From 99e7dd5ef9ac2c5d2c6eb8b53700b8fbda35f1bb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=B4=BE=E6=99=93?= Date: Fri, 24 May 2019 19:27:00 +0800 Subject: [PATCH] add yolov3 ce (#2312) --- PaddleCV/yolov3/.run_ce.sh | 7 ++ PaddleCV/yolov3/README.md | 5 +- PaddleCV/yolov3/README_cn.md | 3 +- PaddleCV/yolov3/_ce.py | 48 +++++++++ PaddleCV/yolov3/box_utils.py | 62 ++++++++---- PaddleCV/yolov3/config.py | 7 +- PaddleCV/yolov3/eval.py | 19 ++-- PaddleCV/yolov3/image_utils.py | 90 ++++++++--------- PaddleCV/yolov3/infer.py | 14 +-- PaddleCV/yolov3/models/darknet.py | 110 +++++++++++--------- PaddleCV/yolov3/models/yolov3.py | 162 ++++++++++++++++-------------- PaddleCV/yolov3/reader.py | 85 ++++++++-------- PaddleCV/yolov3/train.py | 58 +++++++---- PaddleCV/yolov3/utility.py | 7 +- 14 files changed, 397 insertions(+), 280 deletions(-) create mode 100644 PaddleCV/yolov3/.run_ce.sh create mode 100644 PaddleCV/yolov3/_ce.py diff --git a/PaddleCV/yolov3/.run_ce.sh b/PaddleCV/yolov3/.run_ce.sh new file mode 100644 index 00000000..761e0377 --- /dev/null +++ b/PaddleCV/yolov3/.run_ce.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +#This file is only used for continuous evaluation. +export CUDA_VISIBLE_DEVICES=0 +python train.py --enable_ce True --use_multiprocess False --snapshot_iter 100 --max_iter 200 | python _ce.py +export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +python train.py --enable_ce True --use_multiprocess False --snapshot_iter 100 --max_iter 200 | python _ce.py diff --git a/PaddleCV/yolov3/README.md b/PaddleCV/yolov3/README.md index d2747df2..3750af11 100644 --- a/PaddleCV/yolov3/README.md +++ b/PaddleCV/yolov3/README.md @@ -62,7 +62,7 @@ The data catalog structure is as follows: │   ├── 000000000139.jpg │   ├── 000000000285.jpg | ... - + ``` ## Training @@ -170,7 +170,7 @@ Inference speed（Tesla P40）: | input size | 608x608 | 416x416 | 320x320 | |:-------------:| :-----: | :-----: | :-----: | -| infer speed | 48 ms/frame | 29 ms/frame |24 ms/frame | +| infer speed | 48 ms/frame | 29 ms/frame |24 ms/frame | Visualization of infer result is shown as below: @@ -181,4 +181,3 @@ Visualization of infer result is shown as below:
YOLOv3 Visualization Examples

- diff --git a/PaddleCV/yolov3/README_cn.md b/PaddleCV/yolov3/README_cn.md index 247dbc7e..51d12127 100644 --- a/PaddleCV/yolov3/README_cn.md +++ b/PaddleCV/yolov3/README_cn.md @@ -172,7 +172,7 @@ Train Loss | input size | 608x608 | 416x416 | 320x320 | |:-------------:| :-----: | :-----: | :-----: | -| infer speed | 48 ms/frame | 29 ms/frame |24 ms/frame | +| infer speed | 48 ms/frame | 29 ms/frame |24 ms/frame | 下图为模型可视化预测结果：

@@ -182,4 +182,3 @@ Train Loss
YOLOv3 预测可视化

- diff --git a/PaddleCV/yolov3/_ce.py b/PaddleCV/yolov3/_ce.py new file mode 100644 index 00000000..c0ce52df --- /dev/null +++ b/PaddleCV/yolov3/_ce.py @@ -0,0 +1,48 @@ +### This file is only used for continuous evaluation test! +from __future__ import print_function +from __future__ import division +from __future__ import absolute_import +import os +import sys +sys.path.append(os.environ['ceroot']) +from kpi import CostKpi +from kpi import DurationKpi + +train_cost_1card_kpi = CostKpi( + 'train_cost_1card', 0.02, 0, actived=True, desc='train cost') +train_duration_1card_kpi = DurationKpi( + 'train_duration_1card', 0.1, 0, actived=True, desc='train duration') +train_cost_8card_kpi = CostKpi( + 'train_cost_8card', 0.02, 0, actived=True, desc='train cost') +train_duration_8card_kpi = DurationKpi( + 'train_duration_8card', 0.1, 0, actived=True, desc='train duration') +tracking_kpis = [ + train_cost_1card_kpi, train_duration_1card_kpi, train_cost_8card_kpi, + train_duration_8card_kpi +] + + +def parse_log(log): + for line in log.split('\n'): + fs = line.strip().split('\t') + print(fs) + if len(fs) == 3 and fs[0] == 'kpis': + print("-----%s" % fs) + kpi_name = fs[1] + kpi_value = float(fs[2]) + yield kpi_name, kpi_value + + +def log_to_ce(log): + kpi_tracker = {} + for kpi in tracking_kpis: + kpi_tracker[kpi.name] = kpi + for (kpi_name, kpi_value) in parse_log(log): + print(kpi_name, kpi_value) + kpi_tracker[kpi_name].add_record(kpi_value) + kpi_tracker[kpi_name].persist() + + +if __name__ == '__main__': + log = sys.stdin.read() + log_to_ce(log) diff --git a/PaddleCV/yolov3/box_utils.py b/PaddleCV/yolov3/box_utils.py index 37ad5d7c..b5bc4250 100644 --- a/PaddleCV/yolov3/box_utils.py +++ b/PaddleCV/yolov3/box_utils.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -47,6 +46,7 @@ def coco_anno_box_to_center_relative(box, img_height, img_width): return np.array([x, y, w, h]) + def clip_relative_box_in_image(x, y, w, h): """Clip relative box coordinates x, y, w, h to [0, 1]""" x1 = max(x - w / 2, 0.) @@ -58,6 +58,7 @@ def clip_relative_box_in_image(x, y, w, h): w = x2 - x1 h = y2 - y1 + def box_xywh_to_xyxy(box): shape = box.shape assert shape[-1] == 4, "Box shape[-1] should be 4." @@ -68,6 +69,7 @@ def box_xywh_to_xyxy(box): box = box.reshape(shape) return box + def box_iou_xywh(box1, box2): assert box1.shape[-1] == 4, "Box1 shape[-1] should be 4." assert box2.shape[-1] == 4, "Box2 shape[-1] should be 4." @@ -92,6 +94,7 @@ def box_iou_xywh(box1, box2): return inter_area / (b1_area + b2_area - inter_area) + def box_iou_xyxy(box1, box2): assert box1.shape[-1] == 4, "Box1 shape[-1] should be 4." assert box2.shape[-1] == 4, "Box2 shape[-1] should be 4." @@ -114,17 +117,21 @@ def box_iou_xyxy(box1, box2): return inter_area / (b1_area + b2_area - inter_area) + def box_crop(boxes, labels, scores, crop, img_shape): x, y, w, h = map(float, crop) im_w, im_h = map(float, img_shape) boxes = boxes.copy() - boxes[:, 0], boxes[:, 2] = (boxes[:, 0] - boxes[:, 2] / 2) * im_w, (boxes[:, 0] + boxes[:, 2] / 2) * im_w - boxes[:, 1], boxes[:, 3] = (boxes[:, 1] - boxes[:, 3] / 2) * im_h, (boxes[:, 1] + boxes[:, 3] / 2) * im_h + boxes[:, 0], boxes[:, 2] = (boxes[:, 0] - boxes[:, 2] / 2) * im_w, ( + boxes[:, 0] + boxes[:, 2] / 2) * im_w + boxes[:, 1], boxes[:, 3] = (boxes[:, 1] - boxes[:, 3] / 2) * im_h, ( + boxes[:, 1] + boxes[:, 3] / 2) * im_h crop_box = np.array([x, y, x + w, y + h]) centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0 - mask = np.logical_and(crop_box[:2] <= centers, centers <= crop_box[2:]).all(axis=1) + mask = np.logical_and(crop_box[:2] <= centers, centers <= crop_box[2:]).all( + axis=1) boxes[:, :2] = np.maximum(boxes[:, :2], crop_box[:2]) boxes[:, 2:] = np.minimum(boxes[:, 2:], crop_box[2:]) @@ -135,12 +142,20 @@ def box_crop(boxes, labels, scores, crop, img_shape): boxes = boxes * np.expand_dims(mask.astype('float32'), axis=1) labels = labels * mask.astype('float32') scores = scores * mask.astype('float32') - boxes[:, 0], boxes[:, 2] = (boxes[:, 0] + boxes[:, 2]) / 2 / w, (boxes[:, 2] - boxes[:, 0]) / w - boxes[:, 1], boxes[:, 3] = (boxes[:, 1] + boxes[:, 3]) / 2 / h, (boxes[:, 3] - boxes[:, 1]) / h + boxes[:, 0], boxes[:, 2] = (boxes[:, 0] + boxes[:, 2]) / 2 / w, ( + boxes[:, 2] - boxes[:, 0]) / w + boxes[:, 1], boxes[:, 3] = (boxes[:, 1] + boxes[:, 3]) / 2 / h, ( + boxes[:, 3] - boxes[:, 1]) / h return boxes, labels, scores, mask.sum() -def draw_boxes_on_image(image_path, boxes, scores, labels, label_names, score_thresh=0.5): + +def draw_boxes_on_image(image_path, + boxes, + scores, + labels, + label_names, + score_thresh=0.5): image = np.array(Image.open(image_path)) plt.figure() _, ax = plt.subplots(1) @@ -158,22 +173,33 @@ def draw_boxes_on_image(image_path, boxes, scores, labels, label_names, score_th if label not in colors: colors[label] = plt.get_cmap('hsv')(label / len(label_names)) x1, y1, x2, y2 = box[0], box[1], box[2], box[3] - rect = plt.Rectangle((x1, y1), x2 - x1, y2 - y1, - fill=False, linewidth=2.0, - edgecolor=colors[label]) + rect = plt.Rectangle( + (x1, y1), + x2 - x1, + y2 - y1, + fill=False, + linewidth=2.0, + edgecolor=colors[label]) ax.add_patch(rect) - ax.text(x1, y1, '{} {:.4f}'.format(label_names[label], score), - verticalalignment='bottom', horizontalalignment='left', - bbox={'facecolor': colors[label], 'alpha': 0.5, 'pad': 0}, - fontsize=8, color='white') - print("\t {:15s} at {:25} score: {:.5f}".format( - label_names[int(label)], str(list(map(int, list(box)))), score)) + ax.text( + x1, + y1, + '{} {:.4f}'.format(label_names[label], score), + verticalalignment='bottom', + horizontalalignment='left', + bbox={'facecolor': colors[label], + 'alpha': 0.5, + 'pad': 0}, + fontsize=8, + color='white') + print("\t {:15s} at {:25} score: {:.5f}".format(label_names[int( + label)], str(list(map(int, list(box)))), score)) image_name = image_name.replace('jpg', 'png') plt.axis('off') plt.gca().xaxis.set_major_locator(plt.NullLocator()) plt.gca().yaxis.set_major_locator(plt.NullLocator()) - plt.savefig("./output/{}".format(image_name), bbox_inches='tight', pad_inches=0.0) + plt.savefig( + "./output/{}".format(image_name), bbox_inches='tight', pad_inches=0.0) print("Detect result save at ./output/{}\n".format(image_name)) plt.cla() plt.close('all') - diff --git a/PaddleCV/yolov3/config.py b/PaddleCV/yolov3/config.py index b7e1eb1c..784cffed 100644 --- a/PaddleCV/yolov3/config.py +++ b/PaddleCV/yolov3/config.py @@ -33,7 +33,6 @@ _C.gt_min_area = -1 # max target box number in an image _C.max_box_num = 50 - # # Training options # @@ -53,7 +52,6 @@ _C.nms_posk = 100 # score threshold for draw box in debug mode _C.draw_thresh = 0.5 - # # Model options # @@ -65,7 +63,9 @@ _C.pixel_means = [0.485, 0.456, 0.406] _C.pixel_stds = [0.229, 0.224, 0.225] # anchors box weight and height -_C.anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326] +_C.anchors = [ + 10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326 +] # anchor mask of each yolo layer _C.anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] @@ -73,7 +73,6 @@ _C.anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] # IoU threshold to ignore objectness loss of pred box _C.ignore_thresh = .7 - # # SOLVER options # diff --git a/PaddleCV/yolov3/eval.py b/PaddleCV/yolov3/eval.py index 0381ec44..7393829f 100644 --- a/PaddleCV/yolov3/eval.py +++ b/PaddleCV/yolov3/eval.py @@ -64,12 +64,12 @@ def eval(): w = x2 - x1 + 1 h = y2 - y1 + 1 bbox = [x1, y1, w, h] - + res = { - 'image_id': im_id, - 'category_id': label_ids[int(label)], - 'bbox': list(map(float, bbox)), - 'score': float(score) + 'image_id': im_id, + 'category_id': label_ids[int(label)], + 'bbox': list(map(float, bbox)), + 'score': float(score) } result.append(res) return result @@ -79,11 +79,10 @@ def eval(): total_time = 0 for batch_id, batch_data in enumerate(test_reader()): start_time = time.time() - batch_outputs = exe.run( - fetch_list=[v.name for v in fetch_list], - feed=feeder.feed(batch_data), - return_numpy=False, - use_program_cache=True) + batch_outputs = exe.run(fetch_list=[v.name for v in fetch_list], + feed=feeder.feed(batch_data), + return_numpy=False, + use_program_cache=True) lod = batch_outputs[0].lod()[0] nmsed_boxes = np.array(batch_outputs[0]) if nmsed_boxes.shape[1] != 6: diff --git a/PaddleCV/yolov3/image_utils.py b/PaddleCV/yolov3/image_utils.py index 2e713525..16edd255 100644 --- a/PaddleCV/yolov3/image_utils.py +++ b/PaddleCV/yolov3/image_utils.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -30,46 +29,41 @@ def random_distort(img): def random_brightness(img, lower=0.5, upper=1.5): e = np.random.uniform(lower, upper) return ImageEnhance.Brightness(img).enhance(e) - + def random_contrast(img, lower=0.5, upper=1.5): e = np.random.uniform(lower, upper) return ImageEnhance.Contrast(img).enhance(e) - + def random_color(img, lower=0.5, upper=1.5): e = np.random.uniform(lower, upper) return ImageEnhance.Color(img).enhance(e) - + ops = [random_brightness, random_contrast, random_color] np.random.shuffle(ops) - + img = Image.fromarray(img) img = ops[0](img) img = ops[1](img) img = ops[2](img) img = np.asarray(img) - + return img -def random_crop(img, - boxes, - labels, - scores, - scales=[0.3, 1.0], - max_ratio=2.0, - constraints=None, +def random_crop(img, + boxes, + labels, + scores, + scales=[0.3, 1.0], + max_ratio=2.0, + constraints=None, max_trial=50): if len(boxes) == 0: return img, boxes if not constraints: - constraints = [ - (0.1, 1.0), - (0.3, 1.0), - (0.5, 1.0), - (0.7, 1.0), - (0.9, 1.0), - (0.0, 1.0)] + constraints = [(0.1, 1.0), (0.3, 1.0), (0.5, 1.0), (0.7, 1.0), + (0.9, 1.0), (0.0, 1.0)] img = Image.fromarray(img) w, h = img.size @@ -83,12 +77,9 @@ def random_crop(img, crop_w = int(w * scale * np.sqrt(aspect_ratio)) crop_x = random.randrange(w - crop_w) crop_y = random.randrange(h - crop_h) - crop_box = np.array([[ - (crop_x + crop_w / 2.0) / w, - (crop_y + crop_h / 2.0) / h, - crop_w / float(w), - crop_h /float(h) - ]]) + crop_box = np.array([[(crop_x + crop_w / 2.0) / w, + (crop_y + crop_h / 2.0) / h, + crop_w / float(w), crop_h / float(h)]]) iou = box_utils.box_iou_xywh(crop_box, boxes) if min_iou <= iou.min() and max_iou >= iou.max(): @@ -101,19 +92,21 @@ def random_crop(img, box_utils.box_crop(boxes, labels, scores, crop, (w, h)) if box_num < 1: continue - img = img.crop((crop[0], crop[1], crop[0] + crop[2], + img = img.crop((crop[0], crop[1], crop[0] + crop[2], crop[1] + crop[3])).resize(img.size, Image.LANCZOS) img = np.asarray(img) return img, crop_boxes, crop_labels, crop_scores img = np.asarray(img) return img, boxes, labels, scores + def random_flip(img, gtboxes, thresh=0.5): if random.random() > thresh: img = img[:, ::-1, :] gtboxes[:, 0] = 1.0 - gtboxes[:, 0] return img, gtboxes + def random_interp(img, size, interp=None): interp_method = [ cv2.INTER_NEAREST, @@ -121,28 +114,29 @@ def random_interp(img, size, interp=None): cv2.INTER_AREA, cv2.INTER_CUBIC, cv2.INTER_LANCZOS4, - ] + ] if not interp or interp not in interp_method: interp = interp_method[random.randint(0, len(interp_method) - 1)] h, w, _ = img.shape im_scale_x = size / float(w) im_scale_y = size / float(h) - img = cv2.resize(img, None, None, fx=im_scale_x, fy=im_scale_y, - interpolation=interp) + img = cv2.resize( + img, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=interp) return img -def random_expand(img, - gtboxes, - max_ratio=4., - fill=None, - keep_ratio=True, + +def random_expand(img, + gtboxes, + max_ratio=4., + fill=None, + keep_ratio=True, thresh=0.5): if random.random() > thresh: return img, gtboxes if max_ratio < 1.0: return img, gtboxes - + h, w, c = img.shape ratio_x = random.uniform(1, max_ratio) if keep_ratio: @@ -151,15 +145,15 @@ def random_expand(img, ratio_y = random.uniform(1, max_ratio) oh = int(h * ratio_y) ow = int(w * ratio_x) - off_x = random.randint(0, ow -w) - off_y = random.randint(0, oh -h) + off_x = random.randint(0, ow - w) + off_y = random.randint(0, oh - h) out_img = np.zeros((oh, ow, c)) if fill and len(fill) == c: for i in range(c): out_img[:, :, i] = fill[i] * 255.0 - out_img[off_y: off_y + h, off_x: off_x + w, :] = img + out_img[off_y:off_y + h, off_x:off_x + w, :] = img gtboxes[:, 0] = ((gtboxes[:, 0] * w) + off_x) / float(ow) gtboxes[:, 1] = ((gtboxes[:, 1] * h) + off_y) / float(oh) gtboxes[:, 2] = gtboxes[:, 2] / ratio_x @@ -167,21 +161,17 @@ def random_expand(img, return out_img.astype('uint8'), gtboxes + def shuffle_gtbox(gtbox, gtlabel, gtscore): - gt = np.concatenate([gtbox, gtlabel[:, np.newaxis], - gtscore[:, np.newaxis]], axis=1) + gt = np.concatenate( + [gtbox, gtlabel[:, np.newaxis], gtscore[:, np.newaxis]], axis=1) idx = np.arange(gt.shape[0]) np.random.shuffle(idx) gt = gt[idx, :] return gt[:, :4], gt[:, 4], gt[:, 5] -def image_mixup(img1, - gtboxes1, - gtlabels1, - gtscores1, - img2, - gtboxes2, - gtlabels2, + +def image_mixup(img1, gtboxes1, gtlabels1, gtscores1, img2, gtboxes2, gtlabels2, gtscores2): factor = np.random.beta(1.5, 1.5) factor = max(0.0, min(1.0, factor)) @@ -229,7 +219,8 @@ def image_mixup(img1, gtscores[:gt_num] = gtscores_all[:gt_num] return img.astype('uint8'), gtboxes, gtlabels, gtscores -def image_augment(img, gtboxes, gtlabels, gtscores, size, means=None): + +def image_augment(img, gtboxes, gtlabels, gtscores, size, means=None): img = random_distort(img) img, gtboxes = random_expand(img, gtboxes, fill=means) img, gtboxes, gtlabels, gtscores = \ @@ -240,4 +231,3 @@ def image_augment(img, gtboxes, gtlabels, gtscores, size, means=None): return img.astype('float32'), gtboxes.astype('float32'), \ gtlabels.astype('int32'), gtscores.astype('float32') - diff --git a/PaddleCV/yolov3/infer.py b/PaddleCV/yolov3/infer.py index 58615ccf..5520efff 100644 --- a/PaddleCV/yolov3/infer.py +++ b/PaddleCV/yolov3/infer.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - import os import time import numpy as np @@ -54,14 +53,14 @@ def infer(): if image_name.split('.')[-1] in ['jpg', 'png']: image_names.append(image_name) for image_name in image_names: - infer_reader = reader.infer(input_size, os.path.join(cfg.image_path, image_name)) + infer_reader = reader.infer(input_size, + os.path.join(cfg.image_path, image_name)) label_names, _ = reader.get_label_infos() data = next(infer_reader()) im_shape = data[0][2] - outputs = exe.run( - fetch_list=[v.name for v in fetch_list], - feed=feeder.feed(data), - return_numpy=False) + outputs = exe.run(fetch_list=[v.name for v in fetch_list], + feed=feeder.feed(data), + return_numpy=False) bboxes = np.array(outputs[0]) if bboxes.shape[1] != 6: print("No object found in {}".format(image_name)) @@ -71,7 +70,8 @@ def infer(): boxes = bboxes[:, 2:].astype('float32') path = os.path.join(cfg.image_path, image_name) - box_utils.draw_boxes_on_image(path, boxes, scores, labels, label_names, cfg.draw_thresh) + box_utils.draw_boxes_on_image(path, boxes, scores, labels, label_names, + cfg.draw_thresh) if __name__ == '__main__': diff --git a/PaddleCV/yolov3/models/darknet.py b/PaddleCV/yolov3/models/darknet.py index bfce6f3b..9b9b7dd6 100644 --- a/PaddleCV/yolov3/models/darknet.py +++ b/PaddleCV/yolov3/models/darknet.py @@ -17,6 +17,7 @@ from paddle.fluid.param_attr import ParamAttr from paddle.fluid.initializer import Constant from paddle.fluid.regularizer import L2Decay + def conv_bn_layer(input, ch_out, filter_size, @@ -32,8 +33,9 @@ def conv_bn_layer(input, stride=stride, padding=padding, act=None, - param_attr=ParamAttr(initializer=fluid.initializer.Normal(0., 0.02), - name=name+".conv.weights"), + param_attr=ParamAttr( + initializer=fluid.initializer.Normal(0., 0.02), + name=name + ".conv.weights"), bias_attr=False) bn_name = name + ".bn" @@ -42,72 +44,88 @@ def conv_bn_layer(input, act=None, is_test=is_test, param_attr=ParamAttr( - initializer=fluid.initializer.Normal(0., 0.02), - regularizer=L2Decay(0.), - name=bn_name + '.scale'), + initializer=fluid.initializer.Normal(0., 0.02), + regularizer=L2Decay(0.), + name=bn_name + '.scale'), bias_attr=ParamAttr( - initializer=fluid.initializer.Constant(0.0), - regularizer=L2Decay(0.), - name=bn_name + '.offset'), + initializer=fluid.initializer.Constant(0.0), + regularizer=L2Decay(0.), + name=bn_name + '.offset'), moving_mean_name=bn_name + '.mean', moving_variance_name=bn_name + '.var') if act == 'leaky': out = fluid.layers.leaky_relu(x=out, alpha=0.1) return out -def downsample(input, - ch_out, - filter_size=3, - stride=2, - padding=1, - is_test=True, + +def downsample(input, + ch_out, + filter_size=3, + stride=2, + padding=1, + is_test=True, name=None): - return conv_bn_layer(input, - ch_out=ch_out, - filter_size=filter_size, - stride=stride, - padding=padding, - is_test=is_test, - name=name) + return conv_bn_layer( + input, + ch_out=ch_out, + filter_size=filter_size, + stride=stride, + padding=padding, + is_test=is_test, + name=name) + def basicblock(input, ch_out, is_test=True, name=None): - conv1 = conv_bn_layer(input, ch_out, 1, 1, 0, - is_test=is_test, name=name+".0") - conv2 = conv_bn_layer(conv1, ch_out*2, 3, 1, 1, - is_test=is_test, name=name+".1") + conv1 = conv_bn_layer( + input, ch_out, 1, 1, 0, is_test=is_test, name=name + ".0") + conv2 = conv_bn_layer( + conv1, ch_out * 2, 3, 1, 1, is_test=is_test, name=name + ".1") out = fluid.layers.elementwise_add(x=input, y=conv2, act=None) return out + def layer_warp(block_func, input, ch_out, count, is_test=True, name=None): - res_out = block_func(input, ch_out, is_test=is_test, - name='{}.0'.format(name)) + res_out = block_func( + input, ch_out, is_test=is_test, name='{}.0'.format(name)) for j in range(1, count): - res_out = block_func(res_out, ch_out, is_test=is_test, - name='{}.{}'.format(name, j)) + res_out = block_func( + res_out, ch_out, is_test=is_test, name='{}.{}'.format(name, j)) return res_out -DarkNet_cfg = { - 53: ([1,2,8,8,4],basicblock) -} + +DarkNet_cfg = {53: ([1, 2, 8, 8, 4], basicblock)} + def add_DarkNet53_conv_body(body_input, is_test=True): stages, block_func = DarkNet_cfg[53] stages = stages[0:5] - conv1 = conv_bn_layer(body_input, ch_out=32, filter_size=3, - stride=1, padding=1, is_test=is_test, - name="yolo_input") - downsample_ = downsample(conv1, ch_out=conv1.shape[1]*2, - is_test=is_test, - name="yolo_input.downsample") + conv1 = conv_bn_layer( + body_input, + ch_out=32, + filter_size=3, + stride=1, + padding=1, + is_test=is_test, + name="yolo_input") + downsample_ = downsample( + conv1, + ch_out=conv1.shape[1] * 2, + is_test=is_test, + name="yolo_input.downsample") blocks = [] for i, stage in enumerate(stages): - block = layer_warp(block_func, downsample_, 32 *(2**i), - stage, is_test=is_test, - name="stage.{}".format(i)) + block = layer_warp( + block_func, + downsample_, + 32 * (2**i), + stage, + is_test=is_test, + name="stage.{}".format(i)) blocks.append(block) - if i < len(stages) - 1: # do not downsaple in the last stage - downsample_ = downsample(block, ch_out=block.shape[1]*2, - is_test=is_test, - name="stage.{}.downsample".format(i)) + if i < len(stages) - 1: # do not downsaple in the last stage + downsample_ = downsample( + block, + ch_out=block.shape[1] * 2, + is_test=is_test, + name="stage.{}.downsample".format(i)) return blocks[-1:-4:-1] - diff --git a/PaddleCV/yolov3/models/yolov3.py b/PaddleCV/yolov3/models/yolov3.py index ef57abdd..fe491249 100644 --- a/PaddleCV/yolov3/models/yolov3.py +++ b/PaddleCV/yolov3/models/yolov3.py @@ -26,26 +26,48 @@ from config import cfg from .darknet import add_DarkNet53_conv_body from .darknet import conv_bn_layer + def yolo_detection_block(input, channel, is_test=True, name=None): assert channel % 2 == 0, \ "channel {} cannot be divided by 2".format(channel) conv = input for j in range(2): - conv = conv_bn_layer(conv, channel, filter_size=1, - stride=1, padding=0, is_test=is_test, - name='{}.{}.0'.format(name, j)) - conv = conv_bn_layer(conv, channel*2, filter_size=3, - stride=1, padding=1, is_test=is_test, - name='{}.{}.1'.format(name, j)) - route = conv_bn_layer(conv, channel, filter_size=1, stride=1, - padding=0, is_test=is_test, - name='{}.2'.format(name)) - tip = conv_bn_layer(route,channel*2, filter_size=3, stride=1, - padding=1, is_test=is_test, - name='{}.tip'.format(name)) + conv = conv_bn_layer( + conv, + channel, + filter_size=1, + stride=1, + padding=0, + is_test=is_test, + name='{}.{}.0'.format(name, j)) + conv = conv_bn_layer( + conv, + channel * 2, + filter_size=3, + stride=1, + padding=1, + is_test=is_test, + name='{}.{}.1'.format(name, j)) + route = conv_bn_layer( + conv, + channel, + filter_size=1, + stride=1, + padding=0, + is_test=is_test, + name='{}.2'.format(name)) + tip = conv_bn_layer( + route, + channel * 2, + filter_size=3, + stride=1, + padding=1, + is_test=is_test, + name='{}.tip'.format(name)) return route, tip -def upsample(input, scale=2,name=None): + +def upsample(input, scale=2, name=None): # get dynamic upsample output shape shape_nchw = fluid.layers.shape(input) shape_hw = fluid.layers.slice(shape_nchw, axes=[0], starts=[2], ends=[4]) @@ -56,16 +78,12 @@ def upsample(input, scale=2,name=None): # reisze by actual_shape out = fluid.layers.resize_nearest( - input=input, - scale=scale, - actual_shape=out_shape, - name=name) + input=input, scale=scale, actual_shape=out_shape, name=name) return out + class YOLOv3(object): - def __init__(self, - is_train=True, - use_random=True): + def __init__(self, is_train=True, use_random=True): self.is_train = is_train self.use_random = use_random self.outputs = [] @@ -77,10 +95,8 @@ class YOLOv3(object): if self.is_train: self.py_reader = fluid.layers.py_reader( capacity=64, - shapes = [[-1] + self.image_shape, - [-1, cfg.max_box_num, 4], - [-1, cfg.max_box_num], - [-1, cfg.max_box_num]], + shapes=[[-1] + self.image_shape, [-1, cfg.max_box_num, 4], + [-1, cfg.max_box_num], [-1, cfg.max_box_num]], lod_levels=[0, 0, 0, 0], dtypes=['float32'] * 2 + ['int32'] + ['float32'], use_double_buffer=True) @@ -88,13 +104,12 @@ class YOLOv3(object): fluid.layers.read_file(self.py_reader) else: self.image = fluid.layers.data( - name='image', shape=self.image_shape, dtype='float32' - ) + name='image', shape=self.image_shape, dtype='float32') self.im_shape = fluid.layers.data( - name="im_shape", shape=[2], dtype='int32') + name="im_shape", shape=[2], dtype='int32') self.im_id = fluid.layers.data( - name="im_id", shape=[1], dtype='int32') - + name="im_id", shape=[1], dtype='int32') + def feeds(self): if not self.is_train: return [self.image, self.im_id, self.im_shape] @@ -110,12 +125,12 @@ class YOLOv3(object): blocks = add_DarkNet53_conv_body(self.image, not self.is_train) for i, block in enumerate(blocks): if i > 0: - block = fluid.layers.concat( - input=[route, block], - axis=1) - route, tip = yolo_detection_block(block, channel=512//(2**i), - is_test=(not self.is_train), - name="yolo_block.{}".format(i)) + block = fluid.layers.concat(input=[route, block], axis=1) + route, tip = yolo_detection_block( + block, + channel=512 // (2**i), + is_test=(not self.is_train), + name="yolo_block.{}".format(i)) # out channel number = mask_num * (5 + class_num) num_filters = len(cfg.anchor_masks[i]) * (cfg.class_num + 5) @@ -126,17 +141,19 @@ class YOLOv3(object): stride=1, padding=0, act=None, - param_attr=ParamAttr(initializer=fluid.initializer.Normal(0., 0.02), - name="yolo_output.{}.conv.weights".format(i)), - bias_attr=ParamAttr(initializer=fluid.initializer.Constant(0.0), - regularizer=L2Decay(0.), - name="yolo_output.{}.conv.bias".format(i))) + param_attr=ParamAttr( + initializer=fluid.initializer.Normal(0., 0.02), + name="yolo_output.{}.conv.weights".format(i)), + bias_attr=ParamAttr( + initializer=fluid.initializer.Constant(0.0), + regularizer=L2Decay(0.), + name="yolo_output.{}.conv.bias".format(i))) self.outputs.append(block_out) if i < len(blocks) - 1: route = conv_bn_layer( input=route, - ch_out=256//(2**i), + ch_out=256 // (2**i), filter_size=1, stride=1, padding=0, @@ -145,42 +162,42 @@ class YOLOv3(object): # upsample route = upsample(route) - for i, out in enumerate(self.outputs): anchor_mask = cfg.anchor_masks[i] if self.is_train: loss = fluid.layers.yolov3_loss( - x=out, - gt_box=self.gtbox, - gt_label=self.gtlabel, - gt_score=self.gtscore, - anchors=cfg.anchors, - anchor_mask=anchor_mask, - class_num=cfg.class_num, - ignore_thresh=cfg.ignore_thresh, - downsample_ratio=self.downsample, - use_label_smooth=cfg.label_smooth, - name="yolo_loss"+str(i)) + x=out, + gt_box=self.gtbox, + gt_label=self.gtlabel, + gt_score=self.gtscore, + anchors=cfg.anchors, + anchor_mask=anchor_mask, + class_num=cfg.class_num, + ignore_thresh=cfg.ignore_thresh, + downsample_ratio=self.downsample, + use_label_smooth=cfg.label_smooth, + name="yolo_loss" + str(i)) self.losses.append(fluid.layers.reduce_mean(loss)) else: - mask_anchors=[] + mask_anchors = [] for m in anchor_mask: mask_anchors.append(cfg.anchors[2 * m]) mask_anchors.append(cfg.anchors[2 * m + 1]) boxes, scores = fluid.layers.yolo_box( - x=out, - img_size=self.im_shape, - anchors=mask_anchors, - class_num=cfg.class_num, - conf_thresh=cfg.valid_thresh, - downsample_ratio=self.downsample, - name="yolo_box"+str(i)) + x=out, + img_size=self.im_shape, + anchors=mask_anchors, + class_num=cfg.class_num, + conf_thresh=cfg.valid_thresh, + downsample_ratio=self.downsample, + name="yolo_box" + str(i)) self.boxes.append(boxes) - self.scores.append(fluid.layers.transpose(scores, perm=[0, 2, 1])) - - self.downsample //= 2 + self.scores.append( + fluid.layers.transpose( + scores, perm=[0, 2, 1])) + self.downsample //= 2 def loss(self): return sum(self.losses) @@ -189,12 +206,11 @@ class YOLOv3(object): yolo_boxes = fluid.layers.concat(self.boxes, axis=1) yolo_scores = fluid.layers.concat(self.scores, axis=2) return fluid.layers.multiclass_nms( - bboxes=yolo_boxes, - scores=yolo_scores, - score_threshold=cfg.valid_thresh, - nms_top_k=cfg.nms_topk, - keep_top_k=cfg.nms_posk, - nms_threshold=cfg.nms_thresh, - background_label=-1, - name="multiclass_nms") - + bboxes=yolo_boxes, + scores=yolo_scores, + score_threshold=cfg.valid_thresh, + nms_top_k=cfg.nms_topk, + keep_top_k=cfg.nms_posk, + nms_threshold=cfg.nms_thresh, + background_label=-1, + name="multiclass_nms") diff --git a/PaddleCV/yolov3/reader.py b/PaddleCV/yolov3/reader.py index 7d1f0de7..d434e2b1 100644 --- a/PaddleCV/yolov3/reader.py +++ b/PaddleCV/yolov3/reader.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -53,21 +52,17 @@ class DataSetReader(object): cfg.dataset)) if mode == 'train': - cfg.train_file_list = os.path.join(cfg.data_dir, + cfg.train_file_list = os.path.join(cfg.data_dir, cfg.train_file_list) - cfg.train_data_dir = os.path.join(cfg.data_dir, - cfg.train_data_dir) + cfg.train_data_dir = os.path.join(cfg.data_dir, cfg.train_data_dir) self.COCO = COCO(cfg.train_file_list) self.img_dir = cfg.train_data_dir elif mode == 'test' or mode == 'infer': - cfg.val_file_list = os.path.join(cfg.data_dir, - cfg.val_file_list) - cfg.val_data_dir = os.path.join(cfg.data_dir, - cfg.val_data_dir) + cfg.val_file_list = os.path.join(cfg.data_dir, cfg.val_file_list) + cfg.val_data_dir = os.path.join(cfg.data_dir, cfg.val_data_dir) self.COCO = COCO(cfg.val_file_list) self.img_dir = cfg.val_data_dir - def _parse_dataset_catagory(self): self.categories = self.COCO.loadCats(self.COCO.getCatIds()) self.num_category = len(self.categories) @@ -76,10 +71,7 @@ class DataSetReader(object): for category in self.categories: self.label_names.append(category['name']) self.label_ids.append(int(category['id'])) - self.category_to_id_map = { - v: i - for i, v in enumerate(self.label_ids) - } + self.category_to_id_map = {v: i for i, v in enumerate(self.label_ids)} print("Load in {} categories.".format(self.num_category)) self.has_parsed_categpry = True @@ -93,7 +85,8 @@ class DataSetReader(object): img_height = img['height'] img_width = img['width'] anno = self.COCO.loadAnns( - self.COCO.getAnnIds(imgIds=img['id'], iscrowd=None)) + self.COCO.getAnnIds( + imgIds=img['id'], iscrowd=None)) gt_index = 0 for target in anno: if target['area'] < cfg.gt_min_area: @@ -102,7 +95,7 @@ class DataSetReader(object): continue box = box_utils.coco_anno_box_to_center_relative( - target['bbox'], img_height, img_width) + target['bbox'], img_height, img_width) if box[2] <= 0 and box[3] <= 0: continue @@ -141,15 +134,15 @@ class DataSetReader(object): if mode == 'infer': return [] else: - return self._parse_images(is_train=(mode=='train')) - - def get_reader(self, - mode, - size=416, - batch_size=None, - shuffle=False, - mixup_iter=0, - random_sizes=[], + return self._parse_images(is_train=(mode == 'train')) + + def get_reader(self, + mode, + size=416, + batch_size=None, + shuffle=False, + mixup_iter=0, + random_sizes=[], image=None): assert mode in ['train', 'test', 'infer'], "Unknow mode type!" if mode != 'infer': @@ -166,9 +159,13 @@ class DataSetReader(object): h, w, _ = im.shape im_scale_x = size / float(w) im_scale_y = size / float(h) - out_img = cv2.resize(im, None, None, - fx=im_scale_x, fy=im_scale_y, - interpolation=cv2.INTER_CUBIC) + out_img = cv2.resize( + im, + None, + None, + fx=im_scale_x, + fy=im_scale_y, + interpolation=cv2.INTER_CUBIC) mean = np.array(mean).reshape((1, 1, -1)) std = np.array(std).reshape((1, 1, -1)) out_img = (out_img / 255.0 - mean) / std @@ -191,12 +188,12 @@ class DataSetReader(object): mixup_gt_labels = np.array(mixup_img['gt_labels']).copy() mixup_gt_scores = np.ones_like(mixup_gt_labels) im, gt_boxes, gt_labels, gt_scores = \ - image_utils.image_mixup(im, gt_boxes, gt_labels, - gt_scores, mixup_im, mixup_gt_boxes, + image_utils.image_mixup(im, gt_boxes, gt_labels, + gt_scores, mixup_im, mixup_gt_boxes, mixup_gt_labels, mixup_gt_scores) im, gt_boxes, gt_labels, gt_scores = \ - image_utils.image_augment(im, gt_boxes, gt_labels, + image_utils.image_augment(im, gt_boxes, gt_labels, gt_scores, size, mean) mean = np.array(mean).reshape((1, 1, -1)) @@ -230,12 +227,13 @@ class DataSetReader(object): img_size = get_img_size(size, random_sizes) while True: img = imgs[read_cnt % len(imgs)] - mixup_img = get_mixup_img(imgs, mixup_iter, total_iter, read_cnt) + mixup_img = get_mixup_img(imgs, mixup_iter, total_iter, + read_cnt) read_cnt += 1 if read_cnt % len(imgs) == 0 and shuffle: np.random.shuffle(imgs) im, gt_boxes, gt_labels, gt_scores = \ - img_reader_with_augment(img, img_size, cfg.pixel_means, + img_reader_with_augment(img, img_size, cfg.pixel_means, cfg.pixel_stds, mixup_img) batch_out.append([im, gt_boxes, gt_labels, gt_scores]) @@ -249,8 +247,7 @@ class DataSetReader(object): imgs = self._parse_images_by_mode(mode) batch_out = [] for img in imgs: - im, im_id, im_shape = img_reader(img, size, - cfg.pixel_means, + im, im_id, im_shape = img_reader(img, size, cfg.pixel_means, cfg.pixel_stds) batch_out.append((im, im_id, im_shape)) if len(batch_out) == batch_size: @@ -262,8 +259,7 @@ class DataSetReader(object): img = {} img['image'] = image img['id'] = 0 - im, im_id, im_shape = img_reader(img, size, - cfg.pixel_means, + im, im_id, im_shape = img_reader(img, size, cfg.pixel_means, cfg.pixel_stds) batch_out = [(im, im_id, im_shape)] yield batch_out @@ -273,17 +269,18 @@ class DataSetReader(object): dsr = DataSetReader() -def train(size=416, - batch_size=64, - shuffle=True, + +def train(size=416, + batch_size=64, + shuffle=True, total_iter=0, mixup_iter=0, random_sizes=[], num_workers=8, max_queue=32, use_multiprocessing=True): - generator = dsr.get_reader('train', size, batch_size, shuffle, - int(mixup_iter/num_workers), random_sizes) + generator = dsr.get_reader('train', size, batch_size, shuffle, + int(mixup_iter / num_workers), random_sizes) if not use_multiprocessing: return generator @@ -316,15 +313,17 @@ def train(size=416, finally: if enqueuer is not None: enqueuer.stop() - + return reader + def test(size=416, batch_size=1): return dsr.get_reader('test', size, batch_size) + def infer(size=416, image=None): return dsr.get_reader('infer', size, image=image) + def get_label_infos(): return dsr.get_label_infos() - diff --git a/PaddleCV/yolov3/train.py b/PaddleCV/yolov3/train.py index 97d23d38..5ad9a774 100644 --- a/PaddleCV/yolov3/train.py +++ b/PaddleCV/yolov3/train.py @@ -33,12 +33,12 @@ from config import cfg def train(): - if cfg.debug: + if cfg.debug or args.enable_ce: fluid.default_startup_program().random_seed = 1000 fluid.default_main_program().random_seed = 1000 random.seed(0) np.random.seed(0) - + if not os.path.exists(cfg.model_save_dir): os.makedirs(cfg.model_save_dir) @@ -76,16 +76,18 @@ def train(): if cfg.pretrain: if not os.path.exists(cfg.pretrain): print("Pretrain weights not found: {}".format(cfg.pretrain)) + def if_exist(var): return os.path.exists(os.path.join(cfg.pretrain, var.name)) + fluid.io.load_vars(exe, cfg.pretrain, predicate=if_exist) - build_strategy= fluid.BuildStrategy() + build_strategy = fluid.BuildStrategy() build_strategy.memory_optimize = True - build_strategy.sync_batch_norm = cfg.syncbn - compile_program = fluid.compiler.CompiledProgram( - fluid.default_main_program()).with_data_parallel( - loss_name=loss.name, build_strategy=build_strategy) + build_strategy.sync_batch_norm = cfg.syncbn + compile_program = fluid.compiler.CompiledProgram(fluid.default_main_program( + )).with_data_parallel( + loss_name=loss.name, build_strategy=build_strategy) random_sizes = [cfg.input_size] if cfg.random_shape: @@ -93,13 +95,17 @@ def train(): total_iter = cfg.max_iter - cfg.start_iter mixup_iter = total_iter - cfg.no_mixup_iter - train_reader = reader.train(input_size, - batch_size=cfg.batch_size, - shuffle=True, - total_iter=total_iter*devices_num, - mixup_iter=mixup_iter*devices_num, - random_sizes=random_sizes, - use_multiprocessing=cfg.use_multiprocess) + shuffle = True + if args.enable_ce: + shuffle = False + train_reader = reader.train( + input_size, + batch_size=cfg.batch_size, + shuffle=shuffle, + total_iter=total_iter * devices_num, + mixup_iter=mixup_iter * devices_num, + random_sizes=random_sizes, + use_multiprocessing=cfg.use_multiprocess) py_reader = model.py_reader py_reader.decorate_paddle_reader(train_reader) @@ -121,7 +127,7 @@ def train(): for iter_id in range(cfg.start_iter, cfg.max_iter): prev_start_time = start_time start_time = time.time() - losses = exe.run(compile_program, + losses = exe.run(compile_program, fetch_list=[v.name for v in fetch_list]) smoothed_loss.add_value(np.mean(np.array(losses[0]))) snapshot_loss += np.mean(np.array(losses[0])) @@ -129,17 +135,27 @@ def train(): lr = np.array(fluid.global_scope().find_var('learning_rate') .get_tensor()) print("Iter {:d}, lr {:.6f}, loss {:.6f}, time {:.5f}".format( - iter_id, lr[0], - smoothed_loss.get_mean_value(), - start_time - prev_start_time)) + iter_id, lr[0], + smoothed_loss.get_mean_value(), start_time - prev_start_time)) sys.stdout.flush() if (iter_id + 1) % cfg.snapshot_iter == 0: save_model("model_iter{}".format(iter_id)) print("Snapshot {} saved, average loss: {}, \ average time: {}".format( - iter_id + 1, - snapshot_loss / float(cfg.snapshot_iter), - snapshot_time / float(cfg.snapshot_iter))) + iter_id + 1, snapshot_loss / float(cfg.snapshot_iter), + snapshot_time / float(cfg.snapshot_iter))) + if args.enable_ce and iter_id == cfg.max_iter - 1: + if devices_num == 1: + print("kpis\ttrain_cost_1card\t%f" % + (snapshot_loss / float(cfg.snapshot_iter))) + print("kpis\ttrain_duration_1card\t%f" % + (snapshot_time / float(cfg.snapshot_iter))) + else: + print("kpis\ttrain_cost_8card\t%f" % + (snapshot_loss / float(cfg.snapshot_iter))) + print("kpis\ttrain_duration_8card\t%f" % + (snapshot_time / float(cfg.snapshot_iter))) + snapshot_loss = 0 snapshot_time = 0 except fluid.core.EOFException: diff --git a/PaddleCV/yolov3/utility.py b/PaddleCV/yolov3/utility.py index 3f5c3c6e..49a7d8fa 100644 --- a/PaddleCV/yolov3/utility.py +++ b/PaddleCV/yolov3/utility.py @@ -120,12 +120,13 @@ def parse_args(): add_arg('nms_posk', int, 100, "The number of boxes of NMS output.") add_arg('debug', bool, False, "Debug mode") # SINGLE EVAL AND DRAW - add_arg('image_path', str, 'image', + add_arg('image_path', str, 'image', "The image path used to inference and visualize.") - add_arg('image_name', str, None, + add_arg('image_name', str, None, "The single image used to inference and visualize. None to inference all images in image_path") - add_arg('draw_thresh', float, 0.5, + add_arg('draw_thresh', float, 0.5, "Confidence score threshold to draw prediction box in image in debug mode") + add_arg('enable_ce', bool, False, "If set True, enable continuous evaluation job.") # yapf: enable args = parser.parse_args() file_name = sys.argv[0] -- GitLab