op = iterator.make_initializer(train_dataset)\n",
" val_init_op = iterator.make_initializer(val_dataset)\n",
"\n",
" image_ids, image, y_true_13, y_true_26, y_true_52 = iterator.get_next()\n",
" y_true = [y_true_13, y_true_26, y_true_52]\n",
"\n",
" image_ids.set_shape([None])\n",
" image.set_shape([None, None, None, 3])\n",
" for y in y_true:\n",
" y.set_shape([None, None, None, None, None])\n",
"\n",
"\n",
" ### Model definition\n",
" yolo_model = yolov3(class_num, anchors, use_label_smooth, use_focal_loss, batch_norm_decay, weight_decay, use_static_shape=False)\n",
"\n",
" with tf.variable_scope('yolov3'):\n",
" pred_feature_maps = yolo_model.forward(image, is_training=is_training)\n",
"\n",
" loss = yolo_model.compute_loss(pred_feature_maps, y_true)\n",
" y_pred = yolo_model.predict(pred_feature_maps)\n",
"\n",
" l2_loss = tf.losses.get_regularization_loss()\n",
"\n",
" saver_to_restore = tf.train.Saver(var_list=tf.contrib.framework.get_variables_to_restore(include=restore_include, exclude=restore_exclude))\n",
" update_vars = tf.contrib.framework.get_variables_to_restore(include=update_part)\n",
"\n",
"\n",
" global_step = tf.Variable(float(current_global_step), trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])\n",
" if use_warm_up:\n",
" learning_rate = tf.cond(tf.less(global_step, train_batch_num * warm_up_epoch), \n",
" lambda: learning_rate_init * global_step / (train_batch_num * warm_up_epoch),\n",
" lambda: config_learning_rate(global_step - train_batch_num * warm_up_epoch))\n",
" else:\n",
" learning_rate = config_learning_rate(global_step)\n",
"\n",
" optimizer = config_optimizer(optimizer_name, learning_rate)\n",
"\n",
" update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
"\n",
" with tf.control_dependencies(update_ops):\n",
" gvs = optimizer.compute_gradients(loss[0] + l2_loss, var_list=update_vars)\n",
" clip_grad_var = [gv if gv[0] is None else [\n",
" tf.clip_by_norm(gv[0], 100.), gv[1]] for gv in gvs]\n",
" train_op = optimizer.apply_gradients(clip_grad_var, global_step=global_step)\n",
"\n",
" if save_optimizer:\n",
" print('Saving optimizer parameters: ON')\n",
" saver_to_save = tf.train.Saver()\n",
" saver_best = tf.train.Saver()\n",
" else:\n",
" print('Saving optimizer parameters: OFF')\n",
"\n",
"\n",
" with tf.Session() as sess:\n",
" sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])\n",
"\n",
" if os.path.exists(restore_path):\n",
" saver_to_restore.restore(sess, restore_path)\n",
"\n",
" print('\\nStart training...: Total epoches =', total_epoches, '\\n')\n",
"\n",
" best_mAP = -np.Inf\n",
"\n",
" for epoch in range(total_epoches):\n",
" sess.run(train_init_op)\n",
" loss_total, loss_xy, loss_wh, loss_conf, loss_class = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()\n",
"\n",
" ### train part\n",
" for i in trange(train_batch_num):\n",
" _, __y_pred, __y_true, __loss, __global_step, __lr = sess.run(\n",
" [train_op, y_pred, y_true, loss, global_step, learning_rate],\n",
" feed_dict={is_training: True})\n",
"\n",
" loss_total.update(__loss[0], len(__y_pred[0]))\n",
" loss_xy.update(__loss[1], len(__y_pred[0]))\n",
" loss_wh.update(__loss[2], len(__y_pred[0]))\n",
" loss_conf.update(__loss[3], len(__y_pred[0]))\n",
" loss_class.update(__loss[4], len(__y_pred[0]))\n",
"\n",
" if __global_step % train_evaluation_step == 0 and __global_step > 0:\n",
" recall, precision = evaluate_on_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __y_pred, __y_true, class_num, nms_threshold)\n",
"\n",
" info = \"Epoch: {}, global_step: {} | loss: total: {:.2f}, xy: {:.2f}, wh: {:.2f}, conf: {:.2f}, class: {:.2f} | \".format(\n",
" epoch, int(__global_step), loss_total.average, loss_xy.average, loss_wh.average, loss_conf.average, loss_class.average)\n",
" info += 'Last batch: rec: {:.3f}, prec: {:.3f} | lr: {:.5g}'.format(recall, precision, __lr)\n",
" print(info)\n",
" \n",
" if np.isnan(loss_total.average):\n",
" print('****' * 10)\n",
" raise ArithmeticError('Gradient exploded!')\n",
"\n",
" ## train end (saving parameters)\n",
" if save_optimizer and epoch % save_epoch == 0 and epoch > 0:\n",
" if loss_total.average <= 2.:\n",
" saver_to_save.save(sess, save_dir + 'model-epoch_{}_step_{}_loss_{:.4f}_lr_{:.5g}'.format(epoch, int(__global_step), loss_total.average, __lr))\n",
"\n",
" ### validation part\n",
" if epoch % val_evaluation_epoch == 0 and epoch >= warm_up_epoch:\n",
" sess.run(val_init_op)\n",
"\n",
" val_loss_total, val_loss_xy, val_loss_wh, val_loss_conf, val_loss_class = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()\n",
"\n",
" val_preds = []\n",
"\n",
" for j in trange(val_img_cnt):\n",
" __image_ids, __y_pred, __loss = sess.run([image_ids, y_pred, loss],\n",
" feed_dict={is_training: False})\n",
" pred_content = get_preds_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __image_ids, __y_pred)\n",
" val_preds.extend(pred_content)\n",
" val_loss_total.update(__loss[0])\n",
" val_loss_xy.update(__loss[1])\n",
" val_loss_wh.update(__loss[2])\n",
" val_loss_conf.update(__loss[3])\n",
" val_loss_class.update(__loss[4])\n",
"\n",
" # calc mAP\n",
" rec_total, prec_total, ap_total = AverageMeter(), AverageMeter(), AverageMeter()\n",
" gt_dict = parse_gt_rec(val_file, 'GZIP', img_size, letterbox_resize)\n",
"\n",
" info = '======> Epoch: {}, global_step: {}, lr: {:.6g} <======\\n'.format(epoch, __global_step, __lr)\n",
"\n",
" for ii in range(class_num):\n",
" npos, nd, rec, prec, ap = voc_eval(gt_dict, val_preds, ii, iou_thres=eval_threshold, use_07_metric=use_voc_07_metric)\n",
" info += 'EVAL: Class {}: Recall: {:.4f}, Precision: {:.4f}, AP: {:.4f}\\n'.format(ii, rec, prec, ap)\n",
" rec_total.update(rec, npos)\n",
" prec_total.update(prec, nd)\n",
" ap_total.update(ap, 1)\n",
"\n",
" mAP = ap_total.average\n",
" info += 'EVAL: Recall: {:.4f}, Precison: {:.4f}, mAP: {:.4f}\\n'.format(rec_total.average, prec_total.average, mAP)\n",
" info += 'EVAL: loss: total: {:.2f}, xy: {:.2f}, wh: {:.2f}, conf: {:.2f}, class: {:.2f}\\n'.format(\n",
" val_loss_total.average, val_loss_xy.average, val_loss_wh.average, val_loss_conf.average, val_loss_class.average)\n",
" print(info)\n",
"\n",
" if save_optimizer and mAP > best_mAP:\n",
" best_mAP = mAP\n",
" saver_best.save(sess, save_dir + 'best_model_Epoch_{}_step_{}_mAP_{:.4f}_loss_{:.4f}_lr_{:.7g}'.format(\n",
" epoch, int(__global_step), best_mAP, val_loss_total.average, __lr))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "HmoSmKIuOpyC",
"colab_type": "code",
"colab": {}
},
"source": [
"## evaluation (test)\n",
"\n",
"import argparse\n",
"\n",
"if not training:\n",
"\n",
" ### ArgumentParser\n",
" parser = argparse.ArgumentParser(description=\"YOLO-V3 eval procedure.\")\n",
"\n",
" # paths\n",
" parser.add_argument(\"--eval_file\", type=str, default=\"/content/gdrive/My Drive/yolo/data/test.tfrecord\",\n",
" help=\"The path of the validation or test txt file.\")\n",
"\n",
" parser.add_argument(\"--restore_path\", type=str, default=\"/content/gdrive/My Drive/yolo/data/darknet_weights/yolov3.ckpt\",\n",
" help=\"The path of the weights to restore.\")\n",
"\n",
" parser.add_argument(\"--anchor_path\", type=str, default=\"./content/gdrive/My Drive/yolo/data/yolo_anchors.txt\",\n",
" help=\"The path of the anchor txt file.\")\n",
"\n",
" parser.add_argument(\"--class_name_path\", type=str, default=\"/content/gdrive/My Drive/yolo/data/classes.txt\",\n",
" help=\"The path of the class names.\")\n",
"\n",
" # some numbers\n",
" parser.add_argument(\"--img_size\", nargs='*', type=int, default=[416, 416],\n",
" help=\"Resize the input image to `img_size`, size format: [width, height]\")\n",
"\n",
" parser.add_argument(\"--letterbox_resize\", type=lambda x: (str(x).lower() == 'true'), default=False,\n",
" help=\"Whether to use the letterbox resize, i.e., keep the original image aspect ratio.\")\n",
"\n",
" parser.add_argument(\"--num_threads\", type=int, default=10,\n",
" help=\"Number of threads for image processing used in tf.data pipeline.\")\n",
"\n",
" parser.add_argument(\"--prefetech_buffer\", type=int, default=5,\n",
" help=\"Prefetech_buffer used in tf.data pipeline.\")\n",
"\n",
" parser.add_argument(\"--nms_threshold\", type=float, default=0.45,\n",
" help=\"IOU threshold in nms operation.\")\n",
"\n",
" parser.add_argument(\"--score_threshold\", type=float, default=0.01,\n",
" help=\"Threshold of the probability of the classes in nms operation.\")\n",
"\n",
" parser.add_argument(\"--nms_topk\", type=int, default=400,\n",
" help=\"Keep at most nms_topk outputs after nms.\")\n",
"\n",
" parser.add_argument(\"--use_voc_07_metric\", type=lambda x: (str(x).lower() == 'true'), default=False,\n",
" help=\"Whether to use the voc 2007 mAP metrics.\")\n",
"\n",
" args = parser.parse_args()\n",
"\n",
" # args params\n",
" args.anchors = parse_anchors(args.anchor_path)\n",
" args.classes = read_class_names(args.class_name_path)\n",
" args.class_num = len(args.classes)\n",
" args.img_cnt = len(open(args.eval_file, 'r').readlines())\n",
"\n",
" # setting placeholders\n",
" is_training = tf.placeholder(dtype=tf.bool, name=\"phase_train\")\n",
" handle_flag = tf.placeholder(tf.string, [], name='iterator_handle_flag')\n",
" pred_boxes_flag = tf.placeholder(tf.float32, [1, None, None])\n",
" pred_scores_flag = tf.placeholder(tf.float32, [1, None, None])\n",
" gpu_nms_op = gpu_nms(pred_boxes_flag, pred_scores_flag, args.class_num, args.nms_topk, args.score_threshold, args.nms_threshold)\n",
"\n",
" ### tf.data pipeline\n",
" val_dataset = tf.data.TFRecordDataset(filenames=args.eval_file, compression_type='GZIP')\n",
" val_dataset = val_dataset.batch(1)\n",
" val_dataset = val_dataset.map(\n",
" lambda x: tf.py_func(get_batch_data, [x, args.class_num, args.img_size, args.anchors, False, False, False, args.letterbox_resize], [tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]),\n",
" num_parallel_calls=args.num_threads\n",
" )\n",
" val_dataset.prefetch(args.prefetech_buffer)\n",
" iterator = val_dataset.make_one_shot_iterator()\n",
"\n",
" image_ids, image, y_true_13, y_true_26, y_true_52 = iterator.get_next()\n",
" image_ids.set_shape([None])\n",
" y_true = [y_true_13, y_true_26, y_true_52]\n",
" image.set_shape([None, args.img_size[1], args.img_size[0], 3])\n",
" for y in y_true:\n",
" y.set_shape([None, None, None, None, None])\n",
"\n",
" ### Model definition\n",
" yolo_model = yolov3(args.class_num, args.anchors)\n",
" with tf.variable_scope('yolov3'):\n",
" pred_feature_maps = yolo_model.forward(image, is_training=is_training)\n",
" loss = yolo_model.compute_loss(pred_feature_maps, y_true)\n",
" y_pred = yolo_model.predict(pred_feature_maps)\n",
"\n",
" saver_to_restore = tf.train.Saver()\n",
"\n",
"\n",
" with tf.Session() as sess:\n",
" sess.run([tf.global_variables_initializer()])\n",
" if os.path.exists(args.restore_path):\n",
" saver_to_restore.restore(sess, args.restore_path)\n",
" else:\n",
" raise ValueError('there is no model to evaluate. You should move/create the checkpoint file to restore path')\n",
"\n",
" print('\\nStart evaluation...\\n')\n",
"\n",
" val_loss_total, val_loss_xy, val_loss_wh, val_loss_conf, val_loss_class = \\\n",
" AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()\n",
" val_preds = []\n",
"\n",
" for j in trange(args.img_cnt):\n",
" __image_ids, __y_pred, __loss = sess.run([image_ids, y_pred, loss], feed_dict={is_training: False})\n",
" pred_content = get_preds_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __image_ids, __y_pred)\n",
"\n",
" val_preds.extend(pred_content)\n",
" val_loss_total.update(__loss[0])\n",
" val_loss_xy.update(__loss[1])\n",
" val_loss_wh.update(__loss[2])\n",
" val_loss_conf.update(__loss[3])\n",
" val_loss_class.update(__loss[4])\n",
"\n",
" rec_total, prec_total, ap_total = AverageMeter(), AverageMeter(), AverageMeter()\n",
" gt_dict = parse_gt_rec(args.eval_file, 'GZIP', args.img_size, args.letterbox_resize)\n",
" print('mAP eval:')\n",
" for ii in range(args.class_num):\n",
" npos, nd, rec, prec, ap = voc_eval(gt_dict, val_preds, ii, iou_thres=0.5, use_07_metric=args.use_voc_07_metric)\n",
" rec_total.update(rec, npos)\n",
" prec_total.update(prec, nd)\n",
" ap_total.update(ap, 1)\n",
" print('Class {}: Recall: {:.4f}, Precision: {:.4f}, AP: {:.4f}'.format(ii, rec, prec, ap))\n",
"\n",
" mAP = ap_total.average\n",
" print('final mAP: {:.4f}'.format(mAP))\n",
" print(\"recall: {:.3f}, precision: {:.3f}\".format(rec_total.average, prec_total.average))\n",
" print(\"total_loss: {:.3f}, loss_xy: {:.3f}, loss_wh: {:.3f}, loss_conf: {:.3f}, loss_class: {:.3f}\".format(\n",
" val_loss_total.average, val_loss_xy.average, val_loss_wh.average, val_loss_conf.average, val_loss_class.average\n",
" ))"
],
"execution_count": 0,
"outputs": []
}
]
}
\ No newline at end of file