diff --git a/README.md b/README.md
index 258e26af..5d1261aa 100755
--- a/README.md
+++ b/README.md
@@ -64,7 +64,7 @@ HS**V** Intensity | +/- 50%
## Speed
https://cloud.google.com/deep-learning-vm/
-**Machine type:** n1-highmem-4 (4 vCPUs, 26 GB memory)
+**Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory)
**CPU platform:** Intel Skylake
**GPUs:** 1-4 x NVIDIA Tesla P100
**HDD:** 100 GB SSD
@@ -72,19 +72,22 @@ https://cloud.google.com/deep-learning-vm/
GPUs | `batch_size` | speed | COCO epoch
--- |---| --- | ---
(P100) | (images) | (s/batch) | (min/epoch)
-1 | 16 | 0.54s | 66min
-2 | 32 | 0.99s | 61min
-4 | 64 | 1.61s | 49min
+1 | 16 | 0.39s | 48min
+2 | 32 | 0.48s | 29min
+4 | 64 | 0.65s | 20min
# Inference
Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder:
-**YOLOv3:** `detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.pt`
-
+**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights`
+
-**YOLOv3-tiny:** `detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.pt`
-
+**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.weights`
+
+
+**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights weights/yolov3-spp.weights`
+
## Webcam
diff --git a/models.py b/models.py
index ef417603..67e8a83d 100755
--- a/models.py
+++ b/models.py
@@ -174,9 +174,6 @@ class Darknet(nn.Module):
self.module_defs[0]['cfg'] = cfg_path
self.module_defs[0]['height'] = img_size
self.hyperparams, self.module_list = create_modules(self.module_defs)
- self.img_size = img_size
- self.loss_names = ['loss', 'xy', 'wh', 'conf', 'cls', 'nT']
- self.losses = []
def forward(self, x, var=None):
img_size = x.shape[-1]
diff --git a/test.py b/test.py
index b614eae6..5d0ca98a 100644
--- a/test.py
+++ b/test.py
@@ -3,6 +3,8 @@ import json
import time
from pathlib import Path
+from torch.utils.data import DataLoader
+
from models import *
from utils.datasets import *
from utils.utils import *
@@ -39,16 +41,21 @@ def test(
model.to(device).eval()
- # Get dataloader
- # dataloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path), batch_size=batch_size)
- dataloader = LoadImagesAndLabels(test_path, batch_size=batch_size, img_size=img_size)
+ # Dataloader
+ dataset = LoadImagesAndLabels(test_path, img_size=img_size)
+ dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=4)
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 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))
@@ -71,7 +78,7 @@ 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[si]) # to original shape
+ scale_coords(img_size, box, (shapes[0][si], shapes[1][si])) # to original shape
box = xyxy2xywh(box) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
@@ -129,7 +136,7 @@ def test(
# Print image mAP and running mean mAP
print(('%11s%11s' + '%11.3g' * 4 + 's') %
- (seen, dataloader.nF, mean_P, mean_R, mean_mAP, time.time() - t))
+ (seen, len(dataset), mean_P, mean_R, mean_mAP, time.time() - t))
# Print mAP per class
print('\nmAP Per Class:')
@@ -139,7 +146,7 @@ def test(
# Save JSON
if save_json:
- imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.img_files]
+ imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataset.img_files]
with open('results.json', 'w') as file:
json.dump(jdict, file)
diff --git a/train.py b/train.py
index 7ec5a46c..da1a07fe 100644
--- a/train.py
+++ b/train.py
@@ -1,6 +1,8 @@
import argparse
import time
+from torch.utils.data import DataLoader
+
import test # Import test.py to get mAP after each epoch
from models import *
from utils.datasets import *
@@ -17,6 +19,7 @@ def train(
accumulate=1,
multi_scale=False,
freeze_backbone=False,
+ num_workers=0
):
weights = 'weights' + os.sep
latest = weights + 'latest.pt'
@@ -34,47 +37,39 @@ def train(
# Initialize model
model = Darknet(cfg, img_size).to(device)
- # Get dataloader
- dataloader = LoadImagesAndLabels(train_path, batch_size, img_size, augment=True)
- # dataloader = torch.utils.data.DataLoader(dataloader, batch_size=batch_size, num_workers=0)
-
+ # Optimizer
lr0 = 0.001 # initial learning rate
+ optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=.9)
+
+ # Dataloader
+ dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
+ dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)
+
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
best_loss = float('inf')
- if resume:
- checkpoint = torch.load(latest, map_location=device)
-
- # Load weights to resume from
+ if resume: # Load previously saved PyTorch model
+ checkpoint = torch.load(latest, map_location=device) # load checkpoint
model.load_state_dict(checkpoint['model'])
-
- # 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 optimizer
- optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=lr0, momentum=.9)
-
start_epoch = checkpoint['epoch'] + 1
if checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
best_loss = checkpoint['best_loss']
-
del checkpoint # current, saved
- else:
- # Initialize model with backbone (optional)
+ else: # Initialize model with backbone (optional)
if cfg.endswith('yolov3.cfg'):
cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
elif cfg.endswith('yolov3-tiny.cfg'):
cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
- # Set optimizer
- optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=.9)
-
if torch.cuda.device_count() > 1:
+ print('WARNING: MultiGPU Issue: https://github.com/ultralytics/yolov3/issues/146')
model = nn.DataParallel(model)
- model.to(device).train()
+
+ # 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
# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[54, 61], gamma=0.1)
@@ -94,10 +89,7 @@ def train(
# scheduler.step()
# Update scheduler (manual)
- if epoch > 250:
- lr = lr0 / 10
- else:
- lr = lr0
+ lr = lr0 / 10 if epoch > 250 else lr0
for x in optimizer.param_groups:
x['lr'] = lr
@@ -107,14 +99,28 @@ def train(
if int(name.split('.')[1]) < cutoff: # if layer < 75
p.requires_grad = False if (epoch == 0) else True
- ui = -1
rloss = defaultdict(float)
for i, (imgs, targets, _, _) in enumerate(dataloader):
- targets = targets.to(device)
- nT = targets.shape[0]
+ # 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)
+
+ nT = len(targets)
if nT == 0: # if no targets continue
continue
+ # Plot images with bounding boxes
+ plot_images = False
+ if plot_images:
+ import matplotlib.pyplot as plt
+ 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')
+
# SGD burn-in
if (epoch == 0) and (i <= n_burnin):
lr = lr0 * (i / n_burnin) ** 4
@@ -125,7 +131,7 @@ def train(
pred = model(imgs.to(device))
# Build targets
- target_list = build_targets(model, targets, pred)
+ target_list = build_targets(model, targets.to(device), pred)
# Compute loss
loss, loss_dict = compute_loss(pred, target_list)
@@ -139,9 +145,8 @@ def train(
optimizer.zero_grad()
# Running epoch-means of tracked metrics
- ui += 1
for key, val in loss_dict.items():
- rloss[key] = (rloss[key] * ui + val) / (ui + 1)
+ rloss[key] = (rloss[key] * i + val) / (i + 1)
s = ('%8s%12s' + '%10.3g' * 7) % (
'%g/%g' % (epoch, epochs - 1),
@@ -154,8 +159,8 @@ def train(
# Multi-Scale training (320 - 608 pixels) every 10 batches
if multi_scale and (i + 1) % 10 == 0:
- dataloader.img_size = random.choice(range(10, 20)) * 32
- print('multi_scale img_size = %g' % dataloader.img_size)
+ dataset.img_size = random.choice(range(10, 20)) * 32
+ print('multi_scale img_size = %g' % dataset.img_size)
# Update best loss
if rloss['total'] < best_loss:
@@ -198,6 +203,7 @@ if __name__ == '__main__':
parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
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')
opt = parser.parse_args()
print(opt, end='\n\n')
@@ -212,4 +218,5 @@ if __name__ == '__main__':
batch_size=opt.batch_size,
accumulate=opt.accumulate,
multi_scale=opt.multi_scale,
+ num_workers=opt.num_workers
)
diff --git a/utils/datasets.py b/utils/datasets.py
index 31fc166b..285e51f9 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -6,8 +6,8 @@ import random
import cv2
import numpy as np
import torch
+from torch.utils.data import Dataset
-# from torch.utils.data import Dataset
from utils.utils import xyxy2xywh
@@ -89,147 +89,105 @@ class LoadWebcam: # for inference
return 0
-class LoadImagesAndLabels: # for training
- def __init__(self, path, batch_size=1, img_size=608, augment=False):
+class LoadImagesAndLabels(Dataset): # for training/testing
+ def __init__(self, path, img_size=416, augment=False):
with open(path, 'r') as file:
self.img_files = file.read().splitlines()
self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))
-
- self.nF = len(self.img_files) # number of image files
- self.nB = math.ceil(self.nF / batch_size) # number of batches
- assert self.nF > 0, 'No images found in %s' % path
-
+ 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')
for x in self.img_files]
- self.batch_size = batch_size
- self.img_size = img_size
- self.augment = augment
- iter(self)
-
- def __iter__(self):
- self.count = -1
- self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
- return self
+ def __len__(self):
+ return len(self.img_files)
def __getitem__(self, index):
- imgs, labels0, img_paths, img_shapes = self.load_images(index, index + 1)
- labels0[:,0] = index % self.batch_size
+ img_path = self.img_files[index]
+ label_path = self.label_files[index]
- labels = torch.zeros(100, 6)
- labels[:min(len(labels0), 100)] = labels0 # max 100 labels per image
- return imgs.squeeze(0), labels, img_paths, img_shapes
+ img = cv2.imread(img_path) # BGR
+ assert img is not None, 'File Not Found ' + img_path
- def __next__(self):
- self.count += 1 # batches
- if self.count >= self.nB:
- raise StopIteration
+ augment_hsv = True
+ if self.augment and augment_hsv:
+ # SV augmentation by 50%
+ fraction = 0.50
+ img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+ S = img_hsv[:, :, 1].astype(np.float32)
+ V = img_hsv[:, :, 2].astype(np.float32)
- ia = self.count * self.batch_size # start index
- ib = min(ia + self.batch_size, self.nF) # end index
+ a = (random.random() * 2 - 1) * fraction + 1
+ S *= a
+ if a > 1:
+ np.clip(S, a_min=0, a_max=255, out=S)
- return self.load_images(ia, ib)
+ a = (random.random() * 2 - 1) * fraction + 1
+ V *= a
+ if a > 1:
+ np.clip(V, a_min=0, a_max=255, out=V)
- def load_images(self, ia, ib):
- img_all, labels_all, img_paths, img_shapes = [], [], [], []
- for index, files_index in enumerate(range(ia, ib)):
- img_path = self.img_files[self.shuffled_vector[files_index]]
- label_path = self.label_files[self.shuffled_vector[files_index]]
+ img_hsv[:, :, 1] = S.astype(np.uint8)
+ img_hsv[:, :, 2] = V.astype(np.uint8)
+ cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
- img = cv2.imread(img_path) # BGR
- assert img is not None, 'File Not Found ' + img_path
+ h, w, _ = img.shape
+ img, ratio, padw, padh = letterbox(img, height=self.img_size)
- augment_hsv = True
- if self.augment and augment_hsv:
- # SV augmentation by 50%
- fraction = 0.50
- img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
- S = img_hsv[:, :, 1].astype(np.float32)
- V = img_hsv[:, :, 2].astype(np.float32)
+ # Load labels
+ if os.path.isfile(label_path):
+ with open(label_path, 'r') as file:
+ lines = file.read().splitlines()
- a = (random.random() * 2 - 1) * fraction + 1
- S *= a
- if a > 1:
- np.clip(S, a_min=0, a_max=255, out=S)
-
- a = (random.random() * 2 - 1) * fraction + 1
- V *= a
- if a > 1:
- np.clip(V, a_min=0, a_max=255, out=V)
-
- img_hsv[:, :, 1] = S.astype(np.uint8)
- img_hsv[:, :, 2] = V.astype(np.uint8)
- cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
-
- h, w, _ = img.shape
- img, ratio, padw, padh = letterbox(img, height=self.img_size)
-
- # Load labels
- if os.path.isfile(label_path):
- # labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 5) # SLOWER
- with open(label_path, 'r') as file:
- lines = file.read().splitlines()
- labels0 = np.array([x.split() for x in lines], dtype=np.float32)
-
- # Normalized xywh to pixel xyxy format
- labels = labels0.copy()
- labels[:, 1] = ratio * w * (labels0[:, 1] - labels0[:, 3] / 2) + padw
- labels[:, 2] = ratio * h * (labels0[:, 2] - labels0[:, 4] / 2) + padh
- labels[:, 3] = ratio * w * (labels0[:, 1] + labels0[:, 3] / 2) + padw
- labels[:, 4] = ratio * h * (labels0[:, 2] + labels0[:, 4] / 2) + padh
- else:
+ x = np.array([x.split() for x in lines], dtype=np.float32)
+ if x.size is 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))
+ # 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))
- plotFlag = False
- if plotFlag:
- import matplotlib.pyplot as plt
- plt.figure(figsize=(10, 10)) if index == 0 else None
- plt.subplot(4, 4, index + 1).imshow(img[:, :, ::-1])
- plt.plot(labels[:, [1, 3, 3, 1, 1]].T, labels[:, [2, 2, 4, 4, 2]].T, '.-')
- plt.axis('off')
+ nL = len(labels)
+ if nL > 0:
+ # convert xyxy to xywh
+ labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) / self.img_size
- nL = len(labels)
- if nL > 0:
- # convert xyxy to xywh
- labels[:, 1:5] = xyxy2xywh(labels[:, 1:5].copy()) / self.img_size
+ if self.augment:
+ # random left-right flip
+ lr_flip = True
+ if lr_flip & (random.random() > 0.5):
+ img = np.fliplr(img)
+ if nL > 0:
+ labels[:, 1] = 1 - labels[:, 1]
- if self.augment:
- # random left-right flip
- lr_flip = True
- if lr_flip & (random.random() > 0.5):
- img = np.fliplr(img)
- if nL > 0:
- labels[:, 1] = 1 - labels[:, 1]
+ # random up-down flip
+ ud_flip = False
+ if ud_flip & (random.random() > 0.5):
+ img = np.flipud(img)
+ if nL > 0:
+ labels[:, 2] = 1 - labels[:, 2]
- # random up-down flip
- ud_flip = False
- if ud_flip & (random.random() > 0.5):
- img = np.flipud(img)
- if nL > 0:
- labels[:, 2] = 1 - labels[:, 2]
-
- if nL > 0:
- labels = np.concatenate((np.zeros((nL, 1), dtype='float32') + index, labels), 1)
- labels_all.append(labels)
-
- img_all.append(img)
- img_paths.append(img_path)
- img_shapes.append((h, w))
+ labels_out = np.zeros((100, 6), dtype=np.float32)
+ if nL > 0:
+ labels_out[:nL, 1:] = labels # max 100 labels per image
# Normalize
- img_all = np.stack(img_all)[:, :, :, ::-1].transpose(0, 3, 1, 2) # list to np.array and BGR to RGB
- img_all = np.ascontiguousarray(img_all, dtype=np.float32) # uint8 to float32
- img_all /= 255.0 # 0 - 255 to 0.0 - 1.0
+ img = img[:, :, ::-1].transpose(2, 0, 1) # list to np.array and BGR to RGB
+ img = np.ascontiguousarray(img, dtype=np.float32) # uint8 to float32
+ img /= 255.0 # 0 - 255 to 0.0 - 1.0
- labels_all = torch.from_numpy(np.concatenate(labels_all, 0))
- return torch.from_numpy(img_all), labels_all, img_paths, img_shapes
-
- def __len__(self):
- return self.nB # number of batches
+ return torch.from_numpy(img), torch.from_numpy(labels_out), img_path, (h, w)
def letterbox(img, height=416, color=(127.5, 127.5, 127.5)): # resize a rectangular image to a padded square
diff --git a/utils/gcp.sh b/utils/gcp.sh
index 5cc27849..50274913 100755
--- a/utils/gcp.sh
+++ b/utils/gcp.sh
@@ -6,39 +6,31 @@ bash yolov3/data/get_coco_dataset.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
-# Start
+# Train
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
cp -r weights yolov3
-cd yolov3 && python3 train.py --batch-size 26
+cd yolov3 && python3 train.py --batch-size 16 --epochs 1
+sudo shutdown
# Resume
python3 train.py --resume
# Detect
-gsutil cp gs://ultralytics/yolov3.pt yolov3/weights
python3 detect.py
-# Clone branch
+# Clone a branch
sudo rm -rf yolov3 && git clone -b multi_gpu --depth 1 https://github.com/ultralytics/yolov3
-cd yolov3 && python3 train.py --batch-size 26
-
-sudo rm -rf yolov3 && git clone -b multigpu --depth 1 https://github.com/alexpolichroniadis/yolov3
-cp coco.data yolov3/cfg
-cd yolov3 && python3 train.py --batch-size 26
# 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
-# Test Darknet
+# Test Darknet training
python3 test.py --img_size 416 --weights ../darknet/backup/yolov3.backup
-# Download and Resume
-sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 && cd yolov3
+# Download with wget
wget https://storage.googleapis.com/ultralytics/yolov3.pt -O weights/latest.pt
-python3 train.py --img_size 416 --batch_size 16 --epochs 1 --resume
-python3 test.py --img_size 416 --weights weights/latest.pt --conf_thres 0.5
# Copy latest.pt to bucket
gsutil cp yolov3/weights/latest.pt gs://ultralytics
@@ -47,8 +39,8 @@ gsutil cp yolov3/weights/latest.pt gs://ultralytics
gsutil cp gs://ultralytics/latest.pt yolov3/weights/latest.pt
wget https://storage.googleapis.com/ultralytics/latest.pt
-# Testing
-sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 && cd yolov3
-python3 train.py --epochs 3 --var 64
+# Trade Studies
+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
sudo shutdown
-
diff --git a/utils/utils.py b/utils/utils.py
index 1e5e691f..1f4e03b0 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -15,6 +15,9 @@ from utils import torch_utils
torch.set_printoptions(linewidth=1320, precision=5, profile='long')
np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
+# Prevent OpenCV from multithreading (to use PyTorch DataLoader)
+cv2.setNumThreads(0)
+
def float3(x): # format floats to 3 decimals
return float(format(x, '.3f'))
@@ -37,10 +40,10 @@ def model_info(model):
# Plots a line-by-line description of a PyTorch model
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
- print('\n%5s %38s %9s %12s %20s %12s %12s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
+ print('\n%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 %38s %9s %12g %20s %12.3g %12.3g' % (
+ print('%5g %40s %9s %12g %20s %10.3g %10.3g' % (
i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
print('Model Summary: %g layers, %g parameters, %g gradients' % (i + 1, n_p, n_g))