updates
This commit is contained in:
Glenn Jocher
parent
8ebb4da5cc
commit
6aef4e6a78
21
test.py
21
test.py
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@ -3,6 +3,8 @@ import json
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import time
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from pathlib import Path
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from torch.utils.data import DataLoader
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from models import *
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from utils.datasets import *
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from utils.utils import *
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@ -39,16 +41,21 @@ def test(
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model.to(device).eval()
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# Get dataloader
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# dataloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path), batch_size=batch_size)
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dataloader = LoadImagesAndLabels(test_path, batch_size=batch_size, img_size=img_size)
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# Dataloader
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dataset = LoadImagesAndLabels(test_path, img_size=img_size)
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dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=0)
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mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0
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print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
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mP, mR, mAPs, TP, jdict = [], [], [], [], []
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AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
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coco91class = coco80_to_coco91_class()
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for (imgs, targets, paths, shapes) in dataloader:
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for imgs, targets, paths, shapes in dataloader:
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# Unpad and collate targets
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for j, t in enumerate(targets):
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t[:, 0] = j
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targets = torch.cat([t[t[:, 5].nonzero()] for t in targets], 0).squeeze(1)
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targets = targets.to(device)
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t = time.time()
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output = model(imgs.to(device))
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@ -71,7 +78,7 @@ def test(
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if save_json:
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# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
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box = detections[:, :4].clone() # xyxy
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scale_coords(img_size, box, shapes[si]) # to original shape
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scale_coords(img_size, box, (shapes[0][si], shapes[1][si])) # to original shape
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box = xyxy2xywh(box) # xywh
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box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
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@ -129,7 +136,7 @@ def test(
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# Print image mAP and running mean mAP
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print(('%11s%11s' + '%11.3g' * 4 + 's') %
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(seen, dataloader.nF, mean_P, mean_R, mean_mAP, time.time() - t))
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(seen, len(dataset), mean_P, mean_R, mean_mAP, time.time() - t))
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# Print mAP per class
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print('\nmAP Per Class:')
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@ -139,7 +146,7 @@ def test(
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# Save JSON
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if save_json:
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imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.img_files]
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imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataset.img_files]
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with open('results.json', 'w') as file:
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json.dump(jdict, file)
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30
train.py
30
train.py
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@ -42,10 +42,8 @@ def train(
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optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=.9)
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# Dataloader
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if num_workers > 0:
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cv2.setNumThreads(0) # to prevent OpenCV from multithreading
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dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
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dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)
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dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=4)
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cutoff = -1 # backbone reaches to cutoff layer
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start_epoch = 0
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@ -103,17 +101,28 @@ def train(
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if int(name.split('.')[1]) < cutoff: # if layer < 75
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p.requires_grad = False if (epoch == 0) else True
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ui = -1
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rloss = defaultdict(float)
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for i, (imgs, targets, _, _) in enumerate(dataloader):
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if targets.shape[1] == 100: # multithreaded 100-size block
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targets = targets.view((-1, 6))
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targets = targets[targets[:, 5].nonzero().squeeze()]
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# Unpad and collate targets
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for j, t in enumerate(targets):
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t[:, 0] = j
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targets = torch.cat([t[t[:, 5].nonzero()] for t in targets], 0).squeeze(1)
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nT = targets.shape[0]
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nT = len(targets)
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if nT == 0: # if no targets continue
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continue
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# Plot images with bounding boxes
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plot_images = False
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if plot_images:
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import matplotlib.pyplot as plt
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plt.figure(figsize=(10, 10))
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for ip in range(batch_size):
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labels = xywh2xyxy(targets[targets[:, 0] == ip, 2:6]).numpy() * img_size
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plt.subplot(3, 3, ip + 1).imshow(imgs[ip].numpy().transpose(1, 2, 0))
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plt.plot(labels[:, [0, 2, 2, 0, 0]].T, labels[:, [1, 1, 3, 3, 1]].T, '.-')
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plt.axis('off')
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# SGD burn-in
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if (epoch == 0) and (i <= n_burnin):
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lr = lr0 * (i / n_burnin) ** 4
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@ -138,9 +147,8 @@ def train(
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optimizer.zero_grad()
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# Running epoch-means of tracked metrics
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ui += 1
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for key, val in loss_dict.items():
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rloss[key] = (rloss[key] * ui + val) / (ui + 1)
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rloss[key] = (rloss[key] * i + val) / (i + 1)
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s = ('%8s%12s' + '%10.3g' * 7) % (
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'%g/%g' % (epoch, epochs - 1),
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@ -197,7 +205,7 @@ if __name__ == '__main__':
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parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
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parser.add_argument('--img-size', type=int, default=32 * 13, help='pixels')
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parser.add_argument('--resume', action='store_true', help='resume training flag')
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parser.add_argument('--num_workers', type=int, default=4, help='number of Pytorch DataLoader workers')
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parser.add_argument('--num_workers', type=int, default=0, help='number of Pytorch DataLoader workers')
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opt = parser.parse_args()
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print(opt, end='\n\n')
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@ -6,6 +6,7 @@ import random
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import cv2
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import numpy as np
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import torch
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from torch.utils.data import Dataset
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from utils.utils import xyxy2xywh
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@ -88,52 +89,23 @@ class LoadWebcam: # for inference
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return 0
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class LoadImagesAndLabels: # for training
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def __init__(self, path, batch_size=1, img_size=608, augment=False):
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class LoadImagesAndLabels(Dataset): # for training/testing
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def __init__(self, path, img_size=416, augment=False):
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with open(path, 'r') as file:
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self.img_files = file.read().splitlines()
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self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))
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self.nF = len(self.img_files) # number of image files
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self.nB = math.ceil(self.nF / batch_size) # number of batches
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assert self.nF > 0, 'No images found in %s' % path
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assert len(self.img_files) > 0, 'No images found in %s' % path
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self.img_size = img_size
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self.augment = augment
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self.label_files = [x.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt')
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for x in self.img_files]
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self.batch_size = batch_size
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self.img_size = img_size
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self.augment = augment
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def __iter__(self):
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self.count = -1
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#self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
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return self
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def __len__(self):
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return len(self.img_files)
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def __getitem__(self, index):
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imgs, labels0, img_paths, img_shapes = self.load_images(index, index + 1)
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labels0[:, 0] = index % self.batch_size
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labels = torch.zeros(100, 6)
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labels[:min(len(labels0), 100)] = labels0 # max 100 labels per image
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return imgs.squeeze(0), labels, img_paths, img_shapes
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def __next__(self):
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self.count += 1 # batches
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if self.count >= self.nB:
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raise StopIteration
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ia = self.count * self.batch_size # start index
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ib = min(ia + self.batch_size, self.nF) # end index
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return self.load_images(ia, ib)
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def load_images(self, ia, ib):
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img_all, labels_all, img_paths, img_shapes = [], [], [], []
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for index, files_index in enumerate(range(ia, ib)):
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img_path = self.img_files[files_index]
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label_path = self.label_files[files_index]
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img_path = self.img_files[index]
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label_path = self.label_files[index]
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img = cv2.imread(img_path) # BGR
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assert img is not None, 'File Not Found ' + img_path
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@ -183,14 +155,6 @@ class LoadImagesAndLabels: # for training
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if self.augment:
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img, labels, M = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.90, 1.10))
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plotFlag = False
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if plotFlag:
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import matplotlib.pyplot as plt
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plt.figure(figsize=(10, 10)) if index == 0 else None
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plt.subplot(4, 4, index + 1).imshow(img[:, :, ::-1])
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plt.plot(labels[:, [1, 3, 3, 1, 1]].T, labels[:, [2, 2, 4, 4, 2]].T, '.-')
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plt.axis('off')
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nL = len(labels)
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if nL > 0:
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# convert xyxy to xywh
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@ -211,29 +175,16 @@ class LoadImagesAndLabels: # for training
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if nL > 0:
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labels[:, 2] = 1 - labels[:, 2]
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labels_out = np.zeros((100, 6), dtype=np.float32)
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if nL > 0:
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labels = np.concatenate((np.zeros((nL, 1), dtype='float32') + index, labels), 1)
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labels_all.append(labels)
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img_all.append(img)
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img_paths.append(img_path)
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img_shapes.append((h, w))
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labels_out[:nL, 1:] = labels # max 100 labels per image
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# Normalize
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img_all = np.stack(img_all)[:, :, :, ::-1].transpose(0, 3, 1, 2) # list to np.array and BGR to RGB
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img_all = np.ascontiguousarray(img_all, dtype=np.float32) # uint8 to float32
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img_all /= 255.0 # 0 - 255 to 0.0 - 1.0
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img = img[:, :, ::-1].transpose(2, 0, 1) # list to np.array and BGR to RGB
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img = np.ascontiguousarray(img, dtype=np.float32) # uint8 to float32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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if len(labels_all) > 0:
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labels_all = np.concatenate(labels_all, 0)
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else:
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labels_all = np.zeros((1, 6), dtype='float32')
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labels_all = torch.from_numpy(labels_all)
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return torch.from_numpy(img_all), labels_all, img_paths, img_shapes
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def __len__(self):
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return self.nB # number of batches
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return torch.from_numpy(img), torch.from_numpy(labels_out), img_path, (h, w)
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def letterbox(img, height=416, color=(127.5, 127.5, 127.5)): # resize a rectangular image to a padded square
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@ -15,6 +15,9 @@ from utils import torch_utils
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torch.set_printoptions(linewidth=1320, precision=5, profile='long')
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np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
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# Prevent OpenCV from multithreading (to use PyTorch DataLoader)
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cv2.setNumThreads(0)
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def float3(x): # format floats to 3 decimals
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return float(format(x, '.3f'))
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