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update segment training (#57)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
This commit is contained in:
Laughing
2022-11-29 05:30:08 -06:00
committed by GitHub
parent d0b0fe2592
commit 3a241e4cea
14 changed files with 460 additions and 144 deletions
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11
ultralytics/yolo/utils/__init__.py
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@ -3,6 +3,7 @@ import logging.config
import os
import platform
import sys
import threading
from pathlib import Path
# Constants
@ -130,3 +131,13 @@ class TryExcept(contextlib.ContextDecorator):
if value:
print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
return True
def threaded(func):
# Multi-threads a target function and returns thread. Usage: @threaded decorator
def wrapper(*args, **kwargs):
thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
thread.start()
return thread
return wrapper

31
ultralytics/yolo/utils/configs/default.yaml
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@ -26,11 +26,11 @@ deterministic: True
local_rank: -1
single_cls: False # train multi-class data as single-class
image_weights: False # use weighted image selection for training
shuffle: True
rect: False # support rectangular training
cos_lr: False # Use cosine LR scheduler
overlap_mask: True # Segmentation masks overlap
mask_ratio: 4 # Segmentation mask downsample ratio
noval: False
# Val/Test settings ----------------------------------------------------------------------------------------------------
save_json: False
@ -43,7 +43,7 @@ plots: False
save_txt: False
# Hyperparameters ------------------------------------------------------------------------------------------------------
lr0: 0.001 # initial learning rate (SGD=1E-2, Adam=1E-3)
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
@ -59,22 +59,23 @@ iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability)
label_smoothing: 0.0
nbs: 64 # nominal batch size
# anchors: 3
augment_hyp:
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability)
# Hydra configs --------------------------------------------------------------------------------------------------------
hydra:

47
ultralytics/yolo/utils/metrics.py
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@ -283,6 +283,50 @@ def smooth(y, f=0.05):
return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed
def plot_pr_curve(px, py, ap, save_dir=Path('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)
ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
ax.set_title('Precision-Recall Curve')
fig.savefig(save_dir, dpi=250)
plt.close(fig)
def plot_mc_curve(px, py, save_dir=Path('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 = smooth(py.mean(0), 0.05)
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)
ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
ax.set_title(f'{ylabel}-Confidence Curve')
fig.savefig(save_dir, dpi=250)
plt.close(fig)
def compute_ap(recall, precision):
""" Compute the average precision, given the recall and precision curves
# Arguments
@ -365,14 +409,11 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names
f1 = 2 * p * r / (p + r + eps)
names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data
names = dict(enumerate(names)) # to dict
# TODO: plot
'''
if plot:
plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names)
plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1')
plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision')
plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall')
'''
i = smooth(f1.mean(0), 0.1).argmax() # max F1 index
p, r, f1 = p[:, i], r[:, i], f1[:, i]

150
ultralytics/yolo/utils/plotting.py
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@ -1,12 +1,16 @@
import contextlib
import math
from pathlib import Path
from urllib.error import URLError
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from PIL import Image, ImageDraw, ImageFont
from ultralytics.yolo.utils import FONT, USER_CONFIG_DIR
from ultralytics.yolo.utils import FONT, USER_CONFIG_DIR, threaded
from .checks import check_font, check_requirements, is_ascii
from .files import increment_path
@ -179,3 +183,147 @@ def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False,
# cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB
return crop
@threaded
def plot_images_and_masks(images, batch_idx, cls, bboxes, masks, paths, confs=None, fname='images.jpg', names=None):
# Plot image grid with labels
if isinstance(images, torch.Tensor):
images = images.cpu().float().numpy()
if isinstance(cls, torch.Tensor):
cls = cls.cpu().numpy()
if isinstance(bboxes, torch.Tensor):
bboxes = bboxes.cpu().numpy()
if isinstance(masks, torch.Tensor):
masks = masks.cpu().numpy().astype(int)
if isinstance(batch_idx, torch.Tensor):
batch_idx = batch_idx.cpu().numpy()
max_size = 1920 # max image size
max_subplots = 16 # max image subplots, i.e. 4x4
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)
if np.max(images[0]) <= 1:
images *= 255 # de-normalise (optional)
# Build Image
mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
for i, im in enumerate(images):
if i == max_subplots: # if last batch has fewer images than we expect
break
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
im = im.transpose(1, 2, 0)
mosaic[y:y + h, x:x + w, :] = im
# Resize (optional)
scale = max_size / ns / max(h, w)
if scale < 1:
h = math.ceil(scale * h)
w = math.ceil(scale * w)
mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
# Annotate
fs = int((h + w) * ns * 0.01) # font size
annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
for i in range(i + 1):
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
if paths:
annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
if len(cls) > 0:
idx = batch_idx == i
boxes = xywh2xyxy(bboxes[idx]).T
classes = cls[idx].astype('int')
labels = confs is None # labels if no conf column
conf = None if labels else confs[idx] # 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 < 1: # absolute coords need scale if image scales
boxes *= scale
boxes[[0, 2]] += x
boxes[[1, 3]] += y
for j, box in enumerate(boxes.T.tolist()):
c = classes[j]
color = colors(c)
c = names[c] if names else c
if labels or conf[j] > 0.25: # 0.25 conf thresh
label = f'{c}' if labels else f'{c} {conf[j]:.1f}'
annotator.box_label(box, label, color=color)
# Plot masks
if len(masks):
if masks.max() > 1.0: # mean that masks are overlap
image_masks = masks[[i]] # (1, 640, 640)
nl = idx.sum()
index = np.arange(nl).reshape(nl, 1, 1) + 1
image_masks = np.repeat(image_masks, nl, axis=0)
image_masks = np.where(image_masks == index, 1.0, 0.0)
else:
image_masks = masks[idx]
im = np.asarray(annotator.im).copy()
for j, box in enumerate(boxes.T.tolist()):
if labels or conf[j] > 0.25: # 0.25 conf thresh
color = colors(classes[j])
mh, mw = image_masks[j].shape
if mh != h or mw != w:
mask = image_masks[j].astype(np.uint8)
mask = cv2.resize(mask, (w, h))
mask = mask.astype(bool)
else:
mask = image_masks[j].astype(bool)
with contextlib.suppress(Exception):
im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
annotator.fromarray(im)
annotator.im.save(fname) # save
def plot_results_with_masks(file="path/to/results.csv", dir="", best=True):
# Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
save_dir = Path(file).parent if file else Path(dir)
fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
ax = ax.ravel()
files = list(save_dir.glob("results*.csv"))
assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot."
for f in files:
try:
data = pd.read_csv(f)
index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] +
0.1 * data.values[:, 11])
s = [x.strip() for x in data.columns]
x = data.values[:, 0]
for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]):
y = data.values[:, j]
# y[y == 0] = np.nan # don't show zero values
ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=2)
if best:
# best
ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3)
ax[i].set_title(s[j] + f"\n{round(y[index], 5)}")
else:
# last
ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3)
ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}")
# if j in [8, 9, 10]: # share train and val loss y axes
# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
except Exception as e:
print(f"Warning: Plotting error for {f}: {e}")
ax[1].legend()
fig.savefig(save_dir / "results.png", dpi=200)
plt.close()
def output_to_target(output, max_det=300):
# Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting
targets = []
for i, o in enumerate(output):
box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
j = torch.full((conf.shape[0], 1), i)
targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
targets = torch.cat(targets, 0).numpy()
return targets[:, 0], targets[:, 1], targets[:, 2:6], targets[:, 6]

16
ultralytics/yolo/utils/torch_utils.py
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@ -245,3 +245,19 @@ class ModelEMA:
def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
# Update EMA attributes
copy_attr(self.ema, model, include, exclude)
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', 'best_fitness', '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
LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")