YOLOv5 v4.0 Release (#1837)
* Update C3 module * Update C3 module * Update C3 module * Update C3 module * update * update * update * update * update * update * update * update * update * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * update * update * update * update * updates * updates * updates * updates * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update datasets * update * update * update * update attempt_downlaod() * merge * merge * update * update * update * update * update * update * update * update * update * update * parameterize eps * comments * gs-multiple * update * max_nms implemented * Create one_cycle() function * update * update * update * update * update * update * update * update study.png * update study.png * Update datasets.py
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Glenn Jocher authored and GitHub committed
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@@ -15,6 +15,7 @@ from threading import Thread
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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from PIL import Image, ExifTags
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from torch.utils.data import Dataset
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from tqdm import tqdm
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@@ -55,7 +56,7 @@ def exif_size(img):
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def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
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rank=-1, world_size=1, workers=8, image_weights=False):
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rank=-1, world_size=1, workers=8, image_weights=False, quad=False):
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# Make sure only the first process in DDP process the dataset first, and the following others can use the cache
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with torch_distributed_zero_first(rank):
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dataset = LoadImagesAndLabels(path, imgsz, batch_size,
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@@ -79,7 +80,7 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
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num_workers=nw,
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sampler=sampler,
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pin_memory=True,
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collate_fn=LoadImagesAndLabels.collate_fn)
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collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn)
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return dataloader, dataset
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@@ -578,6 +579,32 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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l[:, 0] = i # add target image index for build_targets()
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return torch.stack(img, 0), torch.cat(label, 0), path, shapes
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@staticmethod
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def collate_fn4(batch):
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img, label, path, shapes = zip(*batch) # transposed
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n = len(shapes) // 4
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img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
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ho = torch.tensor([[0., 0, 0, 1, 0, 0]])
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wo = torch.tensor([[0., 0, 1, 0, 0, 0]])
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s = torch.tensor([[1, 1, .5, .5, .5, .5]]) # scale
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for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW
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i *= 4
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if random.random() < 0.5:
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im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[
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0].type(img[i].type())
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l = label[i]
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else:
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im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
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l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
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img4.append(im)
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label4.append(l)
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for i, l in enumerate(label4):
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l[:, 0] = i # add target image index for build_targets()
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return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4
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# Ancillary functions --------------------------------------------------------------------------------------------------
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def load_image(self, index):
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@@ -617,7 +644,7 @@ def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
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def load_mosaic(self, index):
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# loads images in a mosaic
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# loads images in a 4-mosaic
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labels4 = []
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s = self.img_size
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@@ -674,6 +701,80 @@ def load_mosaic(self, index):
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return img4, labels4
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def load_mosaic9(self, index):
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# loads images in a 9-mosaic
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labels9 = []
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s = self.img_size
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indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(8)] # 8 additional image indices
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for i, index in enumerate(indices):
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# Load image
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img, _, (h, w) = load_image(self, index)
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# place img in img9
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if i == 0: # center
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img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
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h0, w0 = h, w
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c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates
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elif i == 1: # top
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c = s, s - h, s + w, s
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elif i == 2: # top right
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c = s + wp, s - h, s + wp + w, s
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elif i == 3: # right
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c = s + w0, s, s + w0 + w, s + h
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elif i == 4: # bottom right
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c = s + w0, s + hp, s + w0 + w, s + hp + h
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elif i == 5: # bottom
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c = s + w0 - w, s + h0, s + w0, s + h0 + h
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elif i == 6: # bottom left
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c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
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elif i == 7: # left
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c = s - w, s + h0 - h, s, s + h0
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elif i == 8: # top left
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c = s - w, s + h0 - hp - h, s, s + h0 - hp
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padx, pady = c[:2]
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x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords
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# Labels
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x = self.labels[index]
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labels = x.copy()
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if x.size > 0: # Normalized xywh to pixel xyxy format
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labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padx
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labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + pady
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labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padx
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labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + pady
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labels9.append(labels)
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# Image
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img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax]
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hp, wp = h, w # height, width previous
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# Offset
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yc, xc = [int(random.uniform(0, s)) for x in self.mosaic_border] # mosaic center x, y
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img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
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# Concat/clip labels
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if len(labels9):
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labels9 = np.concatenate(labels9, 0)
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labels9[:, [1, 3]] -= xc
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labels9[:, [2, 4]] -= yc
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np.clip(labels9[:, 1:], 0, 2 * s, out=labels9[:, 1:]) # use with random_perspective
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# img9, labels9 = replicate(img9, labels9) # replicate
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# Augment
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img9, labels9 = random_perspective(img9, labels9,
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degrees=self.hyp['degrees'],
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translate=self.hyp['translate'],
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scale=self.hyp['scale'],
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shear=self.hyp['shear'],
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perspective=self.hyp['perspective'],
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border=self.mosaic_border) # border to remove
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return img9, labels9
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def replicate(img, labels):
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# Replicate labels
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h, w = img.shape[:2]
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@@ -811,12 +912,12 @@ def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shea
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return img, targets
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def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1): # box1(4,n), box2(4,n)
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def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
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# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
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w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
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w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
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ar = np.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16)) # aspect ratio
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return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + 1e-16) > area_thr) & (ar < ar_thr) # candidates
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ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
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return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
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def cutout(image, labels):
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