Add collate_fn() to DataLoader (#163)

Multi-GPU update with custom collate function to allow variable size target vector per image without needing to pad targets.

README.md

@@ -17,7 +17,7 @@ This directory contains python software and an iOS App developed by Ultralytics
 
 # Description
 
-The https://github.com/ultralytics/yolov3 repo contains inference and training code for YOLOv3 in PyTorch. The code works on Linux, MacOS and Windows. Training is done on the COCO dataset by default: https://cocodataset.org/#home. **Credit to Joseph Redmon for YOLO** (https://pjreddie.com/darknet/yolo/) and to **Erik Lindernoren for the PyTorch implementation** this work is based on (https://github.com/eriklindernoren/PyTorch-YOLOv3).
+The https://github.com/ultralytics/yolov3 repo contains inference and training code for YOLOv3 in PyTorch. The code works on Linux, MacOS and Windows. Training is done on the COCO dataset by default: https://cocodataset.org/#home. **Credit to Joseph Redmon for YOLO: ** https://pjreddie.com/darknet/yolo/.
 
 # Requirements
 
@@ -26,6 +26,7 @@ Python 3.7 or later with the following `pip3 install -U -r requirements.txt` pac
 - `numpy`
 - `torch >= 1.0.0`
 - `opencv-python`
+- `tqdm`
 
 # Tutorials
 
@@ -66,15 +67,20 @@ HS**V** Intensity | +/- 50%
 https://cloud.google.com/deep-learning-vm/  
 **Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory)  
 **CPU platform:** Intel Skylake  
-**GPUs:** 1-4 x NVIDIA Tesla P100  
+**GPUs:** 1-4x P100 ($0.493/hr), 1-8x V100 ($0.803/hr)  
 **HDD:** 100 GB SSD  
+**Dataset:** COCO train 2014 
 
-GPUs | `batch_size` | speed | COCO epoch
+GPUs | `batch_size` | batch time | epoch time | epoch cost
---- |---| --- | --- 
+--- |---| --- | --- | --- 
-(P100)   |  (images)  | (s/batch) | (min/epoch)
+<i></i> |  (images)  | (s/batch) |  |
-1 | 16 | 0.39s  | 48min
+1 P100 | 16 | 0.39s  | 48min  | $0.39
-2 | 32 | 0.48s | 29min
+2 P100 | 32 | 0.48s | 29min | $0.47
-4 | 64 | 0.65s | 20min
+4 P100 | 64 | 0.65s | 20min | $0.65
+1 V100 | 16 | 0.25s  | 31min | $0.41
+2 V100 | 32 | 0.29s | 18min | $0.48
+4 V100 | 64 | 0.41s | 13min | $0.70
+8 V100 | 128 | 0.49s | 7min | $0.80
 
 # Inference
 

detect.py

@@ -1,7 +1,5 @@
 import argparse
-import shutil
 import time
-from pathlib import Path
 from sys import platform
 
 from models import *
@@ -32,9 +30,9 @@ def detect(
     # Load weights
     if weights.endswith('.pt'):  # pytorch format
         if weights.endswith('yolov3.pt') and not os.path.exists(weights):
-            if (platform == 'darwin') or (platform == 'linux'):
+            if platform in ('darwin', 'linux'):  # linux/macos
                 os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -O ' + weights)
-        model.load_state_dict(torch.load(weights, map_location='cpu')['model'])
+        model.load_state_dict(torch.load(weights, map_location=device)['model'])
     else:  # darknet format
         _ = load_darknet_weights(model, weights)
 
@@ -49,15 +47,15 @@ def detect(
 
     # Get classes and colors
     classes = load_classes(parse_data_cfg('cfg/coco.data')['names'])
-    colors = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))]
+    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))]
 
     for i, (path, img, im0) in enumerate(dataloader):
         t = time.time()
+        save_path = str(Path(output) / Path(path).name)
         if webcam:
             print('webcam frame %g: ' % (i + 1), end='')
         else:
             print('image %g/%g %s: ' % (i + 1, len(dataloader), path), end='')
-        save_path = str(Path(output) / Path(path).name)
 
         # Get detections
         img = torch.from_numpy(img).unsqueeze(0).to(device)
@@ -81,18 +79,16 @@ def detect(
                 print('%g %ss' % (n, classes[int(c)]), end=', ')
 
             # Draw bounding boxes and labels of detections
-            for x1, y1, x2, y2, conf, cls_conf, cls in detections:
+            for *xyxy, conf, cls_conf, cls in detections:
                 if save_txt:  # Write to file
                     with open(save_path + '.txt', 'a') as file:
-                        file.write('%g %g %g %g %g %g\n' %
+                        file.write(('%g ' * 6 + '\n') % (*xyxy, cls, cls_conf * conf))
-                                   (x1, y1, x2, y2, cls, cls_conf * conf))
 
                 # Add bbox to the image
                 label = '%s %.2f' % (classes[int(cls)], conf)
-                plot_one_box([x1, y1, x2, y2], im0, label=label, color=colors[int(cls)])
+                plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
 
-        dt = time.time() - t
+        print('Done. (%.3fs)' % (time.time() - t))
-        print('Done. (%.3fs)' % dt)
 
         if save_images:  # Save generated image with detections
             cv2.imwrite(save_path, im0)
@@ -100,7 +96,7 @@ def detect(
         if webcam:  # Show live webcam
             cv2.imshow(weights, im0)
 
-    if save_images and (platform == 'darwin'):  # linux/macos
+    if save_images and platform == 'darwin':  # macos
         os.system('open ' + output + ' ' + save_path)
 
 

requirements.txt

@@ -4,3 +4,5 @@ numpy
 opencv-python
 torch >= 1.0.0
 matplotlib
+pycocotools
+tqdm

test.py

@@ -1,7 +1,6 @@
 import argparse
 import json
 import time
-from pathlib import Path
 
 from torch.utils.data import DataLoader
 
@@ -24,41 +23,44 @@ def test(
 ):
     device = torch_utils.select_device()
 
-    # Configure run
-    data_cfg_dict = parse_data_cfg(data_cfg)
-    nC = int(data_cfg_dict['classes'])  # number of classes (80 for COCO)
-    test_path = data_cfg_dict['valid']
-
     if model is None:
         # Initialize model
-        model = Darknet(cfg, img_size)
+        model = Darknet(cfg, img_size).to(device)
 
         # Load weights
         if weights.endswith('.pt'):  # pytorch format
-            model.load_state_dict(torch.load(weights, map_location='cpu')['model'])
+            model.load_state_dict(torch.load(weights, map_location=device)['model'])
         else:  # darknet format
             _ = load_darknet_weights(model, weights)
 
-    model.to(device).eval()
+    if torch.cuda.device_count() > 1:
+        model = nn.DataParallel(model)
+
+    # Configure run
+    data_cfg = parse_data_cfg(data_cfg)
+    nC = int(data_cfg['classes'])  # number of classes (80 for COCO)
+    test_path = data_cfg['valid']
 
     # Dataloader
     dataset = LoadImagesAndLabels(test_path, img_size=img_size)
-    dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=4)
+    dataloader = DataLoader(dataset,
+                            batch_size=batch_size,
+                            num_workers=4,
+                            pin_memory=False,
+                            collate_fn=dataset.collate_fn)
 
+    model.eval()
     mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0
     print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
     mP, mR, mAPs, TP, jdict = [], [], [], [], []
     AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
     coco91class = coco80_to_coco91_class()
-    for imgs, targets, paths, shapes in dataloader:
+    for imgs, targets, paths, shapes in tqdm(dataloader):
-        # Unpad and collate targets
-        for j, t in enumerate(targets):
-            t[:, 0] = j
-        targets = torch.cat([t[t[:, 5].nonzero()] for t in targets], 0).squeeze(1)
-
-        targets = targets.to(device)
         t = time.time()
-        output = model(imgs.to(device))
+        targets = targets.to(device)
+        imgs = imgs.to(device)
+
+        output = model(imgs)
         output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
 
         # Compute average precision for each sample
@@ -78,7 +80,7 @@ def test(
             if save_json:
                 # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
                 box = detections[:, :4].clone()  # xyxy
-                scale_coords(img_size, box, (shapes[0][si], shapes[1][si]))  # to original shape
+                scale_coords(img_size, box, shapes[si])  # to original shape
                 box = xyxy2xywh(box)  # xywh
                 box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
 
@@ -140,7 +142,7 @@ def test(
 
     # Print mAP per class
     print('\nmAP Per Class:')
-    for i, c in enumerate(load_classes(data_cfg_dict['names'])):
+    for i, c in enumerate(load_classes(data_cfg['names'])):
         if AP_accum_count[i]:
             print('%15s: %-.4f' % (c, AP_accum[i] / (AP_accum_count[i])))
 
@@ -191,4 +193,5 @@ if __name__ == '__main__':
             opt.iou_thres,
             opt.conf_thres,
             opt.nms_thres,
-            opt.save_json)
+            opt.save_json
+        )

train.py

@@ -1,6 +1,7 @@
 import argparse
 import time
 
+import torch.distributed as dist
 from torch.utils.data import DataLoader
 
 import test  # Import test.py to get mAP after each epoch
@@ -20,7 +21,7 @@ def train(
         accumulate=1,
         multi_scale=False,
         freeze_backbone=False,
-        num_workers=0
+        num_workers=4
 ):
     weights = 'weights' + os.sep
     latest = weights + 'latest.pt'
@@ -65,52 +66,55 @@ def train(
         dist.init_process_group(backend=opt.dist_backend, init_method=opt.dist_url,world_size=opt.world_size, rank=opt.rank)
         model = torch.nn.parallel.DistributedDataParallel(model)
 
-    # Dataloader
-    dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
-    if torch.cuda.device_count() > 1:
-        train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
-    else:
-        train_sampler=None    
-    dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers,sampler=train_sampler)        
-
     # Transfer learning (train only YOLO layers)
     # for i, (name, p) in enumerate(model.named_parameters()):
     #     p.requires_grad = True if (p.shape[0] == 255) else False
 
-    # Set scheduler
+    # Set scheduler (reduce lr at epoch 250)
-    # scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[54, 61], gamma=0.1)
+    scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[250], gamma=0.1, last_epoch=start_epoch - 1)
+
+    # Dataset
+    dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
+
+    # Initialize distributed training
+    if torch.cuda.device_count() > 1:
+        dist.init_process_group(backend=opt.backend, init_method=opt.dist_url, world_size=opt.world_size, rank=opt.rank)
+        model = torch.nn.parallel.DistributedDataParallel(model)
+        sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+    else:
+        sampler = None
+
+    # Dataloader
+    dataloader = DataLoader(dataset,
+                            batch_size=batch_size,
+                            num_workers=num_workers,
+                            shuffle=True,
+                            pin_memory=False,
+                            collate_fn=dataset.collate_fn,
+                            sampler=sampler)
 
     # Start training
-    t0 = time.time()
+    nB = len(dataloader)
+    t = time.time()
     model_info(model)
-    n_burnin = min(round(len(dataloader) / 5 + 1), 1000)  # burn-in batches
+    n_burnin = min(round(nB / 5 + 1), 1000)  # burn-in batches
-    for epoch in range(epochs):
+    for epoch in range(start_epoch, epochs):
         model.train()
-        epoch += start_epoch
+        print(('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time'))
 
-        print(('\n%8s%12s' + '%10s' * 7) % (
+        # Update scheduler
-            'Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time'))
+        scheduler.step()
-
-        # Update scheduler (automatic)
-        # scheduler.step()
-
-        # Update scheduler (manual)
-        lr = lr0 / 10 if epoch > 250 else lr0
-        for x in optimizer.param_groups:
-            x['lr'] = lr
 
         # Freeze backbone at epoch 0, unfreeze at epoch 1
         if freeze_backbone and epoch < 2:
-            for i, (name, p) in enumerate(model.named_parameters()):
+            for name, p in model.named_parameters():
                 if int(name.split('.')[1]) < cutoff:  # if layer < 75
-                    p.requires_grad = False if (epoch == 0) else True
+                    p.requires_grad = False if epoch == 0 else True
 
-        rloss = defaultdict(float)
+        mloss = defaultdict(float)  # mean loss
         for i, (imgs, targets, _, _) in enumerate(dataloader):
-            # Unpad and collate targets
+            imgs = imgs.to(device)
-            for j, t in enumerate(targets):
+            targets = targets.to(device)
-                t[:, 0] = j
-            targets = torch.cat([t[t[:, 5].nonzero()] for t in targets], 0).squeeze(1)
 
             nT = len(targets)
             if nT == 0:  # if no targets continue
@@ -119,25 +123,26 @@ def train(
             # Plot images with bounding boxes
             plot_images = False
             if plot_images:
-                import matplotlib.pyplot as plt
+                fig = plt.figure(figsize=(10, 10))
-                plt.figure(figsize=(10, 10))
                 for ip in range(batch_size):
                     labels = xywh2xyxy(targets[targets[:, 0] == ip, 2:6]).numpy() * img_size
                     plt.subplot(4, 4, ip + 1).imshow(imgs[ip].numpy().transpose(1, 2, 0))
                     plt.plot(labels[:, [0, 2, 2, 0, 0]].T, labels[:, [1, 1, 3, 3, 1]].T, '.-')
                     plt.axis('off')
+                fig.tight_layout()
+                fig.savefig('batch_%g.jpg' % i, dpi=fig.dpi)
 
             # SGD burn-in
-            if (epoch == 0) and (i <= n_burnin):
+            if epoch == 0 and i <= n_burnin:
                 lr = lr0 * (i / n_burnin) ** 4
                 for x in optimizer.param_groups:
                     x['lr'] = lr
 
             # Run model
-            pred = model(imgs.to(device))
+            pred = model(imgs)
 
             # Build targets
-            target_list = build_targets(model, targets.to(device), pred)
+            target_list = build_targets(model, targets)
 
             # Compute loss
             loss, loss_dict = compute_loss(pred, target_list)
@@ -146,21 +151,19 @@ def train(
             loss.backward()
 
             # Accumulate gradient for x batches before optimizing
-            if (i + 1) % accumulate == 0 or (i + 1) == len(dataloader):
+            if (i + 1) % accumulate == 0 or (i + 1) == nB:
                 optimizer.step()
                 optimizer.zero_grad()
 
             # Running epoch-means of tracked metrics
             for key, val in loss_dict.items():
-                rloss[key] = (rloss[key] * i + val) / (i + 1)
+                mloss[key] = (mloss[key] * i + val) / (i + 1)
 
             s = ('%8s%12s' + '%10.3g' * 7) % (
-                '%g/%g' % (epoch, epochs - 1),
+                '%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nB - 1),
-                '%g/%g' % (i, len(dataloader) - 1),
+                mloss['xy'], mloss['wh'], mloss['conf'], mloss['cls'],
-                rloss['xy'], rloss['wh'], rloss['conf'],
+                mloss['total'], nT, time.time() - t)
-                rloss['cls'], rloss['total'],
+            t = time.time()
-                nT, time.time() - t0)
-            t0 = time.time()
             print(s)
 
             # Multi-Scale training (320 - 608 pixels) every 10 batches
@@ -169,8 +172,8 @@ def train(
                 print('multi_scale img_size = %g' % dataset.img_size)
 
         # Update best loss
-        if rloss['total'] < best_loss:
+        if mloss['total'] < best_loss:
-            best_loss = rloss['total']
+            best_loss = mloss['total']
 
         # Save training results
         save = True
@@ -178,23 +181,24 @@ def train(
             # Save latest checkpoint
             checkpoint = {'epoch': epoch,
                           'best_loss': best_loss,
-                          'model': model.module.state_dict() if type(model) is nn.parallel.DistributedDataParallel else model.state_dict(),
+                          'model': model.module.state_dict() if type(
+                              model) is nn.parallel.DistributedDataParallel else model.state_dict(),
                           'optimizer': optimizer.state_dict()}
             torch.save(checkpoint, latest)
 
             # Save best checkpoint
-            if best_loss == rloss['total']:
+            if best_loss == mloss['total']:
                 os.system('cp ' + latest + ' ' + best)
 
             # Save backup weights every 5 epochs (optional)
-            if (epoch > 0) and (epoch % 5 == 0):
+            if epoch > 0 and epoch % 5 == 0:
-                os.system('cp ' + latest + ' ' + weights + 'backup{}.pt'.format(epoch))
+                os.system('cp ' + latest + ' ' + weights + 'backup%g.pt' % epoch)
 
         # Calculate mAP
         if type(model) is nn.parallel.DistributedDataParallel:
             model = model.module
         with torch.no_grad():
-            P, R, mAP = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, model=model)
+            P, R, mAP = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size)
 
         # Write epoch results
         with open('results.txt', 'a') as file:
@@ -212,10 +216,10 @@ if __name__ == '__main__':
     parser.add_argument('--img-size', type=int, default=32 * 13, help='pixels')
     parser.add_argument('--resume', action='store_true', help='resume training flag')
     parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
-    parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str,help='url used to set up distributed training')
+    parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str, help='distributed training init method')
-    parser.add_argument('--rank', default=0, type=int,help='node rank for distributed training')
+    parser.add_argument('--rank', default=0, type=int, help='distributed training node rank')
     parser.add_argument('--world-size', default=1, type=int, help='number of nodes for distributed training')
-    parser.add_argument('--dist-backend', default='nccl', type=str,help='distributed backend')
+    parser.add_argument('--backend', default='nccl', type=str, help='distributed backend')
     opt = parser.parse_args()
     print(opt, end='\n\n')
 

utils/datasets.py

@@ -2,11 +2,14 @@ import glob
 import math
 import os
 import random
+import shutil
+from pathlib import Path
 
 import cv2
 import numpy as np
 import torch
 from torch.utils.data import Dataset
+from tqdm import tqdm
 
 from utils.utils import xyxy2xywh
 
@@ -97,7 +100,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         assert len(self.img_files) > 0, 'No images found in %s' % path
         self.img_size = img_size
         self.augment = augment
-        self.label_files = [x.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt')
+        self.label_files = [x.replace('images', 'labels').replace('.bmp', '.txt').replace('.jpg', '.txt')
                             for x in self.img_files]
 
     def __len__(self):
@@ -136,58 +139,61 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         img, ratio, padw, padh = letterbox(img, height=self.img_size)
 
         # Load labels
+        labels = []
         if os.path.isfile(label_path):
             with open(label_path, 'r') as file:
                 lines = file.read().splitlines()
 
             x = np.array([x.split() for x in lines], dtype=np.float32)
-            if x.size is 0:
+            if x.size > 0:
-                # Empty labels file
-                labels = np.array([])
-            else:
                 # Normalized xywh to pixel xyxy format
                 labels = x.copy()
                 labels[:, 1] = ratio * w * (x[:, 1] - x[:, 3] / 2) + padw
                 labels[:, 2] = ratio * h * (x[:, 2] - x[:, 4] / 2) + padh
                 labels[:, 3] = ratio * w * (x[:, 1] + x[:, 3] / 2) + padw
                 labels[:, 4] = ratio * h * (x[:, 2] + x[:, 4] / 2) + padh
-        else:
-            labels = np.array([])
 
         # Augment image and labels
         if self.augment:
-            img, labels, M = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.90, 1.10))
+            img, labels = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.90, 1.10))
 
-        nL = len(labels)
+        nL = len(labels)  # number of labels
-        if nL > 0:
+        if nL:
             # convert xyxy to xywh
             labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) / self.img_size
 
         if self.augment:
             # random left-right flip
             lr_flip = True
-            if lr_flip & (random.random() > 0.5):
+            if lr_flip and random.random() > 0.5:
                 img = np.fliplr(img)
-                if nL > 0:
+                if nL:
                     labels[:, 1] = 1 - labels[:, 1]
 
             # random up-down flip
             ud_flip = False
-            if ud_flip & (random.random() > 0.5):
+            if ud_flip and random.random() > 0.5:
                 img = np.flipud(img)
-                if nL > 0:
+                if nL:
                     labels[:, 2] = 1 - labels[:, 2]
 
-        labels_out = np.zeros((100, 6), dtype=np.float32)
+        labels_out = torch.zeros((nL, 6))
-        if nL > 0:
+        if nL:
-            labels_out[:nL, 1:] = labels  # max 100 labels per image
+            labels_out[:, 1:] = torch.from_numpy(labels)
 
         # Normalize
-        img = img[:, :, ::-1].transpose(2, 0, 1)  # list to np.array and BGR to RGB
+        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
         img = np.ascontiguousarray(img, dtype=np.float32)  # uint8 to float32
         img /= 255.0  # 0 - 255 to 0.0 - 1.0
 
-        return torch.from_numpy(img), torch.from_numpy(labels_out), img_path, (h, w)
+        return torch.from_numpy(img), labels_out, img_path, (h, w)
+
+    @staticmethod
+    def collate_fn(batch):
+        img, label, path, hw = list(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, hw
 
 
 def letterbox(img, height=416, color=(127.5, 127.5, 127.5)):  # resize a rectangular image to a padded square
@@ -203,11 +209,13 @@ def letterbox(img, height=416, color=(127.5, 127.5, 127.5)):  # resize a rectang
     return img, ratio, dw, dh
 
 
-def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
+def random_affine(img, targets=(), degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
                   borderValue=(127.5, 127.5, 127.5)):
     # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
     # https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
 
+    if targets is None:
+        targets = []
     border = 0  # width of added border (optional)
     height = max(img.shape[0], img.shape[1]) + border * 2
 
@@ -233,7 +241,6 @@ def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scal
                               borderValue=borderValue)  # BGR order borderValue
 
     # Return warped points also
-    if targets is not None:
     if len(targets) > 0:
         n = targets.shape[0]
         points = targets[:, 1:5].copy()
@@ -269,16 +276,26 @@ def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scal
         targets = targets[i]
         targets[:, 1:5] = xy[i]
 
-        return imw, targets, M
+    return imw, targets
-    else:
-        return imw
 
 
-def convert_tif2bmp(p='../xview/val_images_bmp'):
+def convert_images2bmp():
-    import glob
+    # cv2.imread() jpg at 230 img/s, *.bmp at 400 img/s
-    import cv2
+    for path in ['../coco/images/val2014/', '../coco/images/train2014/']:
-    files = sorted(glob.glob('%s/*.tif' % p))
+        folder = os.sep + Path(path).name
-    for i, f in enumerate(files):
+        output = path.replace(folder, folder + 'bmp')
-        print('%g/%g' % (i + 1, len(files)))
+        if os.path.exists(output):
-        cv2.imwrite(f.replace('.tif', '.bmp'), cv2.imread(f))
+            shutil.rmtree(output)  # delete output folder
-        os.system('rm -rf ' + f)
+        os.makedirs(output)  # make new output folder
+
+        for f in tqdm(glob.glob('%s*.jpg' % path)):
+            save_name = f.replace('.jpg', '.bmp').replace(folder, folder + 'bmp')
+            cv2.imwrite(save_name, cv2.imread(f))
+
+    for label_path in ['../coco/trainvalno5k.txt', '../coco/5k.txt']:
+        with open(label_path, 'r') as file:
+            lines = file.read()
+        lines = lines.replace('2014/', '2014bmp/').replace('.jpg', '.bmp').replace(
+            '/Users/glennjocher/PycharmProjects/', '../')
+        with open(label_path.replace('5k', '5k_bmp'), 'w') as file:
+            file.write(lines)

utils/gcp.sh

@@ -3,13 +3,14 @@
 # New VM
 sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
 bash yolov3/data/get_coco_dataset.sh
+bash yolov3/weights/download_yolov3_weights.sh
 sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
 sudo shutdown
 
 # Train
 sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
 cp -r weights yolov3
-cd yolov3 && python3 train.py --batch-size 16 --epochs 1
+cd yolov3 && python3 train.py --batch-size 48 --epochs 1
 sudo shutdown
 
 # Resume
@@ -20,11 +21,17 @@ python3 detect.py
 
 # Clone a branch
 sudo rm -rf yolov3 && git clone -b multi_gpu --depth 1 https://github.com/ultralytics/yolov3
+cp -r weights yolov3
+cd yolov3 && python3 train.py --batch-size 48 --epochs 1
+sudo shutdown
+
+# Git pull branch
+git pull https://github.com/ultralytics/yolov3 multi_gpu
 
 # Test
 sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
 sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
-cd yolov3 && python3 test.py --save-json --conf-thres 0.005
+cd yolov3 && python3 test.py --save-json --conf-thres 0.001 --img-size 416
 
 # Test Darknet training
 python3 test.py --img_size 416 --weights ../darknet/backup/yolov3.backup
@@ -33,7 +40,7 @@ python3 test.py --img_size 416 --weights ../darknet/backup/yolov3.backup
 wget https://storage.googleapis.com/ultralytics/yolov3.pt -O weights/latest.pt
 
 # Copy latest.pt to bucket
-gsutil cp yolov3/weights/latest.pt gs://ultralytics
+gsutil cp yolov3/weights/latest1gpu.pt gs://ultralytics
 
 # Copy latest.pt from bucket
 gsutil cp gs://ultralytics/latest.pt yolov3/weights/latest.pt

utils/utils.py

@@ -95,7 +95,7 @@ def weights_init_normal(m):
 
 def xyxy2xywh(x):
     # Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
-    y = torch.zeros_like(x) if x.dtype is torch.float32 else np.zeros_like(x)
+    y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
     y[:, 0] = (x[:, 0] + x[:, 2]) / 2
     y[:, 1] = (x[:, 1] + x[:, 3]) / 2
     y[:, 2] = x[:, 2] - x[:, 0]
@@ -105,7 +105,7 @@ def xyxy2xywh(x):
 
 def xywh2xyxy(x):
     # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
-    y = torch.zeros_like(x) if x.dtype is torch.float32 else np.zeros_like(x)
+    y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
     y[:, 0] = (x[:, 0] - x[:, 2] / 2)
     y[:, 1] = (x[:, 1] - x[:, 3] / 2)
     y[:, 2] = (x[:, 0] + x[:, 2] / 2)
@@ -251,7 +251,7 @@ def wh_iou(box1, box2):
 def compute_loss(p, targets):  # predictions, targets
     FT = torch.cuda.FloatTensor if p[0].is_cuda else torch.FloatTensor
     loss, lxy, lwh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
-    txy, twh, tcls, tconf, indices = targets
+    txy, twh, tcls, indices = targets
     MSE = nn.MSELoss()
     CE = nn.CrossEntropyLoss()
     BCE = nn.BCEWithLogitsLoss()
@@ -260,18 +260,21 @@ def compute_loss(p, targets):  # predictions, targets
     # gp = [x.numel() for x in tconf]  # grid points
     for i, pi0 in enumerate(p):  # layer i predictions, i
         b, a, gj, gi = indices[i]  # image, anchor, gridx, gridy
+        tconf = torch.zeros_like(pi0[..., 0])  # conf
 
         # Compute losses
         k = 1  # nT / bs
         if len(b) > 0:
             pi = pi0[b, a, gj, gi]  # predictions closest to anchors
+            tconf[b, a, gj, gi] = 1  # conf
+
             lxy += k * MSE(torch.sigmoid(pi[..., 0:2]), txy[i])  # xy
             lwh += k * MSE(pi[..., 2:4], twh[i])  # wh
             lcls += (k / 4) * CE(pi[..., 5:], tcls[i])
 
         # pos_weight = FT([gp[i] / min(gp) * 4.])
         # BCE = nn.BCEWithLogitsLoss(pos_weight=pos_weight)
-        lconf += (k * 64) * BCE(pi0[..., 4], tconf[i])
+        lconf += (k * 64) * BCE(pi0[..., 4], tconf)
     loss = lxy + lwh + lconf + lcls
 
     # Add to dictionary
@@ -283,15 +286,13 @@ def compute_loss(p, targets):  # predictions, targets
     return loss, d
 
 
-def build_targets(model, targets, pred):
+def build_targets(model, targets):
     # targets = [image, class, x, y, w, h]
     if isinstance(model, nn.parallel.DistributedDataParallel):
         model = model.module
-    yolo_layers = get_yolo_layers(model)
 
-    # anchors = closest_anchor(model, targets)  # [layer, anchor, i, j]
+    txy, twh, tcls, indices = [], [], [], []
-    txy, twh, tcls, tconf, indices = [], [], [], [], []
+    for i, layer in enumerate(get_yolo_layers(model)):
-    for i, layer in enumerate(yolo_layers):
         nG = model.module_list[layer][0].nG  # grid size
         anchor_vec = model.module_list[layer][0].anchor_vec
 
@@ -324,12 +325,7 @@ def build_targets(model, targets, pred):
         # Class
         tcls.append(c)
 
-        # Conf
+    return txy, twh, tcls, indices
-        tci = torch.zeros_like(pred[i][..., 0])
-        tci[b, a, gj, gi] = 1  # conf
-        tconf.append(tci)
-
-    return txy, twh, tcls, tconf, indices
 
 
 def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
@@ -439,15 +435,6 @@ def get_yolo_layers(model):
     return [i for i, x in enumerate(bool_vec) if x]  # [82, 94, 106] for yolov3
 
 
-def return_torch_unique_index(u, uv):
-    n = uv.shape[1]  # number of columns
-    first_unique = torch.zeros(n, device=u.device).long()
-    for j in range(n):
-        first_unique[j] = (uv[:, j:j + 1] == u).all(0).nonzero()[0]
-
-    return first_unique
-
-
 def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
     # Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
     a = torch.load(filename, map_location='cpu')
@@ -480,10 +467,9 @@ def plot_results(start=0):
     # import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt')
     # from utils.utils import *; plot_results()
 
-    plt.figure(figsize=(14, 7))
+    fig = plt.figure(figsize=(14, 7))
     s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Total Loss', 'Precision', 'Recall', 'mAP']
-    files = sorted(glob.glob('results*.txt'))
+    for f in sorted(glob.glob('results*.txt')):
-    for f in files:
         results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11]).T  # column 11 is mAP
         x = range(1, results.shape[1])
         for i in range(8):
@@ -492,3 +478,4 @@ def plot_results(start=0):
             plt.title(s[i])
             if i == 0:
                 plt.legend()
+    fig.tight_layout()

weights/download_yolov3_weights.sh

@@ -18,3 +18,4 @@ wget -c https://pjreddie.com/media/files/darknet53.conv.74
 # ./darknet partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15
 # mv yolov3-tiny.conv.15 ../
 
+cd ..