The image is saved in static/image0.jpg. Note that after an new inference, the result from the last run will be erased.
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sx))) + + def forward(self, x): + return self.F.apply(x) + + +# Mish https://github.com/digantamisra98/Mish -------------------------------------------------------------------------- +class Mish(nn.Module): + @staticmethod + def forward(x): + return x * F.softplus(x).tanh() + + +class MemoryEfficientMish(nn.Module): + class F(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + ctx.save_for_backward(x) + return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) + + @staticmethod + def backward(ctx, grad_output): + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + fx = F.softplus(x).tanh() + return grad_output * (fx + x * sx * (1 - fx * fx)) + + def forward(self, x): + return self.F.apply(x) + + +# FReLU https://arxiv.org/abs/2007.11824 ------------------------------------------------------------------------------- +class FReLU(nn.Module): + def __init__(self, c1, k=3): # ch_in, kernel + super().__init__() + self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) + self.bn = nn.BatchNorm2d(c1) + + def forward(self, x): + return torch.max(x, self.bn(self.conv(x))) diff --git a/utils/add_nms.py b/utils/add_nms.py new file mode 100644 index 0000000..0a1f797 --- /dev/null +++ b/utils/add_nms.py @@ -0,0 +1,155 @@ +import numpy as np +import onnx +from onnx import shape_inference +try: + import onnx_graphsurgeon as gs +except Exception as e: + print('Import onnx_graphsurgeon failure: %s' % e) + +import logging + +LOGGER = logging.getLogger(__name__) + +class RegisterNMS(object): + def __init__( + self, + onnx_model_path: str, + precision: str = "fp32", + ): + + self.graph = gs.import_onnx(onnx.load(onnx_model_path)) + assert self.graph + LOGGER.info("ONNX graph created successfully") + # Fold constants via ONNX-GS that PyTorch2ONNX may have missed + self.graph.fold_constants() + self.precision = precision + self.batch_size = 1 + def infer(self): + """ + Sanitize the graph by cleaning any unconnected nodes, do a topological resort, + and fold constant inputs values. When possible, run shape inference on the + ONNX graph to determine tensor shapes. + """ + for _ in range(3): + count_before = len(self.graph.nodes) + + self.graph.cleanup().toposort() + try: + for node in self.graph.nodes: + for o in node.outputs: + o.shape = None + model = gs.export_onnx(self.graph) + model = shape_inference.infer_shapes(model) + self.graph = gs.import_onnx(model) + except Exception as e: + LOGGER.info(f"Shape inference could not be performed at this time:\n{e}") + try: + self.graph.fold_constants(fold_shapes=True) + except TypeError as e: + LOGGER.error( + "This version of ONNX GraphSurgeon does not support folding shapes, " + f"please upgrade your onnx_graphsurgeon module. Error:\n{e}" + ) + raise + + count_after = len(self.graph.nodes) + if count_before == count_after: + # No new folding occurred in this iteration, so we can stop for now. + break + + def save(self, output_path): + """ + Save the ONNX model to the given location. + Args: + output_path: Path pointing to the location where to write + out the updated ONNX model. + """ + self.graph.cleanup().toposort() + model = gs.export_onnx(self.graph) + onnx.save(model, output_path) + LOGGER.info(f"Saved ONNX model to {output_path}") + + def register_nms( + self, + *, + score_thresh: float = 0.25, + nms_thresh: float = 0.45, + detections_per_img: int = 100, + ): + """ + Register the ``EfficientNMS_TRT`` plugin node. + NMS expects these shapes for its input tensors: + - box_net: [batch_size, number_boxes, 4] + - class_net: [batch_size, number_boxes, number_labels] + Args: + score_thresh (float): The scalar threshold for score (low scoring boxes are removed). + nms_thresh (float): The scalar threshold for IOU (new boxes that have high IOU + overlap with previously selected boxes are removed). + detections_per_img (int): Number of best detections to keep after NMS. + """ + + self.infer() + # Find the concat node at the end of the network + op_inputs = self.graph.outputs + op = "EfficientNMS_TRT" + attrs = { + "plugin_version": "1", + "background_class": -1, # no background class + "max_output_boxes": detections_per_img, + "score_threshold": score_thresh, + "iou_threshold": nms_thresh, + "score_activation": False, + "box_coding": 0, + } + + if self.precision == "fp32": + dtype_output = np.float32 + elif self.precision == "fp16": + dtype_output = np.float16 + else: + raise NotImplementedError(f"Currently not supports precision: {self.precision}") + + # NMS Outputs + output_num_detections = gs.Variable( + name="num_dets", + dtype=np.int32, + shape=[self.batch_size, 1], + ) # A scalar indicating the number of valid detections per batch image. + output_boxes = gs.Variable( + name="det_boxes", + dtype=dtype_output, + shape=[self.batch_size, detections_per_img, 4], + ) + output_scores = gs.Variable( + name="det_scores", + dtype=dtype_output, + shape=[self.batch_size, detections_per_img], + ) + output_labels = gs.Variable( + name="det_classes", + dtype=np.int32, + shape=[self.batch_size, detections_per_img], + ) + + op_outputs = [output_num_detections, output_boxes, output_scores, output_labels] + + # Create the NMS Plugin node with the selected inputs. The outputs of the node will also + # become the final outputs of the graph. + self.graph.layer(op=op, name="batched_nms", inputs=op_inputs, outputs=op_outputs, attrs=attrs) + LOGGER.info(f"Created NMS plugin '{op}' with attributes: {attrs}") + + self.graph.outputs = op_outputs + + self.infer() + + def save(self, output_path): + """ + Save the ONNX model to the given location. + Args: + output_path: Path pointing to the location where to write + out the updated ONNX model. + """ + self.graph.cleanup().toposort() + model = gs.export_onnx(self.graph) + onnx.save(model, output_path) + LOGGER.info(f"Saved ONNX model to {output_path}") diff --git a/utils/autoanchor.py b/utils/autoanchor.py new file mode 100644 index 0000000..f491032 --- /dev/null +++ b/utils/autoanchor.py @@ -0,0 +1,160 @@ +# Auto-anchor utils + +import numpy as np +import torch +import yaml +from scipy.cluster.vq import kmeans +from tqdm import tqdm + +from utils.general import colorstr + + +def check_anchor_order(m): + # Check anchor order against stride order for YOLO Detect() module m, and correct if necessary + a = m.anchor_grid.prod(-1).view(-1) # anchor area + da = a[-1] - a[0] # delta a + ds = m.stride[-1] - m.stride[0] # delta s + if da.sign() != ds.sign(): # same order + print('Reversing anchor order') + m.anchors[:] = m.anchors.flip(0) + m.anchor_grid[:] = m.anchor_grid.flip(0) + + +def check_anchors(dataset, model, thr=4.0, imgsz=640): + # Check anchor fit to data, recompute if necessary + prefix = colorstr('autoanchor: ') + print(f'\n{prefix}Analyzing anchors... ', end='') + m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() + shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) + scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale + wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh + + def metric(k): # compute metric + r = wh[:, None] / k[None] + x = torch.min(r, 1. / r).min(2)[0] # ratio metric + best = x.max(1)[0] # best_x + aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold + bpr = (best > 1. / thr).float().mean() # best possible recall + return bpr, aat + + anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors + bpr, aat = metric(anchors) + print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='') + if bpr < 0.98: # threshold to recompute + print('. Attempting to improve anchors, please wait...') + na = m.anchor_grid.numel() // 2 # number of anchors + try: + anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) + except Exception as e: + print(f'{prefix}ERROR: {e}') + new_bpr = metric(anchors)[0] + if new_bpr > bpr: # replace anchors + anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) + m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference + check_anchor_order(m) + m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss + print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.') + else: + print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.') + print('') # newline + + +def kmean_anchors(path='./data/coco.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): + """ Creates kmeans-evolved anchors from training dataset + + Arguments: + path: path to dataset *.yaml, or a loaded dataset + n: number of anchors + img_size: image size used for training + thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 + gen: generations to evolve anchors using genetic algorithm + verbose: print all results + + Return: + k: kmeans evolved anchors + + Usage: + from utils.autoanchor import *; _ = kmean_anchors() + """ + thr = 1. / thr + prefix = colorstr('autoanchor: ') + + def metric(k, wh): # compute metrics + r = wh[:, None] / k[None] + x = torch.min(r, 1. / r).min(2)[0] # ratio metric + # x = wh_iou(wh, torch.tensor(k)) # iou metric + return x, x.max(1)[0] # x, best_x + + def anchor_fitness(k): # mutation fitness + _, best = metric(torch.tensor(k, dtype=torch.float32), wh) + return (best * (best > thr).float()).mean() # fitness + + def print_results(k): + k = k[np.argsort(k.prod(1))] # sort small to large + x, best = metric(k, wh0) + bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr + print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr') + print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' + f'past_thr={x[x > thr].mean():.3f}-mean: ', end='') + for i, x in enumerate(k): + print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg + return k + + if isinstance(path, str): # *.yaml file + with open(path) as f: + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # model dict + from utils.datasets import LoadImagesAndLabels + dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) + else: + dataset = path # dataset + + # Get label wh + shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) + wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh + + # Filter + i = (wh0 < 3.0).any(1).sum() + if i: + print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.') + wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels + # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 + + # Kmeans calculation + print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...') + s = wh.std(0) # sigmas for whitening + k, dist = kmeans(wh / s, n, iter=30) # points, mean distance + assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}') + k *= s + wh = torch.tensor(wh, dtype=torch.float32) # filtered + wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered + k = print_results(k) + + # Plot + # k, d = [None] * 20, [None] * 20 + # for i in tqdm(range(1, 21)): + # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance + # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) + # ax = ax.ravel() + # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') + # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh + # ax[0].hist(wh[wh[:, 0]<100, 0],400) + # ax[1].hist(wh[wh[:, 1]<100, 1],400) + # fig.savefig('wh.png', dpi=200) + + # Evolve + npr = np.random + f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma + pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:') # progress bar + for _ in pbar: + v = np.ones(sh) + while (v == 1).all(): # mutate until a change occurs (prevent duplicates) + v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) + kg = (k.copy() * v).clip(min=2.0) + fg = anchor_fitness(kg) + if fg > f: + f, k = fg, kg.copy() + pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' + if verbose: + print_results(k) + + return print_results(k) diff --git a/utils/aws/__init__.py b/utils/aws/__init__.py new file mode 100644 index 0000000..e9691f2 --- /dev/null +++ b/utils/aws/__init__.py @@ -0,0 +1 @@ +#init \ No newline at end of file diff --git a/utils/aws/mime.sh b/utils/aws/mime.sh new file mode 100644 index 0000000..c319a83 --- /dev/null +++ b/utils/aws/mime.sh @@ -0,0 +1,26 @@ +# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ +# This script will run on every instance restart, not only on first start +# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- + +Content-Type: multipart/mixed; boundary="//" +MIME-Version: 1.0 + +--// +Content-Type: text/cloud-config; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="cloud-config.txt" + +#cloud-config +cloud_final_modules: +- [scripts-user, always] + +--// +Content-Type: text/x-shellscript; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="userdata.txt" + +#!/bin/bash +# --- paste contents of userdata.sh here --- +--// diff --git a/utils/aws/resume.py b/utils/aws/resume.py new file mode 100644 index 0000000..338685b --- /dev/null +++ b/utils/aws/resume.py @@ -0,0 +1,37 @@ +# Resume all interrupted trainings in yolor/ dir including DDP trainings +# Usage: $ python utils/aws/resume.py + +import os +import sys +from pathlib import Path + +import torch +import yaml + +sys.path.append('./') # to run '$ python *.py' files in subdirectories + +port = 0 # --master_port +path = Path('').resolve() +for last in path.rglob('*/**/last.pt'): + ckpt = torch.load(last) + if ckpt['optimizer'] is None: + continue + + # Load opt.yaml + with open(last.parent.parent / 'opt.yaml') as f: + opt = yaml.load(f, Loader=yaml.SafeLoader) + + # Get device count + d = opt['device'].split(',') # devices + nd = len(d) # number of devices + ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel + + if ddp: # multi-GPU + port += 1 + cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' + else: # single-GPU + cmd = f'python train.py --resume {last}' + + cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread + print(cmd) + os.system(cmd) diff --git a/utils/aws/userdata.sh b/utils/aws/userdata.sh new file mode 100644 index 0000000..5762ae5 --- /dev/null +++ b/utils/aws/userdata.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html +# This script will run only once on first instance start (for a re-start script see mime.sh) +# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir +# Use >300 GB SSD + +cd home/ubuntu +if [ ! -d yolor ]; then + echo "Running first-time script." # install dependencies, download COCO, pull Docker + git clone -b paper https://github.com/WongKinYiu/yolor && sudo chmod -R 777 yolor + cd yolor + bash data/scripts/get_coco.sh && echo "Data done." & + sudo docker pull nvcr.io/nvidia/pytorch:21.08-py3 && echo "Docker done." & + python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & + wait && echo "All tasks done." # finish background tasks +else + echo "Running re-start script." # resume interrupted runs + i=0 + list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' + while IFS= read -r id; do + ((i++)) + echo "restarting container $i: $id" + sudo docker start $id + # sudo docker exec -it $id python train.py --resume # single-GPU + sudo docker exec -d $id python utils/aws/resume.py # multi-scenario + done <<<"$list" +fi diff --git a/utils/datasets.py b/utils/datasets.py new file mode 100644 index 0000000..b6bb8b0 --- /dev/null +++ b/utils/datasets.py @@ -0,0 +1,1320 @@ +# Dataset utils and dataloaders + +import glob +import logging +import math +import os +import random +import shutil +import time +from itertools import repeat +from multiprocessing.pool import ThreadPool +from pathlib import Path +from threading import Thread + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image, ExifTags +from torch.utils.data import Dataset +from tqdm import tqdm + +import pickle +from copy import deepcopy +#from pycocotools import mask as maskUtils +from torchvision.utils import save_image +from torchvision.ops import roi_pool, roi_align, ps_roi_pool, ps_roi_align + +from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \ + resample_segments, clean_str +from utils.torch_utils import torch_distributed_zero_first + +# Parameters +help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' +img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo'] # acceptable image suffixes +vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes +logger = logging.getLogger(__name__) + +# Get orientation exif tag +for orientation in ExifTags.TAGS.keys(): + if ExifTags.TAGS[orientation] == 'Orientation': + break + + +def get_hash(files): + # Returns a single hash value of a list of files + return sum(os.path.getsize(f) for f in files if os.path.isfile(f)) + + +def exif_size(img): + # Returns exif-corrected PIL size + s = img.size # (width, height) + try: + rotation = dict(img._getexif().items())[orientation] + if rotation == 6: # rotation 270 + s = (s[1], s[0]) + elif rotation == 8: # rotation 90 + s = (s[1], s[0]) + except: + pass + + return s + + +def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False, + rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''): + # Make sure only the first process in DDP process the dataset first, and the following others can use the cache + with torch_distributed_zero_first(rank): + dataset = LoadImagesAndLabels(path, imgsz, batch_size, + augment=augment, # augment images + hyp=hyp, # augmentation hyperparameters + rect=rect, # rectangular training + cache_images=cache, + single_cls=opt.single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix) + + batch_size = min(batch_size, len(dataset)) + nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None + loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader + # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader() + dataloader = loader(dataset, + batch_size=batch_size, + num_workers=nw, + sampler=sampler, + pin_memory=True, + collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn) + return dataloader, dataset + + +class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader): + """ Dataloader that reuses workers + + Uses same syntax as vanilla DataLoader + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) + self.iterator = super().__iter__() + + def __len__(self): + return len(self.batch_sampler.sampler) + + def __iter__(self): + for i in range(len(self)): + yield next(self.iterator) + + +class _RepeatSampler(object): + """ Sampler that repeats forever + + Args: + sampler (Sampler) + """ + + def __init__(self, sampler): + self.sampler = sampler + + def __iter__(self): + while True: + yield from iter(self.sampler) + + +class LoadImages: # for inference + def __init__(self, path, img_size=640, stride=32): + p = str(Path(path).absolute()) # os-agnostic absolute path + if '*' in p: + files = sorted(glob.glob(p, recursive=True)) # glob + elif os.path.isdir(p): + files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir + elif os.path.isfile(p): + files = [p] # files + else: + raise Exception(f'ERROR: {p} does not exist') + + images = [x for x in files if x.split('.')[-1].lower() in img_formats] + videos = [x for x in files if x.split('.')[-1].lower() in vid_formats] + ni, nv = len(images), len(videos) + + self.img_size = img_size + self.stride = stride + self.files = images + videos + self.nf = ni + nv # number of files + self.video_flag = [False] * ni + [True] * nv + self.mode = 'image' + if any(videos): + self.new_video(videos[0]) # new video + else: + self.cap = None + assert self.nf > 0, f'No images or videos found in {p}. ' \ + f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}' + + def __iter__(self): + self.count = 0 + return self + + def __next__(self): + if self.count == self.nf: + raise StopIteration + path = self.files[self.count] + + if self.video_flag[self.count]: + # Read video + self.mode = 'video' + ret_val, img0 = self.cap.read() + if not ret_val: + self.count += 1 + self.cap.release() + if self.count == self.nf: # last video + raise StopIteration + else: + path = self.files[self.count] + self.new_video(path) + ret_val, img0 = self.cap.read() + + self.frame += 1 + print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='') + + else: + # Read image + self.count += 1 + img0 = cv2.imread(path) # BGR + assert img0 is not None, 'Image Not Found ' + path + #print(f'image {self.count}/{self.nf} {path}: ', end='') + + # Padded resize + img = letterbox(img0, self.img_size, stride=self.stride)[0] + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return path, img, img0, self.cap + + def new_video(self, path): + self.frame = 0 + self.cap = cv2.VideoCapture(path) + self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + def __len__(self): + return self.nf # number of files + + +class LoadWebcam: # for inference + def __init__(self, pipe='0', img_size=640, stride=32): + self.img_size = img_size + self.stride = stride + + if pipe.isnumeric(): + pipe = eval(pipe) # local camera + # pipe = 'rtsp://192.168.1.64/1' # IP camera + # pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login + # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera + + self.pipe = pipe + self.cap = cv2.VideoCapture(pipe) # video capture object + self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + if cv2.waitKey(1) == ord('q'): # q to quit + self.cap.release() + cv2.destroyAllWindows() + raise StopIteration + + # Read frame + if self.pipe == 0: # local camera + ret_val, img0 = self.cap.read() + img0 = cv2.flip(img0, 1) # flip left-right + else: # IP camera + n = 0 + while True: + n += 1 + self.cap.grab() + if n % 30 == 0: # skip frames + ret_val, img0 = self.cap.retrieve() + if ret_val: + break + + # Print + assert ret_val, f'Camera Error {self.pipe}' + img_path = 'webcam.jpg' + print(f'webcam {self.count}: ', end='') + + # Padded resize + img = letterbox(img0, self.img_size, stride=self.stride)[0] + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return img_path, img, img0, None + + def __len__(self): + return 0 + + +class LoadStreams: # multiple IP or RTSP cameras + def __init__(self, sources='streams.txt', img_size=640, stride=32): + self.mode = 'stream' + self.img_size = img_size + self.stride = stride + + if os.path.isfile(sources): + with open(sources, 'r') as f: + sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())] + else: + sources = [sources] + + n = len(sources) + self.imgs = [None] * n + self.sources = [clean_str(x) for x in sources] # clean source names for later + for i, s in enumerate(sources): + # Start the thread to read frames from the video stream + print(f'{i + 1}/{n}: {s}... ', end='') + url = eval(s) if s.isnumeric() else s + if 'youtube.com/' in str(url) or 'youtu.be/' in str(url): # if source is YouTube video + check_requirements(('pafy', 'youtube_dl')) + import pafy + url = pafy.new(url).getbest(preftype="mp4").url + cap = cv2.VideoCapture(url) + assert cap.isOpened(), f'Failed to open {s}' + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + self.fps = cap.get(cv2.CAP_PROP_FPS) % 100 + + _, self.imgs[i] = cap.read() # guarantee first frame + thread = Thread(target=self.update, args=([i, cap]), daemon=True) + print(f' success ({w}x{h} at {self.fps:.2f} FPS).') + thread.start() + print('') # newline + + # check for common shapes + s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0) # shapes + self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal + if not self.rect: + print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') + + def update(self, index, cap): + # Read next stream frame in a daemon thread + n = 0 + while cap.isOpened(): + n += 1 + # _, self.imgs[index] = cap.read() + cap.grab() + if n == 4: # read every 4th frame + success, im = cap.retrieve() + self.imgs[index] = im if success else self.imgs[index] * 0 + n = 0 + time.sleep(1 / self.fps) # wait time + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + img0 = self.imgs.copy() + if cv2.waitKey(1) == ord('q'): # q to quit + cv2.destroyAllWindows() + raise StopIteration + + # Letterbox + img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0] + + # Stack + img = np.stack(img, 0) + + # Convert + img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416 + img = np.ascontiguousarray(img) + + return self.sources, img, img0, None + + def __len__(self): + return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years + + +def img2label_paths(img_paths): + # Define label paths as a function of image paths + sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings + return ['txt'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1)) for x in img_paths] + + +class LoadImagesAndLabels(Dataset): # for training/testing + def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False, + cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''): + self.img_size = img_size + self.augment = augment + self.hyp = hyp + self.image_weights = image_weights + self.rect = False if image_weights else rect + self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) + self.mosaic_border = [-img_size // 2, -img_size // 2] + self.stride = stride + self.path = path + #self.albumentations = Albumentations() if augment else None + + try: + f = [] # image files + for p in path if isinstance(path, list) else [path]: + p = Path(p) # os-agnostic + if p.is_dir(): # dir + f += glob.glob(str(p / '**' / '*.*'), recursive=True) + # f = list(p.rglob('**/*.*')) # pathlib + elif p.is_file(): # file + with open(p, 'r') as t: + t = t.read().strip().splitlines() + parent = str(p.parent) + os.sep + f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path + # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) + else: + raise Exception(f'{prefix}{p} does not exist') + self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats]) + # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats]) # pathlib + assert self.img_files, f'{prefix}No images found' + except Exception as e: + raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}') + + # Check cache + self.label_files = img2label_paths(self.img_files) # labels + cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') # cached labels + if cache_path.is_file(): + cache, exists = torch.load(cache_path), True # load + #if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache: # changed + # cache, exists = self.cache_labels(cache_path, prefix), False # re-cache + else: + cache, exists = self.cache_labels(cache_path, prefix), False # cache + + # Display cache + nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupted, total + if exists: + d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" + tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results + assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}' + + # Read cache + cache.pop('hash') # remove hash + cache.pop('version') # remove version + labels, shapes, self.segments = zip(*cache.values()) + self.labels = list(labels) + self.shapes = np.array(shapes, dtype=np.float64) + self.img_files = list(cache.keys()) # update + self.label_files = img2label_paths(cache.keys()) # update + if single_cls: + for x in self.labels: + x[:, 0] = 0 + + n = len(shapes) # number of images + bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index + nb = bi[-1] + 1 # number of batches + self.batch = bi # batch index of image + self.n = n + self.indices = range(n) + + # Rectangular Training + if self.rect: + # Sort by aspect ratio + s = self.shapes # wh + ar = s[:, 1] / s[:, 0] # aspect ratio + irect = ar.argsort() + self.img_files = [self.img_files[i] for i in irect] + self.label_files = [self.label_files[i] for i in irect] + self.labels = [self.labels[i] for i in irect] + self.shapes = s[irect] # wh + ar = ar[irect] + + # Set training image shapes + shapes = [[1, 1]] * nb + for i in range(nb): + ari = ar[bi == i] + mini, maxi = ari.min(), ari.max() + if maxi < 1: + shapes[i] = [maxi, 1] + elif mini > 1: + shapes[i] = [1, 1 / mini] + + self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride + + # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) + self.imgs = [None] * n + if cache_images: + if cache_images == 'disk': + self.im_cache_dir = Path(Path(self.img_files[0]).parent.as_posix() + '_npy') + self.img_npy = [self.im_cache_dir / Path(f).with_suffix('.npy').name for f in self.img_files] + self.im_cache_dir.mkdir(parents=True, exist_ok=True) + gb = 0 # Gigabytes of cached images + self.img_hw0, self.img_hw = [None] * n, [None] * n + results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n))) + pbar = tqdm(enumerate(results), total=n) + for i, x in pbar: + if cache_images == 'disk': + if not self.img_npy[i].exists(): + np.save(self.img_npy[i].as_posix(), x[0]) + gb += self.img_npy[i].stat().st_size + else: + self.imgs[i], self.img_hw0[i], self.img_hw[i] = x + gb += self.imgs[i].nbytes + pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)' + pbar.close() + + def cache_labels(self, path=Path('./labels.cache'), prefix=''): + # Cache dataset labels, check images and read shapes + x = {} # dict + nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate + pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files)) + for i, (im_file, lb_file) in enumerate(pbar): + try: + # verify images + im = Image.open(im_file) + im.verify() # PIL verify + shape = exif_size(im) # image size + segments = [] # instance segments + assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' + assert im.format.lower() in img_formats, f'invalid image format {im.format}' + + # verify labels + if os.path.isfile(lb_file): + nf += 1 # label found + with open(lb_file, 'r') as f: + l = [x.split() for x in f.read().strip().splitlines()] + if any([len(x) > 8 for x in l]): # is segment + classes = np.array([x[0] for x in l], dtype=np.float32) + segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...) + l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) + l = np.array(l, dtype=np.float32) + if len(l): + assert l.shape[1] == 5, 'labels require 5 columns each' + assert (l >= 0).all(), 'negative labels' + assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels' + assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels' + else: + ne += 1 # label empty + l = np.zeros((0, 5), dtype=np.float32) + else: + nm += 1 # label missing + l = np.zeros((0, 5), dtype=np.float32) + x[im_file] = [l, shape, segments] + except Exception as e: + nc += 1 + print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}') + + pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " \ + f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted" + pbar.close() + + if nf == 0: + print(f'{prefix}WARNING: No labels found in {path}. See {help_url}') + + x['hash'] = get_hash(self.label_files + self.img_files) + x['results'] = nf, nm, ne, nc, i + 1 + x['version'] = 0.1 # cache version + torch.save(x, path) # save for next time + logging.info(f'{prefix}New cache created: {path}') + return x + + def __len__(self): + return len(self.img_files) + + # def __iter__(self): + # self.count = -1 + # print('ran dataset iter') + # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) + # return self + + def __getitem__(self, index): + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp['mosaic'] + if mosaic: + # Load mosaic + if random.random() < 0.8: + img, labels = load_mosaic(self, index) + else: + img, labels = load_mosaic9(self, index) + shapes = None + + # MixUp https://arxiv.org/pdf/1710.09412.pdf + if random.random() < hyp['mixup']: + if random.random() < 0.8: + img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1)) + else: + img2, labels2 = load_mosaic9(self, random.randint(0, len(self.labels) - 1)) + r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0 + img = (img * r + img2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + + else: + # Load image + img, (h0, w0), (h, w) = load_image(self, index) + + # Letterbox + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) + + if self.augment: + # Augment imagespace + if not mosaic: + img, labels = random_perspective(img, labels, + degrees=hyp['degrees'], + translate=hyp['translate'], + scale=hyp['scale'], + shear=hyp['shear'], + perspective=hyp['perspective']) + + + #img, labels = self.albumentations(img, labels) + + # Augment colorspace + augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) + + # Apply cutouts + # if random.random() < 0.9: + # labels = cutout(img, labels) + + if random.random() < hyp['paste_in']: + sample_labels, sample_images, sample_masks = [], [], [] + while len(sample_labels) < 30: + sample_labels_, sample_images_, sample_masks_ = load_samples(self, random.randint(0, len(self.labels) - 1)) + sample_labels += sample_labels_ + sample_images += sample_images_ + sample_masks += sample_masks_ + #print(len(sample_labels)) + if len(sample_labels) == 0: + break + labels = pastein(img, labels, sample_labels, sample_images, sample_masks) + + nL = len(labels) # number of labels + if nL: + labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh + labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1 + labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1 + + if self.augment: + # flip up-down + if random.random() < hyp['flipud']: + img = np.flipud(img) + if nL: + labels[:, 2] = 1 - labels[:, 2] + + # flip left-right + if random.random() < hyp['fliplr']: + img = np.fliplr(img) + if nL: + labels[:, 1] = 1 - labels[:, 1] + + labels_out = torch.zeros((nL, 6)) + if nL: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return torch.from_numpy(img), labels_out, self.img_files[index], shapes + + @staticmethod + def collate_fn(batch): + img, label, path, shapes = zip(*batch) # transposed + for i, l in enumerate(label): + l[:, 0] = i # add target image index for build_targets() + return torch.stack(img, 0), torch.cat(label, 0), path, shapes + + @staticmethod + def collate_fn4(batch): + img, label, path, shapes = zip(*batch) # transposed + n = len(shapes) // 4 + img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] + + ho = torch.tensor([[0., 0, 0, 1, 0, 0]]) + wo = torch.tensor([[0., 0, 1, 0, 0, 0]]) + s = torch.tensor([[1, 1, .5, .5, .5, .5]]) # scale + for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW + i *= 4 + if random.random() < 0.5: + im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[ + 0].type(img[i].type()) + l = label[i] + else: + im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2) + l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s + img4.append(im) + label4.append(l) + + for i, l in enumerate(label4): + l[:, 0] = i # add target image index for build_targets() + + return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4 + + +# Ancillary functions -------------------------------------------------------------------------------------------------- +def load_image(self, index): + # loads 1 image from dataset, returns img, original hw, resized hw + img = self.imgs[index] + if img is None: # not cached + path = self.img_files[index] + img = cv2.imread(path) # BGR + assert img is not None, 'Image Not Found ' + path + h0, w0 = img.shape[:2] # orig hw + r = self.img_size / max(h0, w0) # resize image to img_size + if r != 1: # always resize down, only resize up if training with augmentation + interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR + img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp) + return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized + else: + return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized + + +def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): + r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains + hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) + dtype = img.dtype # uint8 + + x = np.arange(0, 256, dtype=np.int16) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) + cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed + + +def hist_equalize(img, clahe=True, bgr=False): + # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255 + yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) + if clahe: + c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + yuv[:, :, 0] = c.apply(yuv[:, :, 0]) + else: + yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram + return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB + + +def load_mosaic(self, index): + # loads images in a 4-mosaic + + labels4, segments4 = [], [] + s = self.img_size + yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + #img4, labels4, segments4 = remove_background(img4, labels4, segments4) + #sample_segments(img4, labels4, segments4, probability=self.hyp['copy_paste']) + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, probability=self.hyp['copy_paste']) + img4, labels4 = random_perspective(img4, labels4, segments4, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + + return img4, labels4 + + +def load_mosaic9(self, index): + # loads images in a 9-mosaic + + labels9, segments9 = [], [] + s = self.img_size + indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, index) + + # place img in img9 + if i == 0: # center + img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + h0, w0 = h, w + c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates + elif i == 1: # top + c = s, s - h, s + w, s + elif i == 2: # top right + c = s + wp, s - h, s + wp + w, s + elif i == 3: # right + c = s + w0, s, s + w0 + w, s + h + elif i == 4: # bottom right + c = s + w0, s + hp, s + w0 + w, s + hp + h + elif i == 5: # bottom + c = s + w0 - w, s + h0, s + w0, s + h0 + h + elif i == 6: # bottom left + c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h + elif i == 7: # left + c = s - w, s + h0 - h, s, s + h0 + elif i == 8: # top left + c = s - w, s + h0 - hp - h, s, s + h0 - hp + + padx, pady = c[:2] + x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padx, pady) for x in segments] + labels9.append(labels) + segments9.extend(segments) + + # Image + img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] + hp, wp = h, w # height, width previous + + # Offset + yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border] # mosaic center x, y + img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] + + # Concat/clip labels + labels9 = np.concatenate(labels9, 0) + labels9[:, [1, 3]] -= xc + labels9[:, [2, 4]] -= yc + c = np.array([xc, yc]) # centers + segments9 = [x - c for x in segments9] + + for x in (labels9[:, 1:], *segments9): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img9, labels9 = replicate(img9, labels9) # replicate + + # Augment + #img9, labels9, segments9 = remove_background(img9, labels9, segments9) + img9, labels9, segments9 = copy_paste(img9, labels9, segments9, probability=self.hyp['copy_paste']) + img9, labels9 = random_perspective(img9, labels9, segments9, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + + return img9, labels9 + + +def load_samples(self, index): + # loads images in a 4-mosaic + + labels4, segments4 = [], [] + s = self.img_size + yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + #img4, labels4, segments4 = remove_background(img4, labels4, segments4) + sample_labels, sample_images, sample_masks = sample_segments(img4, labels4, segments4, probability=0.5) + + return sample_labels, sample_images, sample_masks + + +def copy_paste(img, labels, segments, probability=0.5): + # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) + n = len(segments) + if probability and n: + h, w, c = img.shape # height, width, channels + im_new = np.zeros(img.shape, np.uint8) + for j in random.sample(range(n), k=round(probability * n)): + l, s = labels[j], segments[j] + box = w - l[3], l[2], w - l[1], l[4] + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + if (ioa < 0.30).all(): # allow 30% obscuration of existing labels + labels = np.concatenate((labels, [[l[0], *box]]), 0) + segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) + cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED) + + result = cv2.bitwise_and(src1=img, src2=im_new) + result = cv2.flip(result, 1) # augment segments (flip left-right) + i = result > 0 # pixels to replace + # i[:, :] = result.max(2).reshape(h, w, 1) # act over ch + img[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug + + return img, labels, segments + + +def remove_background(img, labels, segments): + # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) + n = len(segments) + h, w, c = img.shape # height, width, channels + im_new = np.zeros(img.shape, np.uint8) + img_new = np.ones(img.shape, np.uint8) * 114 + for j in range(n): + cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED) + + result = cv2.bitwise_and(src1=img, src2=im_new) + + i = result > 0 # pixels to replace + img_new[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug + + return img_new, labels, segments + + +def sample_segments(img, labels, segments, probability=0.5): + # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) + n = len(segments) + sample_labels = [] + sample_images = [] + sample_masks = [] + if probability and n: + h, w, c = img.shape # height, width, channels + for j in random.sample(range(n), k=round(probability * n)): + l, s = labels[j], segments[j] + box = l[1].astype(int).clip(0,w-1), l[2].astype(int).clip(0,h-1), l[3].astype(int).clip(0,w-1), l[4].astype(int).clip(0,h-1) + + #print(box) + if (box[2] <= box[0]) or (box[3] <= box[1]): + continue + + sample_labels.append(l[0]) + + mask = np.zeros(img.shape, np.uint8) + + cv2.drawContours(mask, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED) + sample_masks.append(mask[box[1]:box[3],box[0]:box[2],:]) + + result = cv2.bitwise_and(src1=img, src2=mask) + i = result > 0 # pixels to replace + mask[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug + #print(box) + sample_images.append(mask[box[1]:box[3],box[0]:box[2],:]) + + return sample_labels, sample_images, sample_masks + + +def replicate(img, labels): + # Replicate labels + h, w = img.shape[:2] + boxes = labels[:, 1:].astype(int) + x1, y1, x2, y2 = boxes.T + s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) + for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices + x1b, y1b, x2b, y2b = boxes[i] + bh, bw = y2b - y1b, x2b - x1b + yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y + x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] + img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) + + return img, labels + + +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): + # Resize and pad image while meeting stride-multiple constraints + shape = img.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return img, ratio, (dw, dh) + + +def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, + border=(0, 0)): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + + height = img.shape[0] + border[0] * 2 # shape(h,w,c) + width = img.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -img.shape[1] / 2 # x translation (pixels) + C[1, 2] = -img.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1.1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) + T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + # Visualize + # import matplotlib.pyplot as plt + # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() + # ax[0].imshow(img[:, :, ::-1]) # base + # ax[1].imshow(img2[:, :, ::-1]) # warped + + # Transform label coordinates + n = len(targets) + if n: + use_segments = any(x.any() for x in segments) + new = np.zeros((n, 4)) + if use_segments: # warp segments + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + + else: # warp boxes + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # clip + new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) + new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) + targets = targets[i] + targets[:, 1:5] = new[i] + + return img, targets + + +def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) + # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio + return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates + + +def bbox_ioa(box1, box2): + # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 + box2 = box2.transpose() + + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + + # Intersection area + inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ + (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) + + # box2 area + box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 + + # Intersection over box2 area + return inter_area / box2_area + + +def cutout(image, labels): + # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 + h, w = image.shape[:2] + + # create random masks + scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction + for s in scales: + mask_h = random.randint(1, int(h * s)) + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + # apply random color mask + image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] + + # return unobscured labels + if len(labels) and s > 0.03: + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + labels = labels[ioa < 0.60] # remove >60% obscured labels + + return labels + + +def pastein(image, labels, sample_labels, sample_images, sample_masks): + # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 + h, w = image.shape[:2] + + # create random masks + scales = [0.75] * 2 + [0.5] * 4 + [0.25] * 4 + [0.125] * 4 + [0.0625] * 6 # image size fraction + for s in scales: + if random.random() < 0.2: + continue + mask_h = random.randint(1, int(h * s)) + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + if len(labels): + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + else: + ioa = np.zeros(1) + + if (ioa < 0.30).all() and len(sample_labels) and (xmax > xmin+20) and (ymax > ymin+20): # allow 30% obscuration of existing labels + sel_ind = random.randint(0, len(sample_labels)-1) + #print(len(sample_labels)) + #print(sel_ind) + #print((xmax-xmin, ymax-ymin)) + #print(image[ymin:ymax, xmin:xmax].shape) + #print([[sample_labels[sel_ind], *box]]) + #print(labels.shape) + hs, ws, cs = sample_images[sel_ind].shape + r_scale = min((ymax-ymin)/hs, (xmax-xmin)/ws) + r_w = int(ws*r_scale) + r_h = int(hs*r_scale) + + if (r_w > 10) and (r_h > 10): + r_mask = cv2.resize(sample_masks[sel_ind], (r_w, r_h)) + r_image = cv2.resize(sample_images[sel_ind], (r_w, r_h)) + temp_crop = image[ymin:ymin+r_h, xmin:xmin+r_w] + m_ind = r_mask > 0 + if m_ind.astype(np.int).sum() > 60: + temp_crop[m_ind] = r_image[m_ind] + #print(sample_labels[sel_ind]) + #print(sample_images[sel_ind].shape) + #print(temp_crop.shape) + box = np.array([xmin, ymin, xmin+r_w, ymin+r_h], dtype=np.float32) + if len(labels): + labels = np.concatenate((labels, [[sample_labels[sel_ind], *box]]), 0) + else: + labels = np.array([[sample_labels[sel_ind], *box]]) + + image[ymin:ymin+r_h, xmin:xmin+r_w] = temp_crop + + return labels + +class Albumentations: + # YOLOv5 Albumentations class (optional, only used if package is installed) + def __init__(self): + self.transform = None + import albumentations as A + + self.transform = A.Compose([ + A.CLAHE(p=0.01), + A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.01), + A.RandomGamma(gamma_limit=[80, 120], p=0.01), + A.Blur(p=0.01), + A.MedianBlur(p=0.01), + A.ToGray(p=0.01), + A.ImageCompression(quality_lower=75, p=0.01),], + bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels'])) + + #logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p)) + + def __call__(self, im, labels, p=1.0): + if self.transform and random.random() < p: + new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed + im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) + return im, labels + + +def create_folder(path='./new'): + # Create folder + if os.path.exists(path): + shutil.rmtree(path) # delete output folder + os.makedirs(path) # make new output folder + + +def flatten_recursive(path='../coco'): + # Flatten a recursive directory by bringing all files to top level + new_path = Path(path + '_flat') + create_folder(new_path) + for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)): + shutil.copyfile(file, new_path / Path(file).name) + + +def extract_boxes(path='../coco/'): # from utils.datasets import *; extract_boxes('../coco128') + # Convert detection dataset into classification dataset, with one directory per class + + path = Path(path) # images dir + shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None # remove existing + files = list(path.rglob('*.*')) + n = len(files) # number of files + for im_file in tqdm(files, total=n): + if im_file.suffix[1:] in img_formats: + # image + im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB + h, w = im.shape[:2] + + # labels + lb_file = Path(img2label_paths([str(im_file)])[0]) + if Path(lb_file).exists(): + with open(lb_file, 'r') as f: + lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + + for j, x in enumerate(lb): + c = int(x[0]) # class + f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename + if not f.parent.is_dir(): + f.parent.mkdir(parents=True) + + b = x[1:] * [w, h, w, h] # box + # b[2:] = b[2:].max() # rectangle to square + b[2:] = b[2:] * 1.2 + 3 # pad + b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int) + + b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image + b[[1, 3]] = np.clip(b[[1, 3]], 0, h) + assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' + + +def autosplit(path='../coco', weights=(0.9, 0.1, 0.0), annotated_only=False): + """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files + Usage: from utils.datasets import *; autosplit('../coco') + Arguments + path: Path to images directory + weights: Train, val, test weights (list) + annotated_only: Only use images with an annotated txt file + """ + path = Path(path) # images dir + files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], []) # image files only + n = len(files) # number of files + indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split + + txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files + [(path / x).unlink() for x in txt if (path / x).exists()] # remove existing + + print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) + for i, img in tqdm(zip(indices, files), total=n): + if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label + with open(path / txt[i], 'a') as f: + f.write(str(img) + '\n') # add image to txt file + + +def load_segmentations(self, index): + key = '/work/handsomejw66/coco17/' + self.img_files[index] + #print(key) + # /work/handsomejw66/coco17/ + return self.segs[key] diff --git a/utils/general.py b/utils/general.py new file mode 100644 index 0000000..faf908f --- /dev/null +++ b/utils/general.py @@ -0,0 +1,891 @@ +# YOLOR general utils + +import glob +import logging +import math +import os +import platform +import random +import re +import subprocess +import time +from pathlib import Path + +import cv2 +import numpy as np +import pandas as pd +import torch +import torchvision +import yaml + +from utils.google_utils import gsutil_getsize +from utils.metrics import fitness +from utils.torch_utils import init_torch_seeds + +# Settings +torch.set_printoptions(linewidth=320, precision=5, profile='long') +np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 +pd.options.display.max_columns = 10 +cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) +os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads + + +def set_logging(rank=-1): + logging.basicConfig( + format="%(message)s", + level=logging.INFO if rank in [-1, 0] else logging.WARN) + + +def init_seeds(seed=0): + # Initialize random number generator (RNG) seeds + random.seed(seed) + np.random.seed(seed) + init_torch_seeds(seed) + + +def get_latest_run(search_dir='.'): + # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) + last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) + return max(last_list, key=os.path.getctime) if last_list else '' + + +def isdocker(): + # Is environment a Docker container + return Path('/workspace').exists() # or Path('/.dockerenv').exists() + + +def emojis(str=''): + # Return platform-dependent emoji-safe version of string + return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str + + +def check_online(): + # Check internet connectivity + import socket + try: + socket.create_connection(("1.1.1.1", 443), 5) # check host accesability + return True + except OSError: + return False + + +def check_git_status(): + # Recommend 'git pull' if code is out of date + print(colorstr('github: '), end='') + try: + assert Path('.git').exists(), 'skipping check (not a git repository)' + assert not isdocker(), 'skipping check (Docker image)' + assert check_online(), 'skipping check (offline)' + + cmd = 'git fetch && git config --get remote.origin.url' + url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git') # github repo url + branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out + n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind + if n > 0: + s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \ + f"Use 'git pull' to update or 'git clone {url}' to download latest." + else: + s = f'up to date with {url} ✅' + print(emojis(s)) # emoji-safe + except Exception as e: + print(e) + + +def check_requirements(requirements='requirements.txt', exclude=()): + # Check installed dependencies meet requirements (pass *.txt file or list of packages) + import pkg_resources as pkg + prefix = colorstr('red', 'bold', 'requirements:') + if isinstance(requirements, (str, Path)): # requirements.txt file + file = Path(requirements) + if not file.exists(): + print(f"{prefix} {file.resolve()} not found, check failed.") + return + requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude] + else: # list or tuple of packages + requirements = [x for x in requirements if x not in exclude] + + n = 0 # number of packages updates + for r in requirements: + try: + pkg.require(r) + except Exception as e: # DistributionNotFound or VersionConflict if requirements not met + n += 1 + print(f"{prefix} {e.req} not found and is required by YOLOR, attempting auto-update...") + print(subprocess.check_output(f"pip install '{e.req}'", shell=True).decode()) + + if n: # if packages updated + source = file.resolve() if 'file' in locals() else requirements + s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \ + f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" + print(emojis(s)) # emoji-safe + + +def check_img_size(img_size, s=32): + # Verify img_size is a multiple of stride s + new_size = make_divisible(img_size, int(s)) # ceil gs-multiple + if new_size != img_size: + print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size)) + return new_size + + +def check_imshow(): + # Check if environment supports image displays + try: + assert not isdocker(), 'cv2.imshow() is disabled in Docker environments' + cv2.imshow('test', np.zeros((1, 1, 3))) + cv2.waitKey(1) + cv2.destroyAllWindows() + cv2.waitKey(1) + return True + except Exception as e: + print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}') + return False + + +def check_file(file): + # Search for file if not found + if Path(file).is_file() or file == '': + return file + else: + files = glob.glob('./**/' + file, recursive=True) # find file + assert len(files), f'File Not Found: {file}' # assert file was found + assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique + return files[0] # return file + + +def check_dataset(dict): + # Download dataset if not found locally + val, s = dict.get('val'), dict.get('download') + if val and len(val): + val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path + if not all(x.exists() for x in val): + print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()]) + if s and len(s): # download script + print('Downloading %s ...' % s) + if s.startswith('http') and s.endswith('.zip'): # URL + f = Path(s).name # filename + torch.hub.download_url_to_file(s, f) + r = os.system('unzip -q %s -d ../ && rm %s' % (f, f)) # unzip + else: # bash script + r = os.system(s) + print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure')) # analyze return value + else: + raise Exception('Dataset not found.') + + +def make_divisible(x, divisor): + # Returns x evenly divisible by divisor + return math.ceil(x / divisor) * divisor + + +def clean_str(s): + # Cleans a string by replacing special characters with underscore _ + return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) + + +def one_cycle(y1=0.0, y2=1.0, steps=100): + # lambda function for sinusoidal ramp from y1 to y2 + return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 + + +def colorstr(*input): + # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') + *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string + colors = {'black': '\033[30m', # basic colors + 'red': '\033[31m', + 'green': '\033[32m', + 'yellow': '\033[33m', + 'blue': '\033[34m', + 'magenta': '\033[35m', + 'cyan': '\033[36m', + 'white': '\033[37m', + 'bright_black': '\033[90m', # bright colors + 'bright_red': '\033[91m', + 'bright_green': '\033[92m', + 'bright_yellow': '\033[93m', + 'bright_blue': '\033[94m', + 'bright_magenta': '\033[95m', + 'bright_cyan': '\033[96m', + 'bright_white': '\033[97m', + 'end': '\033[0m', # misc + 'bold': '\033[1m', + 'underline': '\033[4m'} + return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] + + +def labels_to_class_weights(labels, nc=80): + # Get class weights (inverse frequency) from training labels + if labels[0] is None: # no labels loaded + return torch.Tensor() + + labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO + classes = labels[:, 0].astype(np.int) # labels = [class xywh] + weights = np.bincount(classes, minlength=nc) # occurrences per class + + # Prepend gridpoint count (for uCE training) + # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image + # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start + + weights[weights == 0] = 1 # replace empty bins with 1 + weights = 1 / weights # number of targets per class + weights /= weights.sum() # normalize + return torch.from_numpy(weights) + + +def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): + # Produces image weights based on class_weights and image contents + class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels]) + image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1) + # index = random.choices(range(n), weights=image_weights, k=1) # weight image sample + return image_weights + + +def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) + # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ + # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') + # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') + # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco + # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, + 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] + return x + + +def xyxy2xywh(x): + # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center + y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center + y[:, 2] = x[:, 2] - x[:, 0] # width + y[:, 3] = x[:, 3] - x[:, 1] # height + return y + + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): + # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x + y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y + y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x + y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y + return y + + +def xyn2xy(x, w=640, h=640, padw=0, padh=0): + # Convert normalized segments into pixel segments, shape (n,2) + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = w * x[:, 0] + padw # top left x + y[:, 1] = h * x[:, 1] + padh # top left y + return y + + +def segment2box(segment, width=640, height=640): + # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) + x, y = segment.T # segment xy + inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) + x, y, = x[inside], y[inside] + return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy + + +def segments2boxes(segments): + # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) + boxes = [] + for s in segments: + x, y = s.T # segment xy + boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy + return xyxy2xywh(np.array(boxes)) # cls, xywh + + +def resample_segments(segments, n=1000): + # Up-sample an (n,2) segment + for i, s in enumerate(segments): + x = np.linspace(0, len(s) - 1, n) + xp = np.arange(len(s)) + segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy + return segments + + +def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + coords[:, [0, 2]] -= pad[0] # x padding + coords[:, [1, 3]] -= pad[1] # y padding + coords[:, :4] /= gain + clip_coords(coords, img0_shape) + return coords + + +def clip_coords(boxes, img_shape): + # Clip bounding xyxy bounding boxes to image shape (height, width) + boxes[:, 0].clamp_(0, img_shape[1]) # x1 + boxes[:, 1].clamp_(0, img_shape[0]) # y1 + boxes[:, 2].clamp_(0, img_shape[1]) # x2 + boxes[:, 3].clamp_(0, img_shape[0]) # y2 + + +def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): + # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 + box2 = box2.T + + # Get the coordinates of bounding boxes + if x1y1x2y2: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + else: # transform from xywh to xyxy + b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 + b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 + b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 + b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 + + # Intersection area + inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ + (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) + + # Union Area + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + iou = inter / union + + if GIoU or DIoU or CIoU: + cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared + rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared + if DIoU: + return iou - rho2 / c2 # DIoU + elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / (h2 + eps)) - torch.atan(w1 / (h1 + eps)), 2) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU + else: + return iou # IoU + + + + +def bbox_alpha_iou(box1, box2, x1y1x2y2=False, GIoU=False, DIoU=False, CIoU=False, alpha=2, eps=1e-9): + # Returns tsqrt_he IoU of box1 to box2. box1 is 4, box2 is nx4 + box2 = box2.T + + # Get the coordinates of bounding boxes + if x1y1x2y2: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + else: # transform from xywh to xyxy + b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 + b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 + b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 + b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 + + # Intersection area + inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ + (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) + + # Union Area + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + # change iou into pow(iou+eps) + # iou = inter / union + iou = torch.pow(inter/union + eps, alpha) + # beta = 2 * alpha + if GIoU or DIoU or CIoU: + cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = (cw ** 2 + ch ** 2) ** alpha + eps # convex diagonal + rho_x = torch.abs(b2_x1 + b2_x2 - b1_x1 - b1_x2) + rho_y = torch.abs(b2_y1 + b2_y2 - b1_y1 - b1_y2) + rho2 = ((rho_x ** 2 + rho_y ** 2) / 4) ** alpha # center distance + if DIoU: + return iou - rho2 / c2 # DIoU + elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) + with torch.no_grad(): + alpha_ciou = v / ((1 + eps) - inter / union + v) + # return iou - (rho2 / c2 + v * alpha_ciou) # CIoU + return iou - (rho2 / c2 + torch.pow(v * alpha_ciou + eps, alpha)) # CIoU + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + # c_area = cw * ch + eps # convex area + # return iou - (c_area - union) / c_area # GIoU + c_area = torch.max(cw * ch + eps, union) # convex area + return iou - torch.pow((c_area - union) / c_area + eps, alpha) # GIoU + else: + return iou # torch.log(iou+eps) or iou + + +def box_iou(box1, box2): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) + return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter) + + +def wh_iou(wh1, wh2): + # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 + wh1 = wh1[:, None] # [N,1,2] + wh2 = wh2[None] # [1,M,2] + inter = torch.min(wh1, wh2).prod(2) # [N,M] + return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter) + + +def box_giou(box1, box2): + """ + Return generalized intersection-over-union (Jaccard index) between two sets of boxes. + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise generalized IoU values + for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) + union = (area1[:, None] + area2 - inter) + + iou = inter / union + + lti = torch.min(box1[:, None, :2], box2[:, :2]) + rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) + + whi = (rbi - lti).clamp(min=0) # [N,M,2] + areai = whi[:, :, 0] * whi[:, :, 1] + + return iou - (areai - union) / areai + + +def box_ciou(box1, box2, eps: float = 1e-7): + """ + Return complete intersection-over-union (Jaccard index) between two sets of boxes. + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + eps (float, optional): small number to prevent division by zero. Default: 1e-7 + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise complete IoU values + for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) + union = (area1[:, None] + area2 - inter) + + iou = inter / union + + lti = torch.min(box1[:, None, :2], box2[:, :2]) + rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) + + whi = (rbi - lti).clamp(min=0) # [N,M,2] + diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps + + # centers of boxes + x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2 + y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2 + x_g = (box2[:, 0] + box2[:, 2]) / 2 + y_g = (box2[:, 1] + box2[:, 3]) / 2 + # The distance between boxes' centers squared. + centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2 + + w_pred = box1[:, None, 2] - box1[:, None, 0] + h_pred = box1[:, None, 3] - box1[:, None, 1] + + w_gt = box2[:, 2] - box2[:, 0] + h_gt = box2[:, 3] - box2[:, 1] + + v = (4 / (torch.pi ** 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2) + with torch.no_grad(): + alpha = v / (1 - iou + v + eps) + return iou - (centers_distance_squared / diagonal_distance_squared) - alpha * v + + +def box_diou(box1, box2, eps: float = 1e-7): + """ + Return distance intersection-over-union (Jaccard index) between two sets of boxes. + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + eps (float, optional): small number to prevent division by zero. Default: 1e-7 + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise distance IoU values + for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) + union = (area1[:, None] + area2 - inter) + + iou = inter / union + + lti = torch.min(box1[:, None, :2], box2[:, :2]) + rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) + + whi = (rbi - lti).clamp(min=0) # [N,M,2] + diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps + + # centers of boxes + x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2 + y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2 + x_g = (box2[:, 0] + box2[:, 2]) / 2 + y_g = (box2[:, 1] + box2[:, 3]) / 2 + # The distance between boxes' centers squared. + centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2 + + # The distance IoU is the IoU penalized by a normalized + # distance between boxes' centers squared. + return iou - (centers_distance_squared / diagonal_distance_squared) + + +def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, + labels=()): + """Runs Non-Maximum Suppression (NMS) on inference results + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + + nc = prediction.shape[2] - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_det = 300 # maximum number of detections per image + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 5), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + if nc == 1: + x[:, 5:] = x[:, 4:5] # for models with one class, cls_loss is 0 and cls_conf is always 0.5, + # so there is no need to multiplicate. + else: + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + conf, j = x[:, 5:].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if (time.time() - t) > time_limit: + print(f'WARNING: NMS time limit {time_limit}s exceeded') + break # time limit exceeded + + return output + + +def non_max_suppression_kpt(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, + labels=(), kpt_label=False, nc=None, nkpt=None): + """Runs Non-Maximum Suppression (NMS) on inference results + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + if nc is None: + nc = prediction.shape[2] - 5 if not kpt_label else prediction.shape[2] - 56 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_det = 300 # maximum number of detections per image + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + output = [torch.zeros((0,6), device=prediction.device)] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 5), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:5+nc] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + if not kpt_label: + conf, j = x[:, 5:].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + else: + kpts = x[:, 6:] + conf, j = x[:, 5:6].max(1, keepdim=True) + x = torch.cat((box, conf, j.float(), kpts), 1)[conf.view(-1) > conf_thres] + + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if (time.time() - t) > time_limit: + print(f'WARNING: NMS time limit {time_limit}s exceeded') + break # time limit exceeded + + return output + + +def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() + # Strip optimizer from 'f' to finalize training, optionally save as 's' + x = torch.load(f, map_location=torch.device('cpu')) + if x.get('ema'): + x['model'] = x['ema'] # replace model with ema + for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates': # keys + x[k] = None + x['epoch'] = -1 + x['model'].half() # to FP16 + for p in x['model'].parameters(): + p.requires_grad = False + torch.save(x, s or f) + mb = os.path.getsize(s or f) / 1E6 # filesize + print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB") + + +def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''): + # Print mutation results to evolve.txt (for use with train.py --evolve) + a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys + b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values + c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) + print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c)) + + if bucket: + url = 'gs://%s/evolve.txt' % bucket + if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0): + os.system('gsutil cp %s .' % url) # download evolve.txt if larger than local + + with open('evolve.txt', 'a') as f: # append result + f.write(c + b + '\n') + x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows + x = x[np.argsort(-fitness(x))] # sort + np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness + + # Save yaml + for i, k in enumerate(hyp.keys()): + hyp[k] = float(x[0, i + 7]) + with open(yaml_file, 'w') as f: + results = tuple(x[0, :7]) + c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) + f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n') + yaml.dump(hyp, f, sort_keys=False) + + if bucket: + os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload + + +def apply_classifier(x, model, img, im0): + # applies a second stage classifier to yolo outputs + im0 = [im0] if isinstance(im0, np.ndarray) else im0 + for i, d in enumerate(x): # per image + if d is not None and len(d): + d = d.clone() + + # Reshape and pad cutouts + b = xyxy2xywh(d[:, :4]) # boxes + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square + b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad + d[:, :4] = xywh2xyxy(b).long() + + # Rescale boxes from img_size to im0 size + scale_coords(img.shape[2:], d[:, :4], im0[i].shape) + + # Classes + pred_cls1 = d[:, 5].long() + ims = [] + for j, a in enumerate(d): # per item + cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] + im = cv2.resize(cutout, (224, 224)) # BGR + # cv2.imwrite('test%i.jpg' % j, cutout) + + im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 + im /= 255.0 # 0 - 255 to 0.0 - 1.0 + ims.append(im) + + pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction + x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections + + return x + + +def increment_path(path, exist_ok=True, sep=''): + # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc. + path = Path(path) # os-agnostic + if (path.exists() and exist_ok) or (not path.exists()): + return str(path) + else: + dirs = glob.glob(f"{path}{sep}*") # similar paths + matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs] + i = [int(m.groups()[0]) for m in matches if m] # indices + n = max(i) + 1 if i else 2 # increment number + return f"{path}{sep}{n}" # update path diff --git a/utils/google_app_engine/Dockerfile b/utils/google_app_engine/Dockerfile new file mode 100644 index 0000000..0155618 --- /dev/null +++ b/utils/google_app_engine/Dockerfile @@ -0,0 +1,25 @@ +FROM gcr.io/google-appengine/python + +# Create a virtualenv for dependencies. This isolates these packages from +# system-level packages. +# Use -p python3 or -p python3.7 to select python version. Default is version 2. +RUN virtualenv /env -p python3 + +# Setting these environment variables are the same as running +# source /env/bin/activate. +ENV VIRTUAL_ENV /env +ENV PATH /env/bin:$PATH + +RUN apt-get update && apt-get install -y python-opencv + +# Copy the application's requirements.txt and run pip to install all +# dependencies into the virtualenv. +ADD requirements.txt /app/requirements.txt +RUN pip install -r /app/requirements.txt + +# Add the application source code. +ADD . /app + +# Run a WSGI server to serve the application. gunicorn must be declared as +# a dependency in requirements.txt. +CMD gunicorn -b :$PORT main:app diff --git a/utils/google_app_engine/additional_requirements.txt b/utils/google_app_engine/additional_requirements.txt new file mode 100644 index 0000000..5fcc305 --- /dev/null +++ b/utils/google_app_engine/additional_requirements.txt @@ -0,0 +1,4 @@ +# add these requirements in your app on top of the existing ones +pip==18.1 +Flask==1.0.2 +gunicorn==19.9.0 diff --git a/utils/google_app_engine/app.yaml b/utils/google_app_engine/app.yaml new file mode 100644 index 0000000..69b8f68 --- /dev/null +++ b/utils/google_app_engine/app.yaml @@ -0,0 +1,14 @@ +runtime: custom +env: flex + +service: yolorapp + +liveness_check: + initial_delay_sec: 600 + +manual_scaling: + instances: 1 +resources: + cpu: 1 + memory_gb: 4 + disk_size_gb: 20 \ No newline at end of file diff --git a/utils/google_utils.py b/utils/google_utils.py new file mode 100644 index 0000000..f363408 --- /dev/null +++ b/utils/google_utils.py @@ -0,0 +1,123 @@ +# Google utils: https://cloud.google.com/storage/docs/reference/libraries + +import os +import platform +import subprocess +import time +from pathlib import Path + +import requests +import torch + + +def gsutil_getsize(url=''): + # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du + s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8') + return eval(s.split(' ')[0]) if len(s) else 0 # bytes + + +def attempt_download(file, repo='WongKinYiu/yolov7'): + # Attempt file download if does not exist + file = Path(str(file).strip().replace("'", '').lower()) + + if not file.exists(): + try: + response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api + assets = [x['name'] for x in response['assets']] # release assets + tag = response['tag_name'] # i.e. 'v1.0' + except: # fallback plan + assets = ['yolov7.pt', 'yolov7-tiny.pt', 'yolov7x.pt', 'yolov7-d6.pt', 'yolov7-e6.pt', + 'yolov7-e6e.pt', 'yolov7-w6.pt'] + tag = subprocess.check_output('git tag', shell=True).decode().split()[-1] + + name = file.name + if name in assets: + msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/' + redundant = False # second download option + try: # GitHub + url = f'https://github.com/{repo}/releases/download/{tag}/{name}' + print(f'Downloading {url} to {file}...') + torch.hub.download_url_to_file(url, file) + assert file.exists() and file.stat().st_size > 1E6 # check + except Exception as e: # GCP + print(f'Download error: {e}') + assert redundant, 'No secondary mirror' + url = f'https://storage.googleapis.com/{repo}/ckpt/{name}' + print(f'Downloading {url} to {file}...') + os.system(f'curl -L {url} -o {file}') # torch.hub.download_url_to_file(url, weights) + finally: + if not file.exists() or file.stat().st_size < 1E6: # check + file.unlink(missing_ok=True) # remove partial downloads + print(f'ERROR: Download failure: {msg}') + print('') + return + + +def gdrive_download(id='', file='tmp.zip'): + # Downloads a file from Google Drive. from yolov7.utils.google_utils import *; gdrive_download() + t = time.time() + file = Path(file) + cookie = Path('cookie') # gdrive cookie + print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='') + file.unlink(missing_ok=True) # remove existing file + cookie.unlink(missing_ok=True) # remove existing cookie + + # Attempt file download + out = "NUL" if platform.system() == "Windows" else "/dev/null" + os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}') + if os.path.exists('cookie'): # large file + s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' + else: # small file + s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' + r = os.system(s) # execute, capture return + cookie.unlink(missing_ok=True) # remove existing cookie + + # Error check + if r != 0: + file.unlink(missing_ok=True) # remove partial + print('Download error ') # raise Exception('Download error') + return r + + # Unzip if archive + if file.suffix == '.zip': + print('unzipping... ', end='') + os.system(f'unzip -q {file}') # unzip + file.unlink() # remove zip to free space + + print(f'Done ({time.time() - t:.1f}s)') + return r + + +def get_token(cookie="./cookie"): + with open(cookie) as f: + for line in f: + if "download" in line: + return line.split()[-1] + return "" + +# def upload_blob(bucket_name, source_file_name, destination_blob_name): +# # Uploads a file to a bucket +# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python +# +# storage_client = storage.Client() +# bucket = storage_client.get_bucket(bucket_name) +# blob = bucket.blob(destination_blob_name) +# +# blob.upload_from_filename(source_file_name) +# +# print('File {} uploaded to {}.'.format( +# source_file_name, +# destination_blob_name)) +# +# +# def download_blob(bucket_name, source_blob_name, destination_file_name): +# # Uploads a blob from a bucket +# storage_client = storage.Client() +# bucket = storage_client.get_bucket(bucket_name) +# blob = bucket.blob(source_blob_name) +# +# blob.download_to_filename(destination_file_name) +# +# print('Blob {} downloaded to {}.'.format( +# source_blob_name, +# destination_file_name)) diff --git a/utils/loss.py b/utils/loss.py new file mode 100644 index 0000000..bf7ab65 --- /dev/null +++ b/utils/loss.py @@ -0,0 +1,1697 @@ +# Loss functions + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from utils.general import bbox_iou, bbox_alpha_iou, box_iou, box_giou, box_diou, box_ciou, xywh2xyxy +from utils.torch_utils import is_parallel + + +def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 + # return positive, negative label smoothing BCE targets + return 1.0 - 0.5 * eps, 0.5 * eps + + +class BCEBlurWithLogitsLoss(nn.Module): + # BCEwithLogitLoss() with reduced missing label effects. + def __init__(self, alpha=0.05): + super(BCEBlurWithLogitsLoss, self).__init__() + self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() + self.alpha = alpha + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + pred = torch.sigmoid(pred) # prob from logits + dx = pred - true # reduce only missing label effects + # dx = (pred - true).abs() # reduce missing label and false label effects + alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) + loss *= alpha_factor + return loss.mean() + + +class SigmoidBin(nn.Module): + stride = None # strides computed during build + export = False # onnx export + + def __init__(self, bin_count=10, min=0.0, max=1.0, reg_scale = 2.0, use_loss_regression=True, use_fw_regression=True, BCE_weight=1.0, smooth_eps=0.0): + super(SigmoidBin, self).__init__() + + self.bin_count = bin_count + self.length = bin_count + 1 + self.min = min + self.max = max + self.scale = float(max - min) + self.shift = self.scale / 2.0 + + self.use_loss_regression = use_loss_regression + self.use_fw_regression = use_fw_regression + self.reg_scale = reg_scale + self.BCE_weight = BCE_weight + + start = min + (self.scale/2.0) / self.bin_count + end = max - (self.scale/2.0) / self.bin_count + step = self.scale / self.bin_count + self.step = step + #print(f" start = {start}, end = {end}, step = {step} ") + + bins = torch.range(start, end + 0.0001, step).float() + self.register_buffer('bins', bins) + + + self.cp = 1.0 - 0.5 * smooth_eps + self.cn = 0.5 * smooth_eps + + self.BCEbins = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([BCE_weight])) + self.MSELoss = nn.MSELoss() + + def get_length(self): + return self.length + + def forward(self, pred): + assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length) + + pred_reg = (pred[..., 0] * self.reg_scale - self.reg_scale/2.0) * self.step + pred_bin = pred[..., 1:(1+self.bin_count)] + + _, bin_idx = torch.max(pred_bin, dim=-1) + bin_bias = self.bins[bin_idx] + + if self.use_fw_regression: + result = pred_reg + bin_bias + else: + result = bin_bias + result = result.clamp(min=self.min, max=self.max) + + return result + + + def training_loss(self, pred, target): + assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length) + assert pred.shape[0] == target.shape[0], 'pred.shape=%d is not equal to the target.shape=%d' % (pred.shape[0], target.shape[0]) + device = pred.device + + pred_reg = (pred[..., 0].sigmoid() * self.reg_scale - self.reg_scale/2.0) * self.step + pred_bin = pred[..., 1:(1+self.bin_count)] + + diff_bin_target = torch.abs(target[..., None] - self.bins) + _, bin_idx = torch.min(diff_bin_target, dim=-1) + + bin_bias = self.bins[bin_idx] + bin_bias.requires_grad = False + result = pred_reg + bin_bias + + target_bins = torch.full_like(pred_bin, self.cn, device=device) # targets + n = pred.shape[0] + target_bins[range(n), bin_idx] = self.cp + + loss_bin = self.BCEbins(pred_bin, target_bins) # BCE + + if self.use_loss_regression: + loss_regression = self.MSELoss(result, target) # MSE + loss = loss_bin + loss_regression + else: + loss = loss_bin + + out_result = result.clamp(min=self.min, max=self.max) + + return loss, out_result + + +class FocalLoss(nn.Module): + # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super(FocalLoss, self).__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + # p_t = torch.exp(-loss) + # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability + + # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py + pred_prob = torch.sigmoid(pred) # prob from logits + p_t = true * pred_prob + (1 - true) * (1 - pred_prob) + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = (1.0 - p_t) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + + +class QFocalLoss(nn.Module): + # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super(QFocalLoss, self).__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + + pred_prob = torch.sigmoid(pred) # prob from logits + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = torch.abs(true - pred_prob) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + +class RankSort(torch.autograd.Function): + @staticmethod + def forward(ctx, logits, targets, delta_RS=0.50, eps=1e-10): + + classification_grads=torch.zeros(logits.shape).cuda() + + #Filter fg logits + fg_labels = (targets > 0.) + fg_logits = logits[fg_labels] + fg_targets = targets[fg_labels] + fg_num = len(fg_logits) + + #Do not use bg with scores less than minimum fg logit + #since changing its score does not have an effect on precision + threshold_logit = torch.min(fg_logits)-delta_RS + relevant_bg_labels=((targets==0) & (logits>=threshold_logit)) + + relevant_bg_logits = logits[relevant_bg_labels] + relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() + sorting_error=torch.zeros(fg_num).cuda() + ranking_error=torch.zeros(fg_num).cuda() + fg_grad=torch.zeros(fg_num).cuda() + + #sort the fg logits + order=torch.argsort(fg_logits) + #Loops over each positive following the order + for ii in order: + # Difference Transforms (x_ij) + fg_relations=fg_logits-fg_logits[ii] + bg_relations=relevant_bg_logits-fg_logits[ii] + + if delta_RS > 0: + fg_relations=torch.clamp(fg_relations/(2*delta_RS)+0.5,min=0,max=1) + bg_relations=torch.clamp(bg_relations/(2*delta_RS)+0.5,min=0,max=1) + else: + fg_relations = (fg_relations >= 0).float() + bg_relations = (bg_relations >= 0).float() + + # Rank of ii among pos and false positive number (bg with larger scores) + rank_pos=torch.sum(fg_relations) + FP_num=torch.sum(bg_relations) + + # Rank of ii among all examples + rank=rank_pos+FP_num + + # Ranking error of example ii. target_ranking_error is always 0. (Eq. 7) + ranking_error[ii]=FP_num/rank + + # Current sorting error of example ii. (Eq. 7) + current_sorting_error = torch.sum(fg_relations*(1-fg_targets))/rank_pos + + #Find examples in the target sorted order for example ii + iou_relations = (fg_targets >= fg_targets[ii]) + target_sorted_order = iou_relations * fg_relations + + #The rank of ii among positives in sorted order + rank_pos_target = torch.sum(target_sorted_order) + + #Compute target sorting error. (Eq. 8) + #Since target ranking error is 0, this is also total target error + target_sorting_error= torch.sum(target_sorted_order*(1-fg_targets))/rank_pos_target + + #Compute sorting error on example ii + sorting_error[ii] = current_sorting_error - target_sorting_error + + #Identity Update for Ranking Error + if FP_num > eps: + #For ii the update is the ranking error + fg_grad[ii] -= ranking_error[ii] + #For negatives, distribute error via ranking pmf (i.e. bg_relations/FP_num) + relevant_bg_grad += (bg_relations*(ranking_error[ii]/FP_num)) + + #Find the positives that are misranked (the cause of the error) + #These are the ones with smaller IoU but larger logits + missorted_examples = (~ iou_relations) * fg_relations + + #Denominotor of sorting pmf + sorting_pmf_denom = torch.sum(missorted_examples) + + #Identity Update for Sorting Error + if sorting_pmf_denom > eps: + #For ii the update is the sorting error + fg_grad[ii] -= sorting_error[ii] + #For positives, distribute error via sorting pmf (i.e. missorted_examples/sorting_pmf_denom) + fg_grad += (missorted_examples*(sorting_error[ii]/sorting_pmf_denom)) + + #Normalize gradients by number of positives + classification_grads[fg_labels]= (fg_grad/fg_num) + classification_grads[relevant_bg_labels]= (relevant_bg_grad/fg_num) + + ctx.save_for_backward(classification_grads) + + return ranking_error.mean(), sorting_error.mean() + + @staticmethod + def backward(ctx, out_grad1, out_grad2): + g1, =ctx.saved_tensors + return g1*out_grad1, None, None, None + +class aLRPLoss(torch.autograd.Function): + @staticmethod + def forward(ctx, logits, targets, regression_losses, delta=1., eps=1e-5): + classification_grads=torch.zeros(logits.shape).cuda() + + #Filter fg logits + fg_labels = (targets == 1) + fg_logits = logits[fg_labels] + fg_num = len(fg_logits) + + #Do not use bg with scores less than minimum fg logit + #since changing its score does not have an effect on precision + threshold_logit = torch.min(fg_logits)-delta + + #Get valid bg logits + relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) + relevant_bg_logits=logits[relevant_bg_labels] + relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() + rank=torch.zeros(fg_num).cuda() + prec=torch.zeros(fg_num).cuda() + fg_grad=torch.zeros(fg_num).cuda() + + max_prec=0 + #sort the fg logits + order=torch.argsort(fg_logits) + #Loops over each positive following the order + for ii in order: + #x_ij s as score differences with fgs + fg_relations=fg_logits-fg_logits[ii] + #Apply piecewise linear function and determine relations with fgs + fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) + #Discard i=j in the summation in rank_pos + fg_relations[ii]=0 + + #x_ij s as score differences with bgs + bg_relations=relevant_bg_logits-fg_logits[ii] + #Apply piecewise linear function and determine relations with bgs + bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) + + #Compute the rank of the example within fgs and number of bgs with larger scores + rank_pos=1+torch.sum(fg_relations) + FP_num=torch.sum(bg_relations) + #Store the total since it is normalizer also for aLRP Regression error + rank[ii]=rank_pos+FP_num + + #Compute precision for this example to compute classification loss + prec[ii]=rank_pos/rank[ii] + #For stability, set eps to a infinitesmall value (e.g. 1e-6), then compute grads + if FP_num > eps: + fg_grad[ii] = -(torch.sum(fg_relations*regression_losses)+FP_num)/rank[ii] + relevant_bg_grad += (bg_relations*(-fg_grad[ii]/FP_num)) + + #aLRP with grad formulation fg gradient + classification_grads[fg_labels]= fg_grad + #aLRP with grad formulation bg gradient + classification_grads[relevant_bg_labels]= relevant_bg_grad + + classification_grads /= (fg_num) + + cls_loss=1-prec.mean() + ctx.save_for_backward(classification_grads) + + return cls_loss, rank, order + + @staticmethod + def backward(ctx, out_grad1, out_grad2, out_grad3): + g1, =ctx.saved_tensors + return g1*out_grad1, None, None, None, None + + +class APLoss(torch.autograd.Function): + @staticmethod + def forward(ctx, logits, targets, delta=1.): + classification_grads=torch.zeros(logits.shape).cuda() + + #Filter fg logits + fg_labels = (targets == 1) + fg_logits = logits[fg_labels] + fg_num = len(fg_logits) + + #Do not use bg with scores less than minimum fg logit + #since changing its score does not have an effect on precision + threshold_logit = torch.min(fg_logits)-delta + + #Get valid bg logits + relevant_bg_labels=((targets==0)&(logits>=threshold_logit)) + relevant_bg_logits=logits[relevant_bg_labels] + relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda() + rank=torch.zeros(fg_num).cuda() + prec=torch.zeros(fg_num).cuda() + fg_grad=torch.zeros(fg_num).cuda() + + max_prec=0 + #sort the fg logits + order=torch.argsort(fg_logits) + #Loops over each positive following the order + for ii in order: + #x_ij s as score differences with fgs + fg_relations=fg_logits-fg_logits[ii] + #Apply piecewise linear function and determine relations with fgs + fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1) + #Discard i=j in the summation in rank_pos + fg_relations[ii]=0 + + #x_ij s as score differences with bgs + bg_relations=relevant_bg_logits-fg_logits[ii] + #Apply piecewise linear function and determine relations with bgs + bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1) + + #Compute the rank of the example within fgs and number of bgs with larger scores + rank_pos=1+torch.sum(fg_relations) + FP_num=torch.sum(bg_relations) + #Store the total since it is normalizer also for aLRP Regression error + rank[ii]=rank_pos+FP_num + + #Compute precision for this example + current_prec=rank_pos/rank[ii] + + #Compute interpolated AP and store gradients for relevant bg examples + if (max_prec<=current_prec): + max_prec=current_prec + relevant_bg_grad += (bg_relations/rank[ii]) + else: + relevant_bg_grad += (bg_relations/rank[ii])*(((1-max_prec)/(1-current_prec))) + + #Store fg gradients + fg_grad[ii]=-(1-max_prec) + prec[ii]=max_prec + + #aLRP with grad formulation fg gradient + classification_grads[fg_labels]= fg_grad + #aLRP with grad formulation bg gradient + classification_grads[relevant_bg_labels]= relevant_bg_grad + + classification_grads /= fg_num + + cls_loss=1-prec.mean() + ctx.save_for_backward(classification_grads) + + return cls_loss + + @staticmethod + def backward(ctx, out_grad1): + g1, =ctx.saved_tensors + return g1*out_grad1, None, None + + +class ComputeLoss: + # Compute losses + def __init__(self, model, autobalance=False): + super(ComputeLoss, self).__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 + #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.1, .05]) # P3-P7 + #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.5, 0.4, .1]) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance + for k in 'na', 'nc', 'nl', 'anchors': + setattr(self, k, getattr(det, k)) + + def __call__(self, p, targets): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + pxy = ps[:, :2].sigmoid() * 2. - 0.5 + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets + t[range(n), tcls[i]] = self.cp + #t[t==self.cp] = iou.detach().clamp(0).type(t.dtype) + lcls += self.BCEcls(ps[:, 5:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch = [], [], [], [] + gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + + return tcls, tbox, indices, anch + + +class ComputeLossOTA: + # Compute losses + def __init__(self, model, autobalance=False): + super(ComputeLossOTA, self).__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance + for k in 'na', 'nc', 'nl', 'anchors', 'stride': + setattr(self, k, getattr(det, k)) + + def __call__(self, p, targets, imgs): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs) + pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p] + + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + grid = torch.stack([gi, gj], dim=1) + pxy = ps[:, :2].sigmoid() * 2. - 0.5 + #pxy = ps[:, :2].sigmoid() * 3. - 1. + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] + selected_tbox[:, :2] -= grid + iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + selected_tcls = targets[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets + t[range(n), selected_tcls] = self.cp + lcls += self.BCEcls(ps[:, 5:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets, imgs): + + #indices, anch = self.find_positive(p, targets) + indices, anch = self.find_3_positive(p, targets) + #indices, anch = self.find_4_positive(p, targets) + #indices, anch = self.find_5_positive(p, targets) + #indices, anch = self.find_9_positive(p, targets) + + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + + b_idx = targets[:, 0]==batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + + b, a, gj, gi = indices[i] + idx = (b == batch_idx) + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append(torch.ones(size=(len(b),)) * i) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, 4:5]) + p_cls.append(fg_pred[:, 5:]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. + #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] + pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. + pxywh = torch.cat([pxy, pwh], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" + ).sum(-1) + del cls_preds_ + + cost = ( + pair_wise_cls_loss + + 3.0 * pair_wise_iou_loss + ) + + matching_matrix = torch.zeros_like(cost) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = matching_matrix.sum(0) > 0.0 + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + + return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs + + def find_3_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch + + +class ComputeLossBinOTA: + # Compute losses + def __init__(self, model, autobalance=False): + super(ComputeLossBinOTA, self).__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + #MSEangle = nn.MSELoss().to(device) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance + for k in 'na', 'nc', 'nl', 'anchors', 'stride', 'bin_count': + setattr(self, k, getattr(det, k)) + + #xy_bin_sigmoid = SigmoidBin(bin_count=11, min=-0.5, max=1.5, use_loss_regression=False).to(device) + wh_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0, use_loss_regression=False).to(device) + #angle_bin_sigmoid = SigmoidBin(bin_count=31, min=-1.1, max=1.1, use_loss_regression=False).to(device) + self.wh_bin_sigmoid = wh_bin_sigmoid + + def __call__(self, p, targets, imgs): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs) + pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p] + + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2 # x,y, w-bce, h-bce # xy_bin_sigmoid.get_length()*2 + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + grid = torch.stack([gi, gj], dim=1) + selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] + selected_tbox[:, :2] -= grid + + #pxy = ps[:, :2].sigmoid() * 2. - 0.5 + ##pxy = ps[:, :2].sigmoid() * 3. - 1. + #pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + #pbox = torch.cat((pxy, pwh), 1) # predicted box + + #x_loss, px = xy_bin_sigmoid.training_loss(ps[..., 0:12], tbox[i][..., 0]) + #y_loss, py = xy_bin_sigmoid.training_loss(ps[..., 12:24], tbox[i][..., 1]) + w_loss, pw = self.wh_bin_sigmoid.training_loss(ps[..., 2:(3+self.bin_count)], selected_tbox[..., 2] / anchors[i][..., 0]) + h_loss, ph = self.wh_bin_sigmoid.training_loss(ps[..., (3+self.bin_count):obj_idx], selected_tbox[..., 3] / anchors[i][..., 1]) + + pw *= anchors[i][..., 0] + ph *= anchors[i][..., 1] + + px = ps[:, 0].sigmoid() * 2. - 0.5 + py = ps[:, 1].sigmoid() * 2. - 0.5 + + lbox += w_loss + h_loss # + x_loss + y_loss + + #print(f"\n px = {px.shape}, py = {py.shape}, pw = {pw.shape}, ph = {ph.shape} \n") + + pbox = torch.cat((px.unsqueeze(1), py.unsqueeze(1), pw.unsqueeze(1), ph.unsqueeze(1)), 1).to(device) # predicted box + + + + + iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + selected_tcls = targets[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, (1+obj_idx):], self.cn, device=device) # targets + t[range(n), selected_tcls] = self.cp + lcls += self.BCEcls(ps[:, (1+obj_idx):], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., obj_idx], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets, imgs): + + #indices, anch = self.find_positive(p, targets) + indices, anch = self.find_3_positive(p, targets) + #indices, anch = self.find_4_positive(p, targets) + #indices, anch = self.find_5_positive(p, targets) + #indices, anch = self.find_9_positive(p, targets) + + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + + b_idx = targets[:, 0]==batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + + obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2 + + b, a, gj, gi = indices[i] + idx = (b == batch_idx) + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append(torch.ones(size=(len(b),)) * i) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, obj_idx:(obj_idx+1)]) + p_cls.append(fg_pred[:, (obj_idx+1):]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. + #pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. + pw = self.wh_bin_sigmoid.forward(fg_pred[..., 2:(3+self.bin_count)].sigmoid()) * anch[i][idx][:, 0] * self.stride[i] + ph = self.wh_bin_sigmoid.forward(fg_pred[..., (3+self.bin_count):obj_idx].sigmoid()) * anch[i][idx][:, 1] * self.stride[i] + + pxywh = torch.cat([pxy, pw.unsqueeze(1), ph.unsqueeze(1)], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" + ).sum(-1) + del cls_preds_ + + cost = ( + pair_wise_cls_loss + + 3.0 * pair_wise_iou_loss + ) + + matching_matrix = torch.zeros_like(cost) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = matching_matrix.sum(0) > 0.0 + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + + return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs + + def find_3_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch + + +class ComputeLossAuxOTA: + # Compute losses + def __init__(self, model, autobalance=False): + super(ComputeLossAuxOTA, self).__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance + for k in 'na', 'nc', 'nl', 'anchors', 'stride': + setattr(self, k, getattr(det, k)) + + def __call__(self, p, targets, imgs): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + bs_aux, as_aux_, gjs_aux, gis_aux, targets_aux, anchors_aux = self.build_targets2(p[:self.nl], targets, imgs) + bs, as_, gjs, gis, targets, anchors = self.build_targets(p[:self.nl], targets, imgs) + pre_gen_gains_aux = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]] + pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]] + + + # Losses + for i in range(self.nl): # layer index, layer predictions + pi = p[i] + pi_aux = p[i+self.nl] + b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx + b_aux, a_aux, gj_aux, gi_aux = bs_aux[i], as_aux_[i], gjs_aux[i], gis_aux[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + tobj_aux = torch.zeros_like(pi_aux[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + grid = torch.stack([gi, gj], dim=1) + pxy = ps[:, :2].sigmoid() * 2. - 0.5 + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] + selected_tbox[:, :2] -= grid + iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + selected_tcls = targets[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets + t[range(n), selected_tcls] = self.cp + lcls += self.BCEcls(ps[:, 5:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + n_aux = b_aux.shape[0] # number of targets + if n_aux: + ps_aux = pi_aux[b_aux, a_aux, gj_aux, gi_aux] # prediction subset corresponding to targets + grid_aux = torch.stack([gi_aux, gj_aux], dim=1) + pxy_aux = ps_aux[:, :2].sigmoid() * 2. - 0.5 + #pxy_aux = ps_aux[:, :2].sigmoid() * 3. - 1. + pwh_aux = (ps_aux[:, 2:4].sigmoid() * 2) ** 2 * anchors_aux[i] + pbox_aux = torch.cat((pxy_aux, pwh_aux), 1) # predicted box + selected_tbox_aux = targets_aux[i][:, 2:6] * pre_gen_gains_aux[i] + selected_tbox_aux[:, :2] -= grid_aux + iou_aux = bbox_iou(pbox_aux.T, selected_tbox_aux, x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += 0.25 * (1.0 - iou_aux).mean() # iou loss + + # Objectness + tobj_aux[b_aux, a_aux, gj_aux, gi_aux] = (1.0 - self.gr) + self.gr * iou_aux.detach().clamp(0).type(tobj_aux.dtype) # iou ratio + + # Classification + selected_tcls_aux = targets_aux[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t_aux = torch.full_like(ps_aux[:, 5:], self.cn, device=device) # targets + t_aux[range(n_aux), selected_tcls_aux] = self.cp + lcls += 0.25 * self.BCEcls(ps_aux[:, 5:], t_aux) # BCE + + obji = self.BCEobj(pi[..., 4], tobj) + obji_aux = self.BCEobj(pi_aux[..., 4], tobj_aux) + lobj += obji * self.balance[i] + 0.25 * obji_aux * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets, imgs): + + indices, anch = self.find_3_positive(p, targets) + + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + + b_idx = targets[:, 0]==batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + + b, a, gj, gi = indices[i] + idx = (b == batch_idx) + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append(torch.ones(size=(len(b),)) * i) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, 4:5]) + p_cls.append(fg_pred[:, 5:]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. + #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] + pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. + pxywh = torch.cat([pxy, pwh], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" + ).sum(-1) + del cls_preds_ + + cost = ( + pair_wise_cls_loss + + 3.0 * pair_wise_iou_loss + ) + + matching_matrix = torch.zeros_like(cost) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = matching_matrix.sum(0) > 0.0 + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + + return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs + + def build_targets2(self, p, targets, imgs): + + indices, anch = self.find_5_positive(p, targets) + + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + + b_idx = targets[:, 0]==batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + + b, a, gj, gi = indices[i] + idx = (b == batch_idx) + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append(torch.ones(size=(len(b),)) * i) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, 4:5]) + p_cls.append(fg_pred[:, 5:]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8. + #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] + pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. + pxywh = torch.cat([pxy, pwh], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y/(1-y)) , gt_cls_per_image, reduction="none" + ).sum(-1) + del cls_preds_ + + cost = ( + pair_wise_cls_loss + + 3.0 * pair_wise_iou_loss + ) + + matching_matrix = torch.zeros_like(cost) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = matching_matrix.sum(0) > 0.0 + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64) + + return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs + + def find_5_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 1.0 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch + + def find_3_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch diff --git a/utils/metrics.py b/utils/metrics.py new file mode 100644 index 0000000..666b8c7 --- /dev/null +++ b/utils/metrics.py @@ -0,0 +1,223 @@ +# Model validation metrics + +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import torch + +from . import general + + +def fitness(x): + # Model fitness as a weighted combination of metrics + w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] + return (x[:, :4] * w).sum(1) + + +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()): + """ Compute the average precision, given the recall and precision curves. + Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. + # Arguments + tp: True positives (nparray, nx1 or nx10). + conf: Objectness value from 0-1 (nparray). + pred_cls: Predicted object classes (nparray). + target_cls: True object classes (nparray). + plot: Plot precision-recall curve at mAP@0.5 + save_dir: Plot save directory + # Returns + The average precision as computed in py-faster-rcnn. + """ + + # Sort by objectness + i = np.argsort(-conf) + tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] + + # Find unique classes + unique_classes = np.unique(target_cls) + nc = unique_classes.shape[0] # number of classes, number of detections + + # Create Precision-Recall curve and compute AP for each class + px, py = np.linspace(0, 1, 1000), [] # for plotting + ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) + for ci, c in enumerate(unique_classes): + i = pred_cls == c + n_l = (target_cls == c).sum() # number of labels + n_p = i.sum() # number of predictions + + if n_p == 0 or n_l == 0: + continue + else: + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (n_l + 1e-16) # recall curve + r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases + + # Precision + precision = tpc / (tpc + fpc) # precision curve + p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score + + # AP from recall-precision curve + for j in range(tp.shape[1]): + ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) + if plot and j == 0: + py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 + + # Compute F1 (harmonic mean of precision and recall) + f1 = 2 * p * r / (p + r + 1e-16) + if plot: + plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) + plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') + plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') + plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') + + i = f1.mean(0).argmax() # max F1 index + return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32') + + +def compute_ap(recall, precision): + """ Compute the average precision, given the recall and precision curves + # Arguments + recall: The recall curve (list) + precision: The precision curve (list) + # Returns + Average precision, precision curve, recall curve + """ + + # Append sentinel values to beginning and end + mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01])) + mpre = np.concatenate(([1.], precision, [0.])) + + # Compute the precision envelope + mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) + + # Integrate area under curve + method = 'interp' # methods: 'continuous', 'interp' + if method == 'interp': + x = np.linspace(0, 1, 101) # 101-point interp (COCO) + ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate + else: # 'continuous' + i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve + + return ap, mpre, mrec + + +class ConfusionMatrix: + # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix + def __init__(self, nc, conf=0.25, iou_thres=0.45): + self.matrix = np.zeros((nc + 1, nc + 1)) + self.nc = nc # number of classes + self.conf = conf + self.iou_thres = iou_thres + + def process_batch(self, detections, labels): + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + detections (Array[N, 6]), x1, y1, x2, y2, conf, class + labels (Array[M, 5]), class, x1, y1, x2, y2 + Returns: + None, updates confusion matrix accordingly + """ + detections = detections[detections[:, 4] > self.conf] + gt_classes = labels[:, 0].int() + detection_classes = detections[:, 5].int() + iou = general.box_iou(labels[:, 1:], detections[:, :4]) + + x = torch.where(iou > self.iou_thres) + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + else: + matches = np.zeros((0, 3)) + + n = matches.shape[0] > 0 + m0, m1, _ = matches.transpose().astype(np.int16) + for i, gc in enumerate(gt_classes): + j = m0 == i + if n and sum(j) == 1: + self.matrix[gc, detection_classes[m1[j]]] += 1 # correct + else: + self.matrix[self.nc, gc] += 1 # background FP + + if n: + for i, dc in enumerate(detection_classes): + if not any(m1 == i): + self.matrix[dc, self.nc] += 1 # background FN + + def matrix(self): + return self.matrix + + def plot(self, save_dir='', names=()): + try: + import seaborn as sn + + array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize + array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) + + fig = plt.figure(figsize=(12, 9), tight_layout=True) + sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size + labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels + sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, + xticklabels=names + ['background FP'] if labels else "auto", + yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1)) + fig.axes[0].set_xlabel('True') + fig.axes[0].set_ylabel('Predicted') + fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) + except Exception as e: + pass + + def print(self): + for i in range(self.nc + 1): + print(' '.join(map(str, self.matrix[i]))) + + +# Plots ---------------------------------------------------------------------------------------------------------------- + +def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()): + # Precision-recall curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + py = np.stack(py, axis=1) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py.T): + ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) + else: + ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) + + ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) + ax.set_xlabel('Recall') + ax.set_ylabel('Precision') + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + fig.savefig(Path(save_dir), dpi=250) + + +def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'): + # Metric-confidence curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py): + ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) + else: + ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) + + y = py.mean(0) + ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + fig.savefig(Path(save_dir), dpi=250) diff --git a/utils/plots.py b/utils/plots.py new file mode 100644 index 0000000..fdd8d0e --- /dev/null +++ b/utils/plots.py @@ -0,0 +1,489 @@ +# Plotting utils + +import glob +import math +import os +import random +from copy import copy +from pathlib import Path + +import cv2 +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +import torch +import yaml +from PIL import Image, ImageDraw, ImageFont +from scipy.signal import butter, filtfilt + +from utils.general import xywh2xyxy, xyxy2xywh +from utils.metrics import fitness + +# Settings +matplotlib.rc('font', **{'size': 11}) +matplotlib.use('Agg') # for writing to files only + + +def color_list(): + # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb + def hex2rgb(h): + return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) + + return [hex2rgb(h) for h in matplotlib.colors.TABLEAU_COLORS.values()] # or BASE_ (8), CSS4_ (148), XKCD_ (949) + + +def hist2d(x, y, n=100): + # 2d histogram used in labels.png and evolve.png + xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) + hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) + xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) + yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) + return np.log(hist[xidx, yidx]) + + +def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): + # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy + def butter_lowpass(cutoff, fs, order): + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + return butter(order, normal_cutoff, btype='low', analog=False) + + b, a = butter_lowpass(cutoff, fs, order=order) + return filtfilt(b, a, data) # forward-backward filter + + +def plot_one_box(x, img, color=None, label=None, line_thickness=3): + # Plots one bounding box on image img + tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness + color = color or [random.randint(0, 255) for _ in range(3)] + c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) + cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 + cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled + cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + + +def plot_one_box_PIL(box, img, color=None, label=None, line_thickness=None): + img = Image.fromarray(img) + draw = ImageDraw.Draw(img) + line_thickness = line_thickness or max(int(min(img.size) / 200), 2) + draw.rectangle(box, width=line_thickness, outline=tuple(color)) # plot + if label: + fontsize = max(round(max(img.size) / 40), 12) + font = ImageFont.truetype("Arial.ttf", fontsize) + txt_width, txt_height = font.getsize(label) + draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=tuple(color)) + draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font) + return np.asarray(img) + + +def plot_wh_methods(): # from utils.plots import *; plot_wh_methods() + # Compares the two methods for width-height anchor multiplication + # https://github.com/ultralytics/yolov3/issues/168 + x = np.arange(-4.0, 4.0, .1) + ya = np.exp(x) + yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2 + + fig = plt.figure(figsize=(6, 3), tight_layout=True) + plt.plot(x, ya, '.-', label='YOLOv3') + plt.plot(x, yb ** 2, '.-', label='YOLOR ^2') + plt.plot(x, yb ** 1.6, '.-', label='YOLOR ^1.6') + plt.xlim(left=-4, right=4) + plt.ylim(bottom=0, top=6) + plt.xlabel('input') + plt.ylabel('output') + plt.grid() + plt.legend() + fig.savefig('comparison.png', dpi=200) + + +def output_to_target(output): + # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] + targets = [] + for i, o in enumerate(output): + for *box, conf, cls in o.cpu().numpy(): + targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf]) + return np.array(targets) + + +def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16): + # Plot image grid with labels + + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + + # un-normalise + if np.max(images[0]) <= 1: + images *= 255 + + tl = 3 # line thickness + tf = max(tl - 1, 1) # font thickness + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs ** 0.5) # number of subplots (square) + + # Check if we should resize + scale_factor = max_size / max(h, w) + if scale_factor < 1: + h = math.ceil(scale_factor * h) + w = math.ceil(scale_factor * w) + + colors = color_list() # list of colors + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, img in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + + block_x = int(w * (i // ns)) + block_y = int(h * (i % ns)) + + img = img.transpose(1, 2, 0) + if scale_factor < 1: + img = cv2.resize(img, (w, h)) + + mosaic[block_y:block_y + h, block_x:block_x + w, :] = img + if len(targets) > 0: + image_targets = targets[targets[:, 0] == i] + boxes = xywh2xyxy(image_targets[:, 2:6]).T + classes = image_targets[:, 1].astype('int') + labels = image_targets.shape[1] == 6 # labels if no conf column + conf = None if labels else image_targets[:, 6] # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale_factor < 1: # absolute coords need scale if image scales + boxes *= scale_factor + boxes[[0, 2]] += block_x + boxes[[1, 3]] += block_y + for j, box in enumerate(boxes.T): + cls = int(classes[j]) + color = colors[cls % len(colors)] + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j]) + plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl) + + # Draw image filename labels + if paths: + label = Path(paths[i]).name[:40] # trim to 40 char + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf, + lineType=cv2.LINE_AA) + + # Image border + cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3) + + if fname: + r = min(1280. / max(h, w) / ns, 1.0) # ratio to limit image size + mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA) + # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save + Image.fromarray(mosaic).save(fname) # PIL save + return mosaic + + +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): + # Plot LR simulating training for full epochs + optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals + y = [] + for _ in range(epochs): + scheduler.step() + y.append(optimizer.param_groups[0]['lr']) + plt.plot(y, '.-', label='LR') + plt.xlabel('epoch') + plt.ylabel('LR') + plt.grid() + plt.xlim(0, epochs) + plt.ylim(0) + plt.savefig(Path(save_dir) / 'LR.png', dpi=200) + plt.close() + + +def plot_test_txt(): # from utils.plots import *; plot_test() + # Plot test.txt histograms + x = np.loadtxt('test.txt', dtype=np.float32) + box = xyxy2xywh(x[:, :4]) + cx, cy = box[:, 0], box[:, 1] + + fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) + ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) + ax.set_aspect('equal') + plt.savefig('hist2d.png', dpi=300) + + fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) + ax[0].hist(cx, bins=600) + ax[1].hist(cy, bins=600) + plt.savefig('hist1d.png', dpi=200) + + +def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() + # Plot targets.txt histograms + x = np.loadtxt('targets.txt', dtype=np.float32).T + s = ['x targets', 'y targets', 'width targets', 'height targets'] + fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) + ax = ax.ravel() + for i in range(4): + ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std())) + ax[i].legend() + ax[i].set_title(s[i]) + plt.savefig('targets.jpg', dpi=200) + + +def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt() + # Plot study.txt generated by test.py + fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) + # ax = ax.ravel() + + fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) + # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolor-p6', 'yolor-w6', 'yolor-e6', 'yolor-d6']]: + for f in sorted(Path(path).glob('study*.txt')): + y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T + x = np.arange(y.shape[1]) if x is None else np.array(x) + s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] + # for i in range(7): + # ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) + # ax[i].set_title(s[i]) + + j = y[3].argmax() + 1 + ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, + label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) + + ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], + 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') + + ax2.grid(alpha=0.2) + ax2.set_yticks(np.arange(20, 60, 5)) + ax2.set_xlim(0, 57) + ax2.set_ylim(30, 55) + ax2.set_xlabel('GPU Speed (ms/img)') + ax2.set_ylabel('COCO AP val') + ax2.legend(loc='lower right') + plt.savefig(str(Path(path).name) + '.png', dpi=300) + + +def plot_labels(labels, names=(), save_dir=Path(''), loggers=None): + # plot dataset labels + print('Plotting labels... ') + c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes + nc = int(c.max() + 1) # number of classes + colors = color_list() + x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) + + # seaborn correlogram + sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) + plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) + plt.close() + + # matplotlib labels + matplotlib.use('svg') # faster + ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() + ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) + ax[0].set_ylabel('instances') + if 0 < len(names) < 30: + ax[0].set_xticks(range(len(names))) + ax[0].set_xticklabels(names, rotation=90, fontsize=10) + else: + ax[0].set_xlabel('classes') + sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) + sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) + + # rectangles + labels[:, 1:3] = 0.5 # center + labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 + img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) + for cls, *box in labels[:1000]: + ImageDraw.Draw(img).rectangle(box, width=1, outline=colors[int(cls) % 10]) # plot + ax[1].imshow(img) + ax[1].axis('off') + + for a in [0, 1, 2, 3]: + for s in ['top', 'right', 'left', 'bottom']: + ax[a].spines[s].set_visible(False) + + plt.savefig(save_dir / 'labels.jpg', dpi=200) + matplotlib.use('Agg') + plt.close() + + # loggers + for k, v in loggers.items() or {}: + if k == 'wandb' and v: + v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False) + + +def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution() + # Plot hyperparameter evolution results in evolve.txt + with open(yaml_file) as f: + hyp = yaml.load(f, Loader=yaml.SafeLoader) + x = np.loadtxt('evolve.txt', ndmin=2) + f = fitness(x) + # weights = (f - f.min()) ** 2 # for weighted results + plt.figure(figsize=(10, 12), tight_layout=True) + matplotlib.rc('font', **{'size': 8}) + for i, (k, v) in enumerate(hyp.items()): + y = x[:, i + 7] + # mu = (y * weights).sum() / weights.sum() # best weighted result + mu = y[f.argmax()] # best single result + plt.subplot(6, 5, i + 1) + plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none') + plt.plot(mu, f.max(), 'k+', markersize=15) + plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters + if i % 5 != 0: + plt.yticks([]) + print('%15s: %.3g' % (k, mu)) + plt.savefig('evolve.png', dpi=200) + print('\nPlot saved as evolve.png') + + +def profile_idetection(start=0, stop=0, labels=(), save_dir=''): + # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() + ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() + s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] + files = list(Path(save_dir).glob('frames*.txt')) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows + n = results.shape[1] # number of rows + x = np.arange(start, min(stop, n) if stop else n) + results = results[:, x] + t = (results[0] - results[0].min()) # set t0=0s + results[0] = x + for i, a in enumerate(ax): + if i < len(results): + label = labels[fi] if len(labels) else f.stem.replace('frames_', '') + a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) + a.set_title(s[i]) + a.set_xlabel('time (s)') + # if fi == len(files) - 1: + # a.set_ylim(bottom=0) + for side in ['top', 'right']: + a.spines[side].set_visible(False) + else: + a.remove() + except Exception as e: + print('Warning: Plotting error for %s; %s' % (f, e)) + + ax[1].legend() + plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) + + +def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay() + # Plot training 'results*.txt', overlaying train and val losses + s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends + t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles + for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): + results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T + n = results.shape[1] # number of rows + x = range(start, min(stop, n) if stop else n) + fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True) + ax = ax.ravel() + for i in range(5): + for j in [i, i + 5]: + y = results[j, x] + ax[i].plot(x, y, marker='.', label=s[j]) + # y_smooth = butter_lowpass_filtfilt(y) + # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j]) + + ax[i].set_title(t[i]) + ax[i].legend() + ax[i].set_ylabel(f) if i == 0 else None # add filename + fig.savefig(f.replace('.txt', '.png'), dpi=200) + + +def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''): + # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp') + fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) + ax = ax.ravel() + s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall', + 'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95'] + if bucket: + # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id] + files = ['results%g.txt' % x for x in id] + c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id) + os.system(c) + else: + files = list(Path(save_dir).glob('results*.txt')) + assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T + n = results.shape[1] # number of rows + x = range(start, min(stop, n) if stop else n) + for i in range(10): + y = results[i, x] + if i in [0, 1, 2, 5, 6, 7]: + y[y == 0] = np.nan # don't show zero loss values + # y /= y[0] # normalize + label = labels[fi] if len(labels) else f.stem + ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8) + ax[i].set_title(s[i]) + # if i in [5, 6, 7]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + print('Warning: Plotting error for %s; %s' % (f, e)) + + ax[1].legend() + fig.savefig(Path(save_dir) / 'results.png', dpi=200) + + +def output_to_keypoint(output): + # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] + targets = [] + for i, o in enumerate(output): + kpts = o[:,6:] + o = o[:,:6] + for index, (*box, conf, cls) in enumerate(o.detach().cpu().numpy()): + targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf, *list(kpts.detach().cpu().numpy()[index])]) + return np.array(targets) + + +def plot_skeleton_kpts(im, kpts, steps, orig_shape=None): + #Plot the skeleton and keypointsfor coco datatset + palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], + [230, 230, 0], [255, 153, 255], [153, 204, 255], + [255, 102, 255], [255, 51, 255], [102, 178, 255], + [51, 153, 255], [255, 153, 153], [255, 102, 102], + [255, 51, 51], [153, 255, 153], [102, 255, 102], + [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], + [255, 255, 255]]) + + skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], + [7, 13], [6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3], + [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]] + + pose_limb_color = palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]] + pose_kpt_color = palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]] + radius = 5 + num_kpts = len(kpts) // steps + + for kid in range(num_kpts): + r, g, b = pose_kpt_color[kid] + x_coord, y_coord = kpts[steps * kid], kpts[steps * kid + 1] + if not (x_coord % 640 == 0 or y_coord % 640 == 0): + if steps == 3: + conf = kpts[steps * kid + 2] + if conf < 0.5: + continue + cv2.circle(im, (int(x_coord), int(y_coord)), radius, (int(r), int(g), int(b)), -1) + + for sk_id, sk in enumerate(skeleton): + r, g, b = pose_limb_color[sk_id] + pos1 = (int(kpts[(sk[0]-1)*steps]), int(kpts[(sk[0]-1)*steps+1])) + pos2 = (int(kpts[(sk[1]-1)*steps]), int(kpts[(sk[1]-1)*steps+1])) + if steps == 3: + conf1 = kpts[(sk[0]-1)*steps+2] + conf2 = kpts[(sk[1]-1)*steps+2] + if conf1<0.5 or conf2<0.5: + continue + if pos1[0]%640 == 0 or pos1[1]%640==0 or pos1[0]<0 or pos1[1]<0: + continue + if pos2[0] % 640 == 0 or pos2[1] % 640 == 0 or pos2[0]<0 or pos2[1]<0: + continue + cv2.line(im, pos1, pos2, (int(r), int(g), int(b)), thickness=2) diff --git a/utils/torch_utils.py b/utils/torch_utils.py new file mode 100644 index 0000000..1e631b5 --- /dev/null +++ b/utils/torch_utils.py @@ -0,0 +1,374 @@ +# YOLOR PyTorch utils + +import datetime +import logging +import math +import os +import platform +import subprocess +import time +from contextlib import contextmanager +from copy import deepcopy +from pathlib import Path + +import torch +import torch.backends.cudnn as cudnn +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +try: + import thop # for FLOPS computation +except ImportError: + thop = None +logger = logging.getLogger(__name__) + + +@contextmanager +def torch_distributed_zero_first(local_rank: int): + """ + Decorator to make all processes in distributed training wait for each local_master to do something. + """ + if local_rank not in [-1, 0]: + torch.distributed.barrier() + yield + if local_rank == 0: + torch.distributed.barrier() + + +def init_torch_seeds(seed=0): + # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html + torch.manual_seed(seed) + if seed == 0: # slower, more reproducible + cudnn.benchmark, cudnn.deterministic = False, True + else: # faster, less reproducible + cudnn.benchmark, cudnn.deterministic = True, False + + +def date_modified(path=__file__): + # return human-readable file modification date, i.e. '2021-3-26' + t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime) + return f'{t.year}-{t.month}-{t.day}' + + +def git_describe(path=Path(__file__).parent): # path must be a directory + # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe + s = f'git -C {path} describe --tags --long --always' + try: + return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1] + except subprocess.CalledProcessError as e: + return '' # not a git repository + + +def select_device(device='', batch_size=None): + # device = 'cpu' or '0' or '0,1,2,3' + s = f'YOLOR 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string + cpu = device.lower() == 'cpu' + if cpu: + os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False + elif device: # non-cpu device requested + os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable + assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' # check availability + + cuda = not cpu and torch.cuda.is_available() + if cuda: + n = torch.cuda.device_count() + if n > 1 and batch_size: # check that batch_size is compatible with device_count + assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' + space = ' ' * len(s) + for i, d in enumerate(device.split(',') if device else range(n)): + p = torch.cuda.get_device_properties(i) + s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n" # bytes to MB + else: + s += 'CPU\n' + + logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe + return torch.device('cuda:0' if cuda else 'cpu') + + +def time_synchronized(): + # pytorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + + +def profile(x, ops, n=100, device=None): + # profile a pytorch module or list of modules. Example usage: + # x = torch.randn(16, 3, 640, 640) # input + # m1 = lambda x: x * torch.sigmoid(x) + # m2 = nn.SiLU() + # profile(x, [m1, m2], n=100) # profile speed over 100 iterations + + device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + x = x.to(device) + x.requires_grad = True + print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '') + print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}") + for m in ops if isinstance(ops, list) else [ops]: + m = m.to(device) if hasattr(m, 'to') else m # device + m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m # type + dtf, dtb, t = 0., 0., [0., 0., 0.] # dt forward, backward + try: + flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPS + except: + flops = 0 + + for _ in range(n): + t[0] = time_synchronized() + y = m(x) + t[1] = time_synchronized() + try: + _ = y.sum().backward() + t[2] = time_synchronized() + except: # no backward method + t[2] = float('nan') + dtf += (t[1] - t[0]) * 1000 / n # ms per op forward + dtb += (t[2] - t[1]) * 1000 / n # ms per op backward + + s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' + s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' + p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters + print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') + + +def is_parallel(model): + return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) + + +def intersect_dicts(da, db, exclude=()): + # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values + return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape} + + +def initialize_weights(model): + for m in model.modules(): + t = type(m) + if t is nn.Conv2d: + pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif t is nn.BatchNorm2d: + m.eps = 1e-3 + m.momentum = 0.03 + elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]: + m.inplace = True + + +def find_modules(model, mclass=nn.Conv2d): + # Finds layer indices matching module class 'mclass' + return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] + + +def sparsity(model): + # Return global model sparsity + a, b = 0., 0. + for p in model.parameters(): + a += p.numel() + b += (p == 0).sum() + return b / a + + +def prune(model, amount=0.3): + # Prune model to requested global sparsity + import torch.nn.utils.prune as prune + print('Pruning model... ', end='') + for name, m in model.named_modules(): + if isinstance(m, nn.Conv2d): + prune.l1_unstructured(m, name='weight', amount=amount) # prune + prune.remove(m, 'weight') # make permanent + print(' %.3g global sparsity' % sparsity(model)) + + +def fuse_conv_and_bn(conv, bn): + # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ + fusedconv = nn.Conv2d(conv.in_channels, + conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + groups=conv.groups, + bias=True).requires_grad_(False).to(conv.weight.device) + + # prepare filters + w_conv = conv.weight.clone().view(conv.out_channels, -1) + w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) + fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) + + # prepare spatial bias + b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias + b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) + fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) + + return fusedconv + + +def model_info(model, verbose=False, img_size=640): + # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] + n_p = sum(x.numel() for x in model.parameters()) # number parameters + n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients + if verbose: + print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) + for i, (name, p) in enumerate(model.named_parameters()): + name = name.replace('module_list.', '') + print('%5g %40s %9s %12g %20s %10.3g %10.3g' % + (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) + + try: # FLOPS + from thop import profile + stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 + img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input + flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPS + img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float + fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPS + except (ImportError, Exception): + fs = '' + + logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") + + +def load_classifier(name='resnet101', n=2): + # Loads a pretrained model reshaped to n-class output + model = torchvision.models.__dict__[name](pretrained=True) + + # ResNet model properties + # input_size = [3, 224, 224] + # input_space = 'RGB' + # input_range = [0, 1] + # mean = [0.485, 0.456, 0.406] + # std = [0.229, 0.224, 0.225] + + # Reshape output to n classes + filters = model.fc.weight.shape[1] + model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True) + model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True) + model.fc.out_features = n + return model + + +def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) + # scales img(bs,3,y,x) by ratio constrained to gs-multiple + if ratio == 1.0: + return img + else: + h, w = img.shape[2:] + s = (int(h * ratio), int(w * ratio)) # new size + img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize + if not same_shape: # pad/crop img + h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)] + return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean + + +def copy_attr(a, b, include=(), exclude=()): + # Copy attributes from b to a, options to only include [...] and to exclude [...] + for k, v in b.__dict__.items(): + if (len(include) and k not in include) or k.startswith('_') or k in exclude: + continue + else: + setattr(a, k, v) + + +class ModelEMA: + """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models + Keep a moving average of everything in the model state_dict (parameters and buffers). + This is intended to allow functionality like + https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage + A smoothed version of the weights is necessary for some training schemes to perform well. + This class is sensitive where it is initialized in the sequence of model init, + GPU assignment and distributed training wrappers. + """ + + def __init__(self, model, decay=0.9999, updates=0): + # Create EMA + self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA + # if next(model.parameters()).device.type != 'cpu': + # self.ema.half() # FP16 EMA + self.updates = updates # number of EMA updates + self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + # Update EMA parameters + with torch.no_grad(): + self.updates += 1 + d = self.decay(self.updates) + + msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict + for k, v in self.ema.state_dict().items(): + if v.dtype.is_floating_point: + v *= d + v += (1. - d) * msd[k].detach() + + def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): + # Update EMA attributes + copy_attr(self.ema, model, include, exclude) + + +class BatchNormXd(torch.nn.modules.batchnorm._BatchNorm): + def _check_input_dim(self, input): + # The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc + # is this method that is overwritten by the sub-class + # This original goal of this method was for tensor sanity checks + # If you're ok bypassing those sanity checks (eg. if you trust your inference + # to provide the right dimensional inputs), then you can just use this method + # for easy conversion from SyncBatchNorm + # (unfortunately, SyncBatchNorm does not store the original class - if it did + # we could return the one that was originally created) + return + +def revert_sync_batchnorm(module): + # this is very similar to the function that it is trying to revert: + # https://github.com/pytorch/pytorch/blob/c8b3686a3e4ba63dc59e5dcfe5db3430df256833/torch/nn/modules/batchnorm.py#L679 + module_output = module + if isinstance(module, torch.nn.modules.batchnorm.SyncBatchNorm): + new_cls = BatchNormXd + module_output = BatchNormXd(module.num_features, + module.eps, module.momentum, + module.affine, + module.track_running_stats) + if module.affine: + with torch.no_grad(): + module_output.weight = module.weight + module_output.bias = module.bias + module_output.running_mean = module.running_mean + module_output.running_var = module.running_var + module_output.num_batches_tracked = module.num_batches_tracked + if hasattr(module, "qconfig"): + module_output.qconfig = module.qconfig + for name, child in module.named_children(): + module_output.add_module(name, revert_sync_batchnorm(child)) + del module + return module_output + + +class TracedModel(nn.Module): + + def __init__(self, model=None, device=None, img_size=(640,640)): + super(TracedModel, self).__init__() + + print(" Convert model to Traced-model... ") + self.stride = model.stride + self.names = model.names + self.model = model + + self.model = revert_sync_batchnorm(self.model) + self.model.to('cpu') + self.model.eval() + + self.detect_layer = self.model.model[-1] + self.model.traced = True + + rand_example = torch.rand(1, 3, img_size, img_size) + + traced_script_module = torch.jit.trace(self.model, rand_example, strict=False) + #traced_script_module = torch.jit.script(self.model) + traced_script_module.save("traced_model.pt") + print(" traced_script_module saved! ") + self.model = traced_script_module + self.model.to(device) + self.detect_layer.to(device) + print(" model is traced! \n") + + def forward(self, x, augment=False, profile=False): + out = self.model(x) + out = self.detect_layer(out) + return out \ No newline at end of file diff --git a/utils/wandb_logging/__init__.py b/utils/wandb_logging/__init__.py new file mode 100644 index 0000000..84952a8 --- /dev/null +++ b/utils/wandb_logging/__init__.py @@ -0,0 +1 @@ +# init \ No newline at end of file diff --git a/utils/wandb_logging/__pycache__/__init__.cpython-38.pyc b/utils/wandb_logging/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..5caf98e Binary files /dev/null and b/utils/wandb_logging/__pycache__/__init__.cpython-38.pyc differ diff --git a/utils/wandb_logging/__pycache__/wandb_utils.cpython-38.pyc b/utils/wandb_logging/__pycache__/wandb_utils.cpython-38.pyc new file mode 100644 index 0000000..542d787 Binary files /dev/null and b/utils/wandb_logging/__pycache__/wandb_utils.cpython-38.pyc differ diff --git a/utils/wandb_logging/log_dataset.py b/utils/wandb_logging/log_dataset.py new file mode 100644 index 0000000..74cd6c6 --- /dev/null +++ b/utils/wandb_logging/log_dataset.py @@ -0,0 +1,24 @@ +import argparse + +import yaml + +from wandb_utils import WandbLogger + +WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' + + +def create_dataset_artifact(opt): + with open(opt.data) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) # data dict + logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path') + parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') + parser.add_argument('--project', type=str, default='YOLOR', help='name of W&B Project') + opt = parser.parse_args() + opt.resume = False # Explicitly disallow resume check for dataset upload job + + create_dataset_artifact(opt) diff --git a/utils/wandb_logging/wandb_utils.py b/utils/wandb_logging/wandb_utils.py new file mode 100644 index 0000000..aec7c5f --- /dev/null +++ b/utils/wandb_logging/wandb_utils.py @@ -0,0 +1,306 @@ +import json +import sys +from pathlib import Path + +import torch +import yaml +from tqdm import tqdm + +sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path +from utils.datasets import LoadImagesAndLabels +from utils.datasets import img2label_paths +from utils.general import colorstr, xywh2xyxy, check_dataset + +try: + import wandb + from wandb import init, finish +except ImportError: + wandb = None + +WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' + + +def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): + return from_string[len(prefix):] + + +def check_wandb_config_file(data_config_file): + wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path + if Path(wandb_config).is_file(): + return wandb_config + return data_config_file + + +def get_run_info(run_path): + run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) + run_id = run_path.stem + project = run_path.parent.stem + model_artifact_name = 'run_' + run_id + '_model' + return run_id, project, model_artifact_name + + +def check_wandb_resume(opt): + process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None + if isinstance(opt.resume, str): + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + if opt.global_rank not in [-1, 0]: # For resuming DDP runs + run_id, project, model_artifact_name = get_run_info(opt.resume) + api = wandb.Api() + artifact = api.artifact(project + '/' + model_artifact_name + ':latest') + modeldir = artifact.download() + opt.weights = str(Path(modeldir) / "last.pt") + return True + return None + + +def process_wandb_config_ddp_mode(opt): + with open(opt.data) as f: + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict + train_dir, val_dir = None, None + if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): + api = wandb.Api() + train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias) + train_dir = train_artifact.download() + train_path = Path(train_dir) / 'data/images/' + data_dict['train'] = str(train_path) + + if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): + api = wandb.Api() + val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias) + val_dir = val_artifact.download() + val_path = Path(val_dir) / 'data/images/' + data_dict['val'] = str(val_path) + if train_dir or val_dir: + ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') + with open(ddp_data_path, 'w') as f: + yaml.dump(data_dict, f) + opt.data = ddp_data_path + + +class WandbLogger(): + def __init__(self, opt, name, run_id, data_dict, job_type='Training'): + # Pre-training routine -- + self.job_type = job_type + self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict + # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call + if isinstance(opt.resume, str): # checks resume from artifact + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + run_id, project, model_artifact_name = get_run_info(opt.resume) + model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name + assert wandb, 'install wandb to resume wandb runs' + # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config + self.wandb_run = wandb.init(id=run_id, project=project, resume='allow') + opt.resume = model_artifact_name + elif self.wandb: + self.wandb_run = wandb.init(config=opt, + resume="allow", + project='YOLOR' if opt.project == 'runs/train' else Path(opt.project).stem, + name=name, + job_type=job_type, + id=run_id) if not wandb.run else wandb.run + if self.wandb_run: + if self.job_type == 'Training': + if not opt.resume: + wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict + # Info useful for resuming from artifacts + self.wandb_run.config.opt = vars(opt) + self.wandb_run.config.data_dict = wandb_data_dict + self.data_dict = self.setup_training(opt, data_dict) + if self.job_type == 'Dataset Creation': + self.data_dict = self.check_and_upload_dataset(opt) + else: + prefix = colorstr('wandb: ') + print(f"{prefix}Install Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)") + + def check_and_upload_dataset(self, opt): + assert wandb, 'Install wandb to upload dataset' + check_dataset(self.data_dict) + config_path = self.log_dataset_artifact(opt.data, + opt.single_cls, + 'YOLOR' if opt.project == 'runs/train' else Path(opt.project).stem) + print("Created dataset config file ", config_path) + with open(config_path) as f: + wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader) + return wandb_data_dict + + def setup_training(self, opt, data_dict): + self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants + self.bbox_interval = opt.bbox_interval + if isinstance(opt.resume, str): + modeldir, _ = self.download_model_artifact(opt) + if modeldir: + self.weights = Path(modeldir) / "last.pt" + config = self.wandb_run.config + opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str( + self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \ + config.opt['hyp'] + data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume + if 'val_artifact' not in self.__dict__: # If --upload_dataset is set, use the existing artifact, don't download + self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'), + opt.artifact_alias) + self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'), + opt.artifact_alias) + self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None + if self.train_artifact_path is not None: + train_path = Path(self.train_artifact_path) / 'data/images/' + data_dict['train'] = str(train_path) + if self.val_artifact_path is not None: + val_path = Path(self.val_artifact_path) / 'data/images/' + data_dict['val'] = str(val_path) + self.val_table = self.val_artifact.get("val") + self.map_val_table_path() + if self.val_artifact is not None: + self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") + self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) + if opt.bbox_interval == -1: + self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 + return data_dict + + def download_dataset_artifact(self, path, alias): + if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): + dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) + assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" + datadir = dataset_artifact.download() + return datadir, dataset_artifact + return None, None + + def download_model_artifact(self, opt): + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") + assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' + modeldir = model_artifact.download() + epochs_trained = model_artifact.metadata.get('epochs_trained') + total_epochs = model_artifact.metadata.get('total_epochs') + assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % ( + total_epochs) + return modeldir, model_artifact + return None, None + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ + 'original_url': str(path), + 'epochs_trained': epoch + 1, + 'save period': opt.save_period, + 'project': opt.project, + 'total_epochs': opt.epochs, + 'fitness_score': fitness_score + }) + model_artifact.add_file(str(path / 'last.pt'), name='last.pt') + wandb.log_artifact(model_artifact, + aliases=['latest', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) + print("Saving model artifact on epoch ", epoch + 1) + + def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): + with open(data_file) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) # data dict + nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names']) + names = {k: v for k, v in enumerate(names)} # to index dictionary + self.train_artifact = self.create_dataset_table(LoadImagesAndLabels( + data['train']), names, name='train') if data.get('train') else None + self.val_artifact = self.create_dataset_table(LoadImagesAndLabels( + data['val']), names, name='val') if data.get('val') else None + if data.get('train'): + data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') + if data.get('val'): + data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') + path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path + data.pop('download', None) + with open(path, 'w') as f: + yaml.dump(data, f) + + if self.job_type == 'Training': # builds correct artifact pipeline graph + self.wandb_run.use_artifact(self.val_artifact) + self.wandb_run.use_artifact(self.train_artifact) + self.val_artifact.wait() + self.val_table = self.val_artifact.get('val') + self.map_val_table_path() + else: + self.wandb_run.log_artifact(self.train_artifact) + self.wandb_run.log_artifact(self.val_artifact) + return path + + def map_val_table_path(self): + self.val_table_map = {} + print("Mapping dataset") + for i, data in enumerate(tqdm(self.val_table.data)): + self.val_table_map[data[3]] = data[0] + + def create_dataset_table(self, dataset, class_to_id, name='dataset'): + # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging + artifact = wandb.Artifact(name=name, type="dataset") + img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None + img_files = tqdm(dataset.img_files) if not img_files else img_files + for img_file in img_files: + if Path(img_file).is_dir(): + artifact.add_dir(img_file, name='data/images') + labels_path = 'labels'.join(dataset.path.rsplit('images', 1)) + artifact.add_dir(labels_path, name='data/labels') + else: + artifact.add_file(img_file, name='data/images/' + Path(img_file).name) + label_file = Path(img2label_paths([img_file])[0]) + artifact.add_file(str(label_file), + name='data/labels/' + label_file.name) if label_file.exists() else None + table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) + class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) + for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): + height, width = shapes[0] + labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height]) + box_data, img_classes = [], {} + for cls, *xyxy in labels[:, 1:].tolist(): + cls = int(cls) + box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": cls, + "box_caption": "%s" % (class_to_id[cls]), + "scores": {"acc": 1}, + "domain": "pixel"}) + img_classes[cls] = class_to_id[cls] + boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space + table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes), + Path(paths).name) + artifact.add(table, name) + return artifact + + def log_training_progress(self, predn, path, names): + if self.val_table and self.result_table: + class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) + box_data = [] + total_conf = 0 + for *xyxy, conf, cls in predn.tolist(): + if conf >= 0.25: + box_data.append( + {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": int(cls), + "box_caption": "%s %.3f" % (names[cls], conf), + "scores": {"class_score": conf}, + "domain": "pixel"}) + total_conf = total_conf + conf + boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space + id = self.val_table_map[Path(path).name] + self.result_table.add_data(self.current_epoch, + id, + wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), + total_conf / max(1, len(box_data)) + ) + + def log(self, log_dict): + if self.wandb_run: + for key, value in log_dict.items(): + self.log_dict[key] = value + + def end_epoch(self, best_result=False): + if self.wandb_run: + wandb.log(self.log_dict) + self.log_dict = {} + if self.result_artifact: + train_results = wandb.JoinedTable(self.val_table, self.result_table, "id") + self.result_artifact.add(train_results, 'result') + wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch), + ('best' if best_result else '')]) + self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) + self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") + + def finish_run(self): + if self.wandb_run: + if self.log_dict: + wandb.log(self.log_dict) + wandb.run.finish()