multi_gpu (#135)
* updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates
This commit is contained in:
Glenn Jocher
GitHub
+5
-1
@@ -206,8 +206,11 @@ class LoadImagesAndLabels: # for training
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if nL > 0:
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labels[:, 2] = 1 - labels[:, 2]
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if nL > 0:
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labels = np.concatenate((np.zeros((nL, 1), dtype='float32') + index, labels), 1)
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labels_all.append(labels)
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img_all.append(img)
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labels_all.append(torch.from_numpy(labels))
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img_paths.append(img_path)
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img_shapes.append((h, w))
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@@ -216,6 +219,7 @@ class LoadImagesAndLabels: # for training
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img_all = np.ascontiguousarray(img_all, dtype=np.float32)
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img_all /= 255.0
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labels_all = torch.from_numpy(np.concatenate(labels_all, 0))
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return torch.from_numpy(img_all), labels_all, img_paths, img_shapes
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def __len__(self):
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@@ -4,6 +4,7 @@
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sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
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bash yolov3/data/get_coco_dataset.sh
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sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
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sudo shutdown
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# Start
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python3 train.py
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@@ -15,6 +16,14 @@ python3 train.py --resume
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gsutil cp gs://ultralytics/yolov3.pt yolov3/weights
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python3 detect.py
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# Clone branch
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sudo rm -rf yolov3 && git clone -b multi_gpu --depth 1 https://github.com/ultralytics/yolov3
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cd yolov3 && python3 train.py --batch-size 104
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sudo rm -rf yolov3 && git clone -b multigpu --depth 1 https://github.com/alexpolichroniadis/yolov3
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cp coco.data yolov3/cfg
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cd yolov3 && python3 train.py --batch-size 104
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# Test
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sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
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sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
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@@ -16,10 +16,11 @@ def select_device(force_cpu=False):
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device = torch.device('cuda:0' if cuda else 'cpu')
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if torch.cuda.device_count() > 1:
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device = torch.device('cuda' if cuda else 'cpu')
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print('Found %g GPUs' % torch.cuda.device_count())
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print('WARNING Multi-GPU Issue: https://github.com/ultralytics/yolov3/issues/21')
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torch.cuda.set_device(0) # OPTIONAL: Set your GPU if multiple available
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# # print('Using ', torch.cuda.device_count(), ' GPUs')
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# print('Multi-GPU Issue: https://github.com/ultralytics/yolov3/issues/21')
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# torch.cuda.set_device(0) # OPTIONAL: Set your GPU if multiple available
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# print('Using ', torch.cuda.device_count(), ' GPUs')
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print('Using %s %s\n' % (device.type, torch.cuda.get_device_properties(0) if cuda else ''))
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return device
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+119
-121
@@ -1,10 +1,12 @@
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import glob
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import random
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from collections import defaultdict
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from utils import torch_utils
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@@ -25,15 +27,14 @@ def init_seeds(seed=0):
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def load_classes(path):
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"""
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Loads class labels at 'path'
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"""
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# Loads class labels at 'path'
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fp = open(path, 'r')
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names = fp.read().split('\n')
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return list(filter(None, names)) # filter removes empty strings (such as last line)
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def model_info(model): # Plots a line-by-line description of a PyTorch model
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def model_info(model):
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# Plots a line-by-line description of a PyTorch model
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n_p = sum(x.numel() for x in model.parameters()) # number parameters
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n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
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print('\n%5s %50s %9s %12s %20s %12s %12s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
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@@ -41,7 +42,7 @@ def model_info(model): # Plots a line-by-line description of a PyTorch model
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name = name.replace('module_list.', '')
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print('%5g %50s %9s %12g %20s %12.3g %12.3g' % (
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i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
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print('Model Summary: %g layers, %g parameters, %g gradients\n' % (i + 1, n_p, n_g))
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print('Model Summary: %g layers, %g parameters, %g gradients' % (i + 1, n_p, n_g))
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def coco_class_weights(): # frequency of each class in coco train2014
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@@ -66,7 +67,8 @@ def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
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return x
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def plot_one_box(x, img, color=None, label=None, line_thickness=None): # Plots one bounding box on image img
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def plot_one_box(x, img, color=None, label=None, line_thickness=None):
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# Plots one bounding box on image img
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tl = line_thickness or round(0.002 * max(img.shape[0:2])) + 1 # line thickness
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color = color or [random.randint(0, 255) for _ in range(3)]
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c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
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@@ -122,7 +124,7 @@ def scale_coords(img_size, coords, img0_shape):
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def ap_per_class(tp, conf, pred_cls, target_cls):
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""" Compute the average precision, given the recall and precision curves.
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Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.
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Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
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# Arguments
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tp: True positives (list).
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conf: Objectness value from 0-1 (list).
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@@ -176,7 +178,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
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def compute_ap(recall, precision):
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""" Compute the average precision, given the recall and precision curves.
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Code originally from https://github.com/rbgirshick/py-faster-rcnn.
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Source: https://github.com/rbgirshick/py-faster-rcnn.
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# Arguments
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recall: The recall curve (list).
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precision: The precision curve (list).
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@@ -203,105 +205,127 @@ def compute_ap(recall, precision):
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def bbox_iou(box1, box2, x1y1x2y2=True):
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box1 = box1.t()
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# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
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box2 = box2.t()
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"""
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Returns the IoU of two bounding boxes
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"""
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# Get the coordinates of bounding boxes
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if x1y1x2y2:
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# Get the coordinates of bounding boxes
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# x1, y1, x2, y2 = box1
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b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
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b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
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else:
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# x1, y1, w1, h1 = box1
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# Transform from center and width to exact coordinates
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# x, y, w, h = box1
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b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
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b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
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b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
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b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
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# get the coordinates of the intersection rectangle
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inter_rect_x1 = torch.max(b1_x1, b2_x1)
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inter_rect_y1 = torch.max(b1_y1, b2_y1)
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inter_rect_x2 = torch.min(b1_x2, b2_x2)
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inter_rect_y2 = torch.min(b1_y2, b2_y2)
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# Intersection area
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inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1, 0) * torch.clamp(inter_rect_y2 - inter_rect_y1, 0)
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inter_area = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
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(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
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# Union Area
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b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
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b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
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union_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1) + 1e-16) + \
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(b2_x2 - b2_x1) * (b2_y2 - b2_y1) - inter_area
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return inter_area / (b1_area + b2_area - inter_area + 1e-16)
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return inter_area / union_area # iou
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def build_targets(target, anchor_vec, nA, nC, nG):
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"""
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returns nT, nCorrect, tx, ty, tw, th, tconf, tcls
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"""
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nB = len(target) # number of images in batch
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def wh_iou(box1, box2):
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# Returns the IoU of wh1 to wh2. wh1 is 2, wh2 is nx2
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box2 = box2.t()
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txy = torch.zeros(nB, nA, nG, nG, 2) # batch size, anchors, grid size
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twh = torch.zeros(nB, nA, nG, nG, 2)
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tconf = torch.ByteTensor(nB, nA, nG, nG).fill_(0)
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tcls = torch.ByteTensor(nB, nA, nG, nG, nC).fill_(0) # nC = number of classes
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# w, h = box1
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w1, h1 = box1[0], box1[1]
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w2, h2 = box2[0], box2[1]
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for b in range(nB):
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t = target[b]
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nTb = len(t) # number of targets
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if nTb == 0:
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continue
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# Intersection area
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inter_area = torch.min(w1, w2) * torch.min(h1, h2)
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gxy, gwh = t[:, 1:3] * nG, t[:, 3:5] * nG
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# Union Area
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union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area
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# Get grid box indices and prevent overflows (i.e. 13.01 on 13 anchors)
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gi, gj = torch.clamp(gxy.long(), min=0, max=nG - 1).t()
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return inter_area / union_area # iou
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# iou of targets-anchors (using wh only)
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box1 = gwh
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box2 = anchor_vec.unsqueeze(1)
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inter_area = torch.min(box1, box2).prod(2)
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iou = inter_area / (box1.prod(1) + box2.prod(2) - inter_area + 1e-16)
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def compute_loss(p, targets): # predictions, targets
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FT = torch.cuda.FloatTensor if p[0].is_cuda else torch.FloatTensor
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loss, lxy, lwh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
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txy, twh, tcls, tconf, indices = targets
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MSE = nn.MSELoss()
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CE = nn.CrossEntropyLoss()
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BCE = nn.BCEWithLogitsLoss()
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# Select best iou_pred and anchor
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iou_best, a = iou.max(0) # best anchor [0-2] for each target
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# Compute losses
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# gp = [x.numel() for x in tconf] # grid points
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for i, pi0 in enumerate(p): # layer i predictions, i
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b, a, gj, gi = indices[i] # image, anchor, gridx, gridy
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# Select best unique target-anchor combinations
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if nTb > 1:
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iou_order = torch.argsort(-iou_best) # best to worst
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# Compute losses
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k = 1 # nT / bs
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if len(b) > 0:
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pi = pi0[b, a, gj, gi] # predictions closest to anchors
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lxy += k * MSE(torch.sigmoid(pi[..., 0:2]), txy[i]) # xy
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lwh += k * MSE(pi[..., 2:4], twh[i]) # wh
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lcls += (k / 4) * CE(pi[..., 5:], tcls[i])
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# Unique anchor selection
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u = torch.stack((gi, gj, a), 0)[:, iou_order]
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# _, first_unique = np.unique(u, axis=1, return_index=True) # first unique indices
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first_unique = return_torch_unique_index(u, torch.unique(u, dim=1)) # torch alternative
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# pos_weight = FT([gp[i] / min(gp) * 4.])
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# BCE = nn.BCEWithLogitsLoss(pos_weight=pos_weight)
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lconf += (k * 64) * BCE(pi0[..., 4], tconf[i])
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loss = lxy + lwh + lconf + lcls
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i = iou_order[first_unique]
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# best anchor must share significant commonality (iou) with target
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i = i[iou_best[i] > 0.10] # TODO: examine arbitrary threshold
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if len(i) == 0:
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continue
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# Add to dictionary
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d = defaultdict(float)
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losses = [loss.item(), lxy.item(), lwh.item(), lconf.item(), lcls.item()]
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for name, x in zip(['total', 'xy', 'wh', 'conf', 'cls'], losses):
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d[name] = x
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a, gj, gi, t = a[i], gj[i], gi[i], t[i]
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if len(t.shape) == 1:
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t = t.view(1, 5)
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else:
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if iou_best < 0.10:
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continue
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return loss, d
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tc, gxy, gwh = t[:, 0].long(), t[:, 1:3] * nG, t[:, 3:5] * nG
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def build_targets(model, targets, pred):
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# targets = [image, class, x, y, w, h]
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if isinstance(model, nn.DataParallel):
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model = model.module
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yolo_layers = get_yolo_layers(model)
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# anchors = closest_anchor(model, targets) # [layer, anchor, i, j]
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txy, twh, tcls, tconf, indices = [], [], [], [], []
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for i, layer in enumerate(yolo_layers):
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nG = model.module_list[layer][0].nG # grid size
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anchor_vec = model.module_list[layer][0].anchor_vec
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# iou of targets-anchors
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gwh = targets[:, 4:6] * nG
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iou = [wh_iou(x, gwh) for x in anchor_vec]
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iou, a = torch.stack(iou, 0).max(0) # best iou and anchor
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# reject below threshold ious (OPTIONAL)
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j = iou > 0.01
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t, a, gwh = targets[j], a[j], gwh[j]
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# Indices
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b, c = t[:, 0:2].long().t() # target image, class
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gxy = t[:, 2:4] * nG
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gi, gj = gxy.long().t() # grid_i, grid_j
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indices.append((b, a, gj, gi))
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# XY coordinates
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txy[b, a, gj, gi] = gxy - gxy.floor()
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txy.append(gxy - gxy.floor())
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# Width and height
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twh[b, a, gj, gi] = torch.log(gwh / anchor_vec[a]) # yolo method
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# twh[b, a, gj, gi] = torch.sqrt(gwh / anchor_vec[a]) / 2 # power method
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twh.append(torch.log(gwh / anchor_vec[a])) # yolo method
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# twh.append(torch.sqrt(gwh / anchor_vec[a]) / 2) # power method
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# One-hot encoding of label
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tcls[b, a, gj, gi, tc] = 1
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tconf[b, a, gj, gi] = 1
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# Class
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tcls.append(c)
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return txy, twh, tconf, tcls
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# Conf
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tci = torch.zeros_like(pred[i][..., 0])
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tci[b, a, gj, gi] = 1 # conf
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tconf.append(tci)
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return txy, twh, tcls, tconf, indices
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def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
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@@ -314,34 +338,6 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
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output = [None for _ in range(len(prediction))]
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for image_i, pred in enumerate(prediction):
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# Filter out confidence scores below threshold
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# Get score and class with highest confidence
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# cross-class NMS (experimental)
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cross_class_nms = False
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if cross_class_nms:
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a = pred.clone()
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_, indices = torch.sort(-a[:, 4], 0) # sort best to worst
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a = a[indices]
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radius = 30 # area to search for cross-class ious
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for i in range(len(a)):
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if i >= len(a) - 1:
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break
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close = (torch.abs(a[i, 0] - a[i + 1:, 0]) < radius) & (torch.abs(a[i, 1] - a[i + 1:, 1]) < radius)
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close = close.nonzero()
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if len(close) > 0:
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close = close + i + 1
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iou = bbox_iou(a[i:i + 1, :4], a[close.squeeze(), :4].reshape(-1, 4), x1y1x2y2=False)
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bad = close[iou > nms_thres]
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if len(bad) > 0:
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mask = torch.ones(len(a)).type(torch.ByteTensor)
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mask[bad] = 0
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a = a[mask]
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pred = a
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# Experiment: Prior class size rejection
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# x, y, w, h = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3]
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# a = w * h # area
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@@ -355,6 +351,7 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
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# shape_likelihood[:, c] =
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# multivariate_normal.pdf(x, mean=mat['class_mu'][c, :2], cov=mat['class_cov'][c, :2, :2])
|
||||
|
||||
# Filter out confidence scores below threshold
|
||||
class_prob, class_pred = torch.max(F.softmax(pred[:, 5:], 1), 1)
|
||||
v = pred[:, 4] > conf_thres
|
||||
v = v.nonzero().squeeze()
|
||||
@@ -376,9 +373,7 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
|
||||
# Detections ordered as (x1, y1, x2, y2, obj_conf, class_prob, class_pred)
|
||||
detections = torch.cat((pred[:, :5], class_prob.float().unsqueeze(1), class_pred.float().unsqueeze(1)), 1)
|
||||
# Iterate through all predicted classes
|
||||
unique_labels = detections[:, -1].cpu().unique()
|
||||
if prediction.is_cuda:
|
||||
unique_labels = unique_labels.cuda(prediction.device)
|
||||
unique_labels = detections[:, -1].cpu().unique().to(prediction.device)
|
||||
|
||||
nms_style = 'OR' # 'OR' (default), 'AND', 'MERGE' (experimental)
|
||||
for c in unique_labels:
|
||||
@@ -393,15 +388,15 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
|
||||
ind = list(range(len(dc)))
|
||||
if nms_style == 'OR': # default
|
||||
while len(ind):
|
||||
di = dc[ind[0]:ind[0] + 1]
|
||||
det_max.append(di) # save highest conf detection
|
||||
reject = bbox_iou(di, dc[ind]) > nms_thres
|
||||
j = ind[0]
|
||||
det_max.append(dc[j:j + 1]) # save highest conf detection
|
||||
reject = bbox_iou(dc[j], dc[ind]) > nms_thres
|
||||
[ind.pop(i) for i in reversed(reject.nonzero())]
|
||||
# while dc.shape[0]: # SLOWER
|
||||
# while dc.shape[0]: # SLOWER METHOD
|
||||
# det_max.append(dc[:1]) # save highest conf detection
|
||||
# if len(dc) == 1: # Stop if we're at the last detection
|
||||
# break
|
||||
# iou = bbox_iou(dc[:1], dc[1:]) # iou with other boxes
|
||||
# iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
|
||||
# dc = dc[1:][iou < nms_thres] # remove ious > threshold
|
||||
|
||||
# Image Total P R mAP
|
||||
@@ -409,14 +404,14 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
|
||||
|
||||
elif nms_style == 'AND': # requires overlap, single boxes erased
|
||||
while len(dc) > 1:
|
||||
iou = bbox_iou(dc[:1], dc[1:]) # iou with other boxes
|
||||
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
|
||||
if iou.max() > 0.5:
|
||||
det_max.append(dc[:1])
|
||||
dc = dc[1:][iou < nms_thres] # remove ious > threshold
|
||||
|
||||
elif nms_style == 'MERGE': # weighted mixture box
|
||||
while len(dc) > 0:
|
||||
iou = bbox_iou(dc[:1], dc[0:]) # iou with other boxes
|
||||
iou = bbox_iou(dc[0], dc[0:]) # iou with other boxes
|
||||
i = iou > nms_thres
|
||||
|
||||
weights = dc[i, 4:5] * dc[i, 5:6]
|
||||
@@ -435,6 +430,11 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
|
||||
return output
|
||||
|
||||
|
||||
def get_yolo_layers(model):
|
||||
bool_vec = [x['type'] == 'yolo' for x in model.module_defs]
|
||||
return [i for i, x in enumerate(bool_vec) if x] # [82, 94, 106] for yolov3
|
||||
|
||||
|
||||
def return_torch_unique_index(u, uv):
|
||||
n = uv.shape[1] # number of columns
|
||||
first_unique = torch.zeros(n, device=u.device).long()
|
||||
@@ -446,15 +446,13 @@ def return_torch_unique_index(u, uv):
|
||||
|
||||
def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
|
||||
# Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
|
||||
|
||||
a = torch.load(filename, map_location='cpu')
|
||||
a['optimizer'] = []
|
||||
torch.save(a, filename.replace('.pt', '_lite.pt'))
|
||||
|
||||
|
||||
def coco_class_count(path='../coco/labels/train2014/'):
|
||||
# histogram of occurrences per class
|
||||
|
||||
# Histogram of occurrences per class
|
||||
nC = 80 # number classes
|
||||
x = np.zeros(nC, dtype='int32')
|
||||
files = sorted(glob.glob('%s/*.*' % path))
|
||||
@@ -465,8 +463,7 @@ def coco_class_count(path='../coco/labels/train2014/'):
|
||||
|
||||
|
||||
def coco_only_people(path='../coco/labels/val2014/'):
|
||||
# find images with only people
|
||||
|
||||
# Find images with only people
|
||||
files = sorted(glob.glob('%s/*.*' % path))
|
||||
for i, file in enumerate(files):
|
||||
labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
|
||||
@@ -474,19 +471,20 @@ def coco_only_people(path='../coco/labels/val2014/'):
|
||||
print(labels.shape[0], file)
|
||||
|
||||
|
||||
def plot_results():
|
||||
def plot_results(start=0):
|
||||
# Plot YOLO training results file 'results.txt'
|
||||
# import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v1.txt')
|
||||
# import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt')
|
||||
# from utils.utils import *; plot_results()
|
||||
|
||||
plt.figure(figsize=(14, 7))
|
||||
s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Total Loss', 'mAP', 'Recall', 'Precision']
|
||||
s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']
|
||||
files = sorted(glob.glob('results*.txt'))
|
||||
for f in files:
|
||||
results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11]).T # column 11 is mAP
|
||||
x = range(1, results.shape[1])
|
||||
for i in range(8):
|
||||
plt.subplot(2, 4, i + 1)
|
||||
plt.plot(x, results[i, x], marker='.', label=f)
|
||||
plt.plot(results[i, x[start:]], marker='.', label=f)
|
||||
plt.title(s[i])
|
||||
if i == 0:
|
||||
plt.legend()
|
||||
|
||||
Reference in New Issue
Block a user