yp19940913
commented
4 years ago When I built the yolov4 tiny model, I also encountered the same problem. Have you solved the problem now? How does it work? thank you！！！！！！
Open leekyungchoon opened 4 years ago
yp19940913
commented
4 years ago When I built the yolov4 tiny model, I also encountered the same problem. Have you solved the problem now? How does it work? thank you！！！！！！
leekyungchoon
commented
4 years ago When I built the yolov4 tiny model, I also encountered the same problem. Have you solved the problem now? How does it work? thank you！！！！！！
No, I have not yet.
leekyungchoon
commented
4 years ago When I built the yolov4 tiny model, I also encountered the same problem. Have you solved the problem now? How does it work? thank you！！！！！！
If you have not solved this error, I am sharing my code to support yolov4-tiny model. I have updated dataset.py file as below. Hope it helps you.
import os import cv2 import random import numpy as np import tensorflow as tf import core.utils as utils from core.config import cfg
class Dataset(object): """implement Dataset here"""
def __init__(self, FLAGS, is_training: bool, dataset_type: str = "converted_coco"):
self.tiny = FLAGS.tiny
self.strides, self.anchors, NUM_CLASS, XYSCALE = utils.load_config(FLAGS)
self.dataset_type = dataset_type
self.annot_path = (
cfg.TRAIN.ANNOT_PATH if is_training else cfg.TEST.ANNOT_PATH
)
self.input_sizes = (
cfg.TRAIN.INPUT_SIZE if is_training else cfg.TEST.INPUT_SIZE
)
self.batch_size = (
cfg.TRAIN.BATCH_SIZE if is_training else cfg.TEST.BATCH_SIZE
)
self.data_aug = cfg.TRAIN.DATA_AUG if is_training else cfg.TEST.DATA_AUG
self.train_input_sizes = cfg.TRAIN.INPUT_SIZE
self.classes = utils.read_class_names(cfg.YOLO.CLASSES)
self.num_classes = len(self.classes)
self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE
self.max_bbox_per_scale = 150
self.annotations = self.load_annotations()
self.num_samples = len(self.annotations)
self.num_batchs = int(np.ceil(self.num_samples / self.batch_size))
self.batch_count = 0
def load_annotations(self):
with open(self.annot_path, "r") as f:
txt = f.readlines()
if self.dataset_type == "converted_coco":
annotations = [
line.strip()
for line in txt
if len(line.strip().split()[1:]) != 0
]
elif self.dataset_type == "yolo":
annotations = []
for line in txt:
image_path = line.strip()
root, _ = os.path.splitext(image_path)
with open(root + ".txt") as fd:
boxes = fd.readlines()
string = ""
for box in boxes:
box = box.strip()
box = box.split()
class_num = int(box[0])
center_x = float(box[1])
center_y = float(box[2])
half_width = float(box[3]) / 2
half_height = float(box[4]) / 2
string += " {},{},{},{},{}".format(
center_x - half_width,
center_y - half_height,
center_x + half_width,
center_y + half_height,
class_num,
)
annotations.append(image_path + string)
np.random.shuffle(annotations)
return annotations
def __iter__(self):
return self
def __next__(self):
with tf.device("/cpu:0"):
# self.train_input_size = random.choice(self.train_input_sizes)
self.train_input_size = cfg.TRAIN.INPUT_SIZE
self.train_output_sizes = self.train_input_size // self.strides
batch_image = np.zeros(
(
self.batch_size,
self.train_input_size,
self.train_input_size,
3,
),
dtype=np.float32,
)
batch_label_bboxes = []
batch_bboxes = []
for size in self.train_output_sizes:
label_bbox = np.zeros((self.batch_size, size, size, self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32)
batch_label_bboxes.append(label_bbox)
batch_bbox = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32)
batch_bboxes.append(batch_bbox)
num = 0
if self.batch_count < self.num_batchs:
while num < self.batch_size:
index = self.batch_count * self.batch_size + num
if index >= self.num_samples:
index -= self.num_samples
annotation = self.annotations[index]
image, bboxes = self.parse_annotation(annotation)
(
label_bboxes,
bboxes,
) = self.preprocess_true_boxes(bboxes, num_anchors = len(self.train_output_sizes))
batch_image[num, :, :, :] = image
for batch_bbox, bbox in zip(batch_bboxes, bboxes):
batch_bbox[num,:,:] = bbox
for batch_label_bbox, label_bbox in zip (batch_label_bboxes, label_bboxes):
batch_label_bbox[num, :, :, :] = label_bbox
num += 1
self.batch_count += 1
batch_targets = list(zip(batch_label_bboxes, batch_bboxes))
return (
batch_image,
batch_targets
)
else:
self.batch_count = 0
np.random.shuffle(self.annotations)
raise StopIteration
def random_horizontal_flip(self, image, bboxes):
if random.random() < 0.5:
_, w, _ = image.shape
image = image[:, ::-1, :]
bboxes[:, [0, 2]] = w - bboxes[:, [2, 0]]
return image, bboxes
def random_crop(self, image, bboxes):
if random.random() < 0.5:
h, w, _ = image.shape
max_bbox = np.concatenate(
[
np.min(bboxes[:, 0:2], axis=0),
np.max(bboxes[:, 2:4], axis=0),
],
axis=-1,
)
max_l_trans = max_bbox[0]
max_u_trans = max_bbox[1]
max_r_trans = w - max_bbox[2]
max_d_trans = h - max_bbox[3]
crop_xmin = max(
0, int(max_bbox[0] - random.uniform(0, max_l_trans))
)
crop_ymin = max(
0, int(max_bbox[1] - random.uniform(0, max_u_trans))
)
crop_xmax = max(
w, int(max_bbox[2] + random.uniform(0, max_r_trans))
)
crop_ymax = max(
h, int(max_bbox[3] + random.uniform(0, max_d_trans))
)
image = image[crop_ymin:crop_ymax, crop_xmin:crop_xmax]
bboxes[:, [0, 2]] = bboxes[:, [0, 2]] - crop_xmin
bboxes[:, [1, 3]] = bboxes[:, [1, 3]] - crop_ymin
return image, bboxes
def random_translate(self, image, bboxes):
if random.random() < 0.5:
h, w, _ = image.shape
max_bbox = np.concatenate(
[
np.min(bboxes[:, 0:2], axis=0),
np.max(bboxes[:, 2:4], axis=0),
],
axis=-1,
)
max_l_trans = max_bbox[0]
max_u_trans = max_bbox[1]
max_r_trans = w - max_bbox[2]
max_d_trans = h - max_bbox[3]
tx = random.uniform(-(max_l_trans - 1), (max_r_trans - 1))
ty = random.uniform(-(max_u_trans - 1), (max_d_trans - 1))
M = np.array([[1, 0, tx], [0, 1, ty]])
image = cv2.warpAffine(image, M, (w, h))
bboxes[:, [0, 2]] = bboxes[:, [0, 2]] + tx
bboxes[:, [1, 3]] = bboxes[:, [1, 3]] + ty
return image, bboxes
def parse_annotation(self, annotation):
line = annotation.split()
image_path = line[0]
if not os.path.exists(image_path):
raise KeyError("%s does not exist ... " % image_path)
image = cv2.imread(image_path)
if self.dataset_type == "converted_coco":
bboxes = np.array(
[list(map(int, box.split(","))) for box in line[1:]]
)
elif self.dataset_type == "yolo":
height, width, _ = image.shape
bboxes = np.array(
[list(map(float, box.split(","))) for box in line[1:]]
)
bboxes = bboxes * np.array([width, height, width, height, 1])
bboxes = bboxes.astype(np.int64)
if self.data_aug:
image, bboxes = self.random_horizontal_flip(
np.copy(image), np.copy(bboxes)
)
image, bboxes = self.random_crop(np.copy(image), np.copy(bboxes))
image, bboxes = self.random_translate(
np.copy(image), np.copy(bboxes)
)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image, bboxes = utils.image_preprocess(
np.copy(image),
[self.train_input_size, self.train_input_size],
np.copy(bboxes),
)
return image, bboxes
def preprocess_true_boxes(self, bboxes, num_anchors=3):
label = [
np.zeros(
(
self.train_output_sizes[i],
self.train_output_sizes[i],
self.anchor_per_scale,
5 + self.num_classes,
)
)
for i in range(num_anchors)
]
bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(num_anchors)]
bbox_count = np.zeros((num_anchors,))
for bbox in bboxes:
bbox_coor = bbox[:4]
bbox_class_ind = bbox[4]
onehot = np.zeros(self.num_classes, dtype=np.float)
onehot[bbox_class_ind] = 1.0
uniform_distribution = np.full(
self.num_classes, 1.0 / self.num_classes
)
deta = 0.01
smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution
bbox_xywh = np.concatenate(
[
(bbox_coor[2:] + bbox_coor[:2]) * 0.5,
bbox_coor[2:] - bbox_coor[:2],
],
axis=-1,
)
bbox_xywh_scaled = (
1.0 * bbox_xywh[np.newaxis, :] / self.strides[:, np.newaxis]
)
iou = []
exist_positive = False
for i in range(num_anchors):
anchors_xywh = np.zeros((self.anchor_per_scale, 4))
anchors_xywh[:, 0:2] = (
np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5
)
anchors_xywh[:, 2:4] = self.anchors[i]
iou_scale = utils.bbox_iou(
bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh
)
iou.append(iou_scale)
iou_mask = iou_scale > 0.3
if np.any(iou_mask):
xind, yind = np.floor(bbox_xywh_scaled[i, 0:2]).astype(
np.int32
)
label[i][yind, xind, iou_mask, :] = 0
label[i][yind, xind, iou_mask, 0:4] = bbox_xywh
label[i][yind, xind, iou_mask, 4:5] = 1.0
label[i][yind, xind, iou_mask, 5:] = smooth_onehot
bbox_ind = int(bbox_count[i] % self.max_bbox_per_scale)
bboxes_xywh[i][bbox_ind, :4] = bbox_xywh
bbox_count[i] += 1
exist_positive = True
if not exist_positive:
best_anchor_ind = np.argmax(np.array(iou).reshape(-1), axis=-1)
best_detect = int(best_anchor_ind / self.anchor_per_scale)
best_anchor = int(best_anchor_ind % self.anchor_per_scale)
xind, yind = np.floor(
bbox_xywh_scaled[best_detect, 0:2]
).astype(np.int32)
label[best_detect][yind, xind, best_anchor, :] = 0
label[best_detect][yind, xind, best_anchor, 0:4] = bbox_xywh
label[best_detect][yind, xind, best_anchor, 4:5] = 1.0
label[best_detect][yind, xind, best_anchor, 5:] = smooth_onehot
bbox_ind = int(
bbox_count[best_detect] % self.max_bbox_per_scale
)
bboxes_xywh[best_detect][bbox_ind, :4] = bbox_xywh
bbox_count[best_detect] += 1
#label_sbbox, label_mbbox, label_lbbox = label
#sbboxes, mbboxes, lbboxes = bboxes_xywh
return label, bboxes_xywh #label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes
def __len__(self):
return self.num_batchsWhen I built the yolov4 tiny model, I also encountered the same problem. Have you solved the problem now? How does it work? thank you！！！！！！
If you have not solved this error, I am sharing my code to support yolov4-tiny model. I have updated dataset.py file as below. Hope it helps you.
! /usr/bin/env python
coding=utf-8
import os import cv2 import random import numpy as np import tensorflow as tf import core.utils as utils from core.config import cfg
class Dataset(object): """implement Dataset here"""
def __init__(self, FLAGS, is_training: bool, dataset_type: str = "converted_coco"): self.tiny = FLAGS.tiny self.strides, self.anchors, NUM_CLASS, XYSCALE = utils.load_config(FLAGS) self.dataset_type = dataset_type self.annot_path = ( cfg.TRAIN.ANNOT_PATH if is_training else cfg.TEST.ANNOT_PATH ) self.input_sizes = ( cfg.TRAIN.INPUT_SIZE if is_training else cfg.TEST.INPUT_SIZE ) self.batch_size = ( cfg.TRAIN.BATCH_SIZE if is_training else cfg.TEST.BATCH_SIZE ) self.data_aug = cfg.TRAIN.DATA_AUG if is_training else cfg.TEST.DATA_AUG self.train_input_sizes = cfg.TRAIN.INPUT_SIZE self.classes = utils.read_class_names(cfg.YOLO.CLASSES) self.num_classes = len(self.classes) self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE self.max_bbox_per_scale = 150 self.annotations = self.load_annotations() self.num_samples = len(self.annotations) self.num_batchs = int(np.ceil(self.num_samples / self.batch_size)) self.batch_count = 0 def load_annotations(self): with open(self.annot_path, "r") as f: txt = f.readlines() if self.dataset_type == "converted_coco": annotations = [ line.strip() for line in txt if len(line.strip().split()[1:]) != 0 ] elif self.dataset_type == "yolo": annotations = [] for line in txt: image_path = line.strip() root, _ = os.path.splitext(image_path) with open(root + ".txt") as fd: boxes = fd.readlines() string = "" for box in boxes: box = box.strip() box = box.split() class_num = int(box[0]) center_x = float(box[1]) center_y = float(box[2]) half_width = float(box[3]) / 2 half_height = float(box[4]) / 2 string += " {},{},{},{},{}".format( center_x - half_width, center_y - half_height, center_x + half_width, center_y + half_height, class_num, ) annotations.append(image_path + string) np.random.shuffle(annotations) return annotations def __iter__(self): return self def __next__(self): with tf.device("/cpu:0"): # self.train_input_size = random.choice(self.train_input_sizes) self.train_input_size = cfg.TRAIN.INPUT_SIZE self.train_output_sizes = self.train_input_size // self.strides batch_image = np.zeros( ( self.batch_size, self.train_input_size, self.train_input_size, 3, ), dtype=np.float32, ) batch_label_bboxes = [] batch_bboxes = [] for size in self.train_output_sizes: label_bbox = np.zeros((self.batch_size, size, size, self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32) batch_label_bboxes.append(label_bbox) batch_bbox = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32) batch_bboxes.append(batch_bbox) num = 0 if self.batch_count < self.num_batchs: while num < self.batch_size: index = self.batch_count * self.batch_size + num if index >= self.num_samples: index -= self.num_samples annotation = self.annotations[index] image, bboxes = self.parse_annotation(annotation) ( label_bboxes, bboxes, ) = self.preprocess_true_boxes(bboxes, num_anchors = len(self.train_output_sizes)) batch_image[num, :, :, :] = image for batch_bbox, bbox in zip(batch_bboxes, bboxes): batch_bbox[num,:,:] = bbox for batch_label_bbox, label_bbox in zip (batch_label_bboxes, label_bboxes): batch_label_bbox[num, :, :, :] = label_bbox num += 1 self.batch_count += 1 batch_targets = list(zip(batch_label_bboxes, batch_bboxes)) return ( batch_image, batch_targets ) else: self.batch_count = 0 np.random.shuffle(self.annotations) raise StopIteration def random_horizontal_flip(self, image, bboxes): if random.random() < 0.5: _, w, _ = image.shape image = image[:, ::-1, :] bboxes[:, [0, 2]] = w - bboxes[:, [2, 0]] return image, bboxes def random_crop(self, image, bboxes): if random.random() < 0.5: h, w, _ = image.shape max_bbox = np.concatenate( [ np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0), ], axis=-1, ) max_l_trans = max_bbox[0] max_u_trans = max_bbox[1] max_r_trans = w - max_bbox[2] max_d_trans = h - max_bbox[3] crop_xmin = max( 0, int(max_bbox[0] - random.uniform(0, max_l_trans)) ) crop_ymin = max( 0, int(max_bbox[1] - random.uniform(0, max_u_trans)) ) crop_xmax = max( w, int(max_bbox[2] + random.uniform(0, max_r_trans)) ) crop_ymax = max( h, int(max_bbox[3] + random.uniform(0, max_d_trans)) ) image = image[crop_ymin:crop_ymax, crop_xmin:crop_xmax] bboxes[:, [0, 2]] = bboxes[:, [0, 2]] - crop_xmin bboxes[:, [1, 3]] = bboxes[:, [1, 3]] - crop_ymin return image, bboxes def random_translate(self, image, bboxes): if random.random() < 0.5: h, w, _ = image.shape max_bbox = np.concatenate( [ np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0), ], axis=-1, ) max_l_trans = max_bbox[0] max_u_trans = max_bbox[1] max_r_trans = w - max_bbox[2] max_d_trans = h - max_bbox[3] tx = random.uniform(-(max_l_trans - 1), (max_r_trans - 1)) ty = random.uniform(-(max_u_trans - 1), (max_d_trans - 1)) M = np.array([[1, 0, tx], [0, 1, ty]]) image = cv2.warpAffine(image, M, (w, h)) bboxes[:, [0, 2]] = bboxes[:, [0, 2]] + tx bboxes[:, [1, 3]] = bboxes[:, [1, 3]] + ty return image, bboxes def parse_annotation(self, annotation): line = annotation.split() image_path = line[0] if not os.path.exists(image_path): raise KeyError("%s does not exist ... " % image_path) image = cv2.imread(image_path) if self.dataset_type == "converted_coco": bboxes = np.array( [list(map(int, box.split(","))) for box in line[1:]] ) elif self.dataset_type == "yolo": height, width, _ = image.shape bboxes = np.array( [list(map(float, box.split(","))) for box in line[1:]] ) bboxes = bboxes * np.array([width, height, width, height, 1]) bboxes = bboxes.astype(np.int64) if self.data_aug: image, bboxes = self.random_horizontal_flip( np.copy(image), np.copy(bboxes) ) image, bboxes = self.random_crop(np.copy(image), np.copy(bboxes)) image, bboxes = self.random_translate( np.copy(image), np.copy(bboxes) ) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image, bboxes = utils.image_preprocess( np.copy(image), [self.train_input_size, self.train_input_size], np.copy(bboxes), ) return image, bboxes def preprocess_true_boxes(self, bboxes, num_anchors=3): label = [ np.zeros( ( self.train_output_sizes[i], self.train_output_sizes[i], self.anchor_per_scale, 5 + self.num_classes, ) ) for i in range(num_anchors) ] bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(num_anchors)] bbox_count = np.zeros((num_anchors,)) for bbox in bboxes: bbox_coor = bbox[:4] bbox_class_ind = bbox[4] onehot = np.zeros(self.num_classes, dtype=np.float) onehot[bbox_class_ind] = 1.0 uniform_distribution = np.full( self.num_classes, 1.0 / self.num_classes ) deta = 0.01 smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution bbox_xywh = np.concatenate( [ (bbox_coor[2:] + bbox_coor[:2]) * 0.5, bbox_coor[2:] - bbox_coor[:2], ], axis=-1, ) bbox_xywh_scaled = ( 1.0 * bbox_xywh[np.newaxis, :] / self.strides[:, np.newaxis] ) iou = [] exist_positive = False for i in range(num_anchors): anchors_xywh = np.zeros((self.anchor_per_scale, 4)) anchors_xywh[:, 0:2] = ( np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5 ) anchors_xywh[:, 2:4] = self.anchors[i] iou_scale = utils.bbox_iou( bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh ) iou.append(iou_scale) iou_mask = iou_scale > 0.3 if np.any(iou_mask): xind, yind = np.floor(bbox_xywh_scaled[i, 0:2]).astype( np.int32 ) label[i][yind, xind, iou_mask, :] = 0 label[i][yind, xind, iou_mask, 0:4] = bbox_xywh label[i][yind, xind, iou_mask, 4:5] = 1.0 label[i][yind, xind, iou_mask, 5:] = smooth_onehot bbox_ind = int(bbox_count[i] % self.max_bbox_per_scale) bboxes_xywh[i][bbox_ind, :4] = bbox_xywh bbox_count[i] += 1 exist_positive = True if not exist_positive: best_anchor_ind = np.argmax(np.array(iou).reshape(-1), axis=-1) best_detect = int(best_anchor_ind / self.anchor_per_scale) best_anchor = int(best_anchor_ind % self.anchor_per_scale) xind, yind = np.floor( bbox_xywh_scaled[best_detect, 0:2] ).astype(np.int32) label[best_detect][yind, xind, best_anchor, :] = 0 label[best_detect][yind, xind, best_anchor, 0:4] = bbox_xywh label[best_detect][yind, xind, best_anchor, 4:5] = 1.0 label[best_detect][yind, xind, best_anchor, 5:] = smooth_onehot bbox_ind = int( bbox_count[best_detect] % self.max_bbox_per_scale ) bboxes_xywh[best_detect][bbox_ind, :4] = bbox_xywh bbox_count[best_detect] += 1 #label_sbbox, label_mbbox, label_lbbox = label #sbboxes, mbboxes, lbboxes = bboxes_xywh return label, bboxes_xywh #label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes def __len__(self): return self.num_batchs
Thank you for your help and good luck！
ZZHHogan
commented
4 years ago When I built the yolov4 tiny model, I also encountered the same problem. Have you solved the problem now? How does it work? thank you！！！！！！
If you have not solved this error, I am sharing my code to support yolov4-tiny model. I have updated dataset.py file as below. Hope it helps you.
! /usr/bin/env python
coding=utf-8
import os import cv2 import random import numpy as np import tensorflow as tf import core.utils as utils from core.config import cfg
class Dataset(object): """implement Dataset here"""
def __init__(self, FLAGS, is_training: bool, dataset_type: str = "converted_coco"): self.tiny = FLAGS.tiny self.strides, self.anchors, NUM_CLASS, XYSCALE = utils.load_config(FLAGS) self.dataset_type = dataset_type self.annot_path = ( cfg.TRAIN.ANNOT_PATH if is_training else cfg.TEST.ANNOT_PATH ) self.input_sizes = ( cfg.TRAIN.INPUT_SIZE if is_training else cfg.TEST.INPUT_SIZE ) self.batch_size = ( cfg.TRAIN.BATCH_SIZE if is_training else cfg.TEST.BATCH_SIZE ) self.data_aug = cfg.TRAIN.DATA_AUG if is_training else cfg.TEST.DATA_AUG self.train_input_sizes = cfg.TRAIN.INPUT_SIZE self.classes = utils.read_class_names(cfg.YOLO.CLASSES) self.num_classes = len(self.classes) self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE self.max_bbox_per_scale = 150 self.annotations = self.load_annotations() self.num_samples = len(self.annotations) self.num_batchs = int(np.ceil(self.num_samples / self.batch_size)) self.batch_count = 0 def load_annotations(self): with open(self.annot_path, "r") as f: txt = f.readlines() if self.dataset_type == "converted_coco": annotations = [ line.strip() for line in txt if len(line.strip().split()[1:]) != 0 ] elif self.dataset_type == "yolo": annotations = [] for line in txt: image_path = line.strip() root, _ = os.path.splitext(image_path) with open(root + ".txt") as fd: boxes = fd.readlines() string = "" for box in boxes: box = box.strip() box = box.split() class_num = int(box[0]) center_x = float(box[1]) center_y = float(box[2]) half_width = float(box[3]) / 2 half_height = float(box[4]) / 2 string += " {},{},{},{},{}".format( center_x - half_width, center_y - half_height, center_x + half_width, center_y + half_height, class_num, ) annotations.append(image_path + string) np.random.shuffle(annotations) return annotations def __iter__(self): return self def __next__(self): with tf.device("/cpu:0"): # self.train_input_size = random.choice(self.train_input_sizes) self.train_input_size = cfg.TRAIN.INPUT_SIZE self.train_output_sizes = self.train_input_size // self.strides batch_image = np.zeros( ( self.batch_size, self.train_input_size, self.train_input_size, 3, ), dtype=np.float32, ) batch_label_bboxes = [] batch_bboxes = [] for size in self.train_output_sizes: label_bbox = np.zeros((self.batch_size, size, size, self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32) batch_label_bboxes.append(label_bbox) batch_bbox = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32) batch_bboxes.append(batch_bbox) num = 0 if self.batch_count < self.num_batchs: while num < self.batch_size: index = self.batch_count * self.batch_size + num if index >= self.num_samples: index -= self.num_samples annotation = self.annotations[index] image, bboxes = self.parse_annotation(annotation) ( label_bboxes, bboxes, ) = self.preprocess_true_boxes(bboxes, num_anchors = len(self.train_output_sizes)) batch_image[num, :, :, :] = image for batch_bbox, bbox in zip(batch_bboxes, bboxes): batch_bbox[num,:,:] = bbox for batch_label_bbox, label_bbox in zip (batch_label_bboxes, label_bboxes): batch_label_bbox[num, :, :, :] = label_bbox num += 1 self.batch_count += 1 batch_targets = list(zip(batch_label_bboxes, batch_bboxes)) return ( batch_image, batch_targets ) else: self.batch_count = 0 np.random.shuffle(self.annotations) raise StopIteration def random_horizontal_flip(self, image, bboxes): if random.random() < 0.5: _, w, _ = image.shape image = image[:, ::-1, :] bboxes[:, [0, 2]] = w - bboxes[:, [2, 0]] return image, bboxes def random_crop(self, image, bboxes): if random.random() < 0.5: h, w, _ = image.shape max_bbox = np.concatenate( [ np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0), ], axis=-1, ) max_l_trans = max_bbox[0] max_u_trans = max_bbox[1] max_r_trans = w - max_bbox[2] max_d_trans = h - max_bbox[3] crop_xmin = max( 0, int(max_bbox[0] - random.uniform(0, max_l_trans)) ) crop_ymin = max( 0, int(max_bbox[1] - random.uniform(0, max_u_trans)) ) crop_xmax = max( w, int(max_bbox[2] + random.uniform(0, max_r_trans)) ) crop_ymax = max( h, int(max_bbox[3] + random.uniform(0, max_d_trans)) ) image = image[crop_ymin:crop_ymax, crop_xmin:crop_xmax] bboxes[:, [0, 2]] = bboxes[:, [0, 2]] - crop_xmin bboxes[:, [1, 3]] = bboxes[:, [1, 3]] - crop_ymin return image, bboxes def random_translate(self, image, bboxes): if random.random() < 0.5: h, w, _ = image.shape max_bbox = np.concatenate( [ np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0), ], axis=-1, ) max_l_trans = max_bbox[0] max_u_trans = max_bbox[1] max_r_trans = w - max_bbox[2] max_d_trans = h - max_bbox[3] tx = random.uniform(-(max_l_trans - 1), (max_r_trans - 1)) ty = random.uniform(-(max_u_trans - 1), (max_d_trans - 1)) M = np.array([[1, 0, tx], [0, 1, ty]]) image = cv2.warpAffine(image, M, (w, h)) bboxes[:, [0, 2]] = bboxes[:, [0, 2]] + tx bboxes[:, [1, 3]] = bboxes[:, [1, 3]] + ty return image, bboxes def parse_annotation(self, annotation): line = annotation.split() image_path = line[0] if not os.path.exists(image_path): raise KeyError("%s does not exist ... " % image_path) image = cv2.imread(image_path) if self.dataset_type == "converted_coco": bboxes = np.array( [list(map(int, box.split(","))) for box in line[1:]] ) elif self.dataset_type == "yolo": height, width, _ = image.shape bboxes = np.array( [list(map(float, box.split(","))) for box in line[1:]] ) bboxes = bboxes * np.array([width, height, width, height, 1]) bboxes = bboxes.astype(np.int64) if self.data_aug: image, bboxes = self.random_horizontal_flip( np.copy(image), np.copy(bboxes) ) image, bboxes = self.random_crop(np.copy(image), np.copy(bboxes)) image, bboxes = self.random_translate( np.copy(image), np.copy(bboxes) ) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image, bboxes = utils.image_preprocess( np.copy(image), [self.train_input_size, self.train_input_size], np.copy(bboxes), ) return image, bboxes def preprocess_true_boxes(self, bboxes, num_anchors=3): label = [ np.zeros( ( self.train_output_sizes[i], self.train_output_sizes[i], self.anchor_per_scale, 5 + self.num_classes, ) ) for i in range(num_anchors) ] bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(num_anchors)] bbox_count = np.zeros((num_anchors,)) for bbox in bboxes: bbox_coor = bbox[:4] bbox_class_ind = bbox[4] onehot = np.zeros(self.num_classes, dtype=np.float) onehot[bbox_class_ind] = 1.0 uniform_distribution = np.full( self.num_classes, 1.0 / self.num_classes ) deta = 0.01 smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution bbox_xywh = np.concatenate( [ (bbox_coor[2:] + bbox_coor[:2]) * 0.5, bbox_coor[2:] - bbox_coor[:2], ], axis=-1, ) bbox_xywh_scaled = ( 1.0 * bbox_xywh[np.newaxis, :] / self.strides[:, np.newaxis] ) iou = [] exist_positive = False for i in range(num_anchors): anchors_xywh = np.zeros((self.anchor_per_scale, 4)) anchors_xywh[:, 0:2] = ( np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5 ) anchors_xywh[:, 2:4] = self.anchors[i] iou_scale = utils.bbox_iou( bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh ) iou.append(iou_scale) iou_mask = iou_scale > 0.3 if np.any(iou_mask): xind, yind = np.floor(bbox_xywh_scaled[i, 0:2]).astype( np.int32 ) label[i][yind, xind, iou_mask, :] = 0 label[i][yind, xind, iou_mask, 0:4] = bbox_xywh label[i][yind, xind, iou_mask, 4:5] = 1.0 label[i][yind, xind, iou_mask, 5:] = smooth_onehot bbox_ind = int(bbox_count[i] % self.max_bbox_per_scale) bboxes_xywh[i][bbox_ind, :4] = bbox_xywh bbox_count[i] += 1 exist_positive = True if not exist_positive: best_anchor_ind = np.argmax(np.array(iou).reshape(-1), axis=-1) best_detect = int(best_anchor_ind / self.anchor_per_scale) best_anchor = int(best_anchor_ind % self.anchor_per_scale) xind, yind = np.floor( bbox_xywh_scaled[best_detect, 0:2] ).astype(np.int32) label[best_detect][yind, xind, best_anchor, :] = 0 label[best_detect][yind, xind, best_anchor, 0:4] = bbox_xywh label[best_detect][yind, xind, best_anchor, 4:5] = 1.0 label[best_detect][yind, xind, best_anchor, 5:] = smooth_onehot bbox_ind = int( bbox_count[best_detect] % self.max_bbox_per_scale ) bboxes_xywh[best_detect][bbox_ind, :4] = bbox_xywh bbox_count[best_detect] += 1 #label_sbbox, label_mbbox, label_lbbox = label #sbboxes, mbboxes, lbboxes = bboxes_xywh return label, bboxes_xywh #label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes def __len__(self): return self.num_batchs
I met the same problem,too.And your solution fix the bug.Thank you!!!
lipeng1109
commented
4 years ago 当我构建yolov4微型模型时，我也遇到了同样的问题。您现在解决问题了吗？它是如何工作的？谢谢！！！！！！
如果您尚未解决此错误，我将共享我的代码以支持yolov4-tiny模型。我已经更新了dataset.py文件，如下所示。希望对您有帮助。 ＃！/ usr / bin / env python
编码= utf-8
import os import cv2 import随机 导入numpy as np import tensorflow as tf import core.utils as utils from core.config import cfg class Dataset（object）： “”“此处实现数据集”“”
def __init__(self, FLAGS, is_training: bool, dataset_type: str = "converted_coco"): self.tiny = FLAGS.tiny self.strides, self.anchors, NUM_CLASS, XYSCALE = utils.load_config(FLAGS) self.dataset_type = dataset_type self.annot_path = ( cfg.TRAIN.ANNOT_PATH if is_training else cfg.TEST.ANNOT_PATH ) self.input_sizes = ( cfg.TRAIN.INPUT_SIZE if is_training else cfg.TEST.INPUT_SIZE ) self.batch_size = ( cfg.TRAIN.BATCH_SIZE if is_training else cfg.TEST.BATCH_SIZE ) self.data_aug = cfg.TRAIN.DATA_AUG if is_training else cfg.TEST.DATA_AUG self.train_input_sizes = cfg.TRAIN.INPUT_SIZE self.classes = utils.read_class_names(cfg.YOLO.CLASSES) self.num_classes = len(self.classes) self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE self.max_bbox_per_scale = 150 self.annotations = self.load_annotations() self.num_samples = len(self.annotations) self.num_batchs = int(np.ceil(self.num_samples / self.batch_size)) self.batch_count = 0 def load_annotations(self): with open(self.annot_path, "r") as f: txt = f.readlines() if self.dataset_type == "converted_coco": annotations = [ line.strip() for line in txt if len(line.strip().split()[1:]) != 0 ] elif self.dataset_type == "yolo": annotations = [] for line in txt: image_path = line.strip() root, _ = os.path.splitext(image_path) with open(root + ".txt") as fd: boxes = fd.readlines() string = "" for box in boxes: box = box.strip() box = box.split() class_num = int(box[0]) center_x = float(box[1]) center_y = float(box[2]) half_width = float(box[3]) / 2 half_height = float(box[4]) / 2 string += " {},{},{},{},{}".format( center_x - half_width, center_y - half_height, center_x + half_width, center_y + half_height, class_num, ) annotations.append(image_path + string) np.random.shuffle(annotations) return annotations def __iter__(self): return self def __next__(self): with tf.device("/cpu:0"): # self.train_input_size = random.choice(self.train_input_sizes) self.train_input_size = cfg.TRAIN.INPUT_SIZE self.train_output_sizes = self.train_input_size // self.strides batch_image = np.zeros( ( self.batch_size, self.train_input_size, self.train_input_size, 3, ), dtype=np.float32, ) batch_label_bboxes = [] batch_bboxes = [] for size in self.train_output_sizes: label_bbox = np.zeros((self.batch_size, size, size, self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32) batch_label_bboxes.append(label_bbox) batch_bbox = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32) batch_bboxes.append(batch_bbox) num = 0 if self.batch_count < self.num_batchs: while num < self.batch_size: index = self.batch_count * self.batch_size + num if index >= self.num_samples: index -= self.num_samples annotation = self.annotations[index] image, bboxes = self.parse_annotation(annotation) ( label_bboxes, bboxes, ) = self.preprocess_true_boxes(bboxes, num_anchors = len(self.train_output_sizes)) batch_image[num, :, :, :] = image for batch_bbox, bbox in zip(batch_bboxes, bboxes): batch_bbox[num,:,:] = bbox for batch_label_bbox, label_bbox in zip (batch_label_bboxes, label_bboxes): batch_label_bbox[num, :, :, :] = label_bbox num += 1 self.batch_count += 1 batch_targets = list(zip(batch_label_bboxes, batch_bboxes)) return ( batch_image, batch_targets ) else: self.batch_count = 0 np.random.shuffle(self.annotations) raise StopIteration def random_horizontal_flip(self, image, bboxes): if random.random() < 0.5: _, w, _ = image.shape image = image[:, ::-1, :] bboxes[:, [0, 2]] = w - bboxes[:, [2, 0]] return image, bboxes def random_crop(self, image, bboxes): if random.random() < 0.5: h, w, _ = image.shape max_bbox = np.concatenate( [ np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0), ], axis=-1, ) max_l_trans = max_bbox[0] max_u_trans = max_bbox[1] max_r_trans = w - max_bbox[2] max_d_trans = h - max_bbox[3] crop_xmin = max( 0, int(max_bbox[0] - random.uniform(0, max_l_trans)) ) crop_ymin = max( 0, int(max_bbox[1] - random.uniform(0, max_u_trans)) ) crop_xmax = max( w, int(max_bbox[2] + random.uniform(0, max_r_trans)) ) crop_ymax = max( h, int(max_bbox[3] + random.uniform(0, max_d_trans)) ) image = image[crop_ymin:crop_ymax, crop_xmin:crop_xmax] bboxes[:, [0, 2]] = bboxes[:, [0, 2]] - crop_xmin bboxes[:, [1, 3]] = bboxes[:, [1, 3]] - crop_ymin return image, bboxes def random_translate(self, image, bboxes): if random.random() < 0.5: h, w, _ = image.shape max_bbox = np.concatenate( [ np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0), ], axis=-1, ) max_l_trans = max_bbox[0] max_u_trans = max_bbox[1] max_r_trans = w - max_bbox[2] max_d_trans = h - max_bbox[3] tx = random.uniform(-(max_l_trans - 1), (max_r_trans - 1)) ty = random.uniform(-(max_u_trans - 1), (max_d_trans - 1)) M = np.array([[1, 0, tx], [0, 1, ty]]) image = cv2.warpAffine(image, M, (w, h)) bboxes[:, [0, 2]] = bboxes[:, [0, 2]] + tx bboxes[:, [1, 3]] = bboxes[:, [1, 3]] + ty return image, bboxes def parse_annotation(self, annotation): line = annotation.split() image_path = line[0] if not os.path.exists(image_path): raise KeyError("%s does not exist ... " % image_path) image = cv2.imread(image_path) if self.dataset_type == "converted_coco": bboxes = np.array( [list(map(int, box.split(","))) for box in line[1:]] ) elif self.dataset_type == "yolo": height, width, _ = image.shape bboxes = np.array( [list(map(float, box.split(","))) for box in line[1:]] ) bboxes = bboxes * np.array([width, height, width, height, 1]) bboxes = bboxes.astype(np.int64) if self.data_aug: image, bboxes = self.random_horizontal_flip( np.copy(image), np.copy(bboxes) ) image, bboxes = self.random_crop(np.copy(image), np.copy(bboxes)) image, bboxes = self.random_translate( np.copy(image), np.copy(bboxes) ) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image, bboxes = utils.image_preprocess( np.copy(image), [self.train_input_size, self.train_input_size], np.copy(bboxes), ) return image, bboxes def preprocess_true_boxes(self, bboxes, num_anchors=3): label = [ np.zeros( ( self.train_output_sizes[i], self.train_output_sizes[i], self.anchor_per_scale, 5 + self.num_classes, ) ) for i in range(num_anchors) ] bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(num_anchors)] bbox_count = np.zeros((num_anchors,)) for bbox in bboxes: bbox_coor = bbox[:4] bbox_class_ind = bbox[4] onehot = np.zeros(self.num_classes, dtype=np.float) onehot[bbox_class_ind] = 1.0 uniform_distribution = np.full( self.num_classes, 1.0 / self.num_classes ) deta = 0.01 smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution bbox_xywh = np.concatenate( [ (bbox_coor[2:] + bbox_coor[:2]) * 0.5, bbox_coor[2:] - bbox_coor[:2], ], axis=-1, ) bbox_xywh_scaled = ( 1.0 * bbox_xywh[np.newaxis, :] / self.strides[:, np.newaxis] ) iou = [] exist_positive = False for i in range(num_anchors): anchors_xywh = np.zeros((self.anchor_per_scale, 4)) anchors_xywh[:, 0:2] = ( np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5 ) anchors_xywh[:, 2:4] = self.anchors[i] iou_scale = utils.bbox_iou( bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh ) iou.append(iou_scale) iou_mask = iou_scale > 0.3 if np.any(iou_mask): xind, yind = np.floor(bbox_xywh_scaled[i, 0:2]).astype( np.int32 ) label[i][yind, xind, iou_mask, :] = 0 label[i][yind, xind, iou_mask, 0:4] = bbox_xywh label[i][yind, xind, iou_mask, 4:5] = 1.0 label[i][yind, xind, iou_mask, 5:] = smooth_onehot bbox_ind = int(bbox_count[i] % self.max_bbox_per_scale) bboxes_xywh[i][bbox_ind, :4] = bbox_xywh bbox_count[i] += 1 exist_positive = True if not exist_positive: best_anchor_ind = np.argmax(np.array(iou).reshape(-1), axis=-1) best_detect = int(best_anchor_ind / self.anchor_per_scale) best_anchor = int(best_anchor_ind % self.anchor_per_scale) xind, yind = np.floor( bbox_xywh_scaled[best_detect, 0:2] ).astype(np.int32) label[best_detect][yind, xind, best_anchor, :] = 0 label[best_detect][yind, xind, best_anchor, 0:4] = bbox_xywh label[best_detect][yind, xind, best_anchor, 4:5] = 1.0 label[best_detect][yind, xind, best_anchor, 5:] = smooth_onehot bbox_ind = int( bbox_count[best_detect] % self.max_bbox_per_scale ) bboxes_xywh[best_detect][bbox_ind, :4] = bbox_xywh bbox_count[best_detect] += 1 #label_sbbox, label_mbbox, label_lbbox = label #sbboxes, mbboxes, lbboxes = bboxes_xywh return label, bboxes_xywh #label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes def __len__(self): return self.num_batchs我也遇到了同样的问题。您的解决方案已修复了该错误。谢谢！！！
我替换使用上面的代码，仍然报这个错，请问你这边还有其他的改动吗
ev1lQuark
commented
3 years ago 当我构建yolov4微型模型时，我也遇到了同样的问题。您现在解决问题了吗？它是如何工作的？谢谢！！！！！！
如果您尚未解决此错误，我将共享我的代码以支持yolov4-tiny模型。我已经更新了dataset.py文件，如下所示。希望对您有帮助。 ＃！/ usr / bin / env python
编码= utf-8
import os import cv2 import随机 导入numpy as np import tensorflow as tf import core.utils as utils from core.config import cfg class Dataset（object）： “”“此处实现数据集”“”
def __init__(self, FLAGS, is_training: bool, dataset_type: str = "converted_coco"): self.tiny = FLAGS.tiny self.strides, self.anchors, NUM_CLASS, XYSCALE = utils.load_config(FLAGS) self.dataset_type = dataset_type self.annot_path = ( cfg.TRAIN.ANNOT_PATH if is_training else cfg.TEST.ANNOT_PATH ) self.input_sizes = ( cfg.TRAIN.INPUT_SIZE if is_training else cfg.TEST.INPUT_SIZE ) self.batch_size = ( cfg.TRAIN.BATCH_SIZE if is_training else cfg.TEST.BATCH_SIZE ) self.data_aug = cfg.TRAIN.DATA_AUG if is_training else cfg.TEST.DATA_AUG self.train_input_sizes = cfg.TRAIN.INPUT_SIZE self.classes = utils.read_class_names(cfg.YOLO.CLASSES) self.num_classes = len(self.classes) self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE self.max_bbox_per_scale = 150 self.annotations = self.load_annotations() self.num_samples = len(self.annotations) self.num_batchs = int(np.ceil(self.num_samples / self.batch_size)) self.batch_count = 0 def load_annotations(self): with open(self.annot_path, "r") as f: txt = f.readlines() if self.dataset_type == "converted_coco": annotations = [ line.strip() for line in txt if len(line.strip().split()[1:]) != 0 ] elif self.dataset_type == "yolo": annotations = [] for line in txt: image_path = line.strip() root, _ = os.path.splitext(image_path) with open(root + ".txt") as fd: boxes = fd.readlines() string = "" for box in boxes: box = box.strip() box = box.split() class_num = int(box[0]) center_x = float(box[1]) center_y = float(box[2]) half_width = float(box[3]) / 2 half_height = float(box[4]) / 2 string += " {},{},{},{},{}".format( center_x - half_width, center_y - half_height, center_x + half_width, center_y + half_height, class_num, ) annotations.append(image_path + string) np.random.shuffle(annotations) return annotations def __iter__(self): return self def __next__(self): with tf.device("/cpu:0"): # self.train_input_size = random.choice(self.train_input_sizes) self.train_input_size = cfg.TRAIN.INPUT_SIZE self.train_output_sizes = self.train_input_size // self.strides batch_image = np.zeros( ( self.batch_size, self.train_input_size, self.train_input_size, 3, ), dtype=np.float32, ) batch_label_bboxes = [] batch_bboxes = [] for size in self.train_output_sizes: label_bbox = np.zeros((self.batch_size, size, size, self.anchor_per_scale, 5 + self.num_classes), dtype=np.float32) batch_label_bboxes.append(label_bbox) batch_bbox = np.zeros((self.batch_size, self.max_bbox_per_scale, 4), dtype=np.float32) batch_bboxes.append(batch_bbox) num = 0 if self.batch_count < self.num_batchs: while num < self.batch_size: index = self.batch_count * self.batch_size + num if index >= self.num_samples: index -= self.num_samples annotation = self.annotations[index] image, bboxes = self.parse_annotation(annotation) ( label_bboxes, bboxes, ) = self.preprocess_true_boxes(bboxes, num_anchors = len(self.train_output_sizes)) batch_image[num, :, :, :] = image for batch_bbox, bbox in zip(batch_bboxes, bboxes): batch_bbox[num,:,:] = bbox for batch_label_bbox, label_bbox in zip (batch_label_bboxes, label_bboxes): batch_label_bbox[num, :, :, :] = label_bbox num += 1 self.batch_count += 1 batch_targets = list(zip(batch_label_bboxes, batch_bboxes)) return ( batch_image, batch_targets ) else: self.batch_count = 0 np.random.shuffle(self.annotations) raise StopIteration def random_horizontal_flip(self, image, bboxes): if random.random() < 0.5: _, w, _ = image.shape image = image[:, ::-1, :] bboxes[:, [0, 2]] = w - bboxes[:, [2, 0]] return image, bboxes def random_crop(self, image, bboxes): if random.random() < 0.5: h, w, _ = image.shape max_bbox = np.concatenate( [ np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0), ], axis=-1, ) max_l_trans = max_bbox[0] max_u_trans = max_bbox[1] max_r_trans = w - max_bbox[2] max_d_trans = h - max_bbox[3] crop_xmin = max( 0, int(max_bbox[0] - random.uniform(0, max_l_trans)) ) crop_ymin = max( 0, int(max_bbox[1] - random.uniform(0, max_u_trans)) ) crop_xmax = max( w, int(max_bbox[2] + random.uniform(0, max_r_trans)) ) crop_ymax = max( h, int(max_bbox[3] + random.uniform(0, max_d_trans)) ) image = image[crop_ymin:crop_ymax, crop_xmin:crop_xmax] bboxes[:, [0, 2]] = bboxes[:, [0, 2]] - crop_xmin bboxes[:, [1, 3]] = bboxes[:, [1, 3]] - crop_ymin return image, bboxes def random_translate(self, image, bboxes): if random.random() < 0.5: h, w, _ = image.shape max_bbox = np.concatenate( [ np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0), ], axis=-1, ) max_l_trans = max_bbox[0] max_u_trans = max_bbox[1] max_r_trans = w - max_bbox[2] max_d_trans = h - max_bbox[3] tx = random.uniform(-(max_l_trans - 1), (max_r_trans - 1)) ty = random.uniform(-(max_u_trans - 1), (max_d_trans - 1)) M = np.array([[1, 0, tx], [0, 1, ty]]) image = cv2.warpAffine(image, M, (w, h)) bboxes[:, [0, 2]] = bboxes[:, [0, 2]] + tx bboxes[:, [1, 3]] = bboxes[:, [1, 3]] + ty return image, bboxes def parse_annotation(self, annotation): line = annotation.split() image_path = line[0] if not os.path.exists(image_path): raise KeyError("%s does not exist ... " % image_path) image = cv2.imread(image_path) if self.dataset_type == "converted_coco": bboxes = np.array( [list(map(int, box.split(","))) for box in line[1:]] ) elif self.dataset_type == "yolo": height, width, _ = image.shape bboxes = np.array( [list(map(float, box.split(","))) for box in line[1:]] ) bboxes = bboxes * np.array([width, height, width, height, 1]) bboxes = bboxes.astype(np.int64) if self.data_aug: image, bboxes = self.random_horizontal_flip( np.copy(image), np.copy(bboxes) ) image, bboxes = self.random_crop(np.copy(image), np.copy(bboxes)) image, bboxes = self.random_translate( np.copy(image), np.copy(bboxes) ) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image, bboxes = utils.image_preprocess( np.copy(image), [self.train_input_size, self.train_input_size], np.copy(bboxes), ) return image, bboxes def preprocess_true_boxes(self, bboxes, num_anchors=3): label = [ np.zeros( ( self.train_output_sizes[i], self.train_output_sizes[i], self.anchor_per_scale, 5 + self.num_classes, ) ) for i in range(num_anchors) ] bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(num_anchors)] bbox_count = np.zeros((num_anchors,)) for bbox in bboxes: bbox_coor = bbox[:4] bbox_class_ind = bbox[4] onehot = np.zeros(self.num_classes, dtype=np.float) onehot[bbox_class_ind] = 1.0 uniform_distribution = np.full( self.num_classes, 1.0 / self.num_classes ) deta = 0.01 smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution bbox_xywh = np.concatenate( [ (bbox_coor[2:] + bbox_coor[:2]) * 0.5, bbox_coor[2:] - bbox_coor[:2], ], axis=-1, ) bbox_xywh_scaled = ( 1.0 * bbox_xywh[np.newaxis, :] / self.strides[:, np.newaxis] ) iou = [] exist_positive = False for i in range(num_anchors): anchors_xywh = np.zeros((self.anchor_per_scale, 4)) anchors_xywh[:, 0:2] = ( np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5 ) anchors_xywh[:, 2:4] = self.anchors[i] iou_scale = utils.bbox_iou( bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh ) iou.append(iou_scale) iou_mask = iou_scale > 0.3 if np.any(iou_mask): xind, yind = np.floor(bbox_xywh_scaled[i, 0:2]).astype( np.int32 ) label[i][yind, xind, iou_mask, :] = 0 label[i][yind, xind, iou_mask, 0:4] = bbox_xywh label[i][yind, xind, iou_mask, 4:5] = 1.0 label[i][yind, xind, iou_mask, 5:] = smooth_onehot bbox_ind = int(bbox_count[i] % self.max_bbox_per_scale) bboxes_xywh[i][bbox_ind, :4] = bbox_xywh bbox_count[i] += 1 exist_positive = True if not exist_positive: best_anchor_ind = np.argmax(np.array(iou).reshape(-1), axis=-1) best_detect = int(best_anchor_ind / self.anchor_per_scale) best_anchor = int(best_anchor_ind % self.anchor_per_scale) xind, yind = np.floor( bbox_xywh_scaled[best_detect, 0:2] ).astype(np.int32) label[best_detect][yind, xind, best_anchor, :] = 0 label[best_detect][yind, xind, best_anchor, 0:4] = bbox_xywh label[best_detect][yind, xind, best_anchor, 4:5] = 1.0 label[best_detect][yind, xind, best_anchor, 5:] = smooth_onehot bbox_ind = int( bbox_count[best_detect] % self.max_bbox_per_scale ) bboxes_xywh[best_detect][bbox_ind, :4] = bbox_xywh bbox_count[best_detect] += 1 #label_sbbox, label_mbbox, label_lbbox = label #sbboxes, mbboxes, lbboxes = bboxes_xywh return label, bboxes_xywh #label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes def __len__(self): return self.num_batchs我也遇到了同样的问题。您的解决方案已修复了该错误。谢谢！！！
我替换使用上面的代码，仍然报这个错，请问你这边还有其他的改动吗
您好，我遇到了同样的问题，#223的第一条回答解决了我的问题。我是因为自己的数据集对应的*.name文件出现了问题，建议您检查一下config.py中__C.YOLO.CLASSES 对应的.name文件
I tried transfer training based on yolov4-tiny.weights as below command.
python train.py --weights ./data/yolov4-tiny.weights --tiny
But, I am facing errors below.
Restoring weights from: ./data/yolov4-tiny.weights ... Traceback (most recent call last): File "train.py", line 161, in
app.run(main)
File "/home/kclee/.conda/envs/kclee01/lib/python3.7/site-packages/absl/app.py", line 300, in run
_run_main(main, args)
File "/home/kclee/.conda/envs/kclee01/lib/python3.7/site-packages/absl/app.py", line 251, in _run_main
sys.exit(main(argv))
File "train.py", line 153, in main
for image_data, target in trainset:
File "/home/kclee/tensorflow-yolov4-tflite/core/dataset.py", line 122, in next
self.train_output_sizes[2],
IndexError: index 2 is out of bounds for axis 0 with size 2
Anyone help me to solve this error?